The abiologically and biologically driving effects on organic matter in marginal seas revealed by deep learning-assisted model analysis.

The biogeochemical processes of organic matter exhibit notable variability and unpredictability in marginal seas. In this study, the abiologically and biologically driving effects on particulate organic matter (POM) and dissolved organic matter (DOM) were investigated in the Yellow Sea and Bohai Sea of China, by introducing the cutting-edge network inference tool of deep learning. The concentration of particulate organic carbon (POC) was determined to characterize the status of POM, and the fractions and fluorescent properties of DOM were identified through 3D excitation-emission-matrix spectra (3D-EEM) combined parallel factor analysis (PARAFAC).

Substantially underestimated global health risks of current ozone pollution.

Existing assessments might have underappreciated ozone-related health impacts worldwide. Here our study assesses current global ozone pollution using the high-resolution (0.05°) estimation from a geo-ensemble learning model, with key focuses on population exposure and all-cause mortality burden.

Overcoming Nanosilver Resistance: Resensitizing Bacteria and Targeting Evolutionary Mechanisms.

The rapid spread of antimicrobial resistance poses a critical threat to global health and the environment. Antimicrobial nanomaterials, including silver nanoparticles (AgNPs), are being explored as innovative solutions; however, the emergence of nanoresistance challenges their effectiveness. Understanding resistance mechanisms is essential for developing antievolutionary strategies.

How can digitalization be used to develop community resilience in public health emergencies?: A qualitative comparative analysis from China.

Community resilience is critical for the government's response to public health emergencies. With the rapid development of digital technology, leveraging digital tools for grassroots community governance has become increasingly important for the Chinese government. Fuzzy set qualitative comparative analysis (fsQCA) is utilized in this study to establish a framework for investigating the historical development of holistic intelligent governance for community resilience in the context of public health emergencies.

Fluctuating Spinodal-like Structure in the Glacial Phase of d-Mannitol.

The glacial phase can be formed from supercooled liquid (SCL) in certain systems, which is called liquid-liquid transition (LLT). Revealing the nature of the glacial phase especially in a single-component system is crucial for understanding the LLT process. Here, by using flash differential scanning calorimetry and cold-field transmission electron microscopy, the structure of the d-mannitol glacial phase and the phase transition kinetics between the glacial phase and SCL were studied.

Rural residents' willingness to utilize primary care and its influencing factors in the context of graded diagnosis and treatment: a study in Deqing, China.

Background: Graded diagnosis and treatment is a key component in advancing healthcare system reforms and establishing a foundational healthcare framework. Primary care serves as the cornerstone of this system. Understanding the willingness to seek primary care and the factors influencing it can enhance primary care utilization and address the challenges of "difficult access to healthcare" and "high medical costs.

Novel organophosphate esters and their transformation products in offshore sediment from Eastern China: Occurrence, temporal trend, and risk assessment.

Offshore sediment serves as an important sink for traditional organophosphate esters (TOPEs) originating from terrestrial sources. However, the contamination characteristics of novel OPEs (NOPEs) and their hydrolyzed and hydroxylated transformation products (Di- and OH-OPEs) in marine sediment are still unknown. In this study, 34 OPE-associated contaminants were measured in six offshore sediment cores (71 samples) collected from Eastern China.

Mesenchymal Stem Cell Membrane-Derived Composite System for Enhancing the Tumor Treatment Efficacy of Metal-Organic Framework Nanoparticles.

Mesenchymal stem cell (MSC) membrane-coated metal-organic frameworks (MOFs) represent an innovative approach to enhance the uptake and therapeutic efficacy of copper-based MOFs (Cu-MOFs) in tumor cells. By leveraging the natural homing abilities and biocompatibility of MSC membranes, Cu-MOFs can be effectively targeted to tumor sites, promoting increased cellular uptake. This coating not only facilitates superior internalization by cancer cells but also augments the therapeutic outcomes due to the enhanced delivery of copper ions.

Targeting toll-like receptor 7 as a therapeutic development strategy for systemic lupus erythematosus.

Endosomal TLRs (TLR3/7/8/9) are highly analogous innate immunity sensors for various viral or bacterial RNA/DNA molecular patterns. Among them, TLR7, in particular, has been suggested to be a target for various inflammatory disorders and autoimmune diseases including systemic lupus erythematosus (SLE); but few small-molecule inhibitors with elaborated mechanism have been reported in literature. Here, we reported a well-characterized human TLR7-specific small-molecule inhibitor, TH-407b, with promising potency and negligible cytotoxicity through a novel binding mechanism.

Therapeutic targets of antidiabetic drugs and kidney stones: A druggable mendelian randomization study and experimental study in rats.

Diabetes is known to increase the risk of kidney stones, but the influence of antidiabetic drugs on this risk remains uncertain. Genetic instruments for antidiabetic drugs were identified as variants, which were associated with both the expression of genes encoding target proteins of drugs and glycated hemoglobin level (HbA1c). Here, we investigated the effect of antidiabetic drugs on kidney stones in a mendelian randomization (MR) framework, and further explore the potential effect on CaOx stone rat models induced by glyoxylic acid.

Developing high-performance and sustainable polylactic acid/recycled polyolefin blends: Tuning the degree of functional group reaction and performance optimization.

In the current development of the plastics industry, the use of biodegradable and recycled plastics not only effectively reduces the volume of landfills and incineration but also significantly decreases environmental damage. However, the extensive application of biodegradable polylactic acid (PLA) is limited by its poor toughness and thermal properties. The study introduced recycled linear low-density polyethylene (R-LLDPE) and ethylene-octene copolymer (POE) to modify PLA, primarily based on their excellent toughness and thermal resistance.

Diversity of nitrogen-fixing bacteria in rhizosphere during reproduction in the Yellow River delta.

Salinization in the Yellow River delta poses a challenge for agriculture, but nitrogen-fixing bacteria can improve soil nitrogen content and enhance plant growth under salt stress. This study focuses on the salt-tolerant plant , analyzing its plant traits and the diversity of nitrogen-fixing bacteria in its rhizosphere using the gene. Results show a gradual decline in plant height, fresh weight, and microbial diversity from inland to coastal areas.

Deciphering decadal urban ozone trends from historical records since 1980.

Ozone pollution is a major environmental threat to human health. Timely assessment of ozone trends is crucial for informing environmental policy. Here we show that for the most recent decade (2013-2022) in the northern hemisphere, warm-season (April-September) mean daily 8-h average maximum ozone increases much faster in urban regions with top ozone levels (mainly in the North China Plain, 1.

Eimeria: Navigating complex intestinal ecosystems.

Eimeria is an intracellular obligate apicomplexan parasite that parasitizes the intestinal epithelial cells of livestock and poultry, exhibiting strong host and tissue tropism. Parasite-host interactions involve complex networks and vary as the parasites develop in the host. However, understanding the underlying mechanisms remains a challenge.

Machine learning based aerosol and ocean color joint retrieval algorithm for multiangle polarimeters over coastal waters.

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, recently launched in February 2024, carries two multiangle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and SRON Spectropolarimeter for Planetary Exploration One (SPEXone). Measurements from these MAPs will greatly advance ocean ecosystem and aerosol studies as their measurements contain rich information on the microphysical properties of aerosols and hydrosols. The Multi-Angular Polarimetric Ocean coLor (MAPOL) joint retrieval algorithm has been developed to retrieve aerosol and ocean color information, which uses a vector radiative transfer (RT) model as the forward model.

Shengqiyichang decoction regulates antitumor immunity in colorectal cancer by downregulating lymphocyte antigen 6 family member G6D via the protein kinase B/p38 mitogen-activated protein kinase signaling pathway.

The traditional Chinese medicine (TCM) formulation Shengqiyichang Decoction (SQYCD) has been reported to stimulate host immunity, and it has been administered for the treatment of colorectal cancer (CRC). Here, we applied network and bioinformatics analyses to elucidate the mechanisms by which SQYCD ameliorates CRC and validated its modes of action via and experiments. We identified 46 active compounds in SQYCD and selected 237 proteins as potential therapeutic targets in CRC, most notably p38 mitogen-activated protein kinase (p38⍺).

Evolutionary dynamics of stochastic games in set-structured populations.

In structured populations, the ecology of games may vary over neighborhoods. The effect of the ecological variations on population dynamics remains largely unknown. We here incorporate the ecological variations into the set-structured populations to explore the coevolutionary dynamics of the ecology and cooperation.

High-throughput development of tough metallic glass films.

Fast development of metallic glass films with high toughness has been a long-sought goal of humankind in view of their superior properties and great potential for application in the field of soft electronics. However, until now, there has been no effective experimental strategy because of the lack of suitable and precise toughness measurement technology. In the present work, we introduced a feasible route for developing tough metallic glass films using combinatorial material library preparation and high-throughput toughness measurement nanoindentation.

Porous cellulose photonic film via controlled unidirectional interlayer freezing for rapid visual sensing.

Structure color, arising from the interaction of light with regularly arranged sub-micrometer-sized structures, has spurred interest in sensor design. However, typical cellulose nanocrystal (CNC) photonic films derived from biomass, known for their sustainability and cost-effectiveness, often suffer from limited sensitivity and slow response times due to their dense structure. To address this challenge, we have utilized a unidirectional interlayer freezing-photopolymerization strategy to introduce porous structures into CNC photonic films without compromising their vibrant structural color.

Machine Learning Models for Predicting Bioavailability of Traditional and Emerging Aromatic Contaminants in Plant Roots.

To predict the behavior of aromatic contaminants (ACs) in complex soil-plant systems, this study developed machine learning (ML) models to estimate the root concentration factor (RCF) of both traditional (e.g., polycyclic aromatic hydrocarbons, polychlorinated biphenyls) and emerging ACs (e.

Development and validation of a nomogram for predicting 28-day mortality in critically ill patients with acute gastrointestinal injury: prospective observational study.

Objective: Developing and validating a clinical prediction nomogram of 28-day mortality in critically ill patients with acute gastrointestinal injury (AGI).

Methods: Firstly, the construction of a clinical prediction model was developed using data obtained from a prospective observational study from May 2023 to April 2024. Then, data from a prospective multicenter observational study conducted in the intensive care units of 12 teaching hospitals in 2014 were utilized to independently and externally validate the clinical prediction model developed in the first part.