This paper links the nonequilibrium glassy relaxation behavior of otherwise athermal granular materials to those of thermally activated glasses. Thus, it demonstrates a much wider universality among complex glassy materials out of equilibrium. Our three-dimensional molecular dynamics simulations, fully incorporating friction and inelastic collisions, are designed to reproduce experimental behavior of tapped granular piles. A simple theory based on a dynamics of records explains why the typical phenomenology of annealing and aging after a quench should extend to such granular matter, activated by taps, beyond the more familiar realm of polymers, colloids, and magnetic materials that all exhibit thermal fluctuations.Bacterial genomes are being sequenced at an exponentially increasing rate, but our inability to decipher their transcriptional wiring limits our ability to derive new biology from these sequences. De novo determination of regulatory interactions requires accurate prediction of regulators' DNA binding and precise determination of biologically significant binding sites. Here we address these challenges by solving the DNA-specificity code of extracytoplasmic function sigma factors (ECF σs), a major family of bacterial regulators, and determining their putative regulons. We generated an aligned collection of ECF σs and their promoters by leveraging the autoregulatory nature of ECF σs as a means of promoter discovery and analyzed it to identify and characterize the conserved amino acid-nucleotide interactions that determine promoter specificity. This enabled de novo prediction of ECF σ specificity, which we combined with a statistically rigorous phylogenetic footprinting pipeline based on precomputed orthologs to predict the direct targets of ∼67% of ECF σs. This global survey indicated that some ECF σs are conserved global regulators controlling many genes throughout the genome, which are important under many conditions, while others are local regulators, controlling a few closely linked genes in response to specific stimuli in select species. This analysis reveals important organizing principles of bacterial gene regulation and presents a conceptual and computational framework for deciphering gene regulatory networks.This paper develops a method informed by data and models to recover information about investor beliefs. Our approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, our method allows for the direct use of sparse survey evidence to make these bounds more informative. Within our framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, we represent evidence about investor beliefs using a nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. click here We illustrate our method with a prototypical example from macrofinance using asset market data to infer belief restrictions for macroeconomic growth rates.Despite rapid advances in connectome mapping and neuronal genetics, we lack theoretical and computational tools to unveil, in an experimentally testable fashion, the genetic mechanisms that govern neuronal wiring. Here we introduce a computational framework to link the adjacency matrix of a connectome to the expression patterns of its neurons, helping us uncover a set of genetic rules that govern the interactions between neurons in contact. The method incorporates the biological realities of the system, accounting for noise from data collection limitations, as well as spatial restrictions. The resulting methodology allows us to infer a network of 19 innexin interactions that govern the formation of gap junctions in Caenorhabditis elegans, five of which are already supported by experimental data. As advances in single-cell gene expression profiling increase the accuracy and the coverage of the data, the developed framework will allow researchers to systematically infer experimentally testable connection rules, offering mechanistic predictions for synapse and gap junction formation.Mitochondrial and metabolic dysfunction are often implicated in neurological disease, but effective mechanism-based therapies remain elusive. We performed a genome-scale forward genetic screen in a Drosophila model of tauopathy, a class of neurodegenerative disorders characterized by the accumulation of the protein tau, and identified manipulation of the B-vitamin biotin as a potential therapeutic approach in tauopathy. We show that tau transgenic flies have an innate biotin deficiency due to tau-mediated relaxation of chromatin and consequent aberrant expression of multiple biotin-related genes, disrupting both carboxylase and mitochondrial function. Biotin depletion alone causes mitochondrial pathology and neurodegeneration in both flies and human neurons, implicating mitochondrial dysfunction as a mechanism in biotin deficiency. Finally, carboxylase biotin levels are reduced in mammalian tauopathies, including brains of human Alzheimer's disease patients. These results provide insight into pathogenic mechanisms of human biotin deficiency, the resulting effects on neuronal health, and a potential therapeutic pathway in the treatment of tau-mediated neurotoxicity.The formation and migration of disconnections (line defects constrained to the grain boundary [GB] plane with both dislocation and step character) control many of the kinetic and dynamical properties of GBs and the polycrystalline materials of which they are central constituents. We demonstrate that GBs undergo a finite-temperature topological phase transition of the Kosterlitz-Thouless (KT) type. This phase transition corresponds to the screening of long-range interactions between (and unbinding of) disconnections. This phase transition leads to abrupt changes in the behavior of GB migration, GB sliding, and roughening. We analyze this KT transition through mean-field theory, renormalization group theory, and kinetic Monte Carlo simulations and examine how this transition affects microstructure-scale phenomena such as grain growth stagnation, abnormal grain growth, and superplasticity.