TY - CONF AB - We study the almost-sure termination problem for probabilistic programs. First, we show that supermartingales with lower bounds on conditional absolute difference provide a sound approach for the almost-sure termination problem. Moreover, using this approach we can obtain explicit optimal bounds on tail probabilities of non-termination within a given number of steps. Second, we present a new approach based on Central Limit Theorem for the almost-sure termination problem, and show that this approach can establish almost-sure termination of programs which none of the existing approaches can handle. Finally, we discuss algorithmic approaches for the two above methods that lead to automated analysis techniques for almost-sure termination of probabilistic programs. AU - Huang, Mingzhang AU - Fu, Hongfei AU - Chatterjee, Krishnendu ED - Ryu, Sukyoung ID - 5679 SN - 03029743 TI - New approaches for almost-sure termination of probabilistic programs VL - 11275 ER - TY - JOUR AB - The precise control of neural stem cell (NSC) proliferation and differentiation is crucial for the development and function of the human brain. Here, we review the emerging links between the alteration of embryonic and adult neurogenesis and the etiology of neuropsychiatric disorders (NPDs) such as autism spectrum disorders (ASDs) and schizophrenia (SCZ), as well as the advances in stem cell-based modeling and the novel therapeutic targets derived from these studies. AU - Sacco, Roberto AU - Cacci, Emanuele AU - Novarino, Gaia ID - 546 IS - 2 JF - Current Opinion in Neurobiology TI - Neural stem cells in neuropsychiatric disorders VL - 48 ER - TY - GEN AB - This document contains the full list of genes with their respective significance and dN/dS values. (TXT 4499Â kb) AU - Zapata, Luis AU - Pich, Oriol AU - Serrano, Luis AU - Kondrashov, Fyodor AU - Ossowski, Stephan AU - Schaefer, Martin ID - 9812 TI - Additional file 2: Of negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome ER - TY - GEN AB - This document contains additional supporting evidence presented as supplemental tables. (XLSX 50Â kb) AU - Zapata, Luis AU - Pich, Oriol AU - Serrano, Luis AU - Kondrashov, Fyodor AU - Ossowski, Stephan AU - Schaefer, Martin ID - 9811 TI - Additional file 1: Of negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome ER - TY - JOUR AB - Background: Norepinephrine (NE) signaling has a key role in white adipose tissue (WAT) functions, including lipolysis, free fatty acid liberation and, under certain conditions, conversion of white into brite (brown-in-white) adipocytes. However, acute effects of NE stimulation have not been described at the transcriptional network level. Results: We used RNA-seq to uncover a broad transcriptional response. The inference of protein-protein and protein-DNA interaction networks allowed us to identify a set of immediate-early genes (IEGs) with high betweenness, validating our approach and suggesting a hierarchical control of transcriptional regulation. In addition, we identified a transcriptional regulatory network with IEGs as master regulators, including HSF1 and NFIL3 as novel NE-induced IEG candidates. Moreover, a functional enrichment analysis and gene clustering into functional modules suggest a crosstalk between metabolic, signaling, and immune responses. Conclusions: Altogether, our network biology approach explores for the first time the immediate-early systems level response of human adipocytes to acute sympathetic activation, thereby providing a first network basis of early cell fate programs and crosstalks between metabolic and transcriptional networks required for proper WAT function. AU - Higareda Almaraz, Juan AU - Karbiener, Michael AU - Giroud, Maude AU - Pauler, Florian AU - Gerhalter, Teresa AU - Herzig, Stephan AU - Scheideler, Marcel ID - 20 IS - 1 JF - BMC Genomics SN - 1471-2164 TI - Norepinephrine triggers an immediate-early regulatory network response in primary human white adipocytes VL - 19 ER - TY - JOUR AB - We introduce the notion of “non-malleable codes” which relaxes the notion of error correction and error detection. Informally, a code is non-malleable if the message contained in a modified codeword is either the original message, or a completely unrelated value. In contrast to error correction and error detection, non-malleability can be achieved for very rich classes of modifications. We construct an efficient code that is non-malleable with respect to modifications that affect each bit of the codeword arbitrarily (i.e., leave it untouched, flip it, or set it to either 0 or 1), but independently of the value of the other bits of the codeword. Using the probabilistic method, we also show a very strong and general statement: there exists a non-malleable code for every “small enough” family F of functions via which codewords can be modified. Although this probabilistic method argument does not directly yield efficient constructions, it gives us efficient non-malleable codes in the random-oracle model for very general classes of tampering functions—e.g., functions where every bit in the tampered codeword can depend arbitrarily on any 99% of the bits in the original codeword. As an application of non-malleable codes, we show that they provide an elegant algorithmic solution to the task of protecting functionalities implemented in hardware (e.g., signature cards) against “tampering attacks.” In such attacks, the secret state of a physical system is tampered, in the hopes that future interaction with the modified system will reveal some secret information. This problem was previously studied in the work of Gennaro et al. in 2004 under the name “algorithmic tamper proof security” (ATP). We show that non-malleable codes can be used to achieve important improvements over the prior work. In particular, we show that any functionality can be made secure against a large class of tampering attacks, simply by encoding the secret state with a non-malleable code while it is stored in memory. AU - Dziembowski, Stefan AU - Pietrzak, Krzysztof Z AU - Wichs, Daniel ID - 107 IS - 4 JF - Journal of the ACM TI - Non-malleable codes VL - 65 ER - TY - JOUR AB - In epithelial tissues, cells tightly connect to each other through cell–cell junctions, but they also present the remarkable capacity of reorganizing themselves without compromising tissue integrity. Upon injury, simple epithelia efficiently resolve small lesions through the action of actin cytoskeleton contractile structures at the wound edge and cellular rearrangements. However, the underlying mechanisms and how they cooperate are still poorly understood. In this study, we combine live imaging and theoretical modeling to reveal a novel and indispensable role for occluding junctions (OJs) in this process. We demonstrate that OJ loss of function leads to defects in wound-closure dynamics: instead of contracting, wounds dramatically increase their area. OJ mutants exhibit phenotypes in cell shape, cellular rearrangements, and mechanical properties as well as in actin cytoskeleton dynamics at the wound edge. We propose that OJs are essential for wound closure by impacting on epithelial mechanics at the tissue level, which in turn is crucial for correct regulation of the cellular events occurring at the wound edge. AU - Carvalho, Lara AU - Patricio, Pedro AU - Ponte, Susana AU - Heisenberg, Carl-Philipp J AU - Almeida, Luis AU - Nunes, André S. AU - Araújo, Nuno A.M. AU - Jacinto, Antonio ID - 5676 IS - 12 JF - Journal of Cell Biology SN - 00219525 TI - Occluding junctions as novel regulators of tissue mechanics during wound repair VL - 217 ER - TY - CONF AB - Clustering is a cornerstone of unsupervised learning which can be thought as disentangling multiple generative mechanisms underlying the data. In this paper we introduce an algorithmic framework to train mixtures of implicit generative models which we particularize for variational autoencoders. Relying on an additional set of discriminators, we propose a competitive procedure in which the models only need to approximate the portion of the data distribution from which they can produce realistic samples. As a byproduct, each model is simpler to train, and a clustering interpretation arises naturally from the partitioning of the training points among the models. We empirically show that our approach splits the training distribution in a reasonable way and increases the quality of the generated samples. AU - Locatello, Francesco AU - Vincent, Damien AU - Tolstikhin, Ilya AU - Ratsch, Gunnar AU - Gelly, Sylvain AU - Scholkopf, Bernhard ID - 14224 T2 - 6th International Conference on Learning Representations TI - Clustering meets implicit generative models ER - TY - GEN AB - Table S1. Genes with highest betweenness. Table S2. Local and Master regulators up-regulated. Table S3. Local and Master regulators down-regulated (XLSX 23 kb). AU - Higareda Almaraz, Juan AU - Karbiener, Michael AU - Giroud, Maude AU - Pauler, Florian AU - Gerhalter, Teresa AU - Herzig, Stephan AU - Scheideler, Marcel ID - 9807 TI - Additional file 1: Of Norepinephrine triggers an immediate-early regulatory network response in primary human white adipocytes ER - TY - GEN AB - Table S4. Counts per Gene per Million Reads Mapped. (XLSX 2751 kb). AU - Higareda Almaraz, Juan AU - Karbiener, Michael AU - Giroud, Maude AU - Pauler, Florian AU - Gerhalter, Teresa AU - Herzig, Stephan AU - Scheideler, Marcel ID - 9808 TI - Additional file 3: Of Norepinephrine triggers an immediate-early regulatory network response in primary human white adipocytes ER -