Sources of PM exposure and health benefits of clean air actions in Shanghai.

Estimating PM exposure and its health impacts in cities involves large uncertainty due to the limitations of model resolutions. Consequently, attributing the sources of PM-related health impacts at the city level remains challenging. We characterize the health impacts associated with chronic PM exposure and anthropogenic emissions in Shanghai using a chemical transport model (GEOS-Chem) and its adjoint.

Glo-net: A dual task branch based neural network for multi-class glomeruli segmentation.

Accurate segmentation and classification of glomeruli are fundamental to histopathology slide analysis in renal pathology, which helps to characterize individual kidney disease. Accurate segmentation of glomeruli of different types faces two main challenges compared to traditional primitives segmentation in computational image analysis. Limited by small kernel size, traditional convolutional neural networks could hardly understand the complete context information of different glomeruli.

Symmetric deformable registration of multimodal brain magnetic resonance images via appearance residuals.

Background And Objective: Deformable registration of multimodal brain magnetic resonance images presents significant challenges, primarily due to substantial structural variations between subjects and pronounced differences in appearance across imaging modalities.

Methods: Here, we propose to symmetrically register images from two modalities based on appearance residuals from one modality to another. Computed with simple subtraction between modalities, the appearance residuals enhance structural details and form a common representation for simplifying multimodal deformable registration.

Integrating machine learning and nano-QSAR models to predict the oxidative stress potential caused by single and mixed carbon nanomaterials in algal cells.

In silico methods are increasingly important in predicting the ecotoxicity of engineered nanomaterials (ENMs), encompassing both individual and mixture toxicity predictions. It is widely recognized that ENMs trigger oxidative stress effects by generating intracellular reactive oxygen species (ROS), serving as a key mechanism in their cytotoxicity studies. However, existing in silico methods still face significant challenges in predicting the oxidative stress effects induced by ENMs.

Photoreconfigurable Metasurface for Independent Full-Space Control of Terahertz Waves.

We present a novel photoreconfigurable metasurface designed for independent and efficient control of electromagnetic waves with identical incident polarization and frequency across the entire spatial domain. The proposed metasurface features a three-layer architecture: a top layer incorporating a gold circular split ring resonator (CSRR) filled with perovskite material and dual -shaped perovskite resonators; a middle layer of polyimide dielectric; and a bottom layer comprising a perovskite substrate with an oppositely oriented circular split ring resonator filled with gold. By modulating the intensity of a laser beam, we achieve autonomous manipulation of incident circularly polarized terahertz waves in both transmission and reflection modes.

Modelling mixed crop-livestock systems and climate impact assessment in sub-Saharan Africa.

Climate change significantly challenges smallholder mixed crop-livestock (MCL) systems in sub-Saharan Africa (SSA), affecting food and feed production. This study enhances the SIMPLACE modeling framework by incorporating crop-vegetation-livestock models, which contribute to the development of sustainable agricultural practices in response to climate change. Applying such a framework in a domain in West Africa (786,500 km) allowed us to estimate the changes in crop (Maize, Millet, and Sorghum) yield, grass biomass, livestock numbers, and greenhouse gas emission in response to future climate scenarios.

Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators.

Tackling misinformation in mobile social networks a BERT-LSTM approach for enhancing digital literacy.

The rapid proliferation of mobile social networks has significantly accelerated the dissemination of misinformation, posing serious risks to social stability, public health, and democratic processes. Early detection of misinformation is essential yet challenging, particularly in contexts where initial content propagation lacks user feedback and engagement data. This study presents a novel hybrid model that combines Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM) networks to enhance the detection of misinformation using only textual content.

Lower Cadmium Bioavailability and Toxicity in Japonica Rice than in Indica Rice: Mechanisms and Health Implications.

Cadmium (Cd) is efficiently transferred from soil to food crops, notably rice. Research indicates that indica rice grains may accumulate more Cd than japonica cultivars; however, differences in Cd bioavailability (the fraction of ingested rice Cd absorbed into the systemic circulation) and toxicity remain unexplored, thus hindering a comprehensive understanding of exposure and health risks. To address this, a mouse bioassay was conducted to evaluate the relative bioavailability (RBA) of Cd in 35 samples each of japonica and indica rice, determining which type exhibits lower Cd bioavailability.

Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data.

Axle load data and traffic survey data are both important outputs of highway sensors. This study targets highways and ordinary national and provincial highways, seeking to calculate axle load spectrum and equivalent axle times across the network. There is often an association in the spatial extent of traffic survey data and axle load detection data in highway networks.

Language-Guided Semantic Clustering for Remote Sensing Change Detection.

Existing learning-based remote sensing change detection (RSCD) commonly uses semantic-agnostic binary masks as supervision, which hinders their ability to distinguish between different semantic types of changes, resulting in a noisy change mask prediction. To address this issue, this paper presents a Language-guided semantic clustering framework that can effectively transfer the rich semantic information from the contrastive language-image pretraining (CLIP) model for RSCD, dubbed LSC-CD. The LSC-CD considers the strong zero-shot generalization of the CLIP, which makes it easy to transfer the semantic knowledge from the CLIP into the CD model under semantic-agnostic binary mask supervision.

Impacts of Micro/Nanoplastics Combined with Graphene Oxide on Seeds: Insights into Seedling Growth, Oxidative Stress, and Antioxidant Gene Expression.

Global pollution caused by micro/nanoplastics (M/NPs) is threatening agro-ecosystems, compromising food security and human health. Also, the increasing use of graphene-family nanomaterials (GFNs) in agricultural products has led to their widespread presence in agricultural systems. However, there is a large gap in the literature on the combined effects of MNPs and GFNs on agricultural plants.

The Characteristics, Sources, and Health Risks of Volatile Organic Compounds in an Industrial Area of Nanjing.

This study investigates the chemical complexity and toxicity of volatile organic compounds (VOCs) emitted from national petrochemical industrial parks and their effects on air quality in an industrial area of Nanjing, China. Field measurements were conducted from 1 December 2022, to 17 April 2023, focusing on VOC concentrations and speciations, diurnal variations, ozone formation potential (OFP), source identification, and associated health risks. The results revealed an average total VOC (TVOC) concentration of 15.

Outlier Handling Strategy of Ensembled-Based Sequential Convolutional Neural Networks for Sleep Stage Classification.

The pivotal role of sleep has led to extensive research endeavors aimed at automatic sleep stage classification. However, existing methods perform poorly when classifying small groups or individuals, and these results are often considered outliers in terms of overall performance. These outliers may introduce bias during model training, adversely affecting feature selection and diminishing model performance.

Inflow-modulated inputs of dissolved organic matter fuel carbon dioxide emissions from a large hyper-eutrophic lake.

Terrestrial dissolved organic matter (DOM) is potentially reactive and, upon entering lake ecosystems, can be readily degraded to low-molecular-weight organic matter and dissolved CO. However, to date, there has been limited research on the links between long-term variation in the composition of DOM and CO emissions from lakes. Lake Taihu is a large, shallow, and hyper-eutrophic lake where DOM composition is strongly influenced by inputs from the rivers draining cultivated and urbanized landscapes.

Local structural-functional coupling with counterfactual explanations for epilepsy prediction.

The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural connections (SC) and the corresponding functional activation or functional connections (FC). It has been widely used to identify brain disorders. However, the existing research on SC-FC coupling focuses on global and regional scales, and few studies have investigated the impact of brain disorders on this relationship from the perspective of multi-brain region cooperation (i.

Dual signal amplification in ECL biosensors: A novel approach for argonaute2 detection using SAHARA CRISPR-Cas12a technology.

Argonaute 2 (Ago2) is a crucial enzyme in the RNA interference (RNAi) pathway, essential for gene silencing via the cleavage of target messenger RNA (mRNA) mediated by microRNA (miRNA) or small interfering RNA (siRNA). The activity of Ago2 is a significant biomarker for various diseases, including cancer and viral infections, necessitating precise monitoring techniques. Traditional methods for detecting Ago2 activity are often cumbersome and lack the necessary sensitivity and specificity for low-abundance targets in complex samples.

A weak edge estimation based multi-task neural network for OCT segmentation.

Optical Coherence Tomography (OCT) offers high-resolution images of the eye's fundus. This enables thorough analysis of retinal health by doctors, providing a solid basis for diagnosis and treatment. With the development of deep learning, deep learning-based methods are becoming more popular for fundus OCT image segmentation.

Chaos-enhanced manganese electrolysis: nodule suppression and improved efficiency using controllable chaotic electrical signals.

The control and industrial application of chaotic systems is a major obstacle limiting the diffusion of chaos theory. In this study, we proposed a novel, universally applicable methodology for constructing an offset boosting function for chaotic systems. By integrating this approach with traditional techniques, a four-dimensional chaotic system with two-dimensional offset boosting was developed and successfully implemented by a real chaotic circuit for manganese metal electrolysis, replacing conventional DC.

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.

Insight on the optimized electronic structure of carbon nitride on ultrafast water treatment via photocatalytic activation of ferrate.

Ferrate (Fe(VI)) is a widely used water purifier and is easily affected by external factors. Given that the actual water environment conditions are complicated, this study designed an oxygen-doped carbon nitride (CNO) with rich electron sites to explore whether direct electron transfer promotes the degradation efficiency of Fe(VI) for pollutants under visible light. For comparison, we also prepared phosphorus-doped carbon nitride (CNP), which has electron-deficient sites and indirect electron transfer.

Distributional properties of the entropy transformed Weibull distribution and applications to various scientific fields.

A novel two-parameter continuous model titled the entropy-transformed Weibull (ET-W) distribution has been developed via the entropy transformation. A new framework has been investigated and found to meet the criteria of the probability function. By significantly improving the functional shape and having the ability to model the most likely form of the hazard rate function, this novel modification has increased the adaptability of typical model.

Environmental Conditions Modulate Warming Effects on Plant Litter Decomposition Globally.

Empirical studies worldwide show that warming has variable effects on plant litter decomposition, leaving the overall impact of climate change on decomposition uncertain. We conducted a meta-analysis of 109 experimental warming studies across seven continents, using natural and standardised plant material, to assess the overarching effect of warming on litter decomposition and identify potential moderating factors. We determined that at least 5.