The microbiome orchestrates contaminant low-dose phytostimulation.

Our understanding of the physiological mechanisms of the plant hormetic response to countless environmental contaminants is rapidly advancing. However, the microbiome is a critical determinant of plant responses to stressors, thus possibly influencing hormetic responses. Here, we review the otherwise neglected role of microbes in shaping plant stimulation by subtoxic concentrations of contaminants and vice versa.

Vertical and horizontal variabilities of ammonia in an agricultural area of Southeast China.

Ammonia (NH) plays a crucial role in the global nitrogen cycle, the increased NH emissions from agricultural activities impacting air, soil, water quality, and human health. Accurately estimating both the vertical and horizontal transport distances of NH are important for effective pollution control. Therefore, we used a helium-filled balloon mounted sampler to analyze the vertical profiles of NH emissions and their seasonal variations in an agricultural area of southeast China.

A Respiratory Signal Monitoring Method Based on Dual-Pathway Deep Learning Networks in Image-Guided Robotic-Assisted Intervention System.

Background: Percutaneous puncture procedures, guided by image-guided robotic-assisted intervention (IGRI) systems, are susceptible to disruptions in patients' respiratory rhythm due to factors such as pain and psychological distress.

Methods: We developed an IGRI system with a coded structured light camera and a binocular camera. Our system incorporates dual-pathway deep learning networks, combining convolutional long short-term memory (ConvLSTM) and point long short-term memory (PointLSTM) modules for real-time respiratory signal monitoring.

The 2023 report of the synergetic roadmap on carbon neutrality and clean air for China: Carbon reduction, pollution mitigation, greening, and growth.

The response to climate change and air pollution control demonstrates strong synergy across scientific mechanisms, targets, strategies, and governance systems. This report, based on a monitoring indicator system for coordinated governance of air pollution and climate change, employs an interdisciplinary approach combining natural and social sciences. It establishes 20 indicators across five key areas: air pollution and climate change, governance systems and practices, structural transformation and technologies, atmospheric components and emission reduction pathways, and health impacts and co-benefits.

AI-driven approaches for automatic detection of sleep apnea/hypopnea based on human physiological signals: a review.

Sleep apnea/hypopnea is a sleep disorder characterized by repeated pauses in breathing which could induce a series of health problems such as cardiovascular disease (CVD) and even sudden death. Polysomnography (PSG) is the most common way to diagnose sleep apnea/hypopnea. Considering that PSG data acquisition is complex and the diagnosis of sleep apnea/hypopnea requires manual scoring, it is very time-consuming and highly professional.

Association between microplastics and the functionalities of human gut microbiome.

As an integral part of humans, the gut microbiome plays a significant role in the physiological and pathological processes of the host, and dysbiosis of the gut microbiome is linked to various diseases. The impact of microplastics on the diversity and composition of human gut microbiome has been reported previously. However, effects of microplastics on the functionality of the gut microbiome in humans have not been well studied.

How to develop nature-based solutions for revegetation on abandoned farmland in the Loess Plateau of China?

Adequate revegetation of abandoned farmland acts as a defence against desertification and soil loss, and can help remove carbon dioxide in the atmosphere, thereby playing an important role in regulating regional climate change. Legume, a nitrogen-fixation species, which could effectively improve vegetation coverage to control soil erosion, was widely used for revegetation. However, the dynamics of soil and plant development after legume introduction on abandoned farmland remain unclear.

Sensitivity-enhanced potentiometric measurement by incorporating graphitic carbon nitride into the ion-to-electron transducer of potassium ion-selective electrodes.

In recent years, wearable sweat sensors have garnered significant attention for real-time monitoring of human physiological information because of their ability to continuously and non-invasively detect multiple sweat biomarkers. Among these, potentiometric sensors stand out for their low power consumption, low cost, compact design, and real-time monitoring capabilities, making them an ideal alternative for sweat analysis. However, enhancing the sensitivity of ion-selective electrodes (ISEs), a critical parameter of potentiometric sensors, remains a challenging research focus.

A comprehensive time-series dataset linked to cyanobacterial blooms in Lake Taihu.

Lake Taihu has a history of recurrent harmful cyanobacterial blooms. There is a need to better understand the aquatic ecosystem of Lake Taihu in order to improve methods for controlling the cyanobacterial blooms. Based on the field measurement and satellite remote sensing, we produced and collected a time-series dataset, including the water quality, bio-optics, climate, and anthropogenic data of Lake Taihu (THQBCA), which could provide comprehensive information regarding cyanobacterial blooms.

3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness.

Evaluating students' learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data.

Low-Quality Sensor Data-Based Semi-Supervised Learning for Medical Image Segmentation.

Traditional medical image sensors face multiple challenges. First, these sensors typically rely on large amounts of labeled data, which are time-consuming and costly to obtain. Second, when the data volume and image size are large, traditional sensors have limited computational power, making it difficult to effectively train and infer models.

Semantic Scene Completion in Autonomous Driving: A Two-Stream Multi-Vehicle Collaboration Approach.

Vehicle-to-vehicle communication enables capturing sensor information from diverse perspectives, greatly aiding in semantic scene completion in autonomous driving. However, the misalignment of features between ego vehicle and cooperative vehicles leads to ambiguity problems, affecting accuracy and semantic information. In this paper, we propose a Two-Stream Multi-Vehicle collaboration approach (TSMV), which divides the features of collaborative vehicles into two streams and regresses interactively.

Retrieval Integrity Verification and Multi-System Data Interoperability Mechanism of a Blockchain Oracle for Smart Healthcare with Internet of Things (IoT) Integration.

The proliferation of Internet of Things (IoT) technology has significantly enhanced smart healthcare systems, enabling the collection and processing of vast healthcare datasets such as electronic medical records (EMRs) and remote health monitoring (RHM) data. However, this rapid expansion has also introduced critical challenges related to data security, privacy, and system reliability. To address these challenges, we propose a retrieval integrity verification and multi-system data interoperability mechanism for a Blockchain Oracle in smart healthcare with IoT Integration (RIVMD-BO).

Unlocking vegetation health: optimizing GEDI data for accurate chlorophyll content estimation.

Chlorophyll content is a vital indicator for evaluating vegetation health and estimating productivity. This study addresses the issue of Global Ecosystem Dynamics Investigation (GEDI) data discreteness and explores its potential in estimating chlorophyll content. This study used the empirical Bayesian Kriging regression prediction (EBKRP) method to obtain the continuous distribution of GEDI spot parameters in an unknown space.

Does lexical category matter in effects of emotionality on L2 word processing in late proficient Chinese-English bilinguals? An ERP study.

Although the effects of emotionality on word processing might be modulated by lexical category, a body of extant literature has tended to obviate the need of considering this factor. In this study, we attempted to address how lexical category modulates the effects of emotionality on L2 word processing. To this end, event-related potentials (ERPs) were recorded from a group of late proficient Chinese-English bilinguals while they performed a lexical decision task with a set of tightly matched negative, positive, and neutral words across three lexical categories (nouns, verbs, adjectives).

Conversion of coastal wetlands to paddy fields substantially decreases methane oxidation potential and methanotrophic abundance on the eastern coast of China.

Coastal wetland ecosystems play a key role in the global carbon cycle and climate mitigation. The land conversion of coastal wetlands to paddy fields, an increasingly common practice to feed the growing population, has been shown to dramatically stimulate the methane emissions of (CH). However, the knowledge about how such wetland conversion affects the methane oxidation, a key process regulating methane emissions from coastal wetlands, is nearly unknown.

Deciphering the inhibition mechanisms of microplastics on the full-stage sludge anaerobic digestion via enrichment to anaerobic microbes and toxicity of released compounds.

Increasingly accumulated microplastics (MPs) in sludge could affect the sludge treatment process, while the contributions and mechanisms of MP particles and the released organic matters (MP-DOM) are not fully understood. To fill this gap, this study systematically investigates the effects and mechanisms of MPs on sludge anaerobic digestion. In the presence of MPs, the hydrolysis and acidogenesis of organic matters and methanogenesis all decreased due to the inhibition on the activity of anaerobic microorganisms and key enzymes.

A novel analytical tool for complex propagation processes in networks: High-order dynamic equation.

Controlling the spread of epidemics in complex networks has always been an important research problem in the field of network science and has been widely studied by many scholars so far. One of the key problems in the transmission process of epidemics in complex networks is the transmission mechanism. At present, the transmission mechanism in complex networks can be divided into simple transmission and complex transmission.

Secure multiparty computation protocol based on homomorphic encryption and its application in blockchain.

Blockchain technology is a key technology in the current information field and has been widely used in various industries. Blockchain technology faces significant challenges in privacy protection while ensuring data immutability and transparency, so it is crucial to implement private computing in blockchain. To target the privacy issues in blockchain, we design a secure multi-party computation (SMPC) protocol DHSMPC based on homomorphic encryption in this paper.

New quantified climate reconstruction in southern China suggests a potential influence of winter cloud cover across East Asia during the Holocene.

Previous studies have advanced our understanding of paleoclimate features and dynamics in East Asia, particularly within the East Asian monsoon domain (EAMD) since the last glacial period. However, a lack of quantitative reconstructions in the boundary area between tropical and subtropical zones has largely hindered our spatial comprehension of the relationship between precipitation and temperature throughout the EAMD. In this study, we present a continuous pollen record from the Pearl River delta over the past 13.

A waveguide branch-line crossover based on metal gratings.

A waveguide branch-line crossover with broad bandwidth is designed for beam-forming networks in millimeter wavebands. To realize the power coupling between two parallel branch lines, a substrate with metal gratings patched replaces the common E-plane waveguide wall. Compared with the traditional crossover with coupling vias, the metal gratings achieve continuous and efficient power coupling, endowing the proposed crossover with advantages in compactness and broad bandwidth.

3D Printing-Induced Phase Transition in Ferroelectric Porous Polymer Coatings for Stable Zinc Anodes.

The abundant availability of zinc and its environmentally friendly properties have increasingly drawn attention to the potential of aqueous zinc-ion batteries (AZIBs). However, their development is hindered by the formation of zinc dendrites and severe side reactions during charge/discharge cycles. In this work, a novel strategy is introduced to address these challenges by constructing a ferroelectric porous PVDF-HFP protective layer (PH-ZF) on the zinc anode surface using 3D printing technology.

Gonyaulax montresoriae sp. nov. (Dinophyceae) from the Adriatic Sea produces predominantly yessotoxin.

Yessotoxin is one of the shellfish toxins leading to mussel farm closures in the Adriatic Sea of Italy. Two putative Gonyaulax spinifera strains GSA0501 and GSA0602 are known as yessotoxins producers, but their identities have remained elusive since 2005. To address this gap, we established five Gonyaulax strains by incubating sediments from the Adriatic Sea and subsequently isolating single cells.

Riemannian manifold-based disentangled representation learning for multi-site functional connectivity analysis.

Functional connectivity (FC), derived from resting-state functional magnetic resonance imaging (rs-fMRI), has been widely used to characterize brain abnormalities in disorders. FC is usually defined as a correlation matrix that is a symmetric positive definite (SPD) matrix lying on the Riemannian manifold. Recently, a number of learning-based methods have been proposed for FC analysis, while the geometric properties of Riemannian manifold have not yet been fully explored in previous studies.