Joint fluctuation theorems for sequential heat exchange

We study the statistics of heat exchange of a quantum system that collides sequentially with an arbitrary number of ancillas. This can describe, for instance, an accelerated particle going through a bubble chamber. Unlike other approaches in the literature, our focus is on the \emph{joint} probability distribution that heat $Q_1$ is exchanged with ancilla 1, heat $Q_2$ is exchanged with ancilla 2, and so on. This allows one to address questions concerning the correlations between the collisional events. The joint distribution is found to satisfy a Fluctuation theorem of the Jarzynski-W\'ojcik type. Rather surprisingly, this fluctuation theorem links the statistics of multiple collisions with that of independent single collisions, even though the heat exchanges are statistically correlated

Energy barriers between metastable states in first-order quantum phase transitions

A system of neutral atoms trapped in an optical lattice and dispersively coupled to the field of an optical cavity can realize a variation of the Bose-Hubbard model with infinite-range interactions. This model exhibits a first-order quantum phase transition between a Mott insulator and a charge density wave, with spontaneous symmetry breaking between even and odd sites, as was recently observed experimentally [Landig, Nature (London) 532, 476 (2016)10.1038/nature17409]. In the present paper, we approach the analysis of this transition using a variational model which allows us to establish the notion of an energy barrier separating the two phases. Using a discrete WKB method, we then show that the local tunneling of atoms between adjacent sites lowers this energy barrier and hence facilitates the transition. Within our simplified description, we are thus able to augment the phase diagram of the model with information concerning the height of the barrier separating the metastable minima from the global minimum in each phase, which is an essential aspect for the understanding of the reconfiguration dynamics induced by a quench across a quantum critical point.Fil: Wald, Sascha. Sissa - International School For Advanced Studies; Italia. Universitat Saarland; AlemaniaFil: Timpanaro, André M.. Universidade Federal Do Abc; BrasilFil: Cormick, Maria Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; ArgentinaFil: Landi, Gabriel T.. Universidade de Sao Paulo; Brasi

Advances in the genetic classification of amyotrophic lateral sclerosis

Purpose of review Amyotrophic lateral sclerosis (ALS) is an archetypal complex disease wherein disease risk and severity are, for the majority of patients, the product of interaction between multiple genetic and environmental factors. We are in a period of unprecedented discovery with new large-scale genome-wide association study (GWAS) and accelerating discovery of risk genes. However, much of the observed heritability of ALS is undiscovered and we are not yet approaching elucidation of the total genetic architecture, which will be necessary for comprehensive disease subclassification. Recent findings We summarize recent developments and discuss the future. New machine learning models will help to address nonlinear genetic interactions. Statistical power for genetic discovery may be boosted by reducing the search-space using cell-specific epigenetic profiles and expanding our scope to include genetically correlated phenotypes. Structural variation, somatic heterogeneity and consideration of environmental modifiers represent significant challenges which will require integration of multiple technologies and a multidisciplinary approach, including clinicians, geneticists and pathologists. Summary The move away from fully penetrant Mendelian risk genes necessitates new experimental designs and new standards for validation. The challenges are significant, but the potential reward for successful disease subclassification is large-scale and effective personalized medicine