Spin Model for Inverse Melting and Inverse Glass Transition

A spin model that displays inverse melting and inverse glass transition is presented and analyzed. Strong degeneracy of the interacting states of an individual spin leads to entropic preference of the "ferromagnetic" phase, while lower energy associated with the non-interacting states yields a "paramagnetic" phase as temperature decreases. An infinite range model is solved analytically for constant paramagnetic exchange interaction, while for its random exchange, analogous results based on the replica symmetric solution are presented. The qualitative features of this model are shown to resemble a large class of inverse melting phenomena. First and second order transition regimes are identified

Efficiency in Multi-objective Games

In a multi-objective game, each agent individually evaluates each overall action-profile on multiple objectives. I generalize the price of anarchy to multi-objective games and provide a polynomial-time algorithm to assess it. This work asserts that policies on tobacco promote a higher economic efficiency

LP-based Covering Games with Low Price of Anarchy

We present a new class of vertex cover and set cover games. The price of anarchy bounds match the best known constant factor approximation guarantees for the centralized optimization problems for linear and also for submodular costs -- in contrast to all previously studied covering games, where the price of anarchy cannot be bounded by a constant (e.g. [6, 7, 11, 5, 2]). In particular, we describe a vertex cover game with a price of anarchy of 2. The rules of the games capture the structure of the linear programming relaxations of the underlying optimization problems, and our bounds are established by analyzing these relaxations. Furthermore, for linear costs we exhibit linear time best response dynamics that converge to these almost optimal Nash equilibria. These dynamics mimic the classical greedy approximation algorithm of Bar-Yehuda and Even [3]

Analytical chemistry on many-center chiral compounds based on vibrational circular dichroism:Absolute configuration assignments and determination of contaminant levels

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Analysis of temporal gene regulation of Listeria monocytogenes revealed distinct regulatory response modes after exposure to high pressure processing

Background The pathogen Listeria (L.) monocytogenes is known to survive heat, cold, high pressure, and other extreme conditions. Although the response of this pathogen to pH, osmotic, temperature, and oxidative stress has been studied extensively, its reaction to the stress produced by high pressure processing HPP (which is a preservation method in the food industry), and the activated gene regulatory network (GRN) in response to this stress is still largely unknown. Results We used RNA sequencing transcriptome data of L. monocytogenes (ScottA) treated at 400 MPa and 8(circle)C, for 8 min and combined it with current information in the literature to create a transcriptional regulation database, depicting the relationship between transcription factors (TFs) and their target genes (TGs) in L. monocytogenes. We then applied network component analysis (NCA), a matrix decomposition method, to reconstruct the activities of the TFs over time. According to our findings, L. monocytogenes responded to the stress applied during HPP by three statistically different gene regulation modes: survival mode during the first 10 min post-treatment, repair mode during 1 h post-treatment, and re-growth mode beyond 6 h after HPP. We identified the TFs and their TGs that were responsible for each of the modes. We developed a plausible model that could explain the regulatory mechanism that L. monocytogenes activated through the well-studied CIRCE operon via the regulator HrcA during the survival mode. Conclusions Our findings suggest that the timely activation of TFs associated with an immediate stress response, followed by the expression of genes for repair purposes, and then re-growth and metabolism, could be a strategy of L. monocytogenes to survive and recover extreme HPP conditions. We believe that our results give a better understanding of L. monocytogenes behavior after exposure to high pressure that may lead to the design of a specific knock-out process to target the genes or mechanisms. The results can help the food industry select appropriate HPP conditions to prevent L. monocytogenes recovery during food storage.Peer reviewe

The complete genome sequence of Listeria monocytogenes strain S2542 and expression of selected genes under high-pressure processing

Objectives The study aims to generate the whole genome sequence of L. monocytogenes strain S2542 and to compare it to the genomes of strains RO15 and ScottA. In addition, we aimed to compare gene expression profiles of L. monocytogenes strains S2542, ScottA and RO15 after high-pressure processing (HPP) using ddPCR. Results The whole genome sequence of L. monocytogenes S2542 indicates that this strain belongs to serotype 4b, in contrast to the previously reported serotype 1/2a. Strain S2542 appears to be more susceptible to the treatment at 400 MPa compared to RO15 and ScottA strains. In contrast to RO15 and ScottA strains, viable cell counts of strain S2542 were below the limit of detection after HPP (400 MPa/8 min) when stored at 8 degrees C for 24 and 48 h. The transcriptional response of all three strains to HPP was not significantly different.Peer reviewe

High-pressure processing-induced transcriptome response during recovery of Listeria monocytogenes

Background High-pressure processing (HPP) is a commonly used technique in the food industry to inactivate pathogens, including L. monocytogenes. It has been shown that L. monocytogenes is able to recover from HPP injuries and can start to grow again during long-term cold storage. To date, the gene expression profiling of L. monocytogenes during HPP damage recovery at cooling temperature has not been studied. In order identify key genes that play a role in recovery of the damage caused by HPP treatment, we performed RNA-sequencing (RNA-seq) for two L. monocytogenes strains (barotolerant RO15 and barosensitive ScottA) at nine selected time points (up to 48 h) after treatment with two pressure levels (200 and 400 MPa). Results The results showed that a general stress response was activated by SigB after HPP treatment. In addition, the phosphotransferase system (PTS; mostly fructose-, mannose-, galactitol-, cellobiose-, and ascorbate-specific PTS systems), protein folding, and cobalamin biosynthesis were the most upregulated genes during HPP damage recovery. We observed that cell-division-related genes (divIC, dicIVA, ftsE, and ftsX) were downregulated. By contrast, peptidoglycan-synthesis genes (murG, murC, and pbp2A) were upregulated. This indicates that cell-wall repair occurs as a part of HPP damage recovery. We also observed that prophage genes, including anti-CRISPR genes, were induced by HPP. Interestingly, a large amount of RNA-seq data (up to 85%) was mapped to Rli47, which is a non-coding RNA that is upregulated after HPP. Thus, we predicted that Rli47 plays a role in HPP damage recovery in L. monocytogenes. Moreover, gene-deletion experiments showed that amongst peptidoglycan biosynthesis genes, pbp2A mutants are more sensitive to HPP. Conclusions We identified several genes and mechanisms that may play a role in recovery from HPP damage of L. monocytogenes. Our study contributes to new information on pathogen inactivation by HPP.Peer reviewe

Goedel-type Universes and the Landau Problem

We point out a close relation between a family of Goedel-type solutions of 3+1 General Relativity and the Landau problem in S^2, R^2 and H_2; in particular, the classical geodesics correspond to Larmor orbits in the Landau problem. We discuss the extent of this relation, by analyzing the solutions of the Klein-Gordon equation in these backgrounds. For the R^2 case, this relation was independently noticed in hep-th/0306148. Guided by the analogy with the Landau problem, we speculate on the possible holographic description of a single chronologically safe region.Comment: Latex, 21 pages, 1 figure. v2 missing references to previous work on the subject adde

Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments

One of the striking features of evolution is the appearance of novel structures in organisms. Recently, Kirschner and Gerhart have integrated discoveries in evolution, genetics, and developmental biology to form a theory of facilitated variation (FV). The key observation is that organisms are designed such that random genetic changes are channeled in phenotypic directions that are potentially useful. An open question is how FV spontaneously emerges during evolution. Here, we address this by means of computer simulations of two well-studied model systems, logic circuits and RNA secondary structure. We find that evolution of FV is enhanced in environments that change from time to time in a systematic way: the varying environments are made of the same set of subgoals but in different combinations. We find that organisms that evolve under such varying goals not only remember their history but also generalize to future environments, exhibiting high adaptability to novel goals. Rapid adaptation is seen to goals composed of the same subgoals in novel combinations, and to goals where one of the subgoals was never seen in the history of the organism. The mechanisms for such enhanced generation of novelty (generalization) are analyzed, as is the way that organisms store information in their genomes about their past environments. Elements of facilitated variation theory, such as weak regulatory linkage, modularity, and reduced pleiotropy of mutations, evolve spontaneously under these conditions. Thus, environments that change in a systematic, modular fashion seem to promote facilitated variation and allow evolution to generalize to novel conditions

PEDIA: prioritization of exome data by image analysis

Purpose Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis