TY - GEN AB - Methods inspired from machine learning have recently attracted great interest in the computational study of quantum many-particle systems. So far, however, it has proven challenging to deal with microscopic models in which the total number of particles is not conserved. To address this issue, we propose a new variant of neural network states, which we term neural coherent states. Taking the Fröhlich impurity model as a case study, we show that neural coherent states can learn the ground state of non-additive systems very well. In particular, we observe substantial improvement over the standard coherent state estimates in the most challenging intermediate coupling regime. Our approach is generic and does not assume specific details of the system, suggesting wide applications. AU - Rzadkowski, Wojciech AU - Lemeshko, Mikhail AU - Mentink, Johan H. ID - 10762 T2 - arXiv TI - Artificial neural network states for non-additive systems ER - TY - THES AB - Deep learning is best known for its empirical success across a wide range of applications spanning computer vision, natural language processing and speech. Of equal significance, though perhaps less known, are its ramifications for learning theory: deep networks have been observed to perform surprisingly well in the high-capacity regime, aka the overfitting or underspecified regime. Classically, this regime on the far right of the bias-variance curve is associated with poor generalisation; however, recent experiments with deep networks challenge this view. This thesis is devoted to investigating various aspects of underspecification in deep learning. First, we argue that deep learning models are underspecified on two levels: a) any given training dataset can be fit by many different functions, and b) any given function can be expressed by many different parameter configurations. We refer to the second kind of underspecification as parameterisation redundancy and we precisely characterise its extent. Second, we characterise the implicit criteria (the inductive bias) that guide learning in the underspecified regime. Specifically, we consider a nonlinear but tractable classification setting, and show that given the choice, neural networks learn classifiers with a large margin. Third, we consider learning scenarios where the inductive bias is not by itself sufficient to deal with underspecification. We then study different ways of ‘tightening the specification’: i) In the setting of representation learning with variational autoencoders, we propose a hand- crafted regulariser based on mutual information. ii) In the setting of binary classification, we consider soft-label (real-valued) supervision. We derive a generalisation bound for linear networks supervised in this way and verify that soft labels facilitate fast learning. Finally, we explore an application of soft-label supervision to the training of multi-exit models. AU - Bui Thi Mai, Phuong ID - 9418 SN - 2663-337X TI - Underspecification in deep learning ER - TY - CONF AB - The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement such as fairness. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels. AU - Träuble, Frederik AU - Creager, Elliot AU - Kilbertus, Niki AU - Locatello, Francesco AU - Dittadi, Andrea AU - Goyal, Anirudh AU - Schölkopf, Bernhard AU - Bauer, Stefan ID - 14177 T2 - Proceedings of the 38th International Conference on Machine Learning TI - On disentangled representations learned from correlated data VL - 139 ER - TY - CONF AB - Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by supplementing time-series data augmentation techniques with a novel contrastive learning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series. AU - Yèche, Hugo AU - Dresdner, Gideon AU - Locatello, Francesco AU - Hüser, Matthias AU - Rätsch, Gunnar ID - 14176 T2 - Proceedings of 38th International Conference on Machine Learning TI - Neighborhood contrastive learning applied to online patient monitoring VL - 139 ER - TY - CONF AB - When machine learning systems meet real world applications, accuracy is only one of several requirements. In this paper, we assay a complementary perspective originating from the increasing availability of pre-trained and regularly improving state-of-the-art models. While new improved models develop at a fast pace, downstream tasks vary more slowly or stay constant. Assume that we have a large unlabelled data set for which we want to maintain accurate predictions. Whenever a new and presumably better ML models becomes available, we encounter two problems: (i) given a limited budget, which data points should be re-evaluated using the new model?; and (ii) if the new predictions differ from the current ones, should we update? Problem (i) is about compute cost, which matters for very large data sets and models. Problem (ii) is about maintaining consistency of the predictions, which can be highly relevant for downstream applications; our demand is to avoid negative flips, i.e., changing correct to incorrect predictions. In this paper, we formalize the Prediction Update Problem and present an efficient probabilistic approach as answer to the above questions. In extensive experiments on standard classification benchmark data sets, we show that our method outperforms alternative strategies along key metrics for backward-compatible prediction updates. AU - Träuble, Frederik AU - Kügelgen, Julius von AU - Kleindessner, Matthäus AU - Locatello, Francesco AU - Schölkopf, Bernhard AU - Gehler, Peter ID - 14182 SN - 9781713845393 T2 - 35th Conference on Neural Information Processing Systems TI - Backward-compatible prediction updates: A probabilistic approach VL - 34 ER - TY - CONF AB - Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Variational Inference allows practitioners to obtain increasingly good posterior approximations by spending more compute. The main obstacle to widespread adoption of Boosting Variational Inference is the amount of resources necessary to improve over a strong Variational Inference baseline. In our work, we trace this limitation back to the global curvature of the KL-divergence. We characterize how the global curvature impacts time and memory consumption, address the problem with the notion of local curvature, and provide a novel approximate backtracking algorithm for estimating local curvature. We give new theoretical convergence rates for our algorithms and provide experimental validation on synthetic and real-world datasets. AU - Dresdner, Gideon AU - Shekhar, Saurav AU - Pedregosa, Fabian AU - Locatello, Francesco AU - Rätsch, Gunnar ID - 14181 T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence TI - Boosting variational inference with locally adaptive step-sizes ER - TY - CONF AB - Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space. We study the identifiability of the latent representation based on pairs of views of the observations and prove sufficient conditions that allow us to identify the invariant content partition up to an invertible mapping in both generative and discriminative settings. We find numerical simulations with dependent latent variables are consistent with our theory. Lastly, we introduce Causal3DIdent, a dataset of high-dimensional, visually complex images with rich causal dependencies, which we use to study the effect of data augmentations performed in practice. AU - Kügelgen, Julius von AU - Sharma, Yash AU - Gresele, Luigi AU - Brendel, Wieland AU - Schölkopf, Bernhard AU - Besserve, Michel AU - Locatello, Francesco ID - 14179 SN - 9781713845393 T2 - Advances in Neural Information Processing Systems TI - Self-supervised learning with data augmentations provably isolates content from style VL - 34 ER - TY - CONF AB - Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call \emph{functions}. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computation along width and depth, and lends itself well to capacity extension after training. To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct settings: image classification and visual abstract reasoning on Raven Progressive Matrices. In the former, we show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner. In the latter, we find that Neural Interpreters are competitive with respect to the state-of-the-art in terms of systematic generalization. AU - Rahaman, Nasim AU - Gondal, Muhammad Waleed AU - Joshi, Shruti AU - Gehler, Peter AU - Bengio, Yoshua AU - Locatello, Francesco AU - Schölkopf, Bernhard ID - 14180 SN - 9781713845393 T2 - Advances in Neural Information Processing Systems TI - Dynamic inference with neural interpreters VL - 34 ER - TY - JOUR AB - The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities. AU - Scholkopf, Bernhard AU - Locatello, Francesco AU - Bauer, Stefan AU - Ke, Nan Rosemary AU - Kalchbrenner, Nal AU - Goyal, Anirudh AU - Bengio, Yoshua ID - 14117 IS - 5 JF - Proceedings of the IEEE KW - Electrical and Electronic Engineering SN - 0018-9219 TI - Toward causal representation learning VL - 109 ER - TY - CONF AB - Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and real-world impact remain questionable. We introduce a new high-resolution dataset with 1M simulated images and over 1,800 annotated real-world images of the same setup. In contrast to previous work, this new dataset exhibits correlations, a complex underlying structure, and allows to evaluate transfer to unseen simulated and real-world settings where the encoder i) remains in distribution or ii) is out of distribution. We propose new architectures in order to scale disentangled representation learning to realistic high-resolution settings and conduct a large-scale empirical study of disentangled representations on this dataset. We observe that disentanglement is a good predictor for out-of-distribution (OOD) task performance. AU - Dittadi, Andrea AU - Träuble, Frederik AU - Locatello, Francesco AU - Wüthrich, Manuel AU - Agrawal, Vaibhav AU - Winther, Ole AU - Bauer, Stefan AU - Schölkopf, Bernhard ID - 14178 T2 - The Ninth International Conference on Learning Representations TI - On the transfer of disentangled representations in realistic settings ER - TY - GEN AB - The world is structured in countless ways. It may be prudent to enforce corresponding structural properties to a learning algorithm's solution, such as incorporating prior beliefs, natural constraints, or causal structures. Doing so may translate to faster, more accurate, and more flexible models, which may directly relate to real-world impact. In this dissertation, we consider two different research areas that concern structuring a learning algorithm's solution: when the structure is known and when it has to be discovered. AU - Locatello, Francesco ID - 14221 T2 - arXiv TI - Enforcing and discovering structure in machine learning ER - TY - GEN AB - The Birkhoff conjecture says that the boundary of a strictly convex integrable billiard table is necessarily an ellipse. In this article, we consider a stronger notion of integrability, namely, integrability close to the boundary, and prove a local version of this conjecture: a small perturbation of almost every ellipse that preserves integrability near the boundary, is itself an ellipse. We apply this result to study local spectral rigidity of ellipses using the connection between the wave trace of the Laplacian and the dynamics near the boundary and establish rigidity for almost all of them. AU - Koval, Illya ID - 14278 T2 - arXiv TI - Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse ER - TY - THES AB - The design and verification of concurrent systems remains an open challenge due to the non-determinism that arises from the inter-process communication. In particular, concurrent programs are notoriously difficult both to be written correctly and to be analyzed formally, as complex thread interaction has to be accounted for. The difficulties are further exacerbated when concurrent programs get executed on modern-day hardware, which contains various buffering and caching mechanisms for efficiency reasons. This causes further subtle non-determinism, which can often produce very unintuitive behavior of the concurrent programs. Model checking is at the forefront of tackling the verification problem, where the task is to decide, given as input a concurrent system and a desired property, whether the system satisfies the property. The inherent state-space explosion problem in model checking of concurrent systems causes naïve explicit methods not to scale, thus more inventive methods are required. One such method is stateless model checking (SMC), which explores in memory-efficient manner the program executions rather than the states of the program. State-of-the-art SMC is typically coupled with partial order reduction (POR) techniques, which argue that certain executions provably produce identical system behavior, thus limiting the amount of executions one needs to explore in order to cover all possible behaviors. Another method to tackle the state-space explosion is symbolic model checking, where the considered techniques operate on a succinct implicit representation of the input system rather than explicitly accessing the system. In this thesis we present new techniques for verification of concurrent systems. We present several novel POR methods for SMC of concurrent programs under various models of semantics, some of which account for write-buffering mechanisms. Additionally, we present novel algorithms for symbolic model checking of finite-state concurrent systems, where the desired property of the systems is to ensure a formally defined notion of fairness. AU - Toman, Viktor ID - 10199 KW - concurrency KW - verification KW - model checking SN - 2663-337X TI - Improved verification techniques for concurrent systems ER - TY - JOUR AB - We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data. AU - Patxot, Marion AU - Trejo Banos, Daniel AU - Kousathanas, Athanasios AU - Orliac, Etienne J AU - Ojavee, Sven E AU - Moser, Gerhard AU - Sidorenko, Julia AU - Kutalik, Zoltan AU - Magi, Reedik AU - Visscher, Peter M AU - Ronnegard, Lars AU - Robinson, Matthew Richard ID - 8429 IS - 1 JF - Nature Communications TI - Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits VL - 12 ER - TY - CONF AB - Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic CONGEST model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labelled graph under batch changes. We investigate, when a batch of alpha edge label changes arrive, - how much time as a function of alpha we need to update an existing solution, and - how much information the nodes have to keep in local memory between batches in order to update the solution quickly. Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. The diverse time complexity of our model spans from constant time, through time polynomial in alpha, and to alpha time, which we show to be enough for any task. AU - Foerster, Klaus-Tycho AU - Korhonen, Janne AU - Paz, Ami AU - Rybicki, Joel AU - Schmid, Stefan ID - 10854 SN - 9781450380720 T2 - Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems TI - Input-dynamic distributed algorithms for communication networks ER - TY - JOUR AB - Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic \congest model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labeled graph under batch changes. We investigate, when a batch of α edge label changes arrive, \beginitemize \item how much time as a function of α we need to update an existing solution, and \item how much information the nodes have to keep in local memory between batches in order to update the solution quickly. \enditemize Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. In particular, we derive non-trivial upper bounds for two selected, contrasting problems: maintaining a minimum spanning tree and detecting cliques. AU - Foerster, Klaus-Tycho AU - Korhonen, Janne AU - Paz, Ami AU - Rybicki, Joel AU - Schmid, Stefan ID - 10855 IS - 1 JF - Proceedings of the ACM on Measurement and Analysis of Computing Systems KW - Computer Networks and Communications KW - Hardware and Architecture KW - Safety KW - Risk KW - Reliability and Quality KW - Computer Science (miscellaneous) SN - 2476-1249 TI - Input-dynamic distributed algorithms for communication networks VL - 5 ER - TY - JOUR AB - We consider planning problems for graphs, Markov Decision Processes (MDPs), and games on graphs in an explicit state space. While graphs represent the most basic planning model, MDPs represent interaction with nature and games on graphs represent interaction with an adversarial environment. We consider two planning problems with k different target sets: (a) the coverage problem asks whether there is a plan for each individual target set; and (b) the sequential target reachability problem asks whether the targets can be reached in a given sequence. For the coverage problem, we present a linear-time algorithm for graphs, and quadratic conditional lower bound for MDPs and games on graphs. For the sequential target problem, we present a linear-time algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic conditional lower bound for games on graphs. Our results with conditional lower bounds, based on the boolean matrix multiplication (BMM) conjecture and strong exponential time hypothesis (SETH), establish (i) model-separation results showing that for the coverage problem MDPs and games on graphs are harder than graphs, and for the sequential reachability problem games on graphs are harder than MDPs and graphs; and (ii) problem-separation results showing that for MDPs the coverage problem is harder than the sequential target problem. AU - Chatterjee, Krishnendu AU - Dvořák, Wolfgang AU - Henzinger, Monika H AU - Svozil, Alexander ID - 9293 IS - 8 JF - Artificial Intelligence SN - 0004-3702 TI - Algorithms and conditional lower bounds for planning problems VL - 297 ER - TY - GEN AB - We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\leq$ 10\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having >95% probability of contributing >0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data. AU - Robinson, Matthew Richard ID - 13063 TI - Probabilistic inference of the genetic architecture of functional enrichment of complex traits ER - TY - JOUR AB - The high processing cost, poor mechanical properties and moderate performance of Bi2Te3–based alloys used in thermoelectric devices limit the cost-effectiveness of this energy conversion technology. Towards solving these current challenges, in the present work, we detail a low temperature solution-based approach to produce Bi2Te3-Cu2-xTe nanocomposites with improved thermoelectric performance. Our approach consists in combining proper ratios of colloidal nanoparticles and to consolidate the resulting mixture into nanocomposites using a hot press. The transport properties of the nanocomposites are characterized and compared with those of pure Bi2Te3 nanomaterials obtained following the same procedure. In contrast with most previous works, the presence of Cu2-xTe nanodomains does not result in a significant reduction of the lattice thermal conductivity of the reference Bi2Te3 nanomaterial, which is already very low. However, the introduction of Cu2-xTe yields a nearly threefold increase of the power factor associated to a simultaneous increase of the Seebeck coefficient and electrical conductivity at temperatures above 400 K. Taking into account the band alignment of the two materials, we rationalize this increase by considering that Cu2-xTe nanostructures, with a relatively low electron affinity, are able to inject electrons into Bi2Te3, enhancing in this way its electrical conductivity. The simultaneous increase of the Seebeck coefficient is related to the energy filtering of charge carriers at energy barriers within Bi2Te3 domains associated with the accumulation of electrons in regions nearby a Cu2-xTe/Bi2Te3 heterojunction. Overall, with the incorporation of a proper amount of Cu2-xTe nanoparticles, we demonstrate a 250% improvement of the thermoelectric figure of merit of Bi2Te3. AU - Zhang, Yu AU - Xing, Congcong AU - Liu, Yu AU - Li, Mengyao AU - Xiao, Ke AU - Guardia, Pablo AU - Lee, Seungho AU - Han, Xu AU - Moghaddam, Ahmad AU - Roa, Joan J AU - Arbiol, Jordi AU - Ibáñez, Maria AU - Pan, Kai AU - Prato, Mirko AU - Xie, Ying AU - Cabot, Andreu ID - 9304 IS - 8 JF - Chemical Engineering Journal SN - 1385-8947 TI - Influence of copper telluride nanodomains on the transport properties of n-type bismuth telluride VL - 418 ER - TY - JOUR AB - Astrocytes extensively infiltrate the neuropil to regulate critical aspects of synaptic development and function. This process is regulated by transcellular interactions between astrocytes and neurons via cell adhesion molecules. How astrocytes coordinate developmental processes among one another to parse out the synaptic neuropil and form non-overlapping territories is unknown. Here we identify a molecular mechanism regulating astrocyte-astrocyte interactions during development to coordinate astrocyte morphogenesis and gap junction coupling. We show that hepaCAM, a disease-linked, astrocyte-enriched cell adhesion molecule, regulates astrocyte competition for territory and morphological complexity in the developing mouse cortex. Furthermore, conditional deletion of Hepacam from developing astrocytes significantly impairs gap junction coupling between astrocytes and disrupts the balance between synaptic excitation and inhibition. Mutations in HEPACAM cause megalencephalic leukoencephalopathy with subcortical cysts in humans. Therefore, our findings suggest that disruption of astrocyte self-organization mechanisms could be an underlying cause of neural pathology. AU - Baldwin, Katherine T. AU - Tan, Christabel X. AU - Strader, Samuel T. AU - Jiang, Changyu AU - Savage, Justin T. AU - Elorza-Vidal, Xabier AU - Contreras, Ximena AU - Rülicke, Thomas AU - Hippenmeyer, Simon AU - Estévez, Raúl AU - Ji, Ru-Rong AU - Eroglu, Cagla ID - 9793 IS - 15 JF - Neuron SN - 0896-6273 TI - HepaCAM controls astrocyte self-organization and coupling VL - 109 ER - TY - JOUR AB - Copper chalcogenides are outstanding thermoelectric materials for applications in the medium-high temperature range. Among different chalcogenides, while Cu2−xSe is characterized by higher thermoelectric figures of merit, Cu2−xS provides advantages in terms of low cost and element abundance. In the present work, we investigate the effect of different dopants to enhance the Cu2−xS performance and also its thermal stability. Among the tested options, Pb-doped Cu2−xS shows the highest improvement in stability against sulfur volatilization. Additionally, Pb incorporation allows tuning charge carrier concentration, which enables a significant improvement of the power factor. We demonstrate here that the introduction of an optimal additive amount of just 0.3% results in a threefold increase of the power factor in the middle-temperature range (500–800 K) and a record dimensionless thermoelectric figure of merit above 2 at 880 K. AU - Zhang, Yu AU - Xing, Congcong AU - Liu, Yu AU - Spadaro, Maria Chiara AU - Wang, Xiang AU - Li, Mengyao AU - Xiao, Ke AU - Zhang, Ting AU - Guardia, Pablo AU - Lim, Khak Ho AU - Moghaddam, Ahmad Ostovari AU - Llorca, Jordi AU - Arbiol, Jordi AU - Ibáñez, Maria AU - Cabot, Andreu ID - 9305 IS - 7 JF - Nano Energy SN - 2211-2855 TI - Doping-mediated stabilization of copper vacancies to promote thermoelectric properties of Cu2-xS VL - 85 ER - TY - JOUR AB - Plant fitness is largely dependent on the root, the underground organ, which, besides its anchoring function, supplies the plant body with water and all nutrients necessary for growth and development. To exploit the soil effectively, roots must constantly integrate environmental signals and react through adjustment of growth and development. Important components of the root management strategy involve a rapid modulation of the root growth kinetics and growth direction, as well as an increase of the root system radius through formation of lateral roots (LRs). At the molecular level, such a fascinating growth and developmental flexibility of root organ requires regulatory networks that guarantee stability of the developmental program but also allows integration of various environmental inputs. The plant hormone auxin is one of the principal endogenous regulators of root system architecture by controlling primary root growth and formation of LR. In this review, we discuss recent progress in understanding molecular networks where auxin is one of the main players shaping the root system and acting as mediator between endogenous cues and environmental factors. AU - Cavallari, Nicola AU - Artner, Christina AU - Benková, Eva ID - 9212 IS - 7 JF - Cold Spring Harbor Perspectives in Biology SN - 1943-0264 TI - Auxin-regulated lateral root organogenesis VL - 13 ER - TY - JOUR AB - Chronic psychological stress is one of the most important triggers and environmental risk factors for neuropsychiatric disorders. Chronic stress can influence all organs via the secretion of stress hormones, including glucocorticoids by the adrenal glands, which coordinate the stress response across the body. In the brain, glucocorticoid receptors (GR) are expressed by various cell types including microglia, which are its resident immune cells regulating stress-induced inflammatory processes. To study the roles of microglial GR under normal homeostatic conditions and following chronic stress, we generated a mouse model in which the GR gene is depleted in microglia specifically at adulthood to prevent developmental confounds. We first confirmed that microglia were depleted in GR in our model in males and females among the cingulate cortex and the hippocampus, both stress-sensitive brain regions. Then, cohorts of microglial-GR depleted and wild-type (WT) adult female mice were housed for 3 weeks in a standard or stressful condition, using a chronic unpredictable mild stress (CUMS) paradigm. CUMS induced stress-related behavior in both microglial-GR depleted and WT animals as demonstrated by a decrease of both saccharine preference and progressive ratio breakpoint. Nevertheless, the hippocampal microglial and neural mechanisms underlying the adaptation to stress occurred differently between the two genotypes. Upon CUMS exposure, microglial morphology was altered in the WT controls, without any apparent effect in microglial-GR depleted mice. Furthermore, in the standard environment condition, GR depleted-microglia showed increased expression of pro-inflammatory genes, and genes involved in microglial homeostatic functions (such as Trem2, Cx3cr1 and Mertk). On the contrary, in CUMS condition, GR depleted-microglia showed reduced expression levels of pro-inflammatory genes and increased neuroprotective as well as anti-inflammatory genes compared to WT-microglia. Moreover, in microglial-GR depleted mice, but not in WT mice, CUMS led to a significant reduction of CA1 long-term potentiation and paired-pulse ratio. Lastly, differences in adult hippocampal neurogenesis were observed between the genotypes during normal homeostatic conditions, with microglial-GR deficiency increasing the formation of newborn neurons in the dentate gyrus subgranular zone independently from stress exposure. Together, these findings indicate that, although the deletion of microglial GR did not prevent the animal’s ability to respond to stress, it contributed to modulating hippocampal functions in both standard and stressful conditions, notably by shaping the microglial response to chronic stress. AU - Picard, Katherine AU - Bisht, Kanchan AU - Poggini, Silvia AU - Garofalo, Stefano AU - Golia, Maria Teresa AU - Basilico, Bernadette AU - Abdallah, Fatima AU - Ciano Albanese, Naomi AU - Amrein, Irmgard AU - Vernoux, Nathalie AU - Sharma, Kaushik AU - Hui, Chin Wai AU - C. Savage, Julie AU - Limatola, Cristina AU - Ragozzino, Davide AU - Maggi, Laura AU - Branchi, Igor AU - Tremblay, Marie Ève ID - 9953 JF - Brain, Behavior, and Immunity SN - 0889-1591 TI - Microglial-glucocorticoid receptor depletion alters the response of hippocampal microglia and neurons in a chronic unpredictable mild stress paradigm in female mice VL - 97 ER - TY - JOUR AB - Composite materials offer numerous advantages in a wide range of applications, including thermoelectrics. Here, semiconductor–metal composites are produced by just blending nanoparticles of a sulfide semiconductor obtained in aqueous solution and at room temperature with a metallic Cu powder. The obtained blend is annealed in a reducing atmosphere and afterward consolidated into dense polycrystalline pellets through spark plasma sintering (SPS). We observe that, during the annealing process, the presence of metallic copper activates a partial reduction of the PbS, resulting in the formation of PbS–Pb–CuxS composites. The presence of metallic lead during the SPS process habilitates the liquid-phase sintering of the composite. Besides, by comparing the transport properties of PbS, the PbS–Pb–CuxS composites, and PbS–CuxS composites obtained by blending PbS and CuxS nanoparticles, we demonstrate that the presence of metallic lead decisively contributes to a strong increase of the charge carrier concentration through spillover of charge carriers enabled by the low work function of lead. The increase in charge carrier concentration translates into much higher electrical conductivities and moderately lower Seebeck coefficients. These properties translate into power factors up to 2.1 mW m–1 K–2 at ambient temperature, well above those of PbS and PbS + CuxS. Additionally, the presence of multiple phases in the final composite results in a notable decrease in the lattice thermal conductivity. Overall, the introduction of metallic copper in the initial blend results in a significant improvement of the thermoelectric performance of PbS, reaching a dimensionless thermoelectric figure of merit ZT = 1.1 at 750 K, which represents about a 400% increase over bare PbS. Besides, an average ZTave = 0.72 in the temperature range 320–773 K is demonstrated. AU - Li, Mengyao AU - Liu, Yu AU - Zhang, Yu AU - Han, Xu AU - Xiao, Ke AU - Nabahat, Mehran AU - Arbiol, Jordi AU - Llorca, Jordi AU - Ibáñez, Maria AU - Cabot, Andreu ID - 10327 IS - 43 JF - ACS Applied Materials and Interfaces KW - CuxS KW - PbS KW - energy conversion KW - nanocomposite KW - nanoparticle KW - solution synthesis KW - thermoelectric SN - 1944-8244 TI - PbS–Pb–CuxS composites for thermoelectric application VL - 13 ER - TY - JOUR AB - Cu2–xS has become one of the most promising thermoelectric materials for application in the middle-high temperature range. Its advantages include the abundance, low cost, and safety of its elements and a high performance at relatively elevated temperatures. However, stability issues limit its operation current and temperature, thus calling for the optimization of the material performance in the middle temperature range. Here, we present a synthetic protocol for large scale production of covellite CuS nanoparticles at ambient temperature and atmosphere, and using water as a solvent. The crystal phase and stoichiometry of the particles are afterward tuned through an annealing process at a moderate temperature under inert or reducing atmosphere. While annealing under argon results in Cu1.8S nanopowder with a rhombohedral crystal phase, annealing in an atmosphere containing hydrogen leads to tetragonal Cu1.96S. High temperature X-ray diffraction analysis shows the material annealed in argon to transform to the cubic phase at ca. 400 K, while the material annealed in the presence of hydrogen undergoes two phase transitions, first to hexagonal and then to the cubic structure. The annealing atmosphere, temperature, and time allow adjustment of the density of copper vacancies and thus tuning of the charge carrier concentration and material transport properties. In this direction, the material annealed under Ar is characterized by higher electrical conductivities but lower Seebeck coefficients than the material annealed in the presence of hydrogen. By optimizing the charge carrier concentration through the annealing time, Cu2–xS with record figures of merit in the middle temperature range, up to 1.41 at 710 K, is obtained. We finally demonstrate that this strategy, based on a low-cost and scalable solution synthesis process, is also suitable for the production of high performance Cu2–xS layers using high throughput and cost-effective printing technologies. AU - Li, Mengyao AU - Liu, Yu AU - Zhang, Yu AU - Han, Xu AU - Zhang, Ting AU - Zuo, Yong AU - Xie, Chenyang AU - Xiao, Ke AU - Arbiol, Jordi AU - Llorca, Jordi AU - Ibáñez, Maria AU - Liu, Junfeng AU - Cabot, Andreu ID - 9235 IS - 3 JF - ACS Nano KW - General Engineering KW - General Physics and Astronomy KW - General Materials Science SN - 1936-0851 TI - Effect of the annealing atmosphere on crystal phase and thermoelectric properties of copper sulfide VL - 15 ER - TY - JOUR AB - Two common representations of close packings of identical spheres consisting of hexagonal layers, called Barlow stackings, appear abundantly in minerals and metals. These motifs, however, occupy an identical portion of space and bear identical first-order topological signatures as measured by persistent homology. Here we present a novel method based on k-fold covers that unambiguously distinguishes between these patterns. Moreover, our approach provides topological evidence that the FCC motif is the more stable of the two in the context of evolving experimental sphere packings during the transition from disordered to an ordered state. We conclude that our approach can be generalised to distinguish between various Barlow stackings manifested in minerals and metals. AU - Osang, Georg F AU - Edelsbrunner, Herbert AU - Saadatfar, Mohammad ID - 10204 IS - 40 JF - Soft Matter SN - 1744-683X TI - Topological signatures and stability of hexagonal close packing and Barlow stackings VL - 17 ER - TY - CONF AB - Given a finite set A ⊂ ℝ^d, let Cov_{r,k} denote the set of all points within distance r to at least k points of A. Allowing r and k to vary, we obtain a 2-parameter family of spaces that grow larger when r increases or k decreases, called the multicover bifiltration. Motivated by the problem of computing the homology of this bifiltration, we introduce two closely related combinatorial bifiltrations, one polyhedral and the other simplicial, which are both topologically equivalent to the multicover bifiltration and far smaller than a Čech-based model considered in prior work of Sheehy. Our polyhedral construction is a bifiltration of the rhomboid tiling of Edelsbrunner and Osang, and can be efficiently computed using a variant of an algorithm given by these authors as well. Using an implementation for dimension 2 and 3, we provide experimental results. Our simplicial construction is useful for understanding the polyhedral construction and proving its correctness. AU - Corbet, René AU - Kerber, Michael AU - Lesnick, Michael AU - Osang, Georg F ID - 9605 SN - 18688969 T2 - Leibniz International Proceedings in Informatics TI - Computing the multicover bifiltration VL - 189 ER - TY - CONF AB - Isomanifolds are the generalization of isosurfaces to arbitrary dimension and codimension, i.e. submanifolds of ℝ^d defined as the zero set of some multivariate multivalued smooth function f: ℝ^d → ℝ^{d-n}, where n is the intrinsic dimension of the manifold. A natural way to approximate a smooth isomanifold M is to consider its Piecewise-Linear (PL) approximation M̂ based on a triangulation 𝒯 of the ambient space ℝ^d. In this paper, we describe a simple algorithm to trace isomanifolds from a given starting point. The algorithm works for arbitrary dimensions n and d, and any precision D. Our main result is that, when f (or M) has bounded complexity, the complexity of the algorithm is polynomial in d and δ = 1/D (and unavoidably exponential in n). Since it is known that for δ = Ω (d^{2.5}), M̂ is O(D²)-close and isotopic to M, our algorithm produces a faithful PL-approximation of isomanifolds of bounded complexity in time polynomial in d. Combining this algorithm with dimensionality reduction techniques, the dependency on d in the size of M̂ can be completely removed with high probability. We also show that the algorithm can handle isomanifolds with boundary and, more generally, isostratifolds. The algorithm for isomanifolds with boundary has been implemented and experimental results are reported, showing that it is practical and can handle cases that are far ahead of the state-of-the-art. AU - Boissonnat, Jean-Daniel AU - Kachanovich, Siargey AU - Wintraecken, Mathijs ID - 9441 SN - 1868-8969 T2 - 37th International Symposium on Computational Geometry (SoCG 2021) TI - Tracing isomanifolds in Rd in time polynomial in d using Coxeter-Freudenthal-Kuhn triangulations VL - 189 ER - TY - JOUR AB - We consider the core algorithmic problems related to verification of systems with respect to three classical quantitative properties, namely, the mean-payoff, the ratio, and the minimum initial credit for energy property. The algorithmic problem given a graph and a quantitative property asks to compute the optimal value (the infimum value over all traces) from every node of the graph. We consider graphs with bounded treewidth—a class that contains the control flow graphs of most programs. Let n denote the number of nodes of a graph, m the number of edges (for bounded treewidth 𝑚=𝑂(𝑛)) and W the largest absolute value of the weights. Our main theoretical results are as follows. First, for the minimum initial credit problem we show that (1) for general graphs the problem can be solved in 𝑂(𝑛2⋅𝑚) time and the associated decision problem in 𝑂(𝑛⋅𝑚) time, improving the previous known 𝑂(𝑛3⋅𝑚⋅log(𝑛⋅𝑊)) and 𝑂(𝑛2⋅𝑚) bounds, respectively; and (2) for bounded treewidth graphs we present an algorithm that requires 𝑂(𝑛⋅log𝑛) time. Second, for bounded treewidth graphs we present an algorithm that approximates the mean-payoff value within a factor of 1+𝜖 in time 𝑂(𝑛⋅log(𝑛/𝜖)) as compared to the classical exact algorithms on general graphs that require quadratic time. Third, for the ratio property we present an algorithm that for bounded treewidth graphs works in time 𝑂(𝑛⋅log(|𝑎⋅𝑏|))=𝑂(𝑛⋅log(𝑛⋅𝑊)), when the output is 𝑎𝑏, as compared to the previously best known algorithm on general graphs with running time 𝑂(𝑛2⋅log(𝑛⋅𝑊)). We have implemented some of our algorithms and show that they present a significant speedup on standard benchmarks. AU - Chatterjee, Krishnendu AU - Ibsen-Jensen, Rasmus AU - Pavlogiannis, Andreas ID - 9393 JF - Formal Methods in System Design SN - 0925-9856 TI - Faster algorithms for quantitative verification in bounded treewidth graphs VL - 57 ER - TY - JOUR AB - The early development of many organisms involves the folding of cell monolayers, but this behaviour is difficult to reproduce in vitro; therefore, both mechanistic causes and effects of local curvature remain unclear. Here we study epithelial cell monolayers on corrugated hydrogels engineered into wavy patterns, examining how concave and convex curvatures affect cellular and nuclear shape. We find that substrate curvature affects monolayer thickness, which is larger in valleys than crests. We show that this feature generically arises in a vertex model, leading to the hypothesis that cells may sense curvature by modifying the thickness of the tissue. We find that local curvature also affects nuclear morphology and positioning, which we explain by extending the vertex model to take into account membrane–nucleus interactions, encoding thickness modulation in changes to nuclear deformation and position. We propose that curvature governs the spatial distribution of yes-associated proteins via nuclear shape and density changes. We show that curvature also induces significant variations in lamins, chromatin condensation and cell proliferation rate in folded epithelial tissues. Together, this work identifies active cell mechanics and nuclear mechanoadaptation as the key players of the mechanistic regulation of epithelia to substrate curvature. AU - Luciano, Marine AU - Xue, Shi-lei AU - De Vos, Winnok H. AU - Redondo-Morata, Lorena AU - Surin, Mathieu AU - Lafont, Frank AU - Hannezo, Edouard B AU - Gabriele, Sylvain ID - 10365 IS - 12 JF - Nature Physics SN - 1745-2473 TI - Cell monolayers sense curvature by exploiting active mechanics and nuclear mechanoadaptation VL - 17 ER - TY - JOUR AB - In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field. AU - Klionsky, Daniel J. AU - Abdel-Aziz, Amal Kamal AU - Abdelfatah, Sara AU - Abdellatif, Mahmoud AU - Abdoli, Asghar AU - Abel, Steffen AU - Abeliovich, Hagai AU - Abildgaard, Marie H. AU - Abudu, Yakubu Princely AU - Acevedo-Arozena, Abraham AU - Adamopoulos, Iannis E. AU - Adeli, Khosrow AU - Adolph, Timon E. AU - Adornetto, Annagrazia AU - Aflaki, Elma AU - Agam, Galila AU - Agarwal, Anupam AU - Aggarwal, Bharat B. AU - Agnello, Maria AU - Agostinis, Patrizia AU - Agrewala, Javed N. AU - Agrotis, Alexander AU - Aguilar, Patricia V. AU - Ahmad, S. Tariq AU - Ahmed, Zubair M. AU - Ahumada-Castro, Ulises AU - Aits, Sonja AU - Aizawa, Shu AU - Akkoc, Yunus AU - Akoumianaki, Tonia AU - Akpinar, Hafize Aysin AU - Al-Abd, Ahmed M. AU - Al-Akra, Lina AU - Al-Gharaibeh, Abeer AU - Alaoui-Jamali, Moulay A. AU - Alberti, Simon AU - Alcocer-Gómez, Elísabet AU - Alessandri, Cristiano AU - Ali, Muhammad AU - Alim Al-Bari, M. Abdul AU - Aliwaini, Saeb AU - Alizadeh, Javad AU - Almacellas, Eugènia AU - Almasan, Alexandru AU - Alonso, Alicia AU - Alonso, Guillermo D. AU - Altan-Bonnet, Nihal AU - Altieri, Dario C. AU - Álvarez, Élida M.C. AU - Alves, Sara AU - Alves Da Costa, Cristine AU - Alzaharna, Mazen M. AU - Amadio, Marialaura AU - Amantini, Consuelo AU - Amaral, Cristina AU - Ambrosio, Susanna AU - Amer, Amal O. AU - Ammanathan, Veena AU - An, Zhenyi AU - Andersen, Stig U. AU - Andrabi, Shaida A. AU - Andrade-Silva, Magaiver AU - Andres, Allen M. AU - Angelini, Sabrina AU - Ann, David AU - Anozie, Uche C. AU - Ansari, Mohammad Y. AU - Antas, Pedro AU - Antebi, Adam AU - Antón, Zuriñe AU - Anwar, Tahira AU - Apetoh, Lionel AU - Apostolova, Nadezda AU - Araki, Toshiyuki AU - Araki, Yasuhiro AU - Arasaki, Kohei AU - Araújo, Wagner L. AU - Araya, Jun AU - Arden, Catherine AU - Arévalo, Maria Angeles AU - Arguelles, Sandro AU - Arias, Esperanza AU - Arikkath, Jyothi AU - Arimoto, Hirokazu AU - Ariosa, Aileen R. AU - Armstrong-James, Darius AU - Arnauné-Pelloquin, Laetitia AU - Aroca, Angeles AU - Arroyo, Daniela S. AU - Arsov, Ivica AU - Artero, Rubén AU - Asaro, Dalia Maria Lucia AU - Aschner, Michael AU - Ashrafizadeh, Milad AU - Ashur-Fabian, Osnat AU - Atanasov, Atanas G. AU - Au, Alicia K. AU - Auberger, Patrick AU - Auner, Holger W. AU - Aurelian, Laure AU - Autelli, Riccardo AU - Avagliano, Laura AU - Ávalos, Yenniffer AU - Aveic, Sanja AU - Aveleira, Célia Alexandra AU - Avin-Wittenberg, Tamar AU - Aydin, Yucel AU - Ayton, Scott AU - Ayyadevara, Srinivas AU - Azzopardi, Maria AU - Baba, Misuzu AU - Backer, Jonathan M. AU - Backues, Steven K. AU - Bae, Dong Hun AU - Bae, Ok Nam AU - Bae, Soo Han AU - Baehrecke, Eric H. AU - Baek, Ahruem AU - Baek, Seung Hoon AU - Baek, Sung Hee AU - Bagetta, Giacinto AU - Bagniewska-Zadworna, Agnieszka AU - Bai, Hua AU - Bai, Jie AU - Bai, Xiyuan AU - Bai, Yidong AU - Bairagi, Nandadulal AU - Baksi, Shounak AU - Balbi, Teresa AU - Baldari, Cosima T. AU - Balduini, Walter AU - Ballabio, Andrea AU - Ballester, Maria AU - Balazadeh, Salma AU - Balzan, Rena AU - Bandopadhyay, Rina AU - Banerjee, Sreeparna AU - Banerjee, Sulagna AU - Bánréti, Ágnes AU - Bao, Yan AU - Baptista, Mauricio S. AU - Baracca, Alessandra AU - Barbati, Cristiana AU - Bargiela, Ariadna AU - Barilà, Daniela AU - Barlow, Peter G. AU - Barmada, Sami J. AU - Barreiro, Esther AU - Barreto, George E. AU - Bartek, Jiri AU - Bartel, Bonnie AU - Bartolome, Alberto AU - Barve, Gaurav R. AU - Basagoudanavar, Suresh H. AU - Bassham, Diane C. AU - Bast, Robert C. AU - Basu, Alakananda AU - Batoko, Henri AU - Batten, Isabella AU - Baulieu, Etienne E. AU - Baumgarner, Bradley L. AU - Bayry, Jagadeesh AU - Beale, Rupert AU - Beau, Isabelle AU - Beaumatin, Florian AU - Bechara, Luiz R.G. AU - Beck, George R. AU - Beers, Michael F. AU - Begun, Jakob AU - Behrends, Christian AU - Behrens, Georg M.N. AU - Bei, Roberto AU - Bejarano, Eloy AU - Bel, Shai AU - Behl, Christian AU - Belaid, Amine AU - Belgareh-Touzé, Naïma AU - Bellarosa, Cristina AU - Belleudi, Francesca AU - Belló Pérez, Melissa AU - Bello-Morales, Raquel AU - Beltran, Jackeline Soares De Oliveira AU - Beltran, Sebastián AU - Benbrook, Doris Mangiaracina AU - Bendorius, Mykolas AU - Benitez, Bruno A. AU - Benito-Cuesta, Irene AU - Bensalem, Julien AU - Berchtold, Martin W. AU - Berezowska, Sabina AU - Bergamaschi, Daniele AU - Bergami, Matteo AU - Bergmann, Andreas AU - Berliocchi, Laura AU - Berlioz-Torrent, Clarisse AU - Bernard, Amélie AU - Berthoux, Lionel AU - Besirli, Cagri G. AU - Besteiro, Sebastien AU - Betin, Virginie M. AU - Beyaert, Rudi AU - Bezbradica, Jelena S. AU - Bhaskar, Kiran AU - Bhatia-Kissova, Ingrid AU - Bhattacharya, Resham AU - Bhattacharya, Sujoy AU - Bhattacharyya, Shalmoli AU - Bhuiyan, Md Shenuarin AU - Bhutia, Sujit Kumar AU - Bi, Lanrong AU - Bi, Xiaolin AU - Biden, Trevor J. AU - Bijian, Krikor AU - Billes, Viktor A. AU - Binart, Nadine AU - Bincoletto, Claudia AU - Birgisdottir, Asa B. AU - Bjorkoy, Geir AU - Blanco, Gonzalo AU - Blas-Garcia, Ana AU - Blasiak, Janusz AU - Blomgran, Robert AU - Blomgren, Klas AU - Blum, Janice S. AU - Boada-Romero, Emilio AU - Boban, Mirta AU - Boesze-Battaglia, Kathleen AU - Boeuf, Philippe AU - Boland, Barry AU - Bomont, Pascale AU - Bonaldo, Paolo AU - Bonam, Srinivasa Reddy AU - Bonfili, Laura AU - Bonifacino, Juan S. AU - Boone, Brian A. AU - Bootman, Martin D. AU - Bordi, Matteo AU - Borner, Christoph AU - Bornhauser, Beat C. AU - Borthakur, Gautam AU - Bosch, Jürgen AU - Bose, Santanu AU - Botana, Luis M. AU - Botas, Juan AU - Boulanger, Chantal M. AU - Boulton, Michael E. AU - Bourdenx, Mathieu AU - Bourgeois, Benjamin AU - Bourke, Nollaig M. AU - Bousquet, Guilhem AU - Boya, Patricia AU - Bozhkov, Peter V. AU - Bozi, Luiz H.M. AU - Bozkurt, Tolga O. AU - Brackney, Doug E. AU - Brandts, Christian H. AU - Braun, Ralf J. AU - Braus, Gerhard H. AU - Bravo-Sagua, Roberto AU - Bravo-San Pedro, José M. AU - Brest, Patrick AU - Bringer, Marie Agnès AU - Briones-Herrera, Alfredo AU - Broaddus, V. Courtney AU - Brodersen, Peter AU - Brodsky, Jeffrey L. AU - Brody, Steven L. AU - Bronson, Paola G. AU - Bronstein, Jeff M. AU - Brown, Carolyn N. AU - Brown, Rhoderick E. AU - Brum, Patricia C. AU - Brumell, John H. AU - Brunetti-Pierri, Nicola AU - Bruno, Daniele AU - Bryson-Richardson, Robert J. AU - Bucci, Cecilia AU - Buchrieser, Carmen AU - Bueno, Marta AU - Buitrago-Molina, Laura Elisa AU - Buraschi, Simone AU - Buch, Shilpa AU - Buchan, J. Ross AU - Buckingham, Erin M. AU - Budak, Hikmet AU - Budini, Mauricio AU - Bultynck, Geert AU - Burada, Florin AU - Burgoyne, Joseph R. AU - Burón, M. Isabel AU - Bustos, Victor AU - Büttner, Sabrina AU - Butturini, Elena AU - Byrd, Aaron AU - Cabas, Isabel AU - Cabrera-Benitez, Sandra AU - Cadwell, Ken AU - Cai, Jingjing AU - Cai, Lu AU - Cai, Qian AU - Cairó, Montserrat AU - Calbet, Jose A. AU - Caldwell, Guy A. AU - Caldwell, Kim A. AU - Call, Jarrod A. AU - Calvani, Riccardo AU - Calvo, Ana C. AU - Calvo-Rubio Barrera, Miguel AU - Camara, Niels O.S. AU - Camonis, Jacques H. AU - Camougrand, Nadine AU - Campanella, Michelangelo AU - Campbell, Edward M. AU - Campbell-Valois, François Xavier AU - Campello, Silvia AU - Campesi, Ilaria AU - Campos, Juliane C. AU - Camuzard, Olivier AU - Cancino, Jorge AU - Candido De Almeida, Danilo AU - Canesi, Laura AU - Caniggia, Isabella AU - Canonico, Barbara AU - Cantí, Carles AU - Cao, Bin AU - Caraglia, Michele AU - Caramés, Beatriz AU - Carchman, Evie H. AU - Cardenal-Muñoz, Elena AU - Cardenas, Cesar AU - Cardenas, Luis AU - Cardoso, Sandra M. AU - Carew, Jennifer S. AU - Carle, Georges F. AU - Carleton, Gillian AU - Carloni, Silvia AU - Carmona-Gutierrez, Didac AU - Carneiro, Leticia A. AU - Carnevali, Oliana AU - Carosi, Julian M. AU - Carra, Serena AU - Carrier, Alice AU - Carrier, Lucie AU - Carroll, Bernadette AU - Carter, A. Brent AU - Carvalho, Andreia Neves AU - Casanova, Magali AU - Casas, Caty AU - Casas, Josefina AU - Cassioli, Chiara AU - Castillo, Eliseo F. AU - Castillo, Karen AU - Castillo-Lluva, Sonia AU - Castoldi, Francesca AU - Castori, Marco AU - Castro, Ariel F. AU - Castro-Caldas, Margarida AU - Castro-Hernandez, Javier AU - Castro-Obregon, Susana AU - Catz, Sergio D. AU - Cavadas, Claudia AU - Cavaliere, Federica AU - Cavallini, Gabriella AU - Cavinato, Maria AU - Cayuela, Maria L. AU - Cebollada Rica, Paula AU - Cecarini, Valentina AU - Cecconi, Francesco AU - Cechowska-Pasko, Marzanna AU - Cenci, Simone AU - Ceperuelo-Mallafré, Victòria AU - Cerqueira, João J. AU - Cerutti, Janete M. AU - Cervia, Davide AU - Cetintas, Vildan Bozok AU - Cetrullo, Silvia AU - Chae, Han Jung AU - Chagin, Andrei S. AU - Chai, Chee Yin AU - Chakrabarti, Gopal AU - Chakrabarti, Oishee AU - Chakraborty, Tapas AU - Chakraborty, Trinad AU - Chami, Mounia AU - Chamilos, Georgios AU - Chan, David W. AU - Chan, Edmond Y.W. AU - Chan, Edward D. AU - Chan, H. Y.Edwin AU - Chan, Helen H. AU - Chan, Hung AU - Chan, Matthew T.V. AU - Chan, Yau Sang AU - Chandra, Partha K. AU - Chang, Chih Peng AU - Chang, Chunmei AU - Chang, Hao Chun AU - Chang, Kai AU - Chao, Jie AU - Chapman, Tracey AU - Charlet-Berguerand, Nicolas AU - Chatterjee, Samrat AU - Chaube, Shail K. AU - Chaudhary, Anu AU - Chauhan, Santosh AU - Chaum, Edward AU - Checler, Frédéric AU - Cheetham, Michael E. AU - Chen, Chang Shi AU - Chen, Guang Chao AU - Chen, Jian Fu AU - Chen, Liam L. AU - Chen, Leilei AU - Chen, Lin AU - Chen, Mingliang AU - Chen, Mu Kuan AU - Chen, Ning AU - Chen, Quan AU - Chen, Ruey Hwa AU - Chen, Shi AU - Chen, Wei AU - Chen, Weiqiang AU - Chen, Xin Ming AU - Chen, Xiong Wen AU - Chen, Xu AU - Chen, Yan AU - Chen, Ye Guang AU - Chen, Yingyu AU - Chen, Yongqiang AU - Chen, Yu Jen AU - Chen, Yue Qin AU - Chen, Zhefan Stephen AU - Chen, Zhi AU - Chen, Zhi Hua AU - Chen, Zhijian J. AU - Chen, Zhixiang AU - Cheng, Hanhua AU - Cheng, Jun AU - Cheng, Shi Yuan AU - Cheng, Wei AU - Cheng, Xiaodong AU - Cheng, Xiu Tang AU - Cheng, Yiyun AU - Cheng, Zhiyong AU - Chen, Zhong AU - Cheong, Heesun AU - Cheong, Jit Kong AU - Chernyak, Boris V. AU - Cherry, Sara AU - Cheung, Chi Fai Randy AU - Cheung, Chun Hei Antonio AU - Cheung, King Ho AU - Chevet, Eric AU - Chi, Richard J. AU - Chiang, Alan Kwok Shing AU - Chiaradonna, Ferdinando AU - Chiarelli, Roberto AU - Chiariello, Mario AU - Chica, Nathalia AU - Chiocca, Susanna AU - Chiong, Mario AU - Chiou, Shih Hwa AU - Chiramel, Abhilash I. AU - Chiurchiù, Valerio AU - Cho, Dong Hyung AU - Choe, Seong Kyu AU - Choi, Augustine M.K. AU - Choi, Mary E. AU - Choudhury, Kamalika Roy AU - Chow, Norman S. AU - Chu, Charleen T. AU - Chua, Jason P. AU - Chua, John Jia En AU - Chung, Hyewon AU - Chung, Kin Pan AU - Chung, Seockhoon AU - Chung, So Hyang AU - Chung, Yuen Li AU - Cianfanelli, Valentina AU - Ciechomska, Iwona A. AU - Cifuentes, Mariana AU - Cinque, Laura AU - Cirak, Sebahattin AU - Cirone, Mara AU - Clague, Michael J. AU - Clarke, Robert AU - Clementi, Emilio AU - Coccia, Eliana M. AU - Codogno, Patrice AU - Cohen, Ehud AU - Cohen, Mickael M. AU - Colasanti, Tania AU - Colasuonno, Fiorella AU - Colbert, Robert A. AU - Colell, Anna AU - Čolić, Miodrag AU - Coll, Nuria S. AU - Collins, Mark O. AU - Colombo, María I. AU - Colón-Ramos, Daniel A. AU - Combaret, Lydie AU - Comincini, Sergio AU - Cominetti, Márcia R. AU - Consiglio, Antonella AU - Conte, Andrea AU - Conti, Fabrizio AU - Contu, Viorica Raluca AU - Cookson, Mark R. AU - Coombs, Kevin M. AU - Coppens, Isabelle AU - Corasaniti, Maria Tiziana AU - Corkery, Dale P. AU - Cordes, Nils AU - Cortese, Katia AU - Costa, Maria Do Carmo AU - Costantino, Sarah AU - Costelli, Paola AU - Coto-Montes, Ana AU - Crack, Peter J. AU - Crespo, Jose L. AU - Criollo, Alfredo AU - Crippa, Valeria AU - Cristofani, Riccardo AU - Csizmadia, Tamas AU - Cuadrado, Antonio AU - Cui, Bing AU - Cui, Jun AU - Cui, Yixian AU - Cui, Yong AU - Culetto, Emmanuel AU - Cumino, Andrea C. AU - Cybulsky, Andrey V. AU - Czaja, Mark J. AU - Czuczwar, Stanislaw J. AU - D’Adamo, Stefania AU - D’Amelio, Marcello AU - D’Arcangelo, Daniela AU - D’Lugos, Andrew C. AU - D’Orazi, Gabriella AU - Da Silva, James A. AU - Dafsari, Hormos Salimi AU - Dagda, Ruben K. AU - Dagdas, Yasin AU - Daglia, Maria AU - Dai, Xiaoxia AU - Dai, Yun AU - Dai, Yuyuan AU - Dal Col, Jessica AU - Dalhaimer, Paul AU - Dalla Valle, Luisa AU - Dallenga, Tobias AU - Dalmasso, Guillaume AU - Damme, Markus AU - Dando, Ilaria AU - Dantuma, Nico P. AU - Darling, April L. AU - Das, Hiranmoy AU - Dasarathy, Srinivasan AU - Dasari, Santosh K. AU - Dash, Srikanta AU - Daumke, Oliver AU - Dauphinee, Adrian N. AU - Davies, Jeffrey S. AU - Dávila, Valeria A. AU - Davis, Roger J. AU - Davis, Tanja AU - Dayalan Naidu, Sharadha AU - De Amicis, Francesca AU - De Bosscher, Karolien AU - De Felice, Francesca AU - De Franceschi, Lucia AU - De Leonibus, Chiara AU - De Mattos Barbosa, Mayara G. AU - De Meyer, Guido R.Y. AU - De Milito, Angelo AU - De Nunzio, Cosimo AU - De Palma, Clara AU - De Santi, Mauro AU - De Virgilio, Claudio AU - De Zio, Daniela AU - Debnath, Jayanta AU - Debosch, Brian J. AU - Decuypere, Jean Paul AU - Deehan, Mark A. AU - Deflorian, Gianluca AU - Degregori, James AU - Dehay, Benjamin AU - Del Rio, Gabriel AU - Delaney, Joe R. AU - Delbridge, Lea M.D. AU - Delorme-Axford, Elizabeth AU - Delpino, M. Victoria AU - Demarchi, Francesca AU - Dembitz, Vilma AU - Demers, Nicholas D. AU - Deng, Hongbin AU - Deng, Zhiqiang AU - Dengjel, Joern AU - Dent, Paul AU - Denton, Donna AU - Depamphilis, Melvin L. AU - Der, Channing J. AU - Deretic, Vojo AU - Descoteaux, Albert AU - Devis, Laura AU - Devkota, Sushil AU - Devuyst, Olivier AU - Dewson, Grant AU - Dharmasivam, Mahendiran AU - Dhiman, Rohan AU - Di Bernardo, Diego AU - Di Cristina, Manlio AU - Di Domenico, Fabio AU - Di Fazio, Pietro AU - Di Fonzo, Alessio AU - Di Guardo, Giovanni AU - Di Guglielmo, Gianni M. AU - Di Leo, Luca AU - Di Malta, Chiara AU - Di Nardo, Alessia AU - Di Rienzo, Martina AU - Di Sano, Federica AU - Diallinas, George AU - Diao, Jiajie AU - Diaz-Araya, Guillermo AU - Díaz-Laviada, Inés AU - Dickinson, Jared M. AU - Diederich, Marc AU - Dieudé, Mélanie AU - Dikic, Ivan AU - Ding, Shiping AU - Ding, Wen Xing AU - Dini, Luciana AU - Dinić, Jelena AU - Dinic, Miroslav AU - Dinkova-Kostova, Albena T. AU - Dionne, Marc S. AU - Distler, Jörg H.W. AU - Diwan, Abhinav AU - Dixon, Ian M.C. AU - Djavaheri-Mergny, Mojgan AU - Dobrinski, Ina AU - Dobrovinskaya, Oxana AU - Dobrowolski, Radek AU - Dobson, Renwick C.J. AU - Đokić, Jelena AU - Dokmeci Emre, Serap AU - Donadelli, Massimo AU - Dong, Bo AU - Dong, Xiaonan AU - Dong, Zhiwu AU - Dorn, Gerald W. AU - Dotsch, Volker AU - Dou, Huan AU - Dou, Juan AU - Dowaidar, Moataz AU - Dridi, Sami AU - Drucker, Liat AU - Du, Ailian AU - Du, Caigan AU - Du, Guangwei AU - Du, Hai Ning AU - Du, Li Lin AU - Du Toit, André AU - Duan, Shao Bin AU - Duan, Xiaoqiong AU - Duarte, Sónia P. AU - Dubrovska, Anna AU - Dunlop, Elaine A. AU - Dupont, Nicolas AU - Durán, Raúl V. AU - Dwarakanath, Bilikere S. AU - Dyshlovoy, Sergey A. AU - Ebrahimi-Fakhari, Darius AU - Eckhart, Leopold AU - Edelstein, Charles L. AU - Efferth, Thomas AU - Eftekharpour, Eftekhar AU - Eichinger, Ludwig AU - Eid, Nabil AU - Eisenberg, Tobias AU - Eissa, N. Tony AU - Eissa, Sanaa AU - Ejarque, Miriam AU - El Andaloussi, Abdeljabar AU - El-Hage, Nazira AU - El-Naggar, Shahenda AU - Eleuteri, Anna Maria AU - El-Shafey, Eman S. AU - Elgendy, Mohamed AU - Eliopoulos, Aristides G. AU - Elizalde, María M. AU - Elks, Philip M. AU - Elsasser, Hans Peter AU - Elsherbiny, Eslam S. AU - Emerling, Brooke M. AU - Emre, N. C.Tolga AU - Eng, Christina H. AU - Engedal, Nikolai AU - Engelbrecht, Anna Mart AU - Engelsen, Agnete S.T. AU - Enserink, Jorrit M. AU - Escalante, Ricardo AU - Esclatine, Audrey AU - Escobar-Henriques, Mafalda AU - Eskelinen, Eeva Liisa AU - Espert, Lucile AU - Eusebio, Makandjou Ola AU - Fabrias, Gemma AU - Fabrizi, Cinzia AU - Facchiano, Antonio AU - Facchiano, Francesco AU - Fadeel, Bengt AU - Fader, Claudio AU - Faesen, Alex C. AU - Fairlie, W. Douglas AU - Falcó, Alberto AU - Falkenburger, Bjorn H. AU - Fan, Daping AU - Fan, Jie AU - Fan, Yanbo AU - Fang, Evandro F. AU - Fang, Yanshan AU - Fang, Yognqi AU - Fanto, Manolis AU - Farfel-Becker, Tamar AU - Faure, Mathias AU - Fazeli, Gholamreza AU - Fedele, Anthony O. AU - Feldman, Arthur M. AU - Feng, Du AU - Feng, Jiachun AU - Feng, Lifeng AU - Feng, Yibin AU - Feng, Yuchen AU - Feng, Wei AU - Fenz Araujo, Thais AU - Ferguson, Thomas A. AU - Fernández, Álvaro F. AU - Fernandez-Checa, Jose C. AU - Fernández-Veledo, Sonia AU - Fernie, Alisdair R. AU - Ferrante, Anthony W. AU - Ferraresi, Alessandra AU - Ferrari, Merari F. AU - Ferreira, Julio C.B. AU - Ferro-Novick, Susan AU - Figueras, Antonio AU - Filadi, Riccardo AU - Filigheddu, Nicoletta AU - Filippi-Chiela, Eduardo AU - Filomeni, Giuseppe AU - Fimia, Gian Maria AU - Fineschi, Vittorio AU - Finetti, Francesca AU - Finkbeiner, Steven AU - Fisher, Edward A. AU - Fisher, Paul B. AU - Flamigni, Flavio AU - Fliesler, Steven J. AU - Flo, Trude H. AU - Florance, Ida AU - Florey, Oliver AU - Florio, Tullio AU - Fodor, Erika AU - Follo, Carlo AU - Fon, Edward A. AU - Forlino, Antonella AU - Fornai, Francesco AU - Fortini, Paola AU - Fracassi, Anna AU - Fraldi, Alessandro AU - Franco, Brunella AU - Franco, Rodrigo AU - Franconi, Flavia AU - Frankel, Lisa B. AU - Friedman, Scott L. AU - Fröhlich, Leopold F. AU - Frühbeck, Gema AU - Fuentes, Jose M. AU - Fujiki, Yukio AU - Fujita, Naonobu AU - Fujiwara, Yuuki AU - Fukuda, Mitsunori AU - Fulda, Simone AU - Furic, Luc AU - Furuya, Norihiko AU - Fusco, Carmela AU - Gack, Michaela U. AU - Gaffke, Lidia AU - Galadari, Sehamuddin AU - Galasso, Alessia AU - Galindo, Maria F. AU - Gallolu Kankanamalage, Sachith AU - Galluzzi, Lorenzo AU - Galy, Vincent AU - Gammoh, Noor AU - Gan, Boyi AU - Ganley, Ian G. AU - Gao, Feng AU - Gao, Hui AU - Gao, Minghui AU - Gao, Ping AU - Gao, Shou Jiang AU - Gao, Wentao AU - Gao, Xiaobo AU - Garcera, Ana AU - Garcia, Maria Noé AU - Garcia, Verónica E. AU - García-Del Portillo, Francisco AU - Garcia-Escudero, Vega AU - Garcia-Garcia, Aracely AU - Garcia-Macia, Marina AU - García-Moreno, Diana AU - Garcia-Ruiz, Carmen AU - García-Sanz, Patricia AU - Garg, Abhishek D. AU - Gargini, Ricardo AU - Garofalo, Tina AU - Garry, Robert F. AU - Gassen, Nils C. AU - Gatica, Damian AU - Ge, Liang AU - Ge, Wanzhong AU - Geiss-Friedlander, Ruth AU - Gelfi, Cecilia AU - Genschik, Pascal AU - Gentle, Ian E. AU - Gerbino, Valeria AU - Gerhardt, Christoph AU - Germain, Kyla AU - Germain, Marc AU - Gewirtz, David A. AU - Ghasemipour Afshar, Elham AU - Ghavami, Saeid AU - Ghigo, Alessandra AU - Ghosh, Manosij AU - Giamas, Georgios AU - Giampietri, Claudia AU - Giatromanolaki, Alexandra AU - Gibson, Gary E. AU - Gibson, Spencer B. AU - Ginet, Vanessa AU - Giniger, Edward AU - Giorgi, Carlotta AU - Girao, Henrique AU - Girardin, Stephen E. AU - Giridharan, Mridhula AU - Giuliano, Sandy AU - Giulivi, Cecilia AU - Giuriato, Sylvie AU - Giustiniani, Julien AU - Gluschko, Alexander AU - Goder, Veit AU - Goginashvili, Alexander AU - Golab, Jakub AU - Goldstone, David C. AU - Golebiewska, Anna AU - Gomes, Luciana R. AU - Gomez, Rodrigo AU - Gómez-Sánchez, Rubén AU - Gomez-Puerto, Maria Catalina AU - Gomez-Sintes, Raquel AU - Gong, Qingqiu AU - Goni, Felix M. AU - González-Gallego, Javier AU - Gonzalez-Hernandez, Tomas AU - Gonzalez-Polo, Rosa A. AU - Gonzalez-Reyes, Jose A. AU - González-Rodríguez, Patricia AU - Goping, Ing Swie AU - Gorbatyuk, Marina S. AU - Gorbunov, Nikolai V. AU - Görgülü, Kıvanç AU - Gorojod, Roxana M. AU - Gorski, Sharon M. AU - Goruppi, Sandro AU - Gotor, Cecilia AU - Gottlieb, Roberta A. AU - Gozes, Illana AU - Gozuacik, Devrim AU - Graef, Martin AU - Gräler, Markus H. AU - Granatiero, Veronica AU - Grasso, Daniel AU - Gray, Joshua P. AU - Green, Douglas R. AU - Greenhough, Alexander AU - Gregory, Stephen L. AU - Griffin, Edward F. AU - Grinstaff, Mark W. AU - Gros, Frederic AU - Grose, Charles AU - Gross, Angelina S. AU - Gruber, Florian AU - Grumati, Paolo AU - Grune, Tilman AU - Gu, Xueyan AU - Guan, Jun Lin AU - Guardia, Carlos M. AU - Guda, Kishore AU - Guerra, Flora AU - Guerri, Consuelo AU - Guha, Prasun AU - Guillén, Carlos AU - Gujar, Shashi AU - Gukovskaya, Anna AU - Gukovsky, Ilya AU - Gunst, Jan AU - Günther, Andreas AU - Guntur, Anyonya R. AU - Guo, Chuanyong AU - Guo, Chun AU - Guo, Hongqing AU - Guo, Lian Wang AU - Guo, Ming AU - Gupta, Pawan AU - Gupta, Shashi Kumar AU - Gupta, Swapnil AU - Gupta, Veer Bala AU - Gupta, Vivek AU - Gustafsson, Asa B. AU - Gutterman, David D. AU - H.B, Ranjitha AU - Haapasalo, Annakaisa AU - Haber, James E. AU - Hać, Aleksandra AU - Hadano, Shinji AU - Hafrén, Anders J. AU - Haidar, Mansour AU - Hall, Belinda S. AU - Halldén, Gunnel AU - Hamacher-Brady, Anne AU - Hamann, Andrea AU - Hamasaki, Maho AU - Han, Weidong AU - Hansen, Malene AU - Hanson, Phyllis I. . AU - Hao, Zijian AU - Harada, Masaru AU - Harhaji-Trajkovic, Ljubica AU - Hariharan, Nirmala AU - Haroon, Nigil AU - Harris, James AU - Hasegawa, Takafumi AU - Hasima Nagoor, Noor AU - Haspel, Jeffrey A. AU - Haucke, Volker AU - Hawkins, Wayne D. AU - Hay, Bruce A. AU - Haynes, Cole M. AU - Hayrabedyan, Soren B. AU - Hays, Thomas S. AU - He, Congcong AU - He, Qin AU - He, Rong Rong AU - He, You Wen AU - He, Yu Ying AU - Heakal, Yasser AU - Heberle, Alexander M. AU - Hejtmancik, J. Fielding AU - Helgason, Gudmundur Vignir AU - Henkel, Vanessa AU - Herb, Marc AU - Hergovich, Alexander AU - Herman-Antosiewicz, Anna AU - Hernández, Agustín AU - Hernandez, Carlos AU - Hernandez-Diaz, Sergio AU - Hernandez-Gea, Virginia AU - Herpin, Amaury AU - Herreros, Judit AU - Hervás, Javier H. AU - Hesselson, Daniel AU - Hetz, Claudio AU - Heussler, Volker T. AU - Higuchi, Yujiro AU - Hilfiker, Sabine AU - Hill, Joseph A. AU - Hlavacek, William S. AU - Ho, Emmanuel A. AU - Ho, Idy H.T. AU - Ho, Philip Wing Lok AU - Ho, Shu Leong AU - Ho, Wan Yun AU - Hobbs, G. Aaron AU - Hochstrasser, Mark AU - Hoet, Peter H.M. AU - Hofius, Daniel AU - Hofman, Paul AU - Höhn, Annika AU - Holmberg, Carina I. AU - Hombrebueno, Jose R. AU - Yi-Ren Hong, Chang Won Hong AU - Hooper, Lora V. AU - Hoppe, Thorsten AU - Horos, Rastislav AU - Hoshida, Yujin AU - Hsin, I. Lun AU - Hsu, Hsin Yun AU - Hu, Bing AU - Hu, Dong AU - Hu, Li Fang AU - Hu, Ming Chang AU - Hu, Ronggui AU - Hu, Wei AU - Hu, Yu Chen AU - Hu, Zhuo Wei AU - Hua, Fang AU - Hua, Jinlian AU - Hua, Yingqi AU - Huan, Chongmin AU - Huang, Canhua AU - Huang, Chuanshu AU - Huang, Chuanxin AU - Huang, Chunling AU - Huang, Haishan AU - Huang, Kun AU - Huang, Michael L.H. AU - Huang, Rui AU - Huang, Shan AU - Huang, Tianzhi AU - Huang, Xing AU - Huang, Yuxiang Jack AU - Huber, Tobias B. AU - Hubert, Virginie AU - Hubner, Christian A. AU - Hughes, Stephanie M. AU - Hughes, William E. AU - Humbert, Magali AU - Hummer, Gerhard AU - Hurley, James H. AU - Hussain, Sabah AU - Hussain, Salik AU - Hussey, Patrick J. AU - Hutabarat, Martina AU - Hwang, Hui Yun AU - Hwang, Seungmin AU - Ieni, Antonio AU - Ikeda, Fumiyo AU - Imagawa, Yusuke AU - Imai, Yuzuru AU - Imbriano, Carol AU - Imoto, Masaya AU - Inman, Denise M. AU - Inoki, Ken AU - Iovanna, Juan AU - Iozzo, Renato V. AU - Ippolito, Giuseppe AU - Irazoqui, Javier E. AU - Iribarren, Pablo AU - Ishaq, Mohd AU - Ishikawa, Makoto AU - Ishimwe, Nestor AU - Isidoro, Ciro AU - Ismail, Nahed AU - Issazadeh-Navikas, Shohreh AU - Itakura, Eisuke AU - Ito, Daisuke AU - Ivankovic, Davor AU - Ivanova, Saška AU - Iyer, Anand Krishnan V. AU - Izquierdo, José M. AU - Izumi, Masanori AU - Jäättelä, Marja AU - Jabir, Majid Sakhi AU - Jackson, William T. AU - Jacobo-Herrera, Nadia AU - Jacomin, Anne Claire AU - Jacquin, Elise AU - Jadiya, Pooja AU - Jaeschke, Hartmut AU - Jagannath, Chinnaswamy AU - Jakobi, Arjen J. AU - Jakobsson, Johan AU - Janji, Bassam AU - Jansen-Dürr, Pidder AU - Jansson, Patric J. AU - Jantsch, Jonathan AU - Januszewski, Sławomir AU - Jassey, Alagie AU - Jean, Steve AU - Jeltsch-David, Hélène AU - Jendelova, Pavla AU - Jenny, Andreas AU - Jensen, Thomas E. AU - Jessen, Niels AU - Jewell, Jenna L. AU - Ji, Jing AU - Jia, Lijun AU - Jia, Rui AU - Jiang, Liwen AU - Jiang, Qing AU - Jiang, Richeng AU - Jiang, Teng AU - Jiang, Xuejun AU - Jiang, Yu AU - Jimenez-Sanchez, Maria AU - Jin, Eun Jung AU - Jin, Fengyan AU - Jin, Hongchuan AU - Jin, Li AU - Jin, Luqi AU - Jin, Meiyan AU - Jin, Si AU - Jo, Eun Kyeong AU - Joffre, Carine AU - Johansen, Terje AU - Johnson, Gail V.W. AU - Johnston, Simon A. AU - Jokitalo, Eija AU - Jolly, Mohit Kumar AU - Joosten, Leo A.B. AU - Jordan, Joaquin AU - Joseph, Bertrand AU - Ju, Dianwen AU - Ju, Jeong Sun AU - Ju, Jingfang AU - Juárez, Esmeralda AU - Judith, Delphine AU - Juhász, Gábor AU - Jun, Youngsoo AU - Jung, Chang Hwa AU - Jung, Sung Chul AU - Jung, Yong Keun AU - Jungbluth, Heinz AU - Jungverdorben, Johannes AU - Just, Steffen AU - Kaarniranta, Kai AU - Kaasik, Allen AU - Kabuta, Tomohiro AU - Kaganovich, Daniel AU - Kahana, Alon AU - Kain, Renate AU - Kajimura, Shinjo AU - Kalamvoki, Maria AU - Kalia, Manjula AU - Kalinowski, Danuta S. AU - Kaludercic, Nina AU - Kalvari, Ioanna AU - Kaminska, Joanna AU - Kaminskyy, Vitaliy O. AU - Kanamori, Hiromitsu AU - Kanasaki, Keizo AU - Kang, Chanhee AU - Kang, Rui AU - Kang, Sang Sun AU - Kaniyappan, Senthilvelrajan AU - Kanki, Tomotake AU - Kanneganti, Thirumala Devi AU - Kanthasamy, Anumantha G. AU - Kanthasamy, Arthi AU - Kantorow, Marc AU - Kapuy, Orsolya AU - Karamouzis, Michalis V. AU - Karim, Md Razaul AU - Karmakar, Parimal AU - Katare, Rajesh G. AU - Kato, Masaru AU - Kaufmann, Stefan H.E. AU - Kauppinen, Anu AU - Kaushal, Gur P. AU - Kaushik, Susmita AU - Kawasaki, Kiyoshi AU - Kazan, Kemal AU - Ke, Po Yuan AU - Keating, Damien J. AU - Keber, Ursula AU - Kehrl, John H. AU - Keller, Kate E. AU - Keller, Christian W. AU - Kemper, Jongsook Kim AU - Kenific, Candia M. AU - Kepp, Oliver AU - Kermorgant, Stephanie AU - Kern, Andreas AU - Ketteler, Robin AU - Keulers, Tom G. AU - Khalfin, Boris AU - Khalil, Hany AU - Khambu, Bilon AU - Khan, Shahid Y. AU - Khandelwal, Vinoth Kumar Megraj AU - Khandia, Rekha AU - Kho, Widuri AU - Khobrekar, Noopur V. AU - Khuansuwan, Sataree AU - Khundadze, Mukhran AU - Killackey, Samuel A. AU - Kim, Dasol AU - Kim, Deok Ryong AU - Kim, Do Hyung AU - Kim, Dong Eun AU - Kim, Eun Young AU - Kim, Eun Kyoung AU - Kim, Hak Rim AU - Kim, Hee Sik AU - Hyung-Ryong Kim, Unknown AU - Kim, Jeong Hun AU - Kim, Jin Kyung AU - Kim, Jin Hoi AU - Kim, Joungmok AU - Kim, Ju Hwan AU - Kim, Keun Il AU - Kim, Peter K. AU - Kim, Seong Jun AU - Kimball, Scot R. AU - Kimchi, Adi AU - Kimmelman, Alec C. AU - Kimura, Tomonori AU - King, Matthew A. AU - Kinghorn, Kerri J. AU - Kinsey, Conan G. AU - Kirkin, Vladimir AU - Kirshenbaum, Lorrie A. AU - Kiselev, Sergey L. AU - Kishi, Shuji AU - Kitamoto, Katsuhiko AU - Kitaoka, Yasushi AU - Kitazato, Kaio AU - Kitsis, Richard N. AU - Kittler, Josef T. AU - Kjaerulff, Ole AU - Klein, Peter S. AU - Klopstock, Thomas AU - Klucken, Jochen AU - Knævelsrud, Helene AU - Knorr, Roland L. AU - Ko, Ben C.B. AU - Ko, Fred AU - Ko, Jiunn Liang AU - Kobayashi, Hotaka AU - Kobayashi, Satoru AU - Koch, Ina AU - Koch, Jan C. AU - Koenig, Ulrich AU - Kögel, Donat AU - Koh, Young Ho AU - Koike, Masato AU - Kohlwein, Sepp D. AU - Kocaturk, Nur M. AU - Komatsu, Masaaki AU - König, Jeannette AU - Kono, Toru AU - Kopp, Benjamin T. AU - Korcsmaros, Tamas AU - Korkmaz, Gözde AU - Korolchuk, Viktor I. AU - Korsnes, Mónica Suárez AU - Koskela, Ali AU - Kota, Janaiah AU - Kotake, Yaichiro AU - Kotler, Monica L. AU - Kou, Yanjun AU - Koukourakis, Michael I. AU - Koustas, Evangelos AU - Kovacs, Attila L. AU - Kovács, Tibor AU - Koya, Daisuke AU - Kozako, Tomohiro AU - Kraft, Claudine AU - Krainc, Dimitri AU - Krämer, Helmut AU - Krasnodembskaya, Anna D. AU - Kretz-Remy, Carole AU - Kroemer, Guido AU - Ktistakis, Nicholas T. AU - Kuchitsu, Kazuyuki AU - Kuenen, Sabine AU - Kuerschner, Lars AU - Kukar, Thomas AU - Kumar, Ajay AU - Kumar, Ashok AU - Kumar, Deepak AU - Kumar, Dhiraj AU - Kumar, Sharad AU - Kume, Shinji AU - Kumsta, Caroline AU - Kundu, Chanakya N. AU - Kundu, Mondira AU - Kunnumakkara, Ajaikumar B. AU - Kurgan, Lukasz AU - Kutateladze, Tatiana G. AU - Kutlu, Ozlem AU - Kwak, Seong Ae AU - Kwon, Ho Jeong AU - Kwon, Taeg Kyu AU - Kwon, Yong Tae AU - Kyrmizi, Irene AU - La Spada, Albert AU - Labonté, Patrick AU - Ladoire, Sylvain AU - Laface, Ilaria AU - Lafont, Frank AU - Lagace, Diane C. AU - Lahiri, Vikramjit AU - Lai, Zhibing AU - Laird, Angela S. AU - Lakkaraju, Aparna AU - Lamark, Trond AU - Lan, Sheng Hui AU - Landajuela, Ane AU - Lane, Darius J.R. AU - Lane, Jon D. AU - Lang, Charles H. AU - Lange, Carsten AU - Langel, Ülo AU - Langer, Rupert AU - Lapaquette, Pierre AU - Laporte, Jocelyn AU - Larusso, Nicholas F. AU - Lastres-Becker, Isabel AU - Lau, Wilson Chun Yu AU - Laurie, Gordon W. AU - Lavandero, Sergio AU - Law, Betty Yuen Kwan AU - Law, Helen Ka Wai AU - Layfield, Rob AU - Le, Weidong AU - Le Stunff, Herve AU - Leary, Alexandre Y. AU - Lebrun, Jean Jacques AU - Leck, Lionel Y.W. AU - Leduc-Gaudet, Jean Philippe AU - Lee, Changwook AU - Lee, Chung Pei AU - Lee, Da Hye AU - Lee, Edward B. AU - Lee, Erinna F. AU - Lee, Gyun Min AU - Lee, He Jin AU - Lee, Heung Kyu AU - Lee, Jae Man AU - Lee, Jason S. AU - Lee, Jin A. AU - Lee, Joo Yong AU - Lee, Jun Hee AU - Lee, Michael AU - Lee, Min Goo AU - Lee, Min Jae AU - Lee, Myung Shik AU - Lee, Sang Yoon AU - Lee, Seung Jae AU - Lee, Stella Y. AU - Lee, Sung Bae AU - Lee, Won Hee AU - Lee, Ying Ray AU - Lee, Yong Ho AU - Lee, Youngil AU - Lefebvre, Christophe AU - Legouis, Renaud AU - Lei, Yu L. AU - Lei, Yuchen AU - Leikin, Sergey AU - Leitinger, Gerd AU - Lemus, Leticia AU - Leng, Shuilong AU - Lenoir, Olivia AU - Lenz, Guido AU - Lenz, Heinz Josef AU - Lenzi, Paola AU - León, Yolanda AU - Leopoldino, Andréia M. AU - Leschczyk, Christoph AU - Leskelä, Stina AU - Letellier, Elisabeth AU - Leung, Chi Ting AU - Leung, Po Sing AU - Leventhal, Jeremy S. AU - Levine, Beth AU - Lewis, Patrick A. AU - Ley, Klaus AU - Li, Bin AU - Li, Da Qiang AU - Li, Jianming AU - Li, Jing AU - Li, Jiong AU - Li, Ke AU - Li, Liwu AU - Li, Mei AU - Li, Min AU - Li, Min AU - Li, Ming AU - Li, Mingchuan AU - Li, Pin Lan AU - Li, Ming Qing AU - Li, Qing AU - Li, Sheng AU - Li, Tiangang AU - Li, Wei AU - Li, Wenming AU - Li, Xue AU - Li, Yi Ping AU - Li, Yuan AU - Li, Zhiqiang AU - Li, Zhiyong AU - Li, Zhiyuan AU - Lian, Jiqin AU - Liang, Chengyu AU - Liang, Qiangrong AU - Liang, Weicheng AU - Liang, Yongheng AU - Liang, Yong Tian AU - Liao, Guanghong AU - Liao, Lujian AU - Liao, Mingzhi AU - Liao, Yung Feng AU - Librizzi, Mariangela AU - Lie, Pearl P.Y. AU - Lilly, Mary A. AU - Lim, Hyunjung J. AU - Lima, Thania R.R. AU - Limana, Federica AU - Lin, Chao AU - Lin, Chih Wen AU - Lin, Dar Shong AU - Lin, Fu Cheng AU - Lin, Jiandie D. AU - Lin, Kurt M. AU - Lin, Kwang Huei AU - Lin, Liang Tzung AU - Lin, Pei Hui AU - Lin, Qiong AU - Lin, Shaofeng AU - Lin, Su Ju AU - Lin, Wenyu AU - Lin, Xueying AU - Lin, Yao Xin AU - Lin, Yee Shin AU - Linden, Rafael AU - Lindner, Paula AU - Ling, Shuo Chien AU - Lingor, Paul AU - Linnemann, Amelia K. AU - Liou, Yih Cherng AU - Lipinski, Marta M. AU - Lipovšek, Saška AU - Lira, Vitor A. AU - Lisiak, Natalia AU - Liton, Paloma B. AU - Liu, Chao AU - Liu, Ching Hsuan AU - Liu, Chun Feng AU - Liu, Cui Hua AU - Liu, Fang AU - Liu, Hao AU - Liu, Hsiao Sheng AU - Liu, Hua Feng AU - Liu, Huifang AU - Liu, Jia AU - Liu, Jing AU - Liu, Julia AU - Liu, Leyuan AU - Liu, Longhua AU - Liu, Meilian AU - Liu, Qin AU - Liu, Wei AU - Liu, Wende AU - Liu, Xiao Hong AU - Liu, Xiaodong AU - Liu, Xingguo AU - Liu, Xu AU - Liu, Xuedong AU - Liu, Yanfen AU - Liu, Yang AU - Liu, Yang AU - Liu, Yueyang AU - Liu, Yule AU - Livingston, J. Andrew AU - Lizard, Gerard AU - Lizcano, Jose M. AU - Ljubojevic-Holzer, Senka AU - Lleonart, Matilde E. AU - Llobet-Navàs, David AU - Llorente, Alicia AU - Lo, Chih Hung AU - Lobato-Márquez, Damián AU - Long, Qi AU - Long, Yun Chau AU - Loos, Ben AU - Loos, Julia A. AU - López, Manuela G. AU - López-Doménech, Guillermo AU - López-Guerrero, José Antonio AU - López-Jiménez, Ana T. AU - López-Pérez, Óscar AU - López-Valero, Israel AU - Lorenowicz, Magdalena J. AU - Lorente, Mar AU - Lorincz, Peter AU - Lossi, Laura AU - Lotersztajn, Sophie AU - Lovat, Penny E. AU - Lovell, Jonathan F. AU - Lovy, Alenka AU - Lőw, Péter AU - Lu, Guang AU - Lu, Haocheng AU - Lu, Jia Hong AU - Lu, Jin Jian AU - Lu, Mengji AU - Lu, Shuyan AU - Luciani, Alessandro AU - Lucocq, John M. AU - Ludovico, Paula AU - Luftig, Micah A. AU - Luhr, Morten AU - Luis-Ravelo, Diego AU - Lum, Julian J. AU - Luna-Dulcey, Liany AU - Lund, Anders H. AU - Lund, Viktor K. AU - Lünemann, Jan D. AU - Lüningschrör, Patrick AU - Luo, Honglin AU - Luo, Rongcan AU - Luo, Shouqing AU - Luo, Zhi AU - Luparello, Claudio AU - Lüscher, Bernhard AU - Luu, Luan AU - Lyakhovich, Alex AU - Lyamzaev, Konstantin G. AU - Lystad, Alf Håkon AU - Lytvynchuk, Lyubomyr AU - Ma, Alvin C. AU - Ma, Changle AU - Ma, Mengxiao AU - Ma, Ning Fang AU - Ma, Quan Hong AU - Ma, Xinliang AU - Ma, Yueyun AU - Ma, Zhenyi AU - Macdougald, Ormond A. AU - Macian, Fernando AU - Macintosh, Gustavo C. AU - Mackeigan, Jeffrey P. AU - Macleod, Kay F. AU - Maday, Sandra AU - Madeo, Frank AU - Madesh, Muniswamy AU - Madl, Tobias AU - Madrigal-Matute, Julio AU - Maeda, Akiko AU - Maejima, Yasuhiro AU - Magarinos, Marta AU - Mahavadi, Poornima AU - Maiani, Emiliano AU - Maiese, Kenneth AU - Maiti, Panchanan AU - Maiuri, Maria Chiara AU - Majello, Barbara AU - Major, Michael B. AU - Makareeva, Elena AU - Malik, Fayaz AU - Mallilankaraman, Karthik AU - Malorni, Walter AU - Maloyan, Alina AU - Mammadova, Najiba AU - Man, Gene Chi Wai AU - Manai, Federico AU - Mancias, Joseph D. AU - Mandelkow, Eva Maria AU - Mandell, Michael A. AU - Manfredi, Angelo A. AU - Manjili, Masoud H. AU - Manjithaya, Ravi AU - Manque, Patricio AU - Manshian, Bella B. AU - Manzano, Raquel AU - Manzoni, Claudia AU - Mao, Kai AU - Marchese, Cinzia AU - Marchetti, Sandrine AU - Marconi, Anna Maria AU - Marcucci, Fabrizio AU - Mardente, Stefania AU - Mareninova, Olga A. AU - Margeta, Marta AU - Mari, Muriel AU - Marinelli, Sara AU - Marinelli, Oliviero AU - Mariño, Guillermo AU - Mariotto, Sofia AU - Marshall, Richard S. AU - Marten, Mark R. AU - Martens, Sascha AU - Martin, Alexandre P.J. AU - Martin, Katie R. AU - Martin, Sara AU - Martin, Shaun AU - Martín-Segura, Adrián AU - Martín-Acebes, Miguel A. AU - Martin-Burriel, Inmaculada AU - Martin-Rincon, Marcos AU - Martin-Sanz, Paloma AU - Martina, José A. AU - Martinet, Wim AU - Martinez, Aitor AU - Martinez, Ana AU - Martinez, Jennifer AU - Martinez Velazquez, Moises AU - Martinez-Lopez, Nuria AU - Martinez-Vicente, Marta AU - Martins, Daniel O. AU - Martins, Joilson O. AU - Martins, Waleska K. AU - Martins-Marques, Tania AU - Marzetti, Emanuele AU - Masaldan, Shashank AU - Masclaux-Daubresse, Celine AU - Mashek, Douglas G. AU - Massa, Valentina AU - Massieu, Lourdes AU - Masson, Glenn R. AU - Masuelli, Laura AU - Masyuk, Anatoliy I. AU - Masyuk, Tetyana V. AU - Matarrese, Paola AU - Matheu, Ander AU - Matoba, Satoaki AU - Matsuzaki, Sachiko AU - Mattar, Pamela AU - Matte, Alessandro AU - Mattoscio, Domenico AU - Mauriz, José L. AU - Mauthe, Mario AU - Mauvezin, Caroline AU - Maverakis, Emanual AU - Maycotte, Paola AU - Mayer, Johanna AU - Mazzoccoli, Gianluigi AU - Mazzoni, Cristina AU - Mazzulli, Joseph R. AU - Mccarty, Nami AU - Mcdonald, Christine AU - Mcgill, Mitchell R. AU - Mckenna, Sharon L. AU - Mclaughlin, Beth Ann AU - Mcloughlin, Fionn AU - Mcniven, Mark A. AU - Mcwilliams, Thomas G. AU - Mechta-Grigoriou, Fatima AU - Medeiros, Tania Catarina AU - Medina, Diego L. AU - Megeney, Lynn A. AU - Megyeri, Klara AU - Mehrpour, Maryam AU - Mehta, Jawahar L. AU - Meijer, Alfred J. AU - Meijer, Annemarie H. AU - Mejlvang, Jakob AU - Meléndez, Alicia AU - Melk, Annette AU - Memisoglu, Gonen AU - Mendes, Alexandrina F. AU - Meng, Delong AU - Meng, Fei AU - Meng, Tian AU - Menna-Barreto, Rubem AU - Menon, Manoj B. AU - Mercer, Carol AU - Mercier, Anne E. AU - Mergny, Jean Louis AU - Merighi, Adalberto AU - Merkley, Seth D. AU - Merla, Giuseppe AU - Meske, Volker AU - Mestre, Ana Cecilia AU - Metur, Shree Padma AU - Meyer, Christian AU - Meyer, Hemmo AU - Mi, Wenyi AU - Mialet-Perez, Jeanne AU - Miao, Junying AU - Micale, Lucia AU - Miki, Yasuo AU - Milan, Enrico AU - Milczarek, Małgorzata AU - Miller, Dana L. AU - Miller, Samuel I. AU - Miller, Silke AU - Millward, Steven W. AU - Milosevic, Ira AU - Minina, Elena A. AU - Mirzaei, Hamed AU - Mirzaei, Hamid Reza AU - Mirzaei, Mehdi AU - Mishra, Amit AU - Mishra, Nandita AU - Mishra, Paras Kumar AU - Misirkic Marjanovic, Maja AU - Misasi, Roberta AU - Misra, Amit AU - Misso, Gabriella AU - Mitchell, Claire AU - Mitou, Geraldine AU - Miura, Tetsuji AU - Miyamoto, Shigeki AU - Miyazaki, Makoto AU - Miyazaki, Mitsunori AU - Miyazaki, Taiga AU - Miyazawa, Keisuke AU - Mizushima, Noboru AU - Mogensen, Trine H. AU - Mograbi, Baharia AU - Mohammadinejad, Reza AU - Mohamud, Yasir AU - Mohanty, Abhishek AU - Mohapatra, Sipra AU - Möhlmann, Torsten AU - Mohmmed, Asif AU - Moles, Anna AU - Moley, Kelle H. AU - Molinari, Maurizio AU - Mollace, Vincenzo AU - Møller, Andreas Buch AU - Mollereau, Bertrand AU - Mollinedo, Faustino AU - Montagna, Costanza AU - Monteiro, Mervyn J. AU - Montella, Andrea AU - Montes, L. Ruth AU - Montico, Barbara AU - Mony, Vinod K. AU - Monzio Compagnoni, Giacomo AU - Moore, Michael N. AU - Moosavi, Mohammad A. AU - Mora, Ana L. AU - Mora, Marina AU - Morales-Alamo, David AU - Moratalla, Rosario AU - Moreira, Paula I. AU - Morelli, Elena AU - Moreno, Sandra AU - Moreno-Blas, Daniel AU - Moresi, Viviana AU - Morga, Benjamin AU - Morgan, Alwena H. AU - Morin, Fabrice AU - Morishita, Hideaki AU - Moritz, Orson L. AU - Moriyama, Mariko AU - Moriyasu, Yuji AU - Morleo, Manuela AU - Morselli, Eugenia AU - Moruno-Manchon, Jose F. AU - Moscat, Jorge AU - Mostowy, Serge AU - Motori, Elisa AU - Moura, Andrea Felinto AU - Moustaid-Moussa, Naima AU - Mrakovcic, Maria AU - Muciño-Hernández, Gabriel AU - Mukherjee, Anupam AU - Mukhopadhyay, Subhadip AU - Mulcahy Levy, Jean M. AU - Mulero, Victoriano AU - Muller, Sylviane AU - Münch, Christian AU - Munjal, Ashok AU - Munoz-Canoves, Pura AU - Muñoz-Galdeano, Teresa AU - Münz, Christian AU - Murakawa, Tomokazu AU - Muratori, Claudia AU - Murphy, Brona M. AU - Murphy, J. Patrick AU - Murthy, Aditya AU - Myöhänen, Timo T. AU - Mysorekar, Indira U. AU - Mytych, Jennifer AU - Nabavi, Seyed Mohammad AU - Nabissi, Massimo AU - Nagy, Péter AU - Nah, Jihoon AU - Nahimana, Aimable AU - Nakagawa, Ichiro AU - Nakamura, Ken AU - Nakatogawa, Hitoshi AU - Nandi, Shyam S. AU - Nanjundan, Meera AU - Nanni, Monica AU - Napolitano, Gennaro AU - Nardacci, Roberta AU - Narita, Masashi AU - Nassif, Melissa AU - Nathan, Ilana AU - Natsumeda, Manabu AU - Naude, Ryno J. AU - Naumann, Christin AU - Naveiras, Olaia AU - Navid, Fatemeh AU - Nawrocki, Steffan T. AU - Nazarko, Taras Y. AU - Nazio, Francesca AU - Negoita, Florentina AU - Neill, Thomas AU - Neisch, Amanda L. AU - Neri, Luca M. AU - Netea, Mihai G. AU - Neubert, Patrick AU - Neufeld, Thomas P. AU - Neumann, Dietbert AU - Neutzner, Albert AU - Newton, Phillip T. AU - Ney, Paul A. AU - Nezis, Ioannis P. AU - Ng, Charlene C.W. AU - Ng, Tzi Bun AU - Nguyen, Hang T.T. AU - Nguyen, Long T. AU - Ni, Hong Min AU - Ní Cheallaigh, Clíona AU - Ni, Zhenhong AU - Nicolao, M. Celeste AU - Nicoli, Francesco AU - Nieto-Diaz, Manuel AU - Nilsson, Per AU - Ning, Shunbin AU - Niranjan, Rituraj AU - Nishimune, Hiroshi AU - Niso-Santano, Mireia AU - Nixon, Ralph A. AU - Nobili, Annalisa AU - Nobrega, Clevio AU - Noda, Takeshi AU - Nogueira-Recalde, Uxía AU - Nolan, Trevor M. AU - Nombela, Ivan AU - Novak, Ivana AU - Novoa, Beatriz AU - Nozawa, Takashi AU - Nukina, Nobuyuki AU - Nussbaum-Krammer, Carmen AU - Nylandsted, Jesper AU - O’Donovan, Tracey R. AU - O’Leary, Seónadh M. AU - O’Rourke, Eyleen J. AU - O’Sullivan, Mary P. AU - O’Sullivan, Timothy E. AU - Oddo, Salvatore AU - Oehme, Ina AU - Ogawa, Michinaga AU - Ogier-Denis, Eric AU - Ogmundsdottir, Margret H. AU - Ogretmen, Besim AU - Oh, Goo Taeg AU - Oh, Seon Hee AU - Oh, Young J. AU - Ohama, Takashi AU - Ohashi, Yohei AU - Ohmuraya, Masaki AU - Oikonomou, Vasileios AU - Ojha, Rani AU - Okamoto, Koji AU - Okazawa, Hitoshi AU - Oku, Masahide AU - Oliván, Sara AU - Oliveira, Jorge M.A. AU - Ollmann, Michael AU - Olzmann, James A. AU - Omari, Shakib AU - Omary, M. Bishr AU - Önal, Gizem AU - Ondrej, Martin AU - Ong, Sang Bing AU - Ong, Sang Ging AU - Onnis, Anna AU - Orellana, Juan A. AU - Orellana-Muñoz, Sara AU - Ortega-Villaizan, Maria Del Mar AU - Ortiz-Gonzalez, Xilma R. AU - Ortona, Elena AU - Osiewacz, Heinz D. AU - Osman, Abdel Hamid K. AU - Osta, Rosario AU - Otegui, Marisa S. AU - Otsu, Kinya AU - Ott, Christiane AU - Ottobrini, Luisa AU - Ou, Jing Hsiung James AU - Outeiro, Tiago F. AU - Oynebraten, Inger AU - Ozturk, Melek AU - Pagès, Gilles AU - Pahari, Susanta AU - Pajares, Marta AU - Pajvani, Utpal B. AU - Pal, Rituraj AU - Paladino, Simona AU - Pallet, Nicolas AU - Palmieri, Michela AU - Palmisano, Giuseppe AU - Palumbo, Camilla AU - Pampaloni, Francesco AU - Pan, Lifeng AU - Pan, Qingjun AU - Pan, Wenliang AU - Pan, Xin AU - Panasyuk, Ganna AU - Pandey, Rahul AU - Pandey, Udai B. AU - Pandya, Vrajesh AU - Paneni, Francesco AU - Pang, Shirley Y. AU - Panzarini, Elisa AU - Papademetrio, Daniela L. AU - Papaleo, Elena AU - Papinski, Daniel AU - Papp, Diana AU - Park, Eun Chan AU - Park, Hwan Tae AU - Park, Ji Man AU - Park, Jong In AU - Park, Joon Tae AU - Park, Junsoo AU - Park, Sang Chul AU - Park, Sang Youel AU - Parola, Abraham H. AU - Parys, Jan B. AU - Pasquier, Adrien AU - Pasquier, Benoit AU - Passos, João F. AU - Pastore, Nunzia AU - Patel, Hemal H. AU - Patschan, Daniel AU - Pattingre, Sophie AU - Pedraza-Alva, Gustavo AU - Pedraza-Chaverri, Jose AU - Pedrozo, Zully AU - Pei, Gang AU - Pei, Jianming AU - Peled-Zehavi, Hadas AU - Pellegrini, Joaquín M. AU - Pelletier, Joffrey AU - Peñalva, Miguel A. AU - Peng, Di AU - Peng, Ying AU - Penna, Fabio AU - Pennuto, Maria AU - Pentimalli, Francesca AU - Pereira, Cláudia M.F. AU - Pereira, Gustavo J.S. AU - Pereira, Lilian C. AU - Pereira De Almeida, Luis AU - Perera, Nirma D. AU - Pérez-Lara, Ángel AU - Perez-Oliva, Ana B. AU - Pérez-Pérez, María Esther AU - Periyasamy, Palsamy AU - Perl, Andras AU - Perrotta, Cristiana AU - Perrotta, Ida AU - Pestell, Richard G. AU - Petersen, Morten AU - Petrache, Irina AU - Petrovski, Goran AU - Pfirrmann, Thorsten AU - Pfister, Astrid S. AU - Philips, Jennifer A. AU - Pi, Huifeng AU - Picca, Anna AU - Pickrell, Alicia M. AU - Picot, Sandy AU - Pierantoni, Giovanna M. AU - Pierdominici, Marina AU - Pierre, Philippe AU - Pierrefite-Carle, Valérie AU - Pierzynowska, Karolina AU - Pietrocola, Federico AU - Pietruczuk, Miroslawa AU - Pignata, Claudio AU - Pimentel-Muiños, Felipe X. AU - Pinar, Mario AU - Pinheiro, Roberta O. AU - Pinkas-Kramarski, Ronit AU - Pinton, Paolo AU - Pircs, Karolina AU - Piya, Sujan AU - Pizzo, Paola AU - Plantinga, Theo S. AU - Platta, Harald W. AU - Plaza-Zabala, Ainhoa AU - Plomann, Markus AU - Plotnikov, Egor Y. AU - Plun-Favreau, Helene AU - Pluta, Ryszard AU - Pocock, Roger AU - Pöggeler, Stefanie AU - Pohl, Christian AU - Poirot, Marc AU - Poletti, Angelo AU - Ponpuak, Marisa AU - Popelka, Hana AU - Popova, Blagovesta AU - Porta, Helena AU - Porte Alcon, Soledad AU - Portilla-Fernandez, Eliana AU - Post, Martin AU - Potts, Malia B. AU - Poulton, Joanna AU - Powers, Ted AU - Prahlad, Veena AU - Prajsnar, Tomasz K. AU - Praticò, Domenico AU - Prencipe, Rosaria AU - Priault, Muriel AU - Proikas-Cezanne, Tassula AU - Promponas, Vasilis J. AU - Proud, Christopher G. AU - Puertollano, Rosa AU - Puglielli, Luigi AU - Pulinilkunnil, Thomas AU - Puri, Deepika AU - Puri, Rajat AU - Puyal, Julien AU - Qi, Xiaopeng AU - Qi, Yongmei AU - Qian, Wenbin AU - Qiang, Lei AU - Qiu, Yu AU - Quadrilatero, Joe AU - Quarleri, Jorge AU - Raben, Nina AU - Rabinowich, Hannah AU - Ragona, Debora AU - Ragusa, Michael J. AU - Rahimi, Nader AU - Rahmati, Marveh AU - Raia, Valeria AU - Raimundo, Nuno AU - Rajasekaran, Namakkal Soorappan AU - Ramachandra Rao, Sriganesh AU - Rami, Abdelhaq AU - Ramírez-Pardo, Ignacio AU - Ramsden, David B. AU - Randow, Felix AU - Rangarajan, Pundi N. AU - Ranieri, Danilo AU - Rao, Hai AU - Rao, Lang AU - Rao, Rekha AU - Rathore, Sumit AU - Ratnayaka, J. Arjuna AU - Ratovitski, Edward A. AU - Ravanan, Palaniyandi AU - Ravegnini, Gloria AU - Ray, Swapan K. AU - Razani, Babak AU - Rebecca, Vito AU - Reggiori, Fulvio AU - Régnier-Vigouroux, Anne AU - Reichert, Andreas S. AU - Reigada, David AU - Reiling, Jan H. AU - Rein, Theo AU - Reipert, Siegfried AU - Rekha, Rokeya Sultana AU - Ren, Hongmei AU - Ren, Jun AU - Ren, Weichao AU - Renault, Tristan AU - Renga, Giorgia AU - Reue, Karen AU - Rewitz, Kim AU - Ribeiro De Andrade Ramos, Bruna AU - Riazuddin, S. Amer AU - Ribeiro-Rodrigues, Teresa M. AU - Ricci, Jean Ehrland AU - Ricci, Romeo AU - Riccio, Victoria AU - Richardson, Des R. AU - Rikihisa, Yasuko AU - Risbud, Makarand V. AU - Risueño, Ruth M. AU - Ritis, Konstantinos AU - Rizza, Salvatore AU - Rizzuto, Rosario AU - Roberts, Helen C. AU - Roberts, Luke D. AU - Robinson, Katherine J. AU - Roccheri, Maria Carmela AU - Rocchi, Stephane AU - Rodney, George G. AU - Rodrigues, Tiago AU - Rodrigues Silva, Vagner Ramon AU - Rodriguez, Amaia AU - Rodriguez-Barrueco, Ruth AU - Rodriguez-Henche, Nieves AU - Rodriguez-Rocha, Humberto AU - Roelofs, Jeroen AU - Rogers, Robert S. AU - Rogov, Vladimir V. AU - Rojo, Ana I. AU - Rolka, Krzysztof AU - Romanello, Vanina AU - Romani, Luigina AU - Romano, Alessandra AU - Romano, Patricia S. AU - Romeo-Guitart, David AU - Romero, Luis C. AU - Romero, Montserrat AU - Roney, Joseph C. AU - Rongo, Christopher AU - Roperto, Sante AU - Rosenfeldt, Mathias T. AU - Rosenstiel, Philip AU - Rosenwald, Anne G. AU - Roth, Kevin A. AU - Roth, Lynn AU - Roth, Steven AU - Rouschop, Kasper M.A. AU - Roussel, Benoit D. AU - Roux, Sophie AU - Rovere-Querini, Patrizia AU - Roy, Ajit AU - Rozieres, Aurore AU - Ruano, Diego AU - Rubinsztein, David C. AU - Rubtsova, Maria P. AU - Ruckdeschel, Klaus AU - Ruckenstuhl, Christoph AU - Rudolf, Emil AU - Rudolf, Rüdiger AU - Ruggieri, Alessandra AU - Ruparelia, Avnika Ashok AU - Rusmini, Paola AU - Russell, Ryan R. AU - Russo, Gian Luigi AU - Russo, Maria AU - Russo, Rossella AU - Ryabaya, Oxana O. AU - Ryan, Kevin M. AU - Ryu, Kwon Yul AU - Sabater-Arcis, Maria AU - Sachdev, Ulka AU - Sacher, Michael AU - Sachse, Carsten AU - Sadhu, Abhishek AU - Sadoshima, Junichi AU - Safren, Nathaniel AU - Saftig, Paul AU - Sagona, Antonia P. AU - Sahay, Gaurav AU - Sahebkar, Amirhossein AU - Sahin, Mustafa AU - Sahin, Ozgur AU - Sahni, Sumit AU - Saito, Nayuta AU - Saito, Shigeru AU - Saito, Tsunenori AU - Sakai, Ryohei AU - Sakai, Yasuyoshi AU - Sakamaki, Jun Ichi AU - Saksela, Kalle AU - Salazar, Gloria AU - Salazar-Degracia, Anna AU - Salekdeh, Ghasem H. AU - Saluja, Ashok K. AU - Sampaio-Marques, Belém AU - Sanchez, Maria Cecilia AU - Sanchez-Alcazar, Jose A. AU - Sanchez-Vera, Victoria AU - Sancho-Shimizu, Vanessa AU - Sanderson, J. Thomas AU - Sandri, Marco AU - Santaguida, Stefano AU - Santambrogio, Laura AU - Santana, Magda M. AU - Santoni, Giorgio AU - Sanz, Alberto AU - Sanz, Pascual AU - Saran, Shweta AU - Sardiello, Marco AU - Sargeant, Timothy J. AU - Sarin, Apurva AU - Sarkar, Chinmoy AU - Sarkar, Sovan AU - Sarrias, Maria Rosa AU - Sarkar, Surajit AU - Sarmah, Dipanka Tanu AU - Sarparanta, Jaakko AU - Sathyanarayan, Aishwarya AU - Sathyanarayanan, Ranganayaki AU - Scaglione, K. Matthew AU - Scatozza, Francesca AU - Schaefer, Liliana AU - Schafer, Zachary T. AU - Schaible, Ulrich E. AU - Schapira, Anthony H.V. AU - Scharl, Michael AU - Schatzl, Hermann M. AU - Schein, Catherine H. AU - Scheper, Wiep AU - Scheuring, David AU - Schiaffino, Maria Vittoria AU - Schiappacassi, Monica AU - Schindl, Rainer AU - Schlattner, Uwe AU - Schmidt, Oliver AU - Schmitt, Roland AU - Schmidt, Stephen D. AU - Schmitz, Ingo AU - Schmukler, Eran AU - Schneider, Anja AU - Schneider, Bianca E. AU - Schober, Romana AU - Schoijet, Alejandra C. AU - Schott, Micah B. AU - Schramm, Michael AU - Schröder, Bernd AU - Schuh, Kai AU - Schüller, Christoph AU - Schulze, Ryan J. AU - Schürmanns, Lea AU - Schwamborn, Jens C. AU - Schwarten, Melanie AU - Scialo, Filippo AU - Sciarretta, Sebastiano AU - Scott, Melanie J. AU - Scotto, Kathleen W. AU - Scovassi, A. Ivana AU - Scrima, Andrea AU - Scrivo, Aurora AU - Sebastian, David AU - Sebti, Salwa AU - Sedej, Simon AU - Segatori, Laura AU - Segev, Nava AU - Seglen, Per O. AU - Seiliez, Iban AU - Seki, Ekihiro AU - Selleck, Scott B. AU - Sellke, Frank W. AU - Selsby, Joshua T. AU - Sendtner, Michael AU - Senturk, Serif AU - Seranova, Elena AU - Sergi, Consolato AU - Serra-Moreno, Ruth AU - Sesaki, Hiromi AU - Settembre, Carmine AU - Setty, Subba Rao Gangi AU - Sgarbi, Gianluca AU - Sha, Ou AU - Shacka, John J. AU - Shah, Javeed A. AU - Shang, Dantong AU - Shao, Changshun AU - Shao, Feng AU - Sharbati, Soroush AU - Sharkey, Lisa M. AU - Sharma, Dipali AU - Sharma, Gaurav AU - Sharma, Kulbhushan AU - Sharma, Pawan AU - Sharma, Surendra AU - Shen, Han Ming AU - Shen, Hongtao AU - Shen, Jiangang AU - Shen, Ming AU - Shen, Weili AU - Shen, Zheni AU - Sheng, Rui AU - Sheng, Zhi AU - Sheng, Zu Hang AU - Shi, Jianjian AU - Shi, Xiaobing AU - Shi, Ying Hong AU - Shiba-Fukushima, Kahori AU - Shieh, Jeng Jer AU - Shimada, Yohta AU - Shimizu, Shigeomi AU - Shimozawa, Makoto AU - Shintani, Takahiro AU - Shoemaker, Christopher J. AU - Shojaei, Shahla AU - Shoji, Ikuo AU - Shravage, Bhupendra V. AU - Shridhar, Viji AU - Shu, Chih Wen AU - Shu, Hong Bing AU - Shui, Ke AU - Shukla, Arvind K. AU - Shutt, Timothy E. AU - Sica, Valentina AU - Siddiqui, Aleem AU - Sierra, Amanda AU - Sierra-Torre, Virginia AU - Signorelli, Santiago AU - Sil, Payel AU - Silva, Bruno J.De Andrade AU - Silva, Johnatas D. AU - Silva-Pavez, Eduardo AU - Silvente-Poirot, Sandrine AU - Simmonds, Rachel E. AU - Simon, Anna Katharina AU - Simon, Hans Uwe AU - Simons, Matias AU - Singh, Anurag AU - Singh, Lalit P. AU - Singh, Rajat AU - Singh, Shivendra V. AU - Singh, Shrawan K. AU - Singh, Sudha B. AU - Singh, Sunaina AU - Singh, Surinder Pal AU - Sinha, Debasish AU - Sinha, Rohit Anthony AU - Sinha, Sangita AU - Sirko, Agnieszka AU - Sirohi, Kapil AU - Sivridis, Efthimios L. AU - Skendros, Panagiotis AU - Skirycz, Aleksandra AU - Slaninová, Iva AU - Smaili, Soraya S. AU - Smertenko, Andrei AU - Smith, Matthew D. AU - Soenen, Stefaan J. AU - Sohn, Eun Jung AU - Sok, Sophia P.M. AU - Solaini, Giancarlo AU - Soldati, Thierry AU - Soleimanpour, Scott A. AU - Soler, Rosa M. AU - Solovchenko, Alexei AU - Somarelli, Jason A. AU - Sonawane, Avinash AU - Song, Fuyong AU - Song, Hyun Kyu AU - Song, Ju Xian AU - Song, Kunhua AU - Song, Zhiyin AU - Soria, Leandro R. AU - Sorice, Maurizio AU - Soukas, Alexander A. AU - Soukup, Sandra Fausia AU - Sousa, Diana AU - Sousa, Nadia AU - Spagnuolo, Paul A. AU - Spector, Stephen A. AU - Srinivas Bharath, M. M. AU - St. Clair, Daret AU - Stagni, Venturina AU - Staiano, Leopoldo AU - Stalnecker, Clint A. AU - Stankov, Metodi V. AU - Stathopulos, Peter B. AU - Stefan, Katja AU - Stefan, Sven Marcel AU - Stefanis, Leonidas AU - Steffan, Joan S. AU - Steinkasserer, Alexander AU - Stenmark, Harald AU - Sterneckert, Jared AU - Stevens, Craig AU - Stoka, Veronika AU - Storch, Stephan AU - Stork, Björn AU - Strappazzon, Flavie AU - Strohecker, Anne Marie AU - Stupack, Dwayne G. AU - Su, Huanxing AU - Su, Ling Yan AU - Su, Longxiang AU - Suarez-Fontes, Ana M. AU - Subauste, Carlos S. AU - Subbian, Selvakumar AU - Subirada, Paula V. AU - Sudhandiran, Ganapasam AU - Sue, Carolyn M. AU - Sui, Xinbing AU - Summers, Corey AU - Sun, Guangchao AU - Sun, Jun AU - Sun, Kang AU - Sun, Meng Xiang AU - Sun, Qiming AU - Sun, Yi AU - Sun, Zhongjie AU - Sunahara, Karen K.S. AU - Sundberg, Eva AU - Susztak, Katalin AU - Sutovsky, Peter AU - Suzuki, Hidekazu AU - Sweeney, Gary AU - Symons, J. David AU - Sze, Stephen Cho Wing AU - Szewczyk, Nathaniel J. AU - Tabęcka-Łonczynska, Anna AU - Tabolacci, Claudio AU - Tacke, Frank AU - Taegtmeyer, Heinrich AU - Tafani, Marco AU - Tagaya, Mitsuo AU - Tai, Haoran AU - Tait, Stephen W.G. AU - Takahashi, Yoshinori AU - Takats, Szabolcs AU - Talwar, Priti AU - Tam, Chit AU - Tam, Shing Yau AU - Tampellini, Davide AU - Tamura, Atsushi AU - Tan, Chong Teik AU - Tan, Eng King AU - Tan, Ya Qin AU - Tanaka, Masaki AU - Tanaka, Motomasa AU - Tang, Daolin AU - Tang, Jingfeng AU - Tang, Tie Shan AU - Tanida, Isei AU - Tao, Zhipeng AU - Taouis, Mohammed AU - Tatenhorst, Lars AU - Tavernarakis, Nektarios AU - Taylor, Allen AU - Taylor, Gregory A. AU - Taylor, Joan M. AU - Tchetina, Elena AU - Tee, Andrew R. AU - Tegeder, Irmgard AU - Teis, David AU - Teixeira, Natercia AU - Teixeira-Clerc, Fatima AU - Tekirdag, Kumsal A. AU - Tencomnao, Tewin AU - Tenreiro, Sandra AU - Tepikin, Alexei V. AU - Testillano, Pilar S. AU - Tettamanti, Gianluca AU - Tharaux, Pierre Louis AU - Thedieck, Kathrin AU - Thekkinghat, Arvind A. AU - Thellung, Stefano AU - Thinwa, Josephine W. AU - Thirumalaikumar, V. P. AU - Thomas, Sufi Mary AU - Thomes, Paul G. AU - Thorburn, Andrew AU - Thukral, Lipi AU - Thum, Thomas AU - Thumm, Michael AU - Tian, Ling AU - Tichy, Ales AU - Till, Andreas AU - Timmerman, Vincent AU - Titorenko, Vladimir I. AU - Todi, Sokol V. AU - Todorova, Krassimira AU - Toivonen, Janne M. AU - Tomaipitinca, Luana AU - Tomar, Dhanendra AU - Tomas-Zapico, Cristina AU - Tomić, Sergej AU - Tong, Benjamin Chun Kit AU - Tong, Chao AU - Tong, Xin AU - Tooze, Sharon A. AU - Torgersen, Maria L. AU - Torii, Satoru AU - Torres-López, Liliana AU - Torriglia, Alicia AU - Towers, Christina G. AU - Towns, Roberto AU - Toyokuni, Shinya AU - Trajkovic, Vladimir AU - Tramontano, Donatella AU - Tran, Quynh Giao AU - Travassos, Leonardo H. AU - Trelford, Charles B. AU - Tremel, Shirley AU - Trougakos, Ioannis P. AU - Tsao, Betty P. AU - Tschan, Mario P. AU - Tse, Hung Fat AU - Tse, Tak Fu AU - Tsugawa, Hitoshi AU - Tsvetkov, Andrey S. AU - Tumbarello, David A. AU - Tumtas, Yasin AU - Tuñón, María J. AU - Turcotte, Sandra AU - Turk, Boris AU - Turk, Vito AU - Turner, Bradley J. AU - Tuxworth, Richard I. AU - Tyler, Jessica K. AU - Tyutereva, Elena V. AU - Uchiyama, Yasuo AU - Ugun-Klusek, Aslihan AU - Uhlig, Holm H. AU - Ułamek-Kozioł, Marzena AU - Ulasov, Ilya V. AU - Umekawa, Midori AU - Ungermann, Christian AU - Unno, Rei AU - Urbe, Sylvie AU - Uribe-Carretero, Elisabet AU - Üstün, Suayib AU - Uversky, Vladimir N. AU - Vaccari, Thomas AU - Vaccaro, Maria I. AU - Vahsen, Björn F. AU - Vakifahmetoglu-Norberg, Helin AU - Valdor, Rut AU - Valente, Maria J. AU - Valko, Ayelén AU - Vallee, Richard B. AU - Valverde, Angela M. AU - Van Den Berghe, Greet AU - Van Der Veen, Stijn AU - Van Kaer, Luc AU - Van Loosdregt, Jorg AU - Van Wijk, Sjoerd J.L. AU - Vandenberghe, Wim AU - Vanhorebeek, Ilse AU - Vannier-Santos, Marcos A. AU - Vannini, Nicola AU - Vanrell, M. Cristina AU - Vantaggiato, Chiara AU - Varano, Gabriele AU - Varela-Nieto, Isabel AU - Varga, Máté AU - Vasconcelos, M. Helena AU - Vats, Somya AU - Vavvas, Demetrios G. AU - Vega-Naredo, Ignacio AU - Vega-Rubin-De-Celis, Silvia AU - Velasco, Guillermo AU - Velázquez, Ariadna P. AU - Vellai, Tibor AU - Vellenga, Edo AU - Velotti, Francesca AU - Verdier, Mireille AU - Verginis, Panayotis AU - Vergne, Isabelle AU - Verkade, Paul AU - Verma, Manish AU - Verstreken, Patrik AU - Vervliet, Tim AU - Vervoorts, Jörg AU - Vessoni, Alexandre T. AU - Victor, Victor M. AU - Vidal, Michel AU - Vidoni, Chiara AU - Vieira, Otilia V. AU - Vierstra, Richard D. AU - Viganó, Sonia AU - Vihinen, Helena AU - Vijayan, Vinoy AU - Vila, Miquel AU - Vilar, Marçal AU - Villalba, José M. AU - Villalobo, Antonio AU - Villarejo-Zori, Beatriz AU - Villarroya, Francesc AU - Villarroya, Joan AU - Vincent, Olivier AU - Vindis, Cecile AU - Viret, Christophe AU - Viscomi, Maria Teresa AU - Visnjic, Dora AU - Vitale, Ilio AU - Vocadlo, David J. AU - Voitsekhovskaja, Olga V. AU - Volonté, Cinzia AU - Volta, Mattia AU - Vomero, Marta AU - Von Haefen, Clarissa AU - Vooijs, Marc A. AU - Voos, Wolfgang AU - Vucicevic, Ljubica AU - Wade-Martins, Richard AU - Waguri, Satoshi AU - Waite, Kenrick A. AU - Wakatsuki, Shuji AU - Walker, David W. AU - Walker, Mark J. AU - Walker, Simon A. AU - Walter, Jochen AU - Wandosell, Francisco G. AU - Wang, Bo AU - Wang, Chao Yung AU - Wang, Chen AU - Wang, Chenran AU - Wang, Chenwei AU - Wang, Cun Yu AU - Wang, Dong AU - Wang, Fangyang AU - Wang, Feng AU - Wang, Fengming AU - Wang, Guansong AU - Wang, Han AU - Wang, Hao AU - Wang, Hexiang AU - Wang, Hong Gang AU - Wang, Jianrong AU - Wang, Jigang AU - Wang, Jiou AU - Wang, Jundong AU - Wang, Kui AU - Wang, Lianrong AU - Wang, Liming AU - Wang, Maggie Haitian AU - Wang, Meiqing AU - Wang, Nanbu AU - Wang, Pengwei AU - Wang, Peipei AU - Wang, Ping AU - Wang, Ping AU - Wang, Qing Jun AU - Wang, Qing AU - Wang, Qing Kenneth AU - Wang, Qiong A. AU - Wang, Wen Tao AU - Wang, Wuyang AU - Wang, Xinnan AU - Wang, Xuejun AU - Wang, Yan AU - Wang, Yanchang AU - Wang, Yanzhuang AU - Wang, Yen Yun AU - Wang, Yihua AU - Wang, Yipeng AU - Wang, Yu AU - Wang, Yuqi AU - Wang, Zhe AU - Wang, Zhenyu AU - Wang, Zhouguang AU - Warnes, Gary AU - Warnsmann, Verena AU - Watada, Hirotaka AU - Watanabe, Eizo AU - Watchon, Maxinne AU - Wawrzyńska, Anna AU - Weaver, Timothy E. AU - Wegrzyn, Grzegorz AU - Wehman, Ann M. AU - Wei, Huafeng AU - Wei, Lei AU - Wei, Taotao AU - Wei, Yongjie AU - Weiergräber, Oliver H. AU - Weihl, Conrad C. AU - Weindl, Günther AU - Weiskirchen, Ralf AU - Wells, Alan AU - Wen, Runxia H. AU - Wen, Xin AU - Werner, Antonia AU - Weykopf, Beatrice AU - Wheatley, Sally P. AU - Whitton, J. Lindsay AU - Whitworth, Alexander J. AU - Wiktorska, Katarzyna AU - Wildenberg, Manon E. AU - Wileman, Tom AU - Wilkinson, Simon AU - Willbold, Dieter AU - Williams, Brett AU - Williams, Robin S.B. AU - Williams, Roger L. AU - Williamson, Peter R. AU - Wilson, Richard A. AU - Winner, Beate AU - Winsor, Nathaniel J. AU - Witkin, Steven S. AU - Wodrich, Harald AU - Woehlbier, Ute AU - Wollert, Thomas AU - Wong, Esther AU - Wong, Jack Ho AU - Wong, Richard W. AU - Wong, Vincent Kam Wai AU - Wong, W. Wei Lynn AU - Wu, An Guo AU - Wu, Chengbiao AU - Wu, Jian AU - Wu, Junfang AU - Wu, Kenneth K. AU - Wu, Min AU - Wu, Shan Ying AU - Wu, Shengzhou AU - Wu, Shu Yan AU - Wu, Shufang AU - Wu, William K.K. AU - Wu, Xiaohong AU - Wu, Xiaoqing AU - Wu, Yao Wen AU - Wu, Yihua AU - Xavier, Ramnik J. AU - Xia, Hongguang AU - Xia, Lixin AU - Xia, Zhengyuan AU - Xiang, Ge AU - Xiang, Jin AU - Xiang, Mingliang AU - Xiang, Wei AU - Xiao, Bin AU - Xiao, Guozhi AU - Xiao, Hengyi AU - Xiao, Hong Tao AU - Xiao, Jian AU - Xiao, Lan AU - Xiao, Shi AU - Xiao, Yin AU - Xie, Baoming AU - Xie, Chuan Ming AU - Xie, Min AU - Xie, Yuxiang AU - Xie, Zhiping AU - Xie, Zhonglin AU - Xilouri, Maria AU - Xu, Congfeng AU - Xu, En AU - Xu, Haoxing AU - Xu, Jing AU - Xu, Jin Rong AU - Xu, Liang AU - Xu, Wen Wen AU - Xu, Xiulong AU - Xue, Yu AU - Yakhine-Diop, Sokhna M.S. AU - Yamaguchi, Masamitsu AU - Yamaguchi, Osamu AU - Yamamoto, Ai AU - Yamashina, Shunhei AU - Yan, Shengmin AU - Yan, Shian Jang AU - Yan, Zhen AU - Yanagi, Yasuo AU - Yang, Chuanbin AU - Yang, Dun Sheng AU - Yang, Huan AU - Yang, Huang Tian AU - Yang, Hui AU - Yang, Jin Ming AU - Yang, Jing AU - Yang, Jingyu AU - Yang, Ling AU - Yang, Liu AU - Yang, Ming AU - Yang, Pei Ming AU - Yang, Qian AU - Yang, Seungwon AU - Yang, Shu AU - Yang, Shun Fa AU - Yang, Wannian AU - Yang, Wei Yuan AU - Yang, Xiaoyong AU - Yang, Xuesong AU - Yang, Yi AU - Yang, Ying AU - Yao, Honghong AU - Yao, Shenggen AU - Yao, Xiaoqiang AU - Yao, Yong Gang AU - Yao, Yong Ming AU - Yasui, Takahiro AU - Yazdankhah, Meysam AU - Yen, Paul M. AU - Yi, Cong AU - Yin, Xiao Ming AU - Yin, Yanhai AU - Yin, Zhangyuan AU - Yin, Ziyi AU - Ying, Meidan AU - Ying, Zheng AU - Yip, Calvin K. AU - Yiu, Stephanie Pei Tung AU - Yoo, Young H. AU - Yoshida, Kiyotsugu AU - Yoshii, Saori R. AU - Yoshimori, Tamotsu AU - Yousefi, Bahman AU - Yu, Boxuan AU - Yu, Haiyang AU - Yu, Jun AU - Yu, Jun AU - Yu, Li AU - Yu, Ming Lung AU - Yu, Seong Woon AU - Yu, Victor C. AU - Yu, W. Haung AU - Yu, Zhengping AU - Yu, Zhou AU - Yuan, Junying AU - Yuan, Ling Qing AU - Yuan, Shilin AU - Yuan, Shyng Shiou F. AU - Yuan, Yanggang AU - Yuan, Zengqiang AU - Yue, Jianbo AU - Yue, Zhenyu AU - Yun, Jeanho AU - Yung, Raymond L. AU - Zacks, David N. AU - Zaffagnini, Gabriele AU - Zambelli, Vanessa O. AU - Zanella, Isabella AU - Zang, Qun S. AU - Zanivan, Sara AU - Zappavigna, Silvia AU - Zaragoza, Pilar AU - Zarbalis, Konstantinos S. AU - Zarebkohan, Amir AU - Zarrouk, Amira AU - Zeitlin, Scott O. AU - Zeng, Jialiu AU - Zeng, Ju Deng AU - Žerovnik, Eva AU - Zhan, Lixuan AU - Zhang, Bin AU - Zhang, Donna D. AU - Zhang, Hanlin AU - Zhang, Hong AU - Zhang, Hong AU - Zhang, Honghe AU - Zhang, Huafeng AU - Zhang, Huaye AU - Zhang, Hui AU - Zhang, Hui Ling AU - Zhang, Jianbin AU - Zhang, Jianhua AU - Zhang, Jing Pu AU - Zhang, Kalin Y.B. AU - Zhang, Leshuai W. AU - Zhang, Lin AU - Zhang, Lisheng AU - Zhang, Lu AU - Zhang, Luoying AU - Zhang, Menghuan AU - Zhang, Peng AU - Zhang, Sheng AU - Zhang, Wei AU - Zhang, Xiangnan AU - Zhang, Xiao Wei AU - Zhang, Xiaolei AU - Zhang, Xiaoyan AU - Zhang, Xin AU - Zhang, Xinxin AU - Zhang, Xu Dong AU - Zhang, Yang AU - Zhang, Yanjin AU - Zhang, Yi AU - Zhang, Ying Dong AU - Zhang, Yingmei AU - Zhang, Yuan Yuan AU - Zhang, Yuchen AU - Zhang, Zhe AU - Zhang, Zhengguang AU - Zhang, Zhibing AU - Zhang, Zhihai AU - Zhang, Zhiyong AU - Zhang, Zili AU - Zhao, Haobin AU - Zhao, Lei AU - Zhao, Shuang AU - Zhao, Tongbiao AU - Zhao, Xiao Fan AU - Zhao, Ying AU - Zhao, Yongchao AU - Zhao, Yongliang AU - Zhao, Yuting AU - Zheng, Guoping AU - Zheng, Kai AU - Zheng, Ling AU - Zheng, Shizhong AU - Zheng, Xi Long AU - Zheng, Yi AU - Zheng, Zu Guo AU - Zhivotovsky, Boris AU - Zhong, Qing AU - Zhou, Ao AU - Zhou, Ben AU - Zhou, Cefan AU - Zhou, Gang AU - Zhou, Hao AU - Zhou, Hong AU - Zhou, Hongbo AU - Zhou, Jie AU - Zhou, Jing AU - Zhou, Jing AU - Zhou, Jiyong AU - Zhou, Kailiang AU - Zhou, Rongjia AU - Zhou, Xu Jie AU - Zhou, Yanshuang AU - Zhou, Yinghong AU - Zhou, Yubin AU - Zhou, Zheng Yu AU - Zhou, Zhou AU - Zhu, Binglin AU - Zhu, Changlian AU - Zhu, Guo Qing AU - Zhu, Haining AU - Zhu, Hongxin AU - Zhu, Hua AU - Zhu, Wei Guo AU - Zhu, Yanping AU - Zhu, Yushan AU - Zhuang, Haixia AU - Zhuang, Xiaohong AU - Zientara-Rytter, Katarzyna AU - Zimmermann, Christine M. AU - Ziviani, Elena AU - Zoladek, Teresa AU - Zong, Wei Xing AU - Zorov, Dmitry B. AU - Zorzano, Antonio AU - Zou, Weiping AU - Zou, Zhen AU - Zou, Zhengzhi AU - Zuryn, Steven AU - Zwerschke, Werner AU - Brand-Saberi, Beate AU - Dong, X. Charlie AU - Kenchappa, Chandra Shekar AU - Li, Zuguo AU - Lin, Yong AU - Oshima, Shigeru AU - Rong, Yueguang AU - Sluimer, Judith C. AU - Stallings, Christina L. AU - Tong, Chun Kit ID - 9298 IS - 1 JF - Autophagy SN - 1554-8627 TI - Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition) VL - 17 ER - TY - JOUR AB - We develop a version of Ekedahl’s geometric sieve for integral quadratic forms of rank at least five. As one ranges over the zeros of such quadratic forms, we use the sieve to compute the density of coprime values of polynomials, and furthermore, to address a question about local solubility in families of varieties parameterised by the zeros. AU - Browning, Timothy D AU - Heath-Brown, Roger ID - 8742 IS - 1 JF - Forum Mathematicum SN - 0933-7741 TI - The geometric sieve for quadrics VL - 33 ER - TY - THES AB - Many security definitions come in two flavors: a stronger “adaptive” flavor, where the adversary can arbitrarily make various choices during the course of the attack, and a weaker “selective” flavor where the adversary must commit to some or all of their choices a-priori. For example, in the context of identity-based encryption, selective security requires the adversary to decide on the identity of the attacked party at the very beginning of the game whereas adaptive security allows the attacker to first see the master public key and some secret keys before making this choice. Often, it appears to be much easier to achieve selective security than it is to achieve adaptive security. A series of several recent works shows how to cleverly achieve adaptive security in several such scenarios including generalized selective decryption [Pan07][FJP15], constrained PRFs [FKPR14], and Yao’s garbled circuits [JW16]. Although the above works expressed vague intuition that they share a common technique, the connection was never made precise. In this work we present a new framework (published at Crypto ’17 [JKK+17a]) that connects all of these works and allows us to present them in a unified and simplified fashion. Having the framework in place, we show how to achieve adaptive security for proxy re-encryption schemes (published at PKC ’19 [FKKP19]) and provide the first adaptive security proofs for continuous group key agreement protocols (published at S&P ’21 [KPW+21]). Questioning optimality of our framework, we then show that currently used proof techniques cannot lead to significantly better security guarantees for "graph-building" games (published at TCC ’21 [KKPW21a]). These games cover generalized selective decryption, as well as the security of prominent constructions for constrained PRFs, continuous group key agreement, and proxy re-encryption. Finally, we revisit the adaptive security of Yao’s garbled circuits and extend the analysis of Jafargholi and Wichs in two directions: While they prove adaptive security only for a modified construction with increased online complexity, we provide the first positive results for the original construction by Yao (published at TCC ’21 [KKP21a]). On the negative side, we prove that the results of Jafargholi and Wichs are essentially optimal by showing that no black-box reduction can provide a significantly better security bound (published at Crypto ’21 [KKPW21c]). AU - Klein, Karen ID - 10035 SN - 2663-337X TI - On the adaptive security of graph-based games ER - TY - CONF AB - The security of cryptographic primitives and protocols against adversaries that are allowed to make adaptive choices (e.g., which parties to corrupt or which queries to make) is notoriously difficult to establish. A broad theoretical framework was introduced by Jafargholi et al. [Crypto’17] for this purpose. In this paper we initiate the study of lower bounds on loss in adaptive security for certain cryptographic protocols considered in the framework. We prove lower bounds that almost match the upper bounds (proven using the framework) for proxy re-encryption, prefix-constrained PRFs and generalized selective decryption, a security game that captures the security of certain group messaging and broadcast encryption schemes. Those primitives have in common that their security game involves an underlying graph that can be adaptively built by the adversary. Some of our lower bounds only apply to a restricted class of black-box reductions which we term “oblivious” (the existing upper bounds are of this restricted type), some apply to the broader but still restricted class of non-rewinding reductions, while our lower bound for proxy re-encryption applies to all black-box reductions. The fact that some of our lower bounds seem to crucially rely on obliviousness or at least a non-rewinding reduction hints to the exciting possibility that the existing upper bounds can be improved by using more sophisticated reductions. Our main conceptual contribution is a two-player multi-stage game called the Builder-Pebbler Game. We can translate bounds on the winning probabilities for various instantiations of this game into cryptographic lower bounds for the above-mentioned primitives using oracle separation techniques. AU - Kamath Hosdurg, Chethan AU - Klein, Karen AU - Pietrzak, Krzysztof Z AU - Walter, Michael ID - 10410 SN - 0302-9743 T2 - 19th International Conference TI - The cost of adaptivity in security games on graphs VL - 13043 ER - TY - CONF AB - The security of cryptographic primitives and protocols against adversaries that are allowed to make adaptive choices (e.g., which parties to corrupt or which queries to make) is notoriously difficult to establish. A broad theoretical framework was introduced by Jafargholi et al. [Crypto’17] for this purpose. In this paper we initiate the study of lower bounds on loss in adaptive security for certain cryptographic protocols considered in the framework. We prove lower bounds that almost match the upper bounds (proven using the framework) for proxy re-encryption, prefix-constrained PRFs and generalized selective decryption, a security game that captures the security of certain group messaging and broadcast encryption schemes. Those primitives have in common that their security game involves an underlying graph that can be adaptively built by the adversary. Some of our lower bounds only apply to a restricted class of black-box reductions which we term “oblivious” (the existing upper bounds are of this restricted type), some apply to the broader but still restricted class of non-rewinding reductions, while our lower bound for proxy re-encryption applies to all black-box reductions. The fact that some of our lower bounds seem to crucially rely on obliviousness or at least a non-rewinding reduction hints to the exciting possibility that the existing upper bounds can be improved by using more sophisticated reductions. Our main conceptual contribution is a two-player multi-stage game called the Builder-Pebbler Game. We can translate bounds on the winning probabilities for various instantiations of this game into cryptographic lower bounds for the above-mentioned primitives using oracle separation techniques. AU - Kamath Hosdurg, Chethan AU - Klein, Karen AU - Pietrzak, Krzysztof Z AU - Walter, Michael ID - 10048 T2 - 19th Theory of Cryptography Conference 2021 TI - The cost of adaptivity in security games on graphs ER - TY - JOUR AB - We prove an adiabatic theorem for the Landau–Pekar equations. This allows us to derive new results on the accuracy of their use as effective equations for the time evolution generated by the Fröhlich Hamiltonian with large coupling constant α. In particular, we show that the time evolution of Pekar product states with coherent phonon field and the electron being trapped by the phonons is well approximated by the Landau–Pekar equations until times short compared to α2. AU - Leopold, Nikolai K AU - Rademacher, Simone Anna Elvira AU - Schlein, Benjamin AU - Seiringer, Robert ID - 10738 IS - 7 JF - Analysis and PDE SN - 2157-5045 TI - The Landau–Pekar equations: Adiabatic theorem and accuracy VL - 14 ER - TY - THES AB - The scalability of concurrent data structures and distributed algorithms strongly depends on reducing the contention for shared resources and the costs of synchronization and communication. We show how such cost reductions can be attained by relaxing the strict consistency conditions required by sequential implementations. In the first part of the thesis, we consider relaxation in the context of concurrent data structures. Specifically, in data structures such as priority queues, imposing strong semantics renders scalability impossible, since a correct implementation of the remove operation should return only the element with highest priority. Intuitively, attempting to invoke remove operations concurrently creates a race condition. This bottleneck can be circumvented by relaxing semantics of the affected data structure, thus allowing removal of the elements which are no longer required to have the highest priority. We prove that the randomized implementations of relaxed data structures provide provable guarantees on the priority of the removed elements even under concurrency. Additionally, we show that in some cases the relaxed data structures can be used to scale the classical algorithms which are usually implemented with the exact ones. In the second part, we study parallel variants of the stochastic gradient descent (SGD) algorithm, which distribute computation among the multiple processors, thus reducing the running time. Unfortunately, in order for standard parallel SGD to succeed, each processor has to maintain a local copy of the necessary model parameter, which is identical to the local copies of other processors; the overheads from this perfect consistency in terms of communication and synchronization can negate the speedup gained by distributing the computation. We show that the consistency conditions required by SGD can be relaxed, allowing the algorithm to be more flexible in terms of tolerating quantized communication, asynchrony, or even crash faults, while its convergence remains asymptotically the same. AU - Nadiradze, Giorgi ID - 10429 SN - 2663-337X TI - On achieving scalability through relaxation ER - TY - CONF AB - Decentralized optimization is emerging as a viable alternative for scalable distributed machine learning, but also introduces new challenges in terms of synchronization costs. To this end, several communication-reduction techniques, such as non-blocking communication, quantization, and local steps, have been explored in the decentralized setting. Due to the complexity of analyzing optimization in such a relaxed setting, this line of work often assumes \emph{global} communication rounds, which require additional synchronization. In this paper, we consider decentralized optimization in the simpler, but harder to analyze, \emph{asynchronous gossip} model, in which communication occurs in discrete, randomly chosen pairings among nodes. Perhaps surprisingly, we show that a variant of SGD called \emph{SwarmSGD} still converges in this setting, even if \emph{non-blocking communication}, \emph{quantization}, and \emph{local steps} are all applied \emph{in conjunction}, and even if the node data distributions and underlying graph topology are both \emph{heterogenous}. Our analysis is based on a new connection with multi-dimensional load-balancing processes. We implement this algorithm and deploy it in a super-computing environment, showing that it can outperform previous decentralized methods in terms of end-to-end training time, and that it can even rival carefully-tuned large-batch SGD for certain tasks. AU - Nadiradze, Giorgi AU - Sabour, Amirmojtaba AU - Davies, Peter AU - Li, Shigang AU - Alistarh, Dan-Adrian ID - 10435 T2 - 35th Conference on Neural Information Processing Systems TI - Asynchronous decentralized SGD with quantized and local updates ER - TY - CONF AB - We study the problem of estimating a rank-$1$ signal in the presence of rotationally invariant noise-a class of perturbations more general than Gaussian noise. Principal Component Analysis (PCA) provides a natural estimator, and sharp results on its performance have been obtained in the high-dimensional regime. Recently, an Approximate Message Passing (AMP) algorithm has been proposed as an alternative estimator with the potential to improve the accuracy of PCA. However, the existing analysis of AMP requires an initialization that is both correlated with the signal and independent of the noise, which is often unrealistic in practice. In this work, we combine the two methods, and propose to initialize AMP with PCA. Our main result is a rigorous asymptotic characterization of the performance of this estimator. Both the AMP algorithm and its analysis differ from those previously derived in the Gaussian setting: at every iteration, our AMP algorithm requires a specific term to account for PCA initialization, while in the Gaussian case, PCA initialization affects only the first iteration of AMP. The proof is based on a two-phase artificial AMP that first approximates the PCA estimator and then mimics the true AMP. Our numerical simulations show an excellent agreement between AMP results and theoretical predictions, and suggest an interesting open direction on achieving Bayes-optimal performance. AU - Mondelli, Marco AU - Venkataramanan, Ramji ID - 10593 SN - 1049-5258 T2 - 35th Conference on Neural Information Processing Systems TI - PCA initialization for approximate message passing in rotationally invariant models VL - 35 ER - TY - CONF AB - The question of how and why the phenomenon of mode connectivity occurs in training deep neural networks has gained remarkable attention in the research community. From a theoretical perspective, two possible explanations have been proposed: (i) the loss function has connected sublevel sets, and (ii) the solutions found by stochastic gradient descent are dropout stable. While these explanations provide insights into the phenomenon, their assumptions are not always satisfied in practice. In particular, the first approach requires the network to have one layer with order of N neurons (N being the number of training samples), while the second one requires the loss to be almost invariant after removing half of the neurons at each layer (up to some rescaling of the remaining ones). In this work, we improve both conditions by exploiting the quality of the features at every intermediate layer together with a milder over-parameterization condition. More specifically, we show that: (i) under generic assumptions on the features of intermediate layers, it suffices that the last two hidden layers have order of N−−√ neurons, and (ii) if subsets of features at each layer are linearly separable, then no over-parameterization is needed to show the connectivity. Our experiments confirm that the proposed condition ensures the connectivity of solutions found by stochastic gradient descent, even in settings where the previous requirements do not hold. AU - Nguyen, Quynh AU - Bréchet, Pierre AU - Mondelli, Marco ID - 10594 SN - 1049-5258 T2 - 35th Conference on Neural Information Processing Systems TI - When are solutions connected in deep networks? VL - 35 ER - TY - JOUR AB - The quantum bits (qubits) on which superconducting quantum computers are based have energy scales corresponding to photons with GHz frequencies. The energy of photons in the gigahertz domain is too low to allow transmission through the noisy room-temperature environment, where the signal would be lost in thermal noise. Optical photons, on the other hand, have much higher energies, and signals can be detected using highly efficient single-photon detectors. Transduction from microwave to optical frequencies is therefore a potential enabling technology for quantum devices. However, in such a device the optical pump can be a source of thermal noise and thus degrade the fidelity; the similarity of input microwave state to the output optical state. In order to investigate the magnitude of this effect we model the sub-Kelvin thermal behavior of an electro-optic transducer based on a lithium niobate whispering gallery mode resonator. We find that there is an optimum power level for a continuous pump, whilst pulsed operation of the pump increases the fidelity of the conversion. AU - Mobassem, Sonia AU - Lambert, Nicholas J. AU - Rueda Sanchez, Alfredo R AU - Fink, Johannes M AU - Leuchs, Gerd AU - Schwefel, Harald G.L. ID - 9815 IS - 4 JF - Quantum Science and Technology TI - Thermal noise in electro-optic devices at cryogenic temperatures VL - 6 ER - TY - GEN AB - Redox mediators could catalyse otherwise slow and energy-inefficient cycling of Li-S and Li-O 2 batteries by shuttling electrons/holes between the electrode and the solid insulating storage materials. For mediators to work efficiently they need to oxidize the solid with fast kinetics yet the lowest possible overpotential. Here, we found that when the redox potentials of mediators are tuned via, e.g., Li + concentration in the electrolyte, they exhibit distinct threshold potentials, where the kinetics accelerate several-fold within a range as small as 10 mV. This phenomenon is independent of types of mediators and electrolyte. The acceleration originates from the overpotentials required to activate fast Li + /e – extraction and the following chemical step at specific abundant surface facets. Efficient redox catalysis at insulating solids requires therefore carefully considering the surface conditions of the storage materials and electrolyte-dependent redox potentials, which may be tuned by salt concentrations or solvents. AU - Cao, Deqing AU - Shen, Xiaoxiao AU - Wang, Aiping AU - Yu, Fengjiao AU - Wu, Yuping AU - Shi, Siqi AU - Freunberger, Stefan Alexander AU - Chen, Yuhui ID - 9978 KW - Catalysis KW - Energy engineering KW - Materials theory and modeling T2 - Research Square TI - Sharp kinetic acceleration potentials during mediated redox catalysis of insulators ER - TY - JOUR AB - P-glycoprotein (ABCB1) and breast cancer resistance protein (ABCG2) restrict at the blood–brain barrier (BBB) the brain distribution of the majority of currently known molecularly targeted anticancer drugs. To improve brain delivery of dual ABCB1/ABCG2 substrates, both ABCB1 and ABCG2 need to be inhibited simultaneously at the BBB. We examined the feasibility of simultaneous ABCB1/ABCG2 inhibition with i.v. co-infusion of erlotinib and tariquidar by studying brain distribution of the model ABCB1/ABCG2 substrate [11C]erlotinib in mice and rhesus macaques with PET. Tolerability of the erlotinib/tariquidar combination was assessed in human embryonic stem cell-derived cerebral organoids. In mice and macaques, baseline brain distribution of [11C]erlotinib was low (brain distribution volume, VT,brain < 0.3 mL/cm3). Co-infusion of erlotinib and tariquidar increased VT,brain in mice by 3.0-fold and in macaques by 3.4- to 5.0-fold, while infusion of erlotinib alone or tariquidar alone led to less pronounced VT,brain increases in both species. Treatment of cerebral organoids with erlotinib/tariquidar led to an induction of Caspase-3-dependent apoptosis. Co-infusion of erlotinib/tariquidar may potentially allow for complete ABCB1/ABCG2 inhibition at the BBB, while simultaneously achieving brain-targeted EGFR inhibition. Our protocol may be applicable to enhance brain delivery of molecularly targeted anticancer drugs for a more effective treatment of brain tumors. AU - Tournier, N AU - Goutal, S AU - Mairinger, S AU - Lozano, IH AU - Filip, T AU - Sauberer, M AU - Caillé, F AU - Breuil, L AU - Stanek, J AU - Freeman, AF AU - Novarino, Gaia AU - Truillet, C AU - Wanek, T AU - Langer, O ID - 8730 IS - 7 JF - Journal of Cerebral Blood Flow and Metabolism SN - 0271-678x TI - Complete inhibition of ABCB1 and ABCG2 at the blood-brain barrier by co-infusion of erlotinib and tariquidar to improve brain delivery of the model ABCB1/ABCG2 substrate [11C]erlotinib VL - 41 ER - TY - JOUR AB - A primary roadblock to our understanding of speciation is that it usually occurs over a timeframe that is too long to study from start to finish. The idea of a speciation continuum provides something of a solution to this problem; rather than observing the entire process, we can simply reconstruct it from the multitude of speciation events that surround us. But what do we really mean when we talk about the speciation continuum, and can it really help us understand speciation? We explored these questions using a literature review and online survey of speciation researchers. Although most researchers were familiar with the concept and thought it was useful, our survey revealed extensive disagreement about what the speciation continuum actually tells us. This is due partly to the lack of a clear definition. Here, we provide an explicit definition that is compatible with the Biological Species Concept. That is, the speciation continuum is a continuum of reproductive isolation. After outlining the logic of the definition in light of alternatives, we explain why attempts to reconstruct the speciation process from present‐day populations will ultimately fail. We then outline how we think the speciation continuum concept can continue to act as a foundation for understanding the continuum of reproductive isolation that surrounds us. AU - Stankowski, Sean AU - Ravinet, Mark ID - 9383 IS - 6 JF - Evolution SN - 0014-3820 TI - Defining the speciation continuum VL - 75 ER - TY - JOUR AB - Growth regulation tailors development in plants to their environment. A prominent example of this is the response to gravity, in which shoots bend up and roots bend down1. This paradox is based on opposite effects of the phytohormone auxin, which promotes cell expansion in shoots while inhibiting it in roots via a yet unknown cellular mechanism2. Here, by combining microfluidics, live imaging, genetic engineering and phosphoproteomics in Arabidopsis thaliana, we advance understanding of how auxin inhibits root growth. We show that auxin activates two distinct, antagonistically acting signalling pathways that converge on rapid regulation of apoplastic pH, a causative determinant of growth. Cell surface-based TRANSMEMBRANE KINASE1 (TMK1) interacts with and mediates phosphorylation and activation of plasma membrane H+-ATPases for apoplast acidification, while intracellular canonical auxin signalling promotes net cellular H+ influx, causing apoplast alkalinization. Simultaneous activation of these two counteracting mechanisms poises roots for rapid, fine-tuned growth modulation in navigating complex soil environments. AU - Li, Lanxin AU - Verstraeten, Inge AU - Roosjen, Mark AU - Takahashi, Koji AU - Rodriguez Solovey, Lesia AU - Merrin, Jack AU - Chen, Jian AU - Shabala, Lana AU - Smet, Wouter AU - Ren, Hong AU - Vanneste, Steffen AU - Shabala, Sergey AU - De Rybel, Bert AU - Weijers, Dolf AU - Kinoshita, Toshinori AU - Gray, William M. AU - Friml, Jiří ID - 10223 IS - 7884 JF - Nature KW - Multidisciplinary SN - 00280836 TI - Cell surface and intracellular auxin signalling for H+ fluxes in root growth VL - 599 ER - TY - JOUR AB - When B cells encounter membrane-bound antigens, the formation and coalescence of B cell antigen receptor (BCR) microclusters amplifies BCR signaling. The ability of B cells to probe the surface of antigen-presenting cells (APCs) and respond to APC-bound antigens requires remodeling of the actin cytoskeleton. Initial BCR signaling stimulates actin-related protein (Arp) 2/3 complex-dependent actin polymerization, which drives B cell spreading as well as the centripetal movement and coalescence of BCR microclusters at the B cell-APC synapse. Sustained actin polymerization depends on concomitant actin filament depolymerization, which enables the recycling of actin monomers and Arp2/3 complexes. Cofilin-mediated severing of actin filaments is a rate-limiting step in the morphological changes that occur during immune synapse formation. Hence, regulators of cofilin activity such as WD repeat-containing protein 1 (Wdr1), LIM domain kinase (LIMK), and coactosin-like 1 (Cotl1) may also be essential for actin-dependent processes in B cells. Wdr1 enhances cofilin-mediated actin disassembly. Conversely, Cotl1 competes with cofilin for binding to actin and LIMK phosphorylates cofilin and prevents it from binding to actin filaments. We now show that Wdr1 and LIMK have distinct roles in BCR-induced assembly of the peripheral actin structures that drive B cell spreading, and that cofilin, Wdr1, and LIMK all contribute to the actin-dependent amplification of BCR signaling at the immune synapse. Depleting Cotl1 had no effect on these processes. Thus, the Wdr1-LIMK-cofilin axis is critical for BCR-induced actin remodeling and for B cell responses to APC-bound antigens. AU - Bolger-Munro, Madison AU - Choi, Kate AU - Cheung, Faith AU - Liu, Yi Tian AU - Dang-Lawson, May AU - Deretic, Nikola AU - Keane, Connor AU - Gold, Michael R. ID - 9379 JF - Frontiers in Cell and Developmental Biology KW - B cell KW - actin KW - immune synapse KW - cell spreading KW - cofilin KW - WDR1 (AIP1) KW - LIM domain kinase KW - B cell receptor (BCR) TI - The Wdr1-LIMK-Cofilin axis controls B cell antigen receptor-induced actin remodeling and signaling at the immune synapse VL - 9 ER - TY - JOUR AB - A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics. AU - Chalk, Matthew J AU - Tkačik, Gašper AU - Marre, Olivier ID - 9362 IS - 4 JF - PLoS ONE TI - Inferring the function performed by a recurrent neural network VL - 16 ER - TY - JOUR AB - Size control is a fundamental question in biology, showing incremental complexity in plants, whose cells possess a rigid cell wall. The phytohormone auxin is a vital growth regulator with central importance for differential growth control. Our results indicate that auxin-reliant growth programs affect the molecular complexity of xyloglucans, the major type of cell wall hemicellulose in eudicots. Auxin-dependent induction and repression of growth coincide with reduced and enhanced molecular complexity of xyloglucans, respectively. In agreement with a proposed function in growth control, genetic interference with xyloglucan side decorations distinctly modulates auxin-dependent differential growth rates. Our work proposes that auxin-dependent growth programs have a spatially defined effect on xyloglucan’s molecular structure, which in turn affects cell wall mechanics and specifies differential, gravitropic hypocotyl growth. AU - Velasquez, Silvia Melina AU - Guo, Xiaoyuan AU - Gallemi, Marçal AU - Aryal, Bibek AU - Venhuizen, Peter AU - Barbez, Elke AU - Dünser, Kai Alexander AU - Darino, Martin AU - Pӗnčík, Aleš AU - Novák, Ondřej AU - Kalyna, Maria AU - Mouille, Gregory AU - Benková, Eva AU - Bhalerao, Rishikesh P. AU - Mravec, Jozef AU - Kleine-Vehn, Jürgen ID - 9986 IS - 17 JF - International Journal of Molecular Sciences KW - auxin KW - growth KW - cell wall KW - xyloglucans KW - hypocotyls KW - gravitropism SN - 1661-6596 TI - Xyloglucan remodeling defines auxin-dependent differential tissue expansion in plants VL - 22 ER - TY - JOUR AB - Transposable elements exist widely throughout plant genomes and play important roles in plant evolution. Auxin is an important regulator that is traditionally associated with root development and drought stress adaptation. The DEEPER ROOTING 1 (DRO1) gene is a key component of rice drought avoidance. Here, we identified a transposon that acts as an autonomous auxin‐responsive promoter and its presence at specific genome positions conveys physiological adaptations related to drought avoidance. Rice varieties with high and auxin‐mediated transcription of DRO1 in the root tip show deeper and longer root phenotypes and are thus better adapted to drought. The INDITTO2 transposon contains an auxin response element and displays auxin‐responsive promoter activity; it is thus able to convey auxin regulation of transcription to genes in its proximity. In the rice Acuce, which displays DRO1‐mediated drought adaptation, the INDITTO2 transposon was found to be inserted at the promoter region of the DRO1 locus. Transgenesis‐based insertion of the INDITTO2 transposon into the DRO1 promoter of the non‐adapted rice variety Nipponbare was sufficient to promote its drought avoidance. Our data identify an example of how transposons can act as promoters and convey hormonal regulation to nearby loci, improving plant fitness in response to different abiotic stresses. AU - Zhao, Y AU - Wu, L AU - Fu, Q AU - Wang, D AU - Li, J AU - Yao, B AU - Yu, S AU - Jiang, L AU - Qian, J AU - Zhou, X AU - Han, L AU - Zhao, S AU - Ma, C AU - Zhang, Y AU - Luo, C AU - Dong, Q AU - Li, S AU - Zhang, L AU - Jiang, X AU - Li, Y AU - Luo, H AU - Li, K AU - Yang, J AU - Luo, Q AU - Li, L AU - Peng, S AU - Huang, H AU - Zuo, Z AU - Liu, C AU - Wang, L AU - Li, C AU - He, X AU - Friml, Jiří AU - Du, Y ID - 9189 IS - 6 JF - Plant, Cell & Environment SN - 0140-7791 TI - INDITTO2 transposon conveys auxin-mediated DRO1 transcription for rice drought avoidance VL - 44 ER - TY - GEN AB - This paper establishes new connections between many-body quantum systems, One-body Reduced Density Matrices Functional Theory (1RDMFT) and Optimal Transport (OT), by interpreting the problem of computing the ground-state energy of a finite dimensional composite quantum system at positive temperature as a non-commutative entropy regularized Optimal Transport problem. We develop a new approach to fully characterize the dual-primal solutions in such non-commutative setting. The mathematical formalism is particularly relevant in quantum chemistry: numerical realizations of the many-electron ground state energy can be computed via a non-commutative version of Sinkhorn algorithm. Our approach allows to prove convergence and robustness of this algorithm, which, to our best knowledge, were unknown even in the two marginal case. Our methods are based on careful a priori estimates in the dual problem, which we believe to be of independent interest. Finally, the above results are extended in 1RDMFT setting, where bosonic or fermionic symmetry conditions are enforced on the problem. AU - Feliciangeli, Dario AU - Gerolin, Augusto AU - Portinale, Lorenzo ID - 9792 T2 - arXiv TI - A non-commutative entropic optimal transport approach to quantum composite systems at positive temperature ER -