Molecular self-assembled helix peptide nanotubes based on some amino acid molecules and their dipeptides: molecular modeling studies.

Context: The paper considers the features of the structure and dipole moments of several amino acids and their dipeptides which play an important role in the formation of the peptide nanotubes based on them. The influence of the features of their chirality (left L and right D) and the alpha-helix conformations of amino acids are taken into account. In particular, amino acids with aromatic rings, such as phenylalanine (Phe/F), and branched-chain amino acids (BCAAs)-leucine (Leu/L) and isoleucine (Ile/I)-as well as corresponding dipeptides (diphenylalanine (FF), dileucine (LL), and diisoleucine (II)) are considered.

A mark-specific quantile regression model.

Quantile regression has become a widely used tool for analysing competing risk data. However, quantile regression for competing risk data with a continuous mark is still scarce. The mark variable is an extension of cause of failure in a classical competing risk model where cause of failure is replaced by a continuous mark only observed at uncensored failure times.

Bayesian analysis of 1D H-NMR spectra.

Extracting spin system parameters from 1D high resolution H-NMR spectra can be an intricate task requiring sophisticate methods. With a few exceptions methods to perform such a total line shape analysis commonly rely on local optimization techniques which for increasing complexity of the underlying spin system tend to reveal local solutions. In this work we propose a full Bayesian modeling approach based on a quantum mechanical model of the spin system.

Animal Pose Estimation Based on Contrastive Learning with Dynamic Conditional Prompts.

Traditional animal pose estimation techniques based on images face significant hurdles, including scarce training data, costly data annotation, and challenges posed by non-rigid deformation. Addressing these issues, we proposed dynamic conditional prompts for the prior knowledge of animal poses in language modalities. Then, we utilized a multimodal (language-image) collaborative training and contrastive learning model to estimate animal poses.

Protein-Protein Interaction Prediction via Structure-Based Deep Learning.

Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy.

Bounds on the rates of statistical divergences and mutual information via stochastic thermodynamics.

Statistical divergences are important tools in data analysis, information theory, and statistical physics, and there exist well-known inequalities on their bounds. However, in many circumstances involving temporal evolution, one needs limitations on the rates of such quantities instead. Here, several general upper bounds on the rates of some f-divergences are derived, valid for any type of stochastic dynamics (both Markovian and non-Markovian), in terms of information-like and/or thermodynamic observables.

Experimental study on the mechanical and electrochemical properties of aqueous emulsifiable diphenylmethane diisocyanate-modified silicon-carbon composite electrodes.

Aqueous emulsifiable diphenylmethane diisocyanate (EMDI) can form strong chemical bonds with aqueous adhesives due to the large number of isocyanate (-NCO) groups, which can enhance the mechanical performance of the adhesives. Currently, sodium carboxymethyl cellulose (CMC)-styrene butadiene rubber (SBR) emulsion aqueous bonding agents are widely used in the preparation of anode materials for lithium-ion batteries (LIBs). In this study, EMDI was added to a porous silicon-carbon composite electrode prepared from CMC-SBR, and the evolution of the mechanical properties of the electrode with the EMDI content was first investigated quasi-static uniaxial tensile and interfacial strength tests.

Oxidized Starch-Reinforced Aqueous Polymer Isocyanate Cured with High-Frequency Heating.

In this research, an oxidized starch/styrene-butadiene rubber system with high capability of absorbing electromagnetic energy was adopted as the main component, the effect of oxidized starch content on the bonding and mechanical properties of aqueous polymer isocyanate (API) after high-frequency curing was evaluated, and the effect mechanisms were explored by combining thermodynamic tests and material characterization methods. Our findings revealed that the addition of oxidized starch enhanced the mechanical properties of API after high-frequency curing and the increase in the amount of oxidized starch enhanced the improvement effect of high-frequency curing on API bonding and mechanical properties. At 5 wt% oxidized starch, high-frequency curing improved API bonding properties by 18.

Manipulation of encapsulated artificial phospholipid membranes using sub-micellar lysolipid concentrations.

Droplet Interface Bilayers (DIBs) constitute a commonly used model of artificial membranes for synthetic biology research applications. However, their practical use is often limited by their requirement to be surrounded by oil. Here we demonstrate in-situ bilayer manipulation of submillimeter, hydrogel-encapsulated droplet interface bilayers (eDIBs).

Hydrochemical synthesis of thin films of mercury chalcogenides (HgS and HgSe): A brief review.

The main aspects of mercury sulfide and mercury selenide thin film synthesis using the chemical deposition method are considered. An analysis of various factors affecting film properties was conducted, including the study of how the nature of the substrate and its pretreatment influence film properties. The effects of the working solution's temperature, deposition time, and annealing conditions on film characteristics were investigated.

Elastic strain-induced amorphization in high-entropy alloys.

Elastic stability is the basis for understanding structural responses to external stimuli in crystalline solids, including melting, incipient plasticity and fracture. In this work, elastic stability is investigated in a series of high-entropy alloys (HEAs) using in situ mechanical tests and atomic-resolution characterization in transmission electron microscopy. Under tensile loading, the HEA lattices are observed to undergo a sudden loss of ordering as the elastic strain reached ∽ 10%.

Study on Confined Water in Flexible Graphene/GO Nanochannels.

The structural evolution of flexible nanochannels within a 2D material membrane, influenced by the ingress of water molecules, plays a crucial role in the membrane's filtration and structural stability. However, the experimental observation of nanoscale water is challenging, and current studies mostly focus on rigid nanochannels. Further investigation on the nanoconfined water is urgently needed, considering the flexibility and deformation of the channel.

Effect of the degree of an initial mutant in Moran processes in structured populations.

We study effects of the mutant's degree on the fixation probability, extinction, and fixation times in Moran processes on Erdös-Rényi and Barabási-Albert graphs. We performed stochastic simulations and used mean-field-type approximations to obtain analytical formulas. We showed that the initial placement of a mutant has a significant impact on the fixation probability and extinction time, while it has no effect on the fixation time.

MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction.

The precise prediction of major histocompatibility complex (MHC)-peptide complex structures is pivotal for understanding cellular immune responses and advancing vaccine design. In this study, we enhanced AlphaFold's capabilities by fine-tuning it with a specialized dataset consisting of exclusively high-resolution class I MHC-peptide crystal structures. This tailored approach aimed to address the generalist nature of AlphaFold's original training, which, while broad-ranging, lacked the granularity necessary for the high-precision demands of class I MHC-peptide interaction prediction.

Traded Plastic, Traded Impacts? Designing Counterfactual Scenarios to Assess Environmental Impacts of Global Plastic Waste Trade.

The global trade of plastic waste has raised environmental concerns, especially regarding pollution in waste-importing countries. However, the overall environmental contribution remains unclear due to uncertain treatment shares between handling plastic waste abroad and domestically. Here, we conduct a life cycle assessment of global plastic waste trade in 2022 across 18 countries and six plastic waste types, alongside three "nontrade" counterfactual scenarios.

AI can empower agriculture for global food security: challenges and prospects in developing nations.

Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG).

Multilevel Monte Carlo Methods for Stochastic Convection-Diffusion Eigenvalue Problems.

We develop new multilevel Monte Carlo (MLMC) methods to estimate the expectation of the smallest eigenvalue of a stochastic convection-diffusion operator with random coefficients. The MLMC method is based on a sequence of finite element (FE) discretizations of the eigenvalue problem on a hierarchy of increasingly finer meshes. For the discretized, algebraic eigenproblems we use both the Rayleigh quotient (RQ) iteration and implicitly restarted Arnoldi (IRA), providing an analysis of the cost in each case.

Genetic interference with HvNotch provides new insights into the role of the Notch-signalling pathway for developmental pattern formation in Hydra.

The Notch-signalling pathway plays an important role in pattern formation in Hydra. Using pharmacological Notch inhibitors (DAPT and SAHM1), it has been demonstrated that HvNotch is required for head regeneration and tentacle patterning in Hydra. HvNotch is also involved in establishing the parent-bud boundary and instructing buds to develop feet and detach from the parent.

Investigation of out-of-distribution detection across various models and training methodologies.

Machine learning-based algorithms demonstrate impressive performance across numerous fields; however, they continue to suffer from certain limitations. Even sophisticated and precise algorithms often make erroneous predictions when implemented with datasets having different distributions compared to the training set. Out-of-distribution (OOD) detection, which distinguishes data with different distributions from that of the training set, is a critical research area necessary to overcome these limitations and create more reliable algorithms.

Reversible Binding Interfaces Made of Microstructured Polymer Brushes.

The polymer brush architecture of the end-tethered polymer molecules is one of the most widely used efficient methods to regulate interfacial interactions in colloidal systems found in live matter and manufactured materials. Emerging applications of polymer brush structures require solutions to new tasks in the control of interfacial interactions. The rapid development of live cell manufacturing relies on scalable and efficient cell harvesting methods.

Generalization analysis of deep CNNs under maximum correntropy criterion.

Convolutional neural networks (CNNs) have gained immense popularity in recent years, finding their utility in diverse fields such as image recognition, natural language processing, and bio-informatics. Despite the remarkable progress made in deep learning theory, most studies on CNNs, especially in regression tasks, tend to heavily rely on the least squares loss function. However, there are situations where such learning algorithms may not suffice, particularly in the presence of heavy-tailed noises or outliers.

A mathematical model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities.

Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts-often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy-is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks of infectious diseases requires a firm understanding of the effectiveness of such containment strategies. To this end, mathematical models provide a promising tool.

The Impact of the Fluid-Solid Coupling Behavior of Macro and Microstructures in the Spiral Cochlea on Hearing.

The cilia of the outer hair cells (OHCs) are the key microstructures involved in cochlear acoustic function, and their interactions with lymph in the cochlea involve complex, highly nonlinear, coupled motion and energy conversions, including macroscopic fluid-solid coupling. Recent optical measurements have shown that the frequency selectivity of the cochlea at high sound levels is entirely mechanical and is determined by the interactions of the hair bundles with the surrounding fluid. In this paper, an analytical mathematical model of the spiral cochlea containing macro- and micromeasurements was developed to investigate how the phonosensitive function of OHCs' motions is influenced by the macrostructural and microstructural fluid-solid coupling in the spiral cochlea.