Combined effects of nanoplastics and 3-BHA at environmentally relevant concentrations significantly aggravated kidney injury via TGF-β/SMAD signaling pathway in mice.

The increasing presence of nanoplastics (NPs) and synthetic antioxidants like 3-tert -Butyl-4-hydroxyanisole (3-BHA) in the environment has attracted widespread attention about their combined toxicological effects on human health, particularly on renal function. This study explored to the combined impacts of NPs and 3-BHA at environmentally relevant concentrations on sub-chronic kidney injury in mice. Firstly, our results confirmed that the accumulation of 80 nm NPs in renal tissues, leading to structural abnormalities such as reduced mitochondrial cristae and increased empty bubbles in mice by transmission electron microscope (TEM) analysis.

Wafer Defect Image Generation Method Based on Improved Styleganv3 Network.

This paper takes a look at training a generator model based on a limited dataset that can fit the distribution of the original dataset, improving the reconstruction ability of wafer datasets. High-fidelity wafer defect image generation remains challenging due to limited real data and poor physical authenticity of existing methods. We propose an enhanced StyleGANv3 framework with two key innovations: (1) a Heterogeneous Kernel Fusion Unit (HKFU) enabling multi-scale defect feature refinement via spatiotemporal attention and dynamic gating; (2) a Dynamic Adaptive Attention Module (DAAM) adaptively boosting discriminator sensitivity.

Bi-Regional Machine Learning Radiomics Based on CT Noninvasively Predicts LOX Expression Level and Overall Survival in Hepatocellular Carcinoma.

Objective: We aimed to assess the association between lysyl oxidases (LOX) expression levels and the prognosis of patients with Hepatocellular carcinoma (HCC) and to establish a CT-based bi-regional radiomics model that can discriminate LOX expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database.

Methods: 294 HCC samples were downloaded from TCGA for gene-based prognostic analysis. Meanwhile, the underlying molecular mechanism of LOX expression and its relationship with the immune microenvironment was investigated.

Leveraging scenario differences for cross-task generalization in water plant transfer machine learning models.

Machine learning (ML) models are increasingly deployed in urban water systems to optimize operations, enhance efficiency, and curb resource consumption amid growing sustainability demands. Yet, their transferability across plants is hampered by scenario differences-variations in environmental factors, protocols, and data distributions-that erode performance and necessitate energy-intensive retraining. While existing strategies focus on minimizing these differences via domain adaptation or fine-tuning, none exploit them as inherent prior knowledge for improved generalization.

Genome-wide identification and analysis of the FLA gene family in maize (Zea mays L.) and its expression in response to abiotic stresses.

Background: Fasciclin-like arabinogalactan proteins (FLAs) are a class of plant-specific proteins that contribute to various aspects of plant growth, development, and adaptation. However, these proteins have not yet been identified or characterized in maize. This study conducted bioinformatics and expression analyses of the ZmFLA gene family.

The Improvement Effects of Intercropping Systems on Saline-Alkali Soils and Their Impact on Microbial Communities.

Saline-alkali soil has poor fertility and low organic matter content, which are key factors that limit agricultural productivity. Intercropping systems can enhance biodiversity in farmlands, thereby increasing the organic matter content. During this process, soil microorganisms respond to environmental changes.

Sensitive Detection of Vitamin B2 in a Microdroplet with a Living Graphene Hydrogel Needle.

As a water-soluble vitamin, Vitamin B2 (VB2) is crucial for the health of living organisms. Therefore, developing sensitive and selective methods for detecting VB2 is essential for the quality control of food and pharmaceuticals as well as for clinical diagnosis. In this study, a cell-embedded living graphene hydrogel needle was prepared under ambient atmospheric conditions, where the electroactive bacteria MR-1 was used to induce the reduction of graphene oxide (GO) to graphene hydrogel under the confinement effect with a glass capillary tube.

Machine Learning-Driven Dynamic Measurement of Environmental Indicators in Multiple Scenes and Multiple Disturbances.

Digital city water management systems require extensive data sensing for various environmental indicators, yet measurement accuracy often falls short under diverse extreme conditions. This study proposes a chemical oxygen demand (COD) measurement method based on ultraviolet-visible spectrum analysis and machine learning (ML), taking into account the removal of interferences, including temperature, pH, turbidity, common anions and cations, as well as COD composition and different water environments. The data collected from the river and wastewater were processed through principal component analysis, and random forest (RF) performed the best among the multiclass models with a mean absolute percentage error (MAPE) of only 6.

Determination of gold content in rock gold ore samples based on closed water bath aqua regia digestion-polyurethane foam enrichment and using flame atomic absorption spectrometry.

At present, due to the uneven distribution and low content of gold (Au) in rock gold ore samples, the separation, enrichment and accurate determination of Au have become daunting tasks in the analysis of rock gold samples. This study established a method for determining the Au content in rock gold ore samples with the advantages of high efficiency, low cost, safety and more environment-friendliness. It was based on closed water bath digestion-polyurethane foam (PUF) enrichment and thiourea release, and then, flame atomic absorption spectrometry was used to determine the Au content.

Ferrous activated sodium percarbonate for controlling sulfide and methane production in sewer sediment.

Methane (CH) and hydrogen sulfide (HS) production from sewers are significantly driven by microbial activities in anaerobic sediments. This study proposes a novel ferrous activated sodium percarbonate advanced oxidation process to inhibit CH and HS production. By targeting microbial activity in the top layer (0-2 cm), the process achieved 74.

Tmx2 Maintains Mitochondrial Function to Support Preimplantation Embryogenesis.

Thioredoxin (TRX)-related transmembrane proteins (TMX), a subgroup of the protein disulfide isomerase (PDI) family, comprise a class of transmembrane proteins with diverse biological functions. Among these, TMX2 (PDIA12) remains one of the least characterized members. Recent studies have identified missense mutations in TMX2 associated with aberrant brain development and cerebellar malformations, highlighting its potential importance in developmental processes.

SbMPK13-mediated SbWRKY10 phosphorylation positively regulates SbGST40 expression required for cadmium tolerance in sorghum.

Cadmium (Cd) stress significantly affects the growth, development, and utilization of sorghum. Although the molecular pathways responding to Cd stress in sorghum remain incompletely understood, glutathione transferase (GST) is recognized for its critical role in plant responses to heavy metals and detoxification processes. In this study, we examined the influence of SbGST40 on Cd accumulation in sorghum.

Comprehensive identification and expression analysis of the CPP gene family in maize (Zea mays L.).

Background: Maize is an important crop. The cysteine-rich polycomb-like protein (CPP) is crucial for plant development and abiotic stress response. However, few reports have been reported on the function of ZmCPPs.

Detection of Different Patterns of Genome-Wide Gene Expression Disturbance in Three Nullisomy Lines in Allotetraploid .

Aneuploidy-related disruptions are generally tolerated in polyploid plants, which exhibit a greater capacity for genomic compensation. In this study, we utilize allotetraploid as a model and generated three aneuploid variants (NC1, NC2, and NC8) to investigate the phenotypic and transcriptional consequences of chromosome loss. Significant phenotypic variations were observed, with the most notable being a marked dwarfing phenotype in the aneuploid materials compared to the euploid Oro.

Federated Machine Learning Enables Risk Management and Privacy Protection in Water Quality.

Real-time water quality risk management in wastewater treatment plants (WWTPs) requires extensive data, and data sharing is still just a slogan due to data privacy issues. Here we show an adaptive water system federated averaging (AWSFA) framework based on federated learning (FL), where the model does not access the data but uses parameters trained by the raw data. The study collected data from six WWTPs between 2018 and 2024, and developed 10 machine learning models for each effluent indicator, with the best performance bidirectional long-term memory network (BM) as Baseline.

Flexible Carbon Source Regulation for Mitigating Greenhouse Gas Emissions in Full-Scale Wastewater Treatment.

Achieving carbon neutrality in wastewater treatment plants (WWTPs) by 2060 requires effective strategies to mitigate greenhouse gas (GHG) emissions. This study explores the potential of flexible carbon source regulation to reduce GHG emissions while improving the nutrient removal efficiency under varying influent conditions. A plant-wide model was developed, calibrated with one year of hourly monitoring data, to quantify GHG emissions in a full-scale WWTP.

Data-driven differentiable model for dynamic prediction and control in wastewater treatment.

Wastewater treatment plants, while critical for environmental protection, face mounting challenges in operational efficiency and sustainability due to increasing urbanization and stricter environmental standards. In this study, we introduce an innovative continuous-time neural framework based on Neural Ordinary Differential Equations (Neural ODEs) to enhance the modeling of sewage treatment processes. Addressing the dual challenges of operational efficiency and sustainable development in urban wastewater treatment plants (WWTPs), our methodology marks a significant departure from traditional approaches by implementing a continuous-time neural framework that captures the inherent dynamics of wastewater treatment processes while reducing computational demands by 95 % compared to discrete-time models.

The sorghum SbMPK3-SbNAC074 module involved in salt tolerance.

Plant NAC transcription factors (TFs) are essential genes that modulate plant responses to abiotic stress. In this study, we identified a novel NAC TF, SbNAC074, in sorghum, which exhibits a response to salt stress. Overexpression of SbNAC074 in tobacco significantly enhanced the salt tolerance of transgenic plants.

The Role of MYC2 Transcription Factors in Plant Secondary Metabolism and Stress Response Mechanisms.

Jasmonates (JAs) are essential signaling molecules that orchestrate plant responses to abiotic and biotic stresses and regulate growth and developmental processes. , a core transcription factor in JA signaling, plays a central role in mediating these processes through transcriptional regulation. However, the broader regulatory functions of , particularly in secondary metabolism and stress signaling pathways, are still not fully understood.

Mutation of TaNRAMP5 impacts cadmium transport in wheat.

Cadmium (Cd) pollution significantly impacts the normal growth, development, and food safety of wheat. Employing modern molecular biology techniques represents an effective strategy for cultivating low-Cd wheat. Natural resistance-associated macrophage protein 5 (NRAMP5) is a critical heavy metal transporter, however, its function in wheat, particularly in response to Cd stress, remains largely unexplored.

BPS causes abnormal blastocyst development by inhibiting cell proliferation.

In recent years, the escalating global utilization of bisphenol S (BPS) has raised growing concerns regarding its potential adverse effects on human health. However, the effects of BPS exposure on mammalian embryonic development and the associated molecular mechanisms remain inadequately characterized. In this study, we systematically investigated BPS toxicity in mouse embryogenesis by exposing embryos to graded concentrations (0-25 μg/mL).

Machine learning strategy secures urban smart drinking water treatment plant through incremental advances.

The integration of machine learning into urban drinking water treatment plants (DWTPs) offers a transformative pathway to ensure drinking water safety while promoting the development of smart, low-carbon cities. However, the effectiveness of these systems is frequently hindered by challenges related to data security and reliability, including imprecise control logic, sensor inconsistencies, and data transmission errors. In this study, we introduce a novel progressive Step-by-Step (SBS) machine learning strategy, initially applied to precise disinfectant dosage control in drinking water treatment and subsequently extended to enhance the data security of the entire water supply system.

Knockdown of Mageb16 disrupts cell proliferation and lineage specification during mouse preimplantation development.

Melanoma antigen family member B16 (Mageb16) is crucial for maintaining the pluripotency and differentiation of embryonic stem cells. However, the expression pattern and biological role of Mageb16 during preimplantation development remain unclear. In this study, we showed that Mageb16 mRNA expression was dynamic throughout preimplantation development, with the highest level occurring at the morula stage.

Hypersaline organic wastewater treatment: Biotechnological advances and engineering challenges.

The sustainable treatment of hypersaline organic wastewater (HSOW) remains a significant challenge in industrial wastewater management, as conventional approaches often fail to meet stringent discharge standards and low-carbon sustainability targets. Halotolerant and halophilic microbial strains offer promising solutions, yet their application is hindered by limited stress resistance, thus hindering effective treatment and achieving near-zero liquid discharge. In this review, we systematically examine endogenous strategies, such as microbial mutualism and genetic engineering, alongside exogenous approaches, including functional materials, electrical and magnetic stimulation, and 3D bioprinting, to improve microbial resilience in hypersaline environments.

Real-time quantification of activated sludge concentration and viscosity through deep learning of microscopic images.

The parameters of activatedg sludge are crucial for the daily operation of wastewater treatment plants (WWTPs). In particular, mixed liquor suspended solids (MLSS) and apparent viscosity provide metrics for the biomass and rheological properties of activated sludge. Traditional methods for determining these parameters are time-consuming, require separate measurements for each index, and fail to provide real-time data for future 'smart' WWTPs.