Predicting infarct outcomes after extended time window thrombectomy in large vessel occlusion using knowledge guided deep learning.

Background: Predicting the final infarct after an extended time window mechanical thrombectomy (MT) is beneficial for treatment planning in acute ischemic stroke (AIS). By introducing guidance from prior knowledge, this study aims to improve the accuracy of the deep learning model for post-MT infarct prediction using pre-MT brain perfusion data.

Methods: This retrospective study collected CT perfusion data at admission for AIS patients receiving MT over 6 hours after symptom onset, from January 2020 to December 2024, across three centers.

Satellites reveal hot spots of ocean changes in the early 21st century.

Ocean change leaves a potentially important imprint on ocean colorimetry. Here, we present an overview and current evaluation of the global ocean color variability from 1998 to 2022, and satellites observe that 36% of oceans (~122 million square kilometers, derived from valid observations) have experienced changes ( < 0.1).

A Photo- and Electrochromic Dual-Responsive Smart Window for Full-Day Photothermal Management.

Smart windows, which can intelligently regulate indoor temperature and improve living comfort provide a promising solution for reducing building energy consumption. Active and passive smart windows have their own advantages and disadvantages, yet it is a challenge to perfectly combine the two to achieve complementary advantages. In this work, photo- and electrical dual-responsive films containing WO, ethylene glycol (EG), and Ag are prepared, which can simultaneously realize photochromism and electrochromism.

Regime shift to extensive valley glaciations over High Mountain Asia during the Early-Middle Pleistocene.

In contrast to the well-established onset of Northern Hemisphere high-latitude glaciation at ~2.7 Ma, the timing and drivers of the intensified glaciation over High Mountain Asia (HMA) remain elusive, as glacial geologic evidence within this region is inherently fragmentary. Here, we offer a spatiotemporally complete view of glacier behavior over HMA spanning the last 3 Ma using transient climate-glaciation simulations to address this challenge.

Multi-modal remote sensory learning for multi-objects over autonomous devices.

Introduction: There has been an increasing focus on object segmentation within remote sensing images in recent years due to advancements in remote sensing technology and the growing significance of these images in both military and civilian realms. In these situations, it is critical to accurately and quickly identify a wide variety of objects. In many computer vision applications, scene recognition in aerial-based remote sensing imagery presents a common issue.

Ionic Diodes: Pioneering the Future of Ionotronics in Electronics and Beyond.

The exploration in ionotronics has driven the advancement in several emerging fields, including energy storage, bioelectronics, flexible electronics, and human-machine interactions. Among these, ionic diode devices that use ions as charge carriers have been extensively explored, exhibiting significant potential for integrating biology and electronics. Unlike traditional diodes, ionic diodes not only perform rectification but also enable novel functions and properties (sensing, energy harvesting, and biological detection).

Short-term spatial prediction of algal blooms in Lake Taihu via machine learning and GOCI observations.

Harmful algal blooms are critical issues in eutrophic lakes worldwide. However, predicting the spatial distribution of algal blooms at the pixel level is still a challenge. In this study, floating algae cover (FAC) was used to extract algal coverage via the Geostationary Ocean Color Imager (GOCI) and GOCI-II satellites.

Modified natural organic waste red kenaf straw as an oil sorbent for oil spill remediation.

The oil contamination issue in water is becoming more and more serious. Consequently, it is imperative to develop advanced materials and methods to address the oil pollution issue in the environment. In this work, red kenaf straw, a type of the agricultural waste, was selected as the raw material, and the acid anhydride was utilized as an environment-friendly esterification reagent to facilitate the conversion of hydrophilic groups into hydrophobic groups through a modification reaction.

mpMRI-based habitat analysis for predicting prognoses in patients with high-grade serous ovarian cancer: a multicenter study.

Purpose: To evaluate the value of multiparametric MRI (mpMRI)-based habitat analysis for predicting prognoses in patients with high-grade serous ovarian cancer (HGSOC), and to develop combined models by integrating habitat analysis with clinical predictors.

Methods: This retrospective study included 503 HGSOC patients from four centers. A K-means algorithm was used to identify voxel clusters and generate habitats on mpMRI.

Combined effects of micro/nanoplastics and ZnO nanoparticles on lactuca sativa seedlings under varied lighting.

Micro/nanoplastics (MNPs) can serve as vectors for engineered nanoparticles (ENPs), thereby eliciting mixture toxicological responses in target species. However, our understanding of the combined environmental impacts of lighting conditions and co-exposure to MNPs and ENPs on crop plants remains limited. Herein, we investigated the individual and joint effects of polystyrene microplastics (PMs; 1 μm) or nanoplastics (PNs; 50-100 nm) and ZnO nanoparticles (ZnO NPs) on seed germination and early seedling growth of lettuce (Lactuca sativa) under the dark condition or the light-dark cycle condition.

The sex matters: sex-biased stimulatory responses to contaminants.

Hormetic regulation is a health hallmark determining susceptibility and lifespan, through which whole-animal performance is also stimulated by low doses of chemical contaminants. However, the sex-dependence of these stimulatory responses remains poorly understood. In this perspective, we highlight compelling evidence of sex-dependent stimulation by contaminants across diverse animal taxa.

Robust Current Sensing in Rectangular Conductors: Elliptical Hall-Effect Sensor Array Optimized via Bio-Inspired GWO-BP Neural Network.

Accurate current sensing in rectangular conductors is challenged by mechanical deformations, including eccentricity (X/Y-axis shifts) and inclination (Z-axis tilt), which distort magnetic field distributions and induce measurement errors. To address this, we propose a bio-inspired error compensation strategy integrating an elliptically configured Hall sensor array with a hybrid Grey Wolf Optimizer (GWO)-enhanced backpropagation neural network. The eccentric displacement and tilt angle of the conductor are quantified via a three-dimensional magnetic field reconstruction and current inversion modeling.

Unlocking Deeper Insights into Medical Images with Machine Learning.

Recent years have witnessed remarkable progress at the intersection of medical imaging, biochemical assays, image analysis, and machine learning [...

Toxicity effects and mechanisms of graphdiyne towards freshwater microalgae Scenedesmus obliquus.

Graphdiyne (GDY), an emerging 2D carbon nanomaterial, holds promise yet remains unexplored for environmental hazards. Herein, we investigated the toxicity effects and mechanisms of GDY towards the freshwater microalga Scenedesmus obliquus at environmentally relevant concentrations. The results revealed that 1 mg/L of GDY inhibited the growth of the algae and significantly induced a decrease in photosynthetic pigments.

Biomimetic Design and Validation of an Adaptive Cable-Driven Elbow Exoskeleton Inspired by the Shrimp Shell.

The application of exoskeleton robots has demonstrated promising effectiveness in promoting the recovery of motor skills in patients with upper limb dysfunction. However, the joint misalignment caused by rigid exoskeletons usually leads to an uncomfortable experience for users. In this work, an adaptive cable-driven elbow exoskeleton inspired by the structural characteristics of the shrimp shell was developed to facilitate the rehabilitation of the elbow joint and to provide more compliant human-exoskeleton interactions.

An advanced three stage lightweight model for underwater human detection.

This study presents StarEye, a lightweight deep learning model designed for underwater human body detection (UHBD) that addresses the challenges of complex underwater environments. The proposed model incorporates several innovative components: a comprehensive underwater dataset construction methodology, a StarBlock-based backbone structure for efficient feature extraction, a Context Anchor Attention (CAA) mechanism integrated into both backbone and neck components, and a Shared Convolution Batch Normalization (SCBN) detection head. Extensive experiments demonstrate that StarEye achieves 91.

Mechanism deciphering of variation of soluble organic carbon storage during the sedimentary period in lacustrine sediments.

Stable organic carbon (OC) burial into lacustrine sediments was the important C fixation path for atmospheric C reduction. However, OC retention effect and stability mechanisms in sediments was still unclear at the molecular scale during the burial period. Chronology and OC fractions were measured for the sediments from two freshwater lakes.

Fluctuating ozone exposures caused trade-offs between vegetative growth and reproduction of two Chinese bean cultivars and ethylenediurea alleviated ozone phytotoxicities.

Ozone (O) pollution has elevated in China, threatening plants and crop production. Ethylenediurea (EDU) is a chemical alleviating O-induced phytotoxicities. This study aimed at revealing fluctuating O exposures effects on Youxian No 3 (Phaseolus vulgaris) and Sukui No 4 (Vigna angularis), two widely grown Chinese bean cultivars, and EDU role in mediating these effects.

Analysis of atmospheric pollutant characteristics and regional transport in coastal area along the East China Sea.

PM and black carbon (BC) are important air pollutants impacting radiation balance, air quality, health, and ecosystems. Ozone (O) levels are increasing despite decreases in other pollutants, posing a challenge for pollution control, especially in coastal cities like Zhoushan, where the monsoonal climate can exacerbate PM and ozone pollution. This study conducted continuous online measurements of major atmospheric pollutants in Zhoushan, Zhejiang Province, in 2020.

Analysis of aerosol chemical components and source apportionment during a long-lasting haze event in the Yangtze River Delta, China.

Based on the chemical composition data of a regional long-lasting haze event that occurred in the Yangtze River Delta (YRD) region from 17 December 2023 to 8 January 2024, the evolutionary characteristics of the chemical components and sources of fine particulate matter (PM) under different pollution levels were comparatively analyzed using PMF (Positive Matrix Factorization) and backward trajectory analysis. SNA (NO, NH, SO) was found to be the primary chemical component of PM, making up 63.6 % (clean days) to 69.

Nucleation and Growth Mechanisms of Micro/Nano Structural Manganese-Trimesic Acid Coordinations for Aqueous Zinc-Ion Batteries.

Nucleation and growth of metal-organic frameworks (MOFs) are critical for controlling their morphology, size, and performance. Guided by the crystal nucleation and growth theory, this study systematically explored the effects of the sequential addition of ligand trimesic acid (BTC) and manganese ions (Mn), ligand-to-metal ion ratio, solvent composition, and surfactants on the nucleation and growth of MnBTC. The regulatory mechanisms of the crystal morphology and internal structure were deeply revealed.

Brain MRIs classification based on 2D SWD-MF-DFA.

Background: To improve imaging classification accuracy, we modify the traditional 2D multifractal trend fluctuation analysis (MF-DFA) method to better preserve local feature values. Inspired by MF-DFA, we develop a novel method for extracting eigenvalues, enhancing the precision of imaging analysis.

New Method: In this paper, we propose an enhanced algorithm building upon the traditional 2D MF-DFA.

Assessing ozone pollution and climate change impacts on winter wheat: flux modeling vs. dose-response modeling.

Surface ozone is a phytotoxic pollutant that damages photosynthetic systems, reduces gas exchange, retards vegetation growth, and decreases yield. In this study, we developed a new ozone flux module within an agroecosystem model framework to enhance our ability to understand, measure, and predict the impact of surface ozone on agricultural productivity. The new module was calibrated and evaluated against historical observational data from multiple sites.

An improved multi-scale feature extraction method for nonlinear signals.

This paper proposes an innovative multi-scale feature extraction method for analyzing electroencephalogram (EEG) and electrocardiogram (ECG) signals. The method utilizes an energy functional derived from the Cahn-Hilliard (CH) phase field equation to extract features, aiming to improve classification accuracy. To validate its effectiveness, we integrate the extracted features with a Support Vector Machine (SVM) classifier, forming the CH-SVM model for both EEG and ECG classification.