Browse Articles

Kinetics of hematological and biochemical biomarkers are key tools for monitoring disease progression in Marburg virus-infected patients in Rwanda

Enhancing agricultural commodity price forecasting with deep learning

Abstract Accurate forecasting of agricultural commodity prices is essential for market planning and policy formulation, especially in agriculture-dependent economies like India. Price volatility, driven by factors such as weather variability and market demand fluctuations, poses significant forecasting challenges. This study evaluates the performance of traditional stochastic models, machine learning techniques, and deep learning approaches in forecasting the prices of 23 commodities using daily wholesale price data from January 2010 to June 2024. Models assessed include Autoregressive Integrated Moving Average, Support Vector Regression, Extreme Gradient Boosting, Multilayer Perceptron, Recurrent Neural Networks, Long Short-Term Memory Networks, Gated Recurrent Units, and Echo State Networks. Results show that deep learning models, particularly Long Short-Term Memory and Gated Recurrent Units, outperform others in capturing complex temporal patterns, achieving superior accuracy across error metrics. The results indicate that deep learning models, particularly Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), demonstrate superior performance in capturing complex temporal patterns. For instance, the GRU model achieved a Root Mean Squared Error (RMSE) of 369.54 for onions and 210.35 for tomatoes, significantly outperforming the ARIMA model, which recorded RMSE values of 1564.62 and 1298.60, respectively. Furthermore, the Mean Absolute Percentage Error (MAPE) for GRU was notably lower, at 14.59% for onions and 10.58% for tomatoes. These results underscore the efficacy of deep learning approaches in addressing the inherent volatility and nonlinear dynamics of agricultural commodity prices. These findings offer valuable insights for policymakers, traders, and farmers, enabling better market interventions, crop planning, and risk management. The study recommends exploring hybrid models and incorporating external factors like weather data to further enhance forecasting reliability.

In situ NMR reveals a pH sensor motif in an outer membrane protein that drives bacterial vesicle production

The outer membrane vesicles (OMVs) produced by diderm bacteria have important roles in cell envelope homeostasis, secretion, interbacterial communication, and pathogenesis. The facultative intracellular pathogen Salmonella enterica Typhimurium (STm) activates OMV biogenesis inside the acidic vacuoles of host cells by upregulating the expression of the OM protein PagC, one of the most robustly activated genes in a host environment. Here, we used solid-state nuclear magnetic resonance (NMR) and electron microscopy (EM), with native bacterial OMVs, to demonstrate that three histidines, essential for the OMV biogenic function of PagC, constitute a key pH-sensing motif. The NMR spectra of PagC in OMVs show that they become protonated around pH 6, and His protonation is associated with specific perturbations of select regions of PagC. The use of bacterial OMVs is a key aspect of this work enabling NMR structural studies in the context of the physiological environment. PagC expression upregulates OMV production in Escherichia coli , replicating its function in STm. Moreover, the presence of PagC drives a striking aggregation of OMVs and increases bacterial cell pellicle formation at acidic pH, pointing to a potential role as an adhesin active in biofilm formation. The data provide experimental evidence for a pH-dependent mechanism of OMV biogenesis and aggregation driven by an OM protein.

Machine learning reveals genes impacting oxidative stress resistance across yeasts

Abstract Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and during interactions with other organisms, including host infection. Here, we characterize the variation in resistance to the ROS-inducing compound tert -butyl hydroperoxide across the ancient yeast subphylum Saccharomycotina and use machine learning (ML) to identify gene families whose sizes are predictive of ROS resistance. The most predictive features are enriched in gene families related to cell wall organization and include two reductase gene families. We estimate the quantitative contributions of features to each species’ classification to guide experimental validation and show that overexpression of the old yellow enzyme (OYE) reductase increases ROS resistance in Kluyveromyces lactis , while Saccharomyces cerevisiae mutants lacking multiple mannosyltransferase-encoding genes are hypersensitive to ROS. Altogether, this work provides a framework for how ML can uncover genetic mechanisms underlying trait variation across diverse species and inform trait manipulation for clinical and biotechnological applications.

A biomimetic multi-component subunit vaccine via ratiometric loading of hierarchical hydrogels

Cold-induced nucleosome dynamics linked to silencing of Arabidopsis FLC

Abstract Temperature influences nucleosome dynamics, and thus chromatin, to regulate gene expression. Such mechanisms underlie the epigenetic silencing of Arabidopsis FLOWERING LOCUS C ( FLC ) by prolonged cold. Here, we show a temperature-dependent transition in local chromatin structure at the H3K27me3 nucleation region, from a modality active for transcription to a state that can be Polycomb silenced. In vivo chromatin measurements and coarse-grained simulations at near-atomistic resolution show that the active transcription state is characterised by a highly dynamic nucleosome arrangement that exposes the FLC transcription start site (TSS). Cold exposure then changes the chromatin by reducing nucleosome dynamics and re-positioning the + 1 nucleosome, leading to transcriptional repression. This local chromatin transition partially depends on VERNALIZATION1 (VRN1), a non-sequence-specific DNA-binding protein. Loss of VRN1 results in hyperaccumulation of H2A.Z, more dynamic nucleosomes and an inability to accumulate H2Aub and H3K27me3. Our work highlights how local nucleosome dynamics link to chromatin structure transitions to integrate temperature inputs into epigenetic switching mechanisms in plants.

Anchoring Highly Unsaturated Nickel(II) Sites into a Metal–Organic Framework for Simultaneous High C ₂ H ₂ Adsorption and Separation

Abstract Separation of acetylene (C 2 H 2 ) from carbon dioxide (CO 2 ) remains a challenge to achieve both high C 2 H 2 uptake and selectivity for a single material. Porous materials with open metal sites (OMSs) have been widely employed to separate various gas mixtures; however, routine OMSs often show the limitation in C 2 H 2 /CO 2 separation due to the comparable binding affinity with the two gases. Herein, we report a new strategy of anchoring highly unsaturated nickel(II) sites into a large‐pore MOF (Ni 2+ @NOTT‐101‐(COOH) 2 ) for simultaneously improving C 2 H 2 adsorption and selectivity. The coordination geometry of the anchored Ni 2+ ion was accurately determined by SCXRD studies, featuring a two‐coordination model with highly unsaturated OMSs after the activation. This highly unsaturated Ni 2+ ion can provide additional binding sites to improve C 2 H 2 adsorption and also enable highly selective binding of C 2 H 2 over CO 2 through the specific and strong π‐ complexation interactions, as revealed by gas‐loaded SCXRD studies and theoretical simulations. This metalated MOF thus exhibits both significantly enhanced C 2 H 2 uptake (201.4 cm 3 g −1 ) and C 2 H 2 /CO 2 selectivity (25.7) than the pristine NOTT‐101‐(COOH) 2 (148.0 cm 3 g −1 and 3.8) at 298 K and 1 bar, making an unprecedented balance between C 2 H 2 adsorption and selectivity to overcome the trade‐off challenge. Breakthrough experiments on equimolar C 2 H 2 /CO 2 mixtures afford both the top‐tier dynamic selectivity (14.4) and C 2 H 2 productivity of 114.5 L kg −1 (>99.5% purity) at ambient conditions.

Investigating hepatic fibrosis heterogeneity by three-dimensional imaging in metabolic dysfunction-associated steatotic liver disease

A fake news detection model using the integration of multimodal attention mechanism and residual convolutional network

Intestinal alkaline phosphatase is a receptor for cholesterol-lowering pentapeptide IIAEK and regulates cholesterol homeostasis in mice

Photocatalytic syntheses and evaluation of biological activities of rare disaccharides, 3-O-α-d-glucopyranosyl-d-arabinose

Abstract Recently, there has been a growing interest in rare sugars due to their potential applications in functional foods and pharmaceuticals. However, sustainable production methods for these compounds remain challenging due to their high cost, lengthy production times, and environmentally harmful reagents. Herein, we report a novel photocatalytic approach for synthesizing rare disaccharides from maltose, an abundant and renewable natural resource, under mild conditions at room temperature and atmospheric pressure using light as the energy source. The photocatalytic treatment of maltose using platinum compound-supported titanium oxide (PtCl/TiO₂) resulted in the formation of rare disaccharides, primarily 3-O-α-d-glucopyranosyl-d-arabinose and glucosyl-erythrose, which were characterized by HPLC, LC/MS, 13C NMR spectroscopy, and optical rotation measurements. Notably, biological evaluation of 3-O-α-d-glucopyranosyl-d-arabinose using HeLa and HEK293 cells demonstrated no cytotoxicity and negligible cellular uptake. Furthermore, enzymatic degradation studies using mouse intestinal α-glucosidase revealed significantly lower degradability compared to maltose, with minimal glucose production observed. These findings suggest that 3-O-α-d-glucopyranosyl-d-arabinose exhibits resistance to digestion and absorption in mammalian systems, highlighting its potential application as a low-calorie sweetener and a functional food ingredient. This study presents an environmentally benign synthetic route to rare disaccharides and demonstrates their promising biological properties.

Experimental study on mechanical performance of corrugated steel-concrete composite bridge decks

Design of a low-delay 4-bit parallel prefix adder using QCA technology

Abstract This paper presents a novel low-delay 4-bit Parallel Prefix Adder (PPA) implemented as a multilayer circuit using Quantum Dot Cellular Automata (QCA) technology. PPAs are among the most suitable architectures for high-speed digital design, offering significant advantages in scalability and performance over traditional Ripple Carry Adders (RCAs) and Carry Flow Adders (CFAs). The proposed design provides a fast, compact, ergonomic, and energy-efficient alternative to QCA adders adopting these architectures. This work enhances existing PPA modules, including XOR gates, Half Adders, Black Modules, and Gray Modules, by tailoring them to optimally fit the core PPA structure. The proposed PPA achieves a 26% reduction in cell count, a 31% reduction in area and a 57% reduction in delay compared to existing PPA designs. Utilizing a hybrid crossover methodology, the design reduces delay by 25% relative to the fastest 4-bit QCA adder reported in the literature and lowers the area-delay cost by 11% compared to the most economical design. Simulated using the QCADesigner-E Version 2.2 software, the proposed adder demonstrates energy dissipation comparable to existing designs, solidifying its practicality and efficiency for high-speed QCA-based applications.

A phototaxis assay to measure sublethal effects of pesticides on bees

A safety message dissemination method in a travel guidance system

The importance of small-island populations for the long-term survival of endangered large-bodied insular mammals

Island populations of large vertebrates have experienced higher extinction rates than mainland populations over long timescales due to demographic stochasticity, genetic drift, and inbreeding. While being more susceptible to extinction and as such potentially targeted for conservation interventions such as genetic rescue, small-island populations can experience relatively less anthropogenic habitat degradation than those on larger islands. Here, we determine the consequences and conservation implications of long-term isolation and recent human activities on genetic diversity of island populations of two forest-dependent mammals endemic to the Wallacea archipelago: the anoa ( Bubalus spp.) and babirusa ( Babyrousa spp.). Using genomic analyses and habitat suitability models, we show that, compared to closely related species, populations on mainland Sulawesi exhibit low heterozygosity, high inbreeding, a high proportion of deleterious alleles, and experience a high rate of anthropogenic disturbance. In contrast, populations on smaller islands occupy higher-quality habitats, possess fewer deleterious mutations despite exhibiting lower heterozygosity and higher inbreeding. Site frequency spectra indicate that these patterns reflect stronger, long-term purging in smaller-island populations. Our results thus suggest that conservation efforts should focus on protecting small-island high-quality habitats and avoiding translocations from mainland populations. This study highlights the crucial role of small offshore islands for the long-term survival of Wallacea’s iconic and indigenous mammals in the face of development on the mainland.

Immunogenicity and safety of mRNA-based seasonal influenza vaccines encoding hemagglutinin and neuraminidase

Abstract Current influenza vaccines induce immune responses to hemagglutinin (HA), a surface glycoprotein of seasonal influenza viruses, but have suboptimal effectiveness. mRNA vaccines may improve protection by targeting additional antigens such as neuraminidase (NA), for which immune responses independently correlate with protection. In this phase 1/2 trial (NCT05333289), healthy adults 18–75 years were randomly assigned to receive different doses of mRNA-1020 or mRNA-1030 (encoding HA and NA at different ratios), mRNA-1010 (encoding HA), or a licensed active comparator (recombinant HA). Primary endpoints were safety and reactogenicity, and HA and NA antibody responses against vaccine-matched influenza strains. Most common local and systemic solicited ARs were injection site pain and fatigue. There were no vaccine-related serious adverse events nor significant associated safety concerns through 181 days. mRNA-1020 and mRNA-1030 elicited high HA-specific immune responses and induced NA-specific immune responses with no additional reactogenicity at equivalent dose levels beyond an mRNA-based, HA-only–containing vaccine.

Occupational carbon footprints and exposure to climate transition risks

Positional BMP signaling orchestrates villus length in the small intestine

Triple Synergy Engineering via Metal‐Free Dual‐Atom Incorporation for Self‐Sustaining Acidic Ammonia Electrosynthesis

Abstract Electrochemical nitrate reduction reaction (NO 3 RR) for ammonia synthesis under acidic conditions offers significant advantages, like direct fertilizer production and prevention of ammonia volatilization. However, three critical challenges persist: instability of metal‐based catalysts, competition from the hydrogen evolution reaction (HER), and proton depletion leading to species imbalance. Here, we developed a novel metal‐free heteronuclear diatomic‐based catalyst that simultaneously addresses these challenges through atomic‐level triple synergy engineering. Silicon–iodine dual‐atoms are precisely anchored on nickel oxide ultrathin nanosheets supported on carbon cloth (Si/I‐NiO@CC) via a gradient‐heating co‐loading method. Si/I‐NiO@CC establishes a self‐sustaining catalytic system, achieving a remarkable Faradaic efficiency of 96.8% at −0.3 V versus RHE and record‐breaking operational stability of 420 h in acidic electrolyte, surpassing the performance of all reported acid NO 3 RR electrocatalysts to date. Advanced in situ spectroscopic characterization combined with electrochemical evaluation reveals the triple synergy mechanism: electron‐deficient Ni δ⁺ and oxygen vacancies generate abundant active sites while mitigating HER competition, iodine‐mediated proton reservoirs dynamically regulate H* coverage to maintain species balance, and covalent Si─O─Ni interfacial bonding inhibits metal leaching and stabilizes the catalytic system. This work establishes a constructive guideline for the rational engineering of high‐efficiency electrocatalysts for selective acidic NO 3 RR.