Fast Mixing for the Low Temperature 2D Ising Model Through Irreversible Parallel Dynamics

We study tunneling and mixing time for a non-reversible probabilistic cellular automaton. With a suitable choice of the parameters, we first show that the stationary distribution is close in total variation to a low temperature Ising model. Then we prove that both the mixing time and the time to exit a metastable state grow polynomially in the size of the system, while this growth is exponential in reversible dynamics. In this model, non-reversibility, parallel updatings and a suitable choice of boundary conditions combine to produce an efficient dynamical stability

Sum of exit times in series of metastable states in probabilistic cellular automata

Reversible Probabilistic Cellular Automata are a special class of automata whose stationary behavior is described by Gibbs--like measures. For those models the dynamics can be trapped for a very long time in states which are very different from the ones typical of stationarity. This phenomenon can be recasted in the framework of metastability theory which is typical of Statistical Mechanics. In this paper we consider a model presenting two not degenerate in energy metastable states which form a series, in the sense that, when the dynamics is started at one of them, before reaching stationarity, the system must necessarily visit the second one. We discuss a rule for combining the exit times from each of the metastable states

Heterogeneous and rate-dependent streptavidin-biotin unbinding revealed by high-speed force spectroscopy and atomistic simulations

Receptor-ligand interactions are essential for biological function and their binding strength is commonly explained in terms of static lock-and-key models based on molecular complementarity. However, detailed information of the full unbinding pathway is often lacking due, in part, to the static nature of atomic structures and ensemble averaging inherent to bulk biophysics approaches. Here we combine molecular dynamics and high-speed force spectroscopy on the streptavidin-biotin complex to determine the binding strength and unbinding pathways over the widest dynamic range. Experiment and simulation show excellent agreement at overlapping velocities and provided evidence of the unbinding mechanisms. During unbinding, biotin crosses multiple energy barriers and visits various intermediate states far from the binding pocket while streptavidin undergoes transient induced fits, all varying with loading rate. This multistate process slows down the transition to the unbound state and favors rebinding, thus explaining the long lifetime of the complex. We provide an atomistic, dynamic picture of the unbinding process, replacing a simple two-state picture with one that involves many routes to the lock and rate-dependent induced-fit motions for intermediates, which might be relevant for other receptor-ligand bonds.Comment: 21 pages, 4 figure

Genomic insights into the vulnerability of sympatric whitefish species flocks

The erosion of habitat heterogeneity can reduce species diversity directly but can also lead to the loss of distinctiveness of sympatric species through speciation reversal. We know little about changes in genomic differentiation during the early stages of these processes, which can be mediated by anthropogenic perturbation. Here, we analyse three sympatric whitefish species (Coregonus spp) sampled across two neighbouring and connected Swiss pre‐alpine lakes, which have been differentially affected by anthropogenic eutrophication. Our data set comprises 16,173 loci genotyped across 138 whitefish using restriction‐site associated DNA sequencing (RADseq). Our analysis suggests that in each of the two lakes the population of a different, but ecologically similar, whitefish species declined following a recent period of eutrophication. Genomic signatures consistent with hybridisation are more pronounced in the more severely impacted lake. Comparisons between sympatric pairs of whitefish species with contrasting ecology, where one is shallow benthic and the other one more profundal pelagic, reveal genomic differentiation that is largely correlated along the genome, while differentiation is uncorrelated between pairs of allopatric provenance with similar ecology. We identify four genomic loci that provide evidence of parallel divergent adaptation between the shallow benthic species and the two different more profundal species. Functional annotations available for two of those loci are consistent with divergent ecological adaptation. Our genomic analysis indicates the action of divergent natural selection between sympatric whitefish species in pre‐alpine lakes and reveals the vulnerability of these species to anthropogenic alterations of the environment and associated adaptive landscape

RHAMNETIN IS A BETTER INHIBITOR OF SARS-COV-2 2’-O-METHYLTRANSFERASE THAN DOLUTEGRAVIR: A COMPUTATIONAL PREDICTION

Background: The 2’-O-methyltransferase is responsible for the capping of SARS-CoV-2 mRNA and consequently the evasion of the host’s immune system. This study aims at identifying prospective natural inhibitors of the active site of SARS-CoV-2 2’O-methyltransferase (2’-OMT) through an in silico approach. Materials and Method: The target was docked against a library of natural compounds obtained from edible African plants using PyRx - virtual screening software. The antiviral agent, Dolutegravir which has a binding affinity score of -8.5 kcal mol−1 with the SARS-CoV-2 2’-OMT was used as a standard. Compounds were screened for bioavailability through the SWISSADME web server using their molecular descriptors. Screenings for pharmacokinetic properties and bioactivity were performed with PKCSM and Molinspiration web servers respectively. The PLIP and Fpocket webservers were used for the binding site analyses. The Galaxy webserver was used for simulating the time-resolved motions of the apo and holo forms of the target while the MDWeb web server was used for the analyses of the trajectory data. Results: The Root-Mean-Square-Deviation (RMSD) induced by Rhamnetin is 1.656A0 as compared to Dolutegravir (1.579A0). The average B-factor induced by Rhamnetin is 113.75 while for Dolutegravir is 78.87; the Root-Mean-Square-Fluctuation (RMSF) for Rhamnetin is 0.75 and for Dolutegravir is 0.67. Also at the active site, Rhamnetin also has a binding affinity score of -9.5 kcal mol−1 and forms 7 hydrogen bonds as compared to Dolutegravir which has -8.5 kcal mol−1 and forms 4 hydrogen bonds respectively. Conclusion: Rhamnetin showed better inhibitory activity at the target’s active site than Dolutegravir

On‐Demand Reconfiguration of Nanomaterials: When Electronics Meets Ionics

Rapid advances in the semiconductor industry, driven largely by device scaling, are now approaching fundamental physical limits and face severe power, performance, and cost constraints. Multifunctional materials and devices may lead to a paradigm shift toward new, intelligent, and efficient computing systems, and are being extensively studied. Herein examines how, by controlling the internal ion distribution in a solid‐state film, a material’s chemical composition and physical properties can be reversibly reconfigured using an applied electric field, at room temperature and after device fabrication. Reconfigurability is observed in a wide range of materials, including commonly used dielectric films, and has led to the development of new device concepts such as resistive random‐access memory. Physical reconfigurability further allows memory and logic operations to be merged in the same device for efficient in‐memory computing and neuromorphic computing systems. By directly changing the chemical composition of the material, coupled electrical, optical, and magnetic effects can also be obtained. A survey of recent fundamental material and device studies that reveal the dynamic ionic processes is included, along with discussions on systematic modeling efforts, device and material challenges, and future research directions.By controlling the internal ion distribution in a solid‐state film, the material’s chemical composition and physical (i.e., electrical, optical, and magnetic) properties can be reversibly reconfigured, in situ, using an applied electric field. The reconfigurability is achieved in a wide range of materials, and can lead to the development of new memory, logic, and multifunctional devices and systems.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141225/1/adma201702770.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141225/2/adma201702770_am.pd

Quantum machine learning: a classical perspective

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde

Analog Photonics Computing for Information Processing, Inference and Optimisation

This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

Multiscale Simulations of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) lack stable secondary and/or tertiary structures under physiological conditions. The have now been recognized to play important roles in numerous biological processes, particularly cellular signaling and regulation. Mutation of IDPs are frequently associated with human diseases, such as cancers and neuron degenerative diseases. Therefore, it is important to understand the structure, dynamics, and interactions of IDPs, so as to establish the mechanistic basis of how intrinsic disorder mediates versatile functions and how such mechanisms may fail in human diseases. However, the heterogeneous structural ensembles of IDPs are not amenable to high resolution characterization solely through experimental measurements, and molecular modelling and simulation are required to study IDP structures, dynamics, and interactions at the atomistic levels. Here, we first applied the state-of-the-art explicit solvent atomistic simulations to an anti-apoptotic protein Bcl-xL and demonstrated how inherent structural disorder may provide a physical basis of protein regulated unfolding in signaling transduction. We have also constructed a series of efficient coarse-grained models to directly simulate the interactions between IDPs and unveiled how the preexisting structural elements accelerate binding of ACTR to NCBD by promoting efficient folding upon encounter. These studies shed important light on how IDPs perform functions in the cellular regulatory network, but also reveal the necessity of new sampling techniques for more efficient simulations of IDPs. We have thus developed a novel sampling technique, called multiscale enhanced sampling (MSES). MSES couples the atomistic model with coarse-grained ones, to accelerate the sampling of atomistic conformational space. Bias from coupling to a coarse-grained model can be removed using Hamiltonian replica exchange. To achieve the best possible efficiency of MSES simulations, we have developed a new hybrid resolution protein model that could capture the essential features of IDP structures, so as to generate local and long-range fluctuations that are largely consistent with those at the atomistic level. We have also developed an advanced replica exchange protocol, to allow the fast conformational transitions observed in the coupled conditions to be rapidly exchanged to the unbiased limit. Application of these strategies to characterize the structural ensembles of a few non-trivial IDPs shows that faster convergence rate can be achieved, demonstrating the great potential of MSES for atomistic simulations of larger and more complex IDPs