StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features.

Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive.

The evolution of calcified anaerobic granular sludge bed informs the deep insight into its agglomeration process.

Calcium-induced agglomeration of anaerobic granular sludge bed (AGSB) has become a critical factor in performance decline of calcified anaerobic reactors. However, the agglomeration process of AGSB and the underlying mechanisms remain unclear and elusive. This study delved into the evolution of calcified AGSB, and four typical states of normal AGSB (Nor-AGSB), calcified dispersed AGSB (Dis-AGSB), calcified dimeric AGSB (Dim-AGSB), and calcified polymeric AGSB (Pol-AGSB) were characterized.

Association of perceived mental health with mortality, and analysis of potential pathways in Italian men and women: Prospective results from the Moli-sani Study cohort.

Background: Perceived mental health (PMH) was reportedly associated with mortality in general populations worldwide. However, little is known about sex differences and pathways potentially linking PMH to mortality. We explored the relationship between PMH and mortality in Italian men and women, and analysed potential explanatory factors.

DPI_CDF: druggable protein identifier using cascade deep forest.

Background: Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory.

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization.

The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods.

Dietary indices underpinning front-of-pack nutrition labels and health outcomes: a systematic review and meta-analysis of prospective cohort studies.

Background: Nutrient profiling systems are increasingly used to characterize the healthfulness of foods for front-of-package (FOP) labeling, which have been proposed as an effective public health strategy to help people make healthier food choices.

Objective: This study aimed to review available evidence from cohort studies that evaluated the association of dietary indices underpinning FOP nutrition labels with all-cause mortality and incidence of cardiovascular diseases (CVDs) or cancer.

Methods: PubMed, Web of Science, and Scopus were systematically searched up to October 2023.

Steroid hormones in systemic sclerosis: associations with disease characteristics and modifications during scleroderma renal crisis.

Objective: The renin-angiotensin-aldosterone system (RAAS) and glucocorticoids (GCs) are involved in vascular remodeling and fibrosis but have not been extensively studied in systemic sclerosis (SSc). Our aim was to investigate the RAAS and GC hormones in SSc patients.

Methods: Serum levels of renin (dosage and activity), aldosterone and its precursors (DOC, B, 18-OH-DOC, 18-OH-B), and GCs (cortisol, cortisone, 11-deoxycortisol, 18-OH-F) were assessed in 122 SSc patients and 52 healthy controls.

CRISPR/Cas9 mutagenesis of the GROWTH-REGULATING FACTOR (GRF) gene family.

Genome editing in plants typically relies on T-DNA plasmids that are mobilized by -mediated transformation to deliver the CRISPR/Cas machinery. Here, we introduce a series of CRISPR/Cas9 T-DNA vectors for minimal settings, such as teaching labs. Gene-specific targeting sequences can be inserted as annealed short oligonucleotides in a single straightforward cloning step.

Association between BMI, RFM and mortality and potential mediators: Prospective findings from the Moli-sani study.

Background: Body mass index (BMI) is the most frequently used adiposity measure, yet it is unable to differentiate fat mass from lean mass. Relative fat mass (RFM) has been proposed as an alternative. This paper aims to study RFM and BMI association with mortality in a general Italian population and potential mediators of such association.

Identification of the ubiquitin-proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network.

The major mechanism of proteolysis in the cytosol and nucleus is the ubiquitin-proteasome pathway (UPP). The highly controlled UPP has an effect on a wide range of cellular processes and substrates, and flaws in the system can lead to the pathogenesis of a number of serious human diseases. Knowledge about UPPs provide useful hints to understand the cellular process and drug discovery.

Selenium and mercury concentrations in biological samples from patients with COVID-19.

Background: Coronavirus disease 2019 (COVID-19) is a systemic disease affecting multiple organs. Furthermore, viral infection depletes several trace elements and promotes complex biochemical reactions in the body. Smoking has been linked to the incidence of COVID-19 and associated mortality, and it may impact clinical effects, viral and bacterial conversion, and treatment outcomes.

Graphene Oxide (GO) Materials-Applications and Toxicity on Living Organisms and Environment.

Graphene-based materials have attracted much attention due to their fascinating properties such as hydrophilicity, high dispersion in aqueous media, robust size, high biocompatibility, and surface functionalization ability due to the presence of functional groups and interactions with biomolecules such as proteins and nucleic acid. Modified methods were developed for safe, direct, inexpensive, and eco-friendly synthesis. However, toxicity to the environment and animal health has been reported, raising concerns about their utilization.

XGB-DrugPred: computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set.

Accurate identification of drug-targets in human body has great significance for designing novel drugs. Compared with traditional experimental methods, prediction of drug-targets via machine learning algorithms has enhanced the attention of many researchers due to fast and accurate prediction. In this study, we propose a machine learning-based method, namely XGB-DrugPred for accurate prediction of druggable proteins.

Tracking the Replication-Competent Zika Virus with Tetracysteine-Tagged Capsid Protein in Living Cells.

Real-time imaging of viruses in living cells considerably facilitates the study of virus-host interactions. However, generating a fluorescently labeled recombinant virus is challenging, especially for Zika virus (ZIKV), which causes microcephaly in neonates. The monocistronic nature of the ZIKV genome represents a major challenge for generating a replication-competent genetically engineered ZIKV suitable for real-time imaging.

Psychological Resilience, Cardiovascular Disease, and Metabolic Disturbances: A Systematic Review.

Background: Positive psychosocial factors can play an important role in the development of cardiovascular disease (CVD). Among them, psychological resilience (PR) is defined as the capacity of responding positively to stressful events. Our aim was to assess whether PR is associated with CVD or metabolic disturbances through a systematic review.

Human drug-pathway association prediction based on network consistency projection.

We present a novel computational method for drug-pathway association prediction based on known drug-pathway associations. The association between a drug and a pathway needs to be examined to not only explain the cause and enable the identification, therapy, and diagnosis of a human disease. Though, biological studies and clinical trials require substantial time and resources to identify drug-pathway associations.

Impact of Nationwide Lockdowns Resulting from the First Wave of the COVID-19 Pandemic on Food Intake, Eating Behaviors, and Diet Quality: A Systematic Review.

The lockdowns resulting from the first wave of the coronavirus disease 2019 (COVID-19) pandemic impacted deeply on all life activities, including diet. We performed a systematic review to investigate changes in food intake, eating behaviors, and diet quality during lockdown as compared with before the lockdown. A literature search was performed using 3 electronic databases from inception until 13 June 2021.

Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network.

Predicting the protein sequence information of enzymes and non-enzymes is an important but a very challenging task. Existing methods use protein geometric structures only or protein sequences alone to predict enzymatic functions. Thus, their prediction results are unsatisfactory.

AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information.

In extremely cold environments, living organisms like plants, animals, fishes, and microbes can die due to the intracellular ice formation in their bodies. To sustain life in such cold environments, some cold-blooded species produced Antifreeze proteins (AFPs), also called ice-binding proteins. AFPs are not only limited to the medical field but also have diverse significance in the area of biotechnology, agriculture, and the food industry.

High Breakdown Current Density in Quasi-1D van der Waals Layered Material TaNiSe.

We synthesized ternary composition chalcogenide TaNiSe, a quasi-one-dimensional (Q1D) material with excellent crystallinity. To utilize the excellent electrical conductivity property of TaNiSe, the breakdown current density () according to thickness change through mechanical exfoliation was measured. It was confirmed that as the thickness decreased, the maximum breakdown voltage () increased, and at 18 nm thickness, 35 MA cm of was measured, which was 35 times higher than that of copper, which is commonly used as an interconnect material.

Ta Ni Se : 1D van der Waals Material with Ambipolar Behavior.

In this study, high-purity and centimeter-scale bulk Ta Ni Se crystals are obtained by controlling the growth temperature and stoichiometric ratio between tantalum, nickel, and selenium. It is demonstrated that the bulk Ta Ni Se crystals could be effectively exfoliated into a few chain-scale nanowires through simple mechanical exfoliation and liquid-phase exfoliation. Also, the calculation of electronic band structures confirms that Ta Ni Se is a semiconducting material with a small bandgap.

An iron-air fuel cell system towards concurrent phosphorus removal and resource recovery in the form of vivianite and energy generation in wastewater treatment: A sustainable technology regarding phosphorus.

Phosphorous (P) recovery from industrial wastewaters solves both P deficiency and P pollution problems. A sequencing batch iron-air fuel cell was set up to recover P from synthetic wastewater containing 0.6 g-P/L NaHPO.