Structural Regulation of NiFe LDH under Spontaneous Corrosion to Enhance the Oxygen Evolution Properties.

Electrochemical water splitting holds promise for sustainable hydrogen production but restricted by the sluggish reaction kinetics at the anodic oxygen evolution. Herein, we present a room-temperature spontaneous corrosion strategy to convert inexpensive iron (Fe) on iron foam substrates into highly active and stable self-supporting nickel iron layered hydroxide (NiFe LDH) catalysts. The corrosion evolution mechanisms are elucidated combining ex-situ scanning electron microscopy (SEM) and X-ray photo electron spectroscopy (XPS) techniques, demonstrating precise control over the concentration of Ni2+ and reaction time to achieve controllable micro-structures of NiFe LDH.

Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations.

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability.

Optimizing Acute Coronary Syndrome Patient Treatment: Leveraging Gated Transformer Models for Precise Risk Prediction and Management.

Background: Acute coronary syndrome (ACS) is a severe cardiovascular disease with globally rising incidence and mortality rates. Traditional risk assessment tools are widely used but are limited due to the complexity of the data.

Methods: This study introduces a gated Transformer model utilizing machine learning to analyze electronic health records (EHRs) for an enhanced prediction of major adverse cardiovascular events (MACEs) in ACS patients.

Kernel Bayesian nonlinear matrix factorization based on variational inference for human-virus protein-protein interaction prediction.

Identification of potential human-virus protein-protein interactions (PPIs) contributes to the understanding of the mechanisms of viral infection and to the development of antiviral drugs. Existing computational models often have more hyperparameters that need to be adjusted manually, which limits their computational efficiency and generalization ability. Based on this, this study proposes a kernel Bayesian logistic matrix decomposition model with automatic rank determination, VKBNMF, for the prediction of human-virus PPIs.

Hierarchical Sea Urchin-like Fe-doped Heazlewoodite for High-Efficient Oxygen Evolution.

Electrochemical water-splitting to produce hydrogen is potential to substitute the traditional industrial coal gasification, but the oxygen evolution kinetics at the anode remains sluggish. In this paper, sea urchin-like Fe doped NiS catalyst growing on nickel foam (NF) substrate is constructed via a simple two-step strategy, including surface iron activation and post sulfuration process. The NF-Fe-NiS obtains at temperature of 130 °C (NF-Fe-NiS-130) features nanoneedle-like arrays which are vertically grown on the particles to form sea urchin-like morphology, features high electrochemical surface area.

Weighted hypergraph learning and adaptive inductive matrix completion for SARS-CoV-2 drug repositioning.

Motivation: The outbreak of the human coronavirus (SARS-CoV-2) has placed a huge burden on public health and the world economy. Compared with de novo drug discovery, drug repurposing is a promising therapeutic strategy that facilitates rapid clinical treatment decisions, shortens the development process, and reduces costs.

Results: In this study, we propose a weighted hypergraph learning and adaptive inductive matrix completion method, WHAIMC, for predicting potential virus-drug associations.

Study on a co-hybrid nano-hydroxyapatite with lignin derivatives and alendronate and the reinforce effect for poly(lactide-co-glycolide).

A novel co-hybrid nano-apatite (n-HA) by introducing lignin derivatives (LDs) and alendronate (ALE) was designed to reinforce poly(lactide-co-glycolide) (PLGA). The effect of different addition methods and contents of LDs, lignin derivatives sorts of lignosulfonate (LS), alkali lignin (AL) and carboxymethyl lignin (CML), and the addition order of ALE on the dispersion of hybrid n-HA, and reinforce effective for PLGA were investigated by FTIR, XRD, TEM, TGA, XPS, N adsorption/desorption, zeta potential, dispersion experiments, universal testing machine, SEM, DSC and POM. The results showed that the addition order could regulate the growth of n-HA crystal planes by binding with Ca, and co-hybrid HA by LDs and ALE possessed better dispersion owing to the synergistic effect.

Preparation and Characterization of a Novel Tragacanth Gum/Chitosan/Sr-Nano-Hydroxyapatite Composite Membrane.

It is a great challenge to obtain an ideal guided bone regeneration (GBR) membrane. In this study, tragacanth gum (GT) was introduced into a chitosan/nano-hydroxyapatite (CS/n-HA) system. The effects of different component ratios and strontium-doped nano-hydroxyapatite (Sr-HA) on the physical-chemical properties and degradation behavior of the CS/Sr-n-HA/GT ternary composite membrane were investigated using Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), contact angle, electromechanical universal tester and in vitro soaking in simulated body fluid (SBF).

Cobalt Oxide Arrays Anchored to Copper Foam as Efficient Binder-free Anode for Lithium Ion Batteries.

The development of lithium-ion batteries with simplified assembling steps and fast charge capability is crucial for current battery applications. In this study, we propose a simple in-situ strategy for the construction of high-dispersive cobalt oxide (CoO) nanoneedle arrays, which grow vertically on a copper foam substrate. It is demonstrated that this nanoneedle CoO electrodes provide abundant electrochemical surface area.

HONMF: integration analysis of multi-omics microbiome data via matrix factorization and hypergraph.

Motivation: The accumulation of multi-omics microbiome data provides an unprecedented opportunity to understand the diversity of bacterial, fungal, and viral components from different conditions. The changes in the composition of viruses, bacteria, and fungi communities have been associated with environments and critical illness. However, identifying and dissecting the heterogeneity of microbial samples and cross-kingdom interactions remains challenging.

Study on the Quality of Mixed Silage of Rapeseed with Alfalfa or Myriophyllum.

The objective of this study was to improve the comprehensive rate of utilization of rapeseed ( L.), Myriophyllum ( L.) spicatum and alfalfa ( L.

Logistic tensor decomposition with sparse subspace learning for prediction of multiple disease types of human-virus protein-protein interactions.

Viral infection involves a large number of protein-protein interactions (PPIs) between the virus and the host, and the identification of these PPIs plays an important role in revealing viral infection and pathogenesis. Existing computational models focus on predicting whether human proteins and viral proteins interact, and rarely take into account the types of diseases associated with these interactions. Although there are computational models based on a matrix and tensor decomposition for predicting multi-type biological interaction relationships, these methods cannot effectively model high-order nonlinear relationships of biological entities and are not suitable for integrating multiple features.

Effects of initial volatile fatty acid concentrations on process characteristics, microbial communities, and metabolic pathways on solid-state anaerobic digestion.

Solid-state anaerobic digestion (SSAD) is vulnerable to excess volatile fatty acids (VFA), mainly acetate and propionate. The co-effects of VFAs and microbial dynamics under VFA accumulation were investigated in SSAD of pig manure and corn straw. Adding 2 and 4 mg/g acetate or propionate caused initial increases in total VFAs, followed by decreases after day 6, resulting in 'mild' VFA accumulation, while adding 6 mg/g caused similarly increased VFAs, but with no subsequent decrease, causing 'severe' VFA accumulation and poor methanation performance.

High-temperature adaptation of an allele is thermoregulated by small RNAs.

Climate change negatively affects crop yield, which hinders efforts to reach agricultural sustainability and food security. Here, we show that a previously unidentified allele of the nitrate transporter gene is required to maintain high yield and high nitrogen use efficiency under high temperatures. We demonstrate that this tolerance to high temperatures in rice accessions harboring the HTNE-2 (high temperature resistant and nitrogen efficient-2) alleles from enhanced translation of the mRNA isoform and the decreased abundance of a unique small RNA (sNRT2.

Utilizing Cell Culture Assisted Anodization to Fabricate Aluminium Oxide with a Gradient Microstep and Nanopore Structure.

Anodic aluminum oxide (AAO) with a gradient microstep and nanopore structure (GMNP) is fabricated by inversely using cell culture to control the reaction areas in the electrochemical anodization, which shows a larger porosity than that of typical planar AAO. The figure of the microstep is influenced by the cell dehydration temperature which controls the cell shrinkage degree. A GMNP AAO with a diameter of 2.

Prevalence and pathology of Cephalopina titillator infestation in Camelus bactrianus from Xinjiang, China.

Background: In camels, nasopharyngeal myiasis is caused by the larvae of Cephalopina titillator, which parasitize the tissues of nasal and paranasal sinuses, pharynx, and larynx. C. titillator infestation adversely affects the health of camels and decreases milk and meat production and even death.

Deciphering discriminative antibiotic resistance genes and pathogens in agricultural soil following chemical and organic fertilizer.

Fertilizers containing rich nutrients can change the profiles of antibiotic resistant pathogens (ARPs) and antibiotic resistance genes (ARGs) in receiving soils; however, the discriminative ARGs and ARPs in agricultural soil following different fertilizer applications remain unknown. Using metagenomic sequencing combined with binning approach, the present study investigated the discriminative ARGs and ARPs under various fertilizer applications (chemical and organic fertilizer) in a 8-year field experiment. VanR, multidrug ARG transporter, vanS, ermA, and arnA were the discriminative ARGs in the chemical fertilizer group, whereas rosB, multidrug transporter, mexW, and aac(3)-I were enhanced in the organic fertilizer group.

Hypergraph clustering based on modularity feature projection for high-order relationship community detection of microorganisms.

Microbial community is an important part of organisms or ecosystems to maintain health and stability. Analyzing the interaction of microorganisms in the ecosystem and mining the co-occurrence module of the microbial community can deepen the understanding of microbial community function. This could also improve the ability to manipulate the microbial community, thus provide new means for ecological restoration, disease treatment and drug development.

Generalized matrix factorization based on weighted hypergraph learning for microbe-drug association prediction.

The complex and diverse microbial communities are closely related to human health, and the research of microbial communities plays an increasingly critical role in drug development and precision medicine. Identifying potential microbe-drug associations not only benefits drug discovery and clinical therapy, but also contributes to a better understanding of the mechanisms of action of microbes. Compared with the complexity and high cost of biological experiments, computational methods can quickly and efficiently predict potential microbe-drug associations, which could be a useful complement to experimental methods.

DeepMNE: Deep Multi-Network Embedding for lncRNA-Disease Association Prediction.

Long non-coding RNA (lncRNA) participates in various biological processes, hence its mutations and disorders play an important role in the pathogenesis of multiple human diseases. Identifying disease-related lncRNAs is crucial for the diagnosis, prevention, and treatment of diseases. Although a large number of computational approaches have been developed, effectively integrating multi-omics data and accurately predicting potential lncRNA-disease associations remains a challenge, especially regarding new lncRNAs and new diseases.

Hypergraph-based logistic matrix factorization for metabolite-disease interaction prediction.

Motivation: Function-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational approaches build networks by calculating pairwise relationships, which is inappropriate for mining higher-order relationships.

An Inductive Logistic Matrix Factorization Model for Predicting Drug-Metabolite Association With Vicus Regularization.

Metabolites are closely related to human disease. The interaction between metabolites and drugs has drawn increasing attention in the field of pharmacomicrobiomics. However, only a small portion of the drug-metabolite interactions were experimentally observed due to the fact that experimental validation is labor-intensive, costly, and time-consuming.

Performance improvement for a 2D convolutional neural network by using SSC encoding on protein-protein interaction tasks.

Background: The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in experimental technology, it is still expensive, laborious, and time-consuming to determine protein-protein interactions (PPIs), and there is a strong demand for effective bioinformatics approaches to identify potential PPIs. Considering the large amount of PPI data, a high-performance processor can be utilized to enhance the capability of the deep learning method and directly predict protein sequences.