Search Results (28)

The increase in industrial production can lead to more complex emissions of O₃ precursors, but the changes in the formation mechanism and source of O₃ are still unclear. Taking Jincheng as the typical industrial city, an observation-based model (OBM) is explored to analyze the changes in O₃ formation in 2022 and 2024. The results indicated that the concentration of NOx and VOCs in 2024 increased by 21.1% and 22.3%, respectively. And the concentrations of alkenes related to industrial processes increased significantly. RO₂+NO is the main pathway for O₃ formation (51.5~54.2%), while VOCs+OH· contributes most to the formation of RO₂. VOC and NOx both play important roles in O₃ formation, and the sensitivity of VOCs increased from 0.76 to 0.84 in 2022 and 2024, with alkenes increasing the most. Industrial processes and coal combustion are the important sources for O₃ and its precursors, and the contribution of the industrial process increased significantly during 2022 and 2024. In summary, the increase in the industrial activity level has led to the increase in alkenes, which has a key impact on the formation of O₃. Controlling the emission of alkene from the industrial process is the direction for the continuous control of O₃ pollution in industrial cities. Full article

Since people spend most of their time in indoor environments, they are continuously exposed to various contaminants that threaten human health. The air quality in these settings is therefore a crucial factor in maintaining health safety. In order to reduce the concentration of indoor air pollutants and improve air quality, photocatalytic oxidation has drawn the attention of researchers. This study aims to provide a comprehensive view of the nanomaterials used in the photocatalytic oxidation of the most common pollutants in indoor environments. The effects of various parameters like humidity, airflow, deposition time, and light intensity were also evaluated, as they can significantly influence photocatalytic reactions. The most common nanomaterials used in photocatalysis are TiO₂-based and, in this study, they were classified and examined based on their morphology. TiO₂ doping with metals and non-metals has demonstrated an enhancement of its adsorption properties and photocatalytic efficiency for the removal of several pollutants. The role of carbon-based nanomaterials in photocatalysis was also evaluated due to their adsorption capabilities towards various pollutants. In addition, other less common photocatalysts such as ZnO, MnO₂, WO₃, CeO₂, and CdS also exhibited high photocatalytic activity for pollutant degradation. Applications of these photocatalysts in air purifiers, paints, and building materials e.g., concrete, glass, and wallpapers, lead to efficient reduction of pollutants in indoor settings. Full article

Volatile organic compounds (VOCs) can impact the actions of drugs due to their effects on drug receptors and the activities of enzymes involved in various metabolic processes, especially those relating to gene regulation. They can disrupt cellular functions and potentially affect human drug metabolism and utilization receptors. They mimic or inhibit the actions of endogenous ligands, leading to carcinogenesis, neurotoxicity, endocrine disruption, and respiratory disorders. Chronic exposure to VOCs due to human occupation can lead to an increased generation of reactive oxygen species (ROS), which could lead to oxidative stress and damage to lipids, affecting the formation and proper functioning of gene regulation, enzyme activity, and cell membranes. The presence of oxidative stress could interfere with drug activity and potentially impact the body’s ability to process and utilize drugs effectively. This is because drugs such as antioxidant drugs play an essential role in cell protection against oxidative damage. Therefore, disruptions in their metabolism could distort the overall health condition through the breakdown of antioxidant defense mechanisms. In this study, the aim is to assess the effect of VOC exposure on drug receptors and the way forward in designing and maintaining optimal drug activity for workers’ overall well-being. Full article

Traditional object detectors require extensive instance-level annotations for training. Conversely, few-shot object detectors, which are generally fine-tuned using limited data from unknown classes, tend to show biases toward base categories and are susceptible to variations within these unknown samples. To mitigate these challenges, we introduce a Two-Stage Fine-Tuning Approach (TFA) named Extreme R-CNN, designed to operate effectively with extremely limited original samples through the integration of sample synthesis and knowledge distillation. Our approach involves synthesizing new training examples via instance clipping and employing various data-augmentation techniques. We enhance the Faster R-CNN architecture by decoupling the regression and classification components of the Region of Interest (RoI), allowing synthetic samples to train the classification head independently of the object-localization process. Comprehensive evaluations on the Microsoft COCO and PASCAL VOC datasets demonstrate significant improvements over baseline methods. Specifically, on the PASCAL VOC dataset, the average precision for novel categories is enhanced by up to 15 percent, while on the more complex Microsoft COCO benchmark it is enhanced by up to 6.1 percent. Remarkably, in the 1-shot scenario, the AP50 of our model exceeds that of the baseline model in the 10-shot setting within the PASCAL VOC dataset, confirming the efficacy of our proposed method. Full article

Microbes produce various bioactive metabolites that can influence plant growth and stress tolerance. In this study, a plant growth-promoting rhizobacterium (PGPR), strain S14, was identified as Micrococcus luteus (designated as MlS14) using de novo whole-genome assembly. The MlS14 genome revealed major gene clusters for the synthesis of indole-3-acetic acid (IAA), terpenoids, and carotenoids. MlS14 produced significant amounts of IAA, and its volatile organic compounds (VOCs), specifically terpenoids, exhibited antifungal activity, suppressing the growth of pathogenic fungi. The presence of yellow pigment in the bacterial colony indicated carotenoid production. Treatment with MlS14 activated the expression of β-glucuronidase (GUS) driven by a promoter containing auxin-responsive elements. The application of MlS14 reshaped the root architecture of Arabidopsis seedlings, causing shorter primary roots, increased lateral root growth, and longer, denser root hairs; these characteristics are typically controlled by elevated exogenous IAA levels. MlS14 positively regulated seedling growth by enhancing photosynthesis, activating antioxidant enzymes, and promoting the production of secondary metabolites with reactive oxygen species (ROS) scavenging activity. Pretreatment with MlS14 reduced H₂O₂ and malondialdehyde (MDA) levels in seedlings under drought and heat stress, resulting in greater fresh weight during the post-stress period. Additionally, exposure to MlS14 stabilized chlorophyll content and growth rate in seedlings under salt stress. MlS14 transcriptionally upregulated genes involved in antioxidant defense and photosynthesis. Furthermore, genes linked to various hormone signaling pathways, such as abscisic acid (ABA), auxin, jasmonic acid (JA), and salicylic acid (SA), displayed increased expression levels, with those involved in ABA synthesis, using carotenoids as precursors, being the most highly induced. Furthermore, MlS14 treatment increased the expression of several transcription factors associated with stress responses, with DREB2A showing the highest level of induction. In conclusion, MlS14 played significant roles in promoting plant growth and stress tolerance. Metabolites such as IAA and carotenoids may function as positive regulators of plant metabolism and hormone signaling pathways essential for growth and adaptation to abiotic stress. Full article

Plasma from electric discharges can be used in the abatement of volatile organic compounds (VOCs). The application of gas-phase pulsed corona discharge (PCD) in air–water mixtures provides favorable conditions for the oxidation of VOCs at unsurpassed energy efficiency. This research investigates the impact of water sprinkling on PCD performance in the oxidation of m-xylene as a model compound. Experimental research into the plasma treatment of continuous air flow was undertaken using the PCD reactor in dry and water-sprinkled modes. Water sprinkling more than doubled the m-xylene oxidation rate, which can be attributed to abundant OH-radicals produced at the plasma–water interface. Water sprinkling substantially reduced the formation of nitrous oxide, which is considered to be a secondary pollutant in the outlet air. Ozone is considered a by-product helping the subsequent photocatalytic oxidation of potential residues and photocatalyst maintenance. The use of water-sprinkled PCD is a promising approach to energy-efficient abatement of VOCs. Full article

This study primarily investigates advanced object detection and time series prediction methods in image-based visual servoing systems, aiming to capture targets better and predict the motion trajectory of robotic arms in advance, thereby enhancing the system’s performance and reliability. The research first implements object detection on the VOC2007 dataset using the Detection Transformer (DETR) and achieves ideal detection scores. The particle swarm optimization algorithm and 3-5-3 polynomial interpolation methods were utilized for trajectory planning, creating a unique dataset through simulation. This dataset contains randomly generated trajectories within the workspace, fully simulating actual working conditions. Significantly, the Bidirectional Long Short-Term Memory (BILSTM) model was improved by substituting its traditional Multilayer Perceptron (MLP) components with Kolmogorov–Arnold Networks (KANs). KANs, inspired by the K-A theorem, improve the network representation ability by placing learnable activation functions on fixed node activation functions. By implementing KANs, the model enhances parameter efficiency and interpretability, thus addressing the typical challenges of MLPs, such as the high parameter count and lack of transparency. The experiments achieved favorable predictive results, indicating that the KAN not only reduces the complexity of the model but also improves learning efficiency and prediction accuracy in dynamic visual servoing environments. Finally, Gazebo software was used in ROS to model and simulate the robotic arm, verify the effectiveness of the algorithm, and achieve visual servoing. Full article

The deep-sea bacterium Spongiibacter nanhainus CSC3.9 has significant inhibitory effects on agricultural pathogenic fungi and human pathogenic bacteria, especially Pseudomonas aeruginosa, the notorious multidrug-resistant pathogen affecting human public health. We demonstrate that the corresponding antibacterial agents against P. aeruginosa PAO1 are volatile organic compounds (VOCs, namely VOC-3.9). Our findings show that VOC-3.9 leads to the abnormal cell division of P. aeruginosa PAO1 by disordering the expression of several essential division proteins associated with septal peptidoglycan synthesis. VOC-3.9 hinders the biofilm formation process and promotes the biofilm dispersion process of P. aeruginosa PAO1 by affecting its quorum sensing systems. VOC-3.9 also weakens the iron uptake capability of P. aeruginosa PAO1, leading to reduced enzymatic activity associated with key metabolic processes, such as reactive oxygen species (ROS) scavenging. Overall, our study paves the way to developing antimicrobial compounds against drug-resistant bacteria by using volatile organic compounds. Full article

Early identification of acute gout is crucial, enabling healthcare professionals to implement targeted interventions for rapid pain relief and preventing disease progression, ensuring improved long-term joint function. In this study, we comprehensively explored the potential early detection of gout flares (GFs) based on nurses’ chief complaint notes in the Emergency Department (ED). Addressing the challenge of identifying GFs prospectively during an ED visit, where documentation is typically minimal, our research focused on employing alternative Natural Language Processing (NLP) techniques to enhance detection accuracy. We investigated GF detection algorithms using both sparse representations by traditional NLP methods and dense encodings by medical domain-specific Large Language Models (LLMs), distinguishing between generative and discriminative models. Three methods were used to alleviate the issue of severe data imbalances, including oversampling, class weights, and focal loss. Extensive empirical studies were performed on the Gout Emergency Department Chief Complaint Corpora. Sparse text representations like tf-idf proved to produce strong performances, achieving F1 scores higher than 0.75. The best deep learning models were RoBERTa-large-PM-M3-Voc and BioGPT, which had the best F1 scores for each dataset, with a 0.8 on the 2019 dataset and a 0.85 F1 score on the 2020 dataset, respectively. We concluded that although discriminative LLMs performed better for this classification task when compared to generative LLMs, a combination of using generative models as feature extractors and employing a support vector machine for classification yielded promising results comparable to those obtained with discriminative models. Full article

Isoprene, a lipophilic and unstable compound with the chemical formula C5H8, is transported to plant chloroplasts via the 2-C-methyl-d-erythritol 4-phosphate (MEP) pathway, which relies on photosynthesis. Although only about 20% of terrestrial plants can synthesize isoprene, those that emit it are more adaptable to oxidative and thermal stresses. To shed light on the still-elusive protective mechanism of isoprene, numerous investigations have been conducted. Isoprene has been shown to react with and quench various reactive oxygen species (ROS) such as singlet oxygen (1O2). Its reduced state and conjugated double bonds suggest that it functions as an antioxidant, although this has yet to be conclusively proven. Despite its low abundance relative to other molecules in plant tissues, recent research has explored several potential roles for isoprene including acting as a scavenger of ROS by serving as an antioxidant; strengthening cell membranes; modulating genomic, proteomic and metabolomic profiles; signaling stress responses among neighboring plants compared with other volatile organic compounds (VOCs); regulating metabolic fluxes of hormones produced through the MEP pathway; or even functioning as a free developmental hormone. Future prospective studies, such as identifying the specific receptors for VOCs along with transcription factors (TFs) and other regulatory proteins participating in the signaling pathways and also metabolomic, transcriptomic and physiological analyses could help in comprehending VOC-induced defense responses in plants under stress conditions. Full article

Enhanced ozone (O₃) pollution has emerged as a pressing environmental concern in China, particularly for densely populated megacities and major city clusters. However, volatile organic compounds (VOCs), the key precursors to O₃ formation, have not been routinely measured. In this study, we characterize the spatial and temporal patterns of VOCs and examine the role of VOCs in O₃ production in five cities (Dongying (DY), Rizhao (RZ), Yantai (YT), Weihai (WH), and Jinan (JN)) in the North China Plain (NCP) for two sampling periods (June and December) in 2021 through continuous field observations. Among various VOC categories, alkanes accounted for the largest proportion of VOCs in the cities. For VOCs, chemical reactivities, aromatic hydrocarbons, and alkenes were dominant contributors to O₃ formation potential (OFP). Unlike inland regions, the contribution to OFP from OVOCs increased greatly at high O₃ concentrations in coastal regions (especially YT). Model simulations during the O₃ episode show that the net O₃ production rates were 27.87, 10.24, and 10.37 ppbv/h in DY, RZ, and JN. The pathway of HO₂ + NO contributed the most to O₃ production in JN and RZ, while RO₂ + NO was the largest contributor to O₃ production in DY. The relative incremental reactivity (RIR) revealed that O₃ formation in DY was the transitional regime, while it was markedly the VOC-limited regime in JN and RZ. The O₃ production response is influenced by NOx concentration and has a clear daily variation pattern (the sensitivity is greater from 15:00 to 17:00). The most efficiencies in O₃ reduction could be achieved by reducing NOx when the NOx concentration is low (less than 20 ppbv in this study). This study reveals the importance of ambient VOCs in O₃ production over the NCP and demonstrates that a better grasp of VOC sources and profiles is critical for in-depth O₃ regulation in the NCP. Full article

SARS-CoV-2 infection remains a global burden. Despite intensive research, the mechanism and dynamics of early viral replication are not completely understood, such as the kinetics of the formation of genomic RNA (gRNA), sub-genomic RNA (sgRNA), and replication centers/organelles (ROs). We employed single-molecule RNA-fluorescence in situ hybridization (smRNA-FISH) to simultaneously detect viral gRNA and sgRNA and immunofluorescence to detect nsp3 protein, a marker for the formation of RO, and carried out a time-course analysis. We found that single molecules of gRNA are visible within the cytoplasm at 30 min post infection (p.i.). Starting from 2 h p.i., most of the viral RNA existed in clusters/speckles, some of which were surrounded by single molecules of sgRNA. These speckles associated with nsp3 protein starting at 3 h p.i., indicating that these were precursors to ROs. Furthermore, RNA replication was asynchronous, as cells with RNA at all stages of replication were found at any given time point. Our probes detected the SARS-CoV-2 variants of concern, and also suggested that the BA.1 strain exhibited a slower rate of replication kinetics than the WA1 strain. Our results provide insights into the kinetics of SARS-CoV-2 early post-entry events, which will facilitate identification of new therapeutic targets for early-stage replication to combat COVID-19. Full article

Underwater object detection plays a significant role in marine ecosystem research and marine species conservation. The improvement of related technologies holds practical significance. Although existing object-detection algorithms have achieved an excellent performance on land, they are not satisfactory in underwater scenarios due to two limitations: the underwater objects are often small, densely distributed, and prone to occlusion characteristics, and underwater embedded devices have limited storage and computational capabilities. In this paper, we propose a high-precision, lightweight underwater detector specifically optimizing for underwater scenarios based on the You Only Look Once Version 8 (YOLOv8) model. Firstly, we replace the Darknet-53 backbone of YOLOv8s with FasterNet-T0, reducing model parameters by 22.52%, FLOPS by 23.59%, and model size by 22.73%, achieving model lightweighting. Secondly, we add a Prediction Head for Small Objects, increase the number of channels for high-resolution feature map detection heads, and decrease the number of channels for low-resolution feature map detection heads. This results in a 1.2% improvement in small-object detection accuracy, while the remaining model parameters and memory consumption are nearly unchanged. Thirdly, we use Deformable ConvNets and Coordinate Attention in the neck part to enhance the accuracy in the detection of irregularly shaped and densely occluded small targets. This is achieved by learning convolution offsets from feature maps and emphasizing the regions of interest (RoIs). Our method achieves 52.12% AP on the underwater dataset UTDAC2020, with only 8.5 M parameters, 25.5 B FLOPS, and 17 MB model size. It surpasses the performance of large model YOLOv8l, at 51.69% AP, with 43.6 M parameters, 164.8 B FLOPS, and 84 MB model size. Furthermore, by increasing the input image resolution to 1280 × 1280 pixels, our model achieves 53.18% AP, making it the state-of-the-art (SOTA) model for the UTDAC2020 underwater dataset. Additionally, we achieve 84.4% mAP on the Pascal VOC dataset, with a substantial reduction in model parameters compared to previous, well-established detectors. The experimental results demonstrate that our proposed lightweight method retains effectiveness on underwater datasets and can be generalized to common datasets. Full article

The effect of wireworm-damaged lettuce roots on the antioxidative defense system (ascorbate–glutathione cycle, photosynthetic pigments) and movement of insect/slug parasitic nematodes towards determined root exudates was studied in a glasshouse experiment. Lettuce seedlings were grown in a substrate soil in the absence/presence of wireworms (Elateridae). The ascorbate–glutathione system and photosynthetic pigments were analyzed by HPLC, while volatile organic compounds (VOC) emitted by lettuce roots were investigated by GC-MS. Herbivore-induced root compounds, namely 2,4-nonadienal, glutathione, and ascorbic acid, were selected for a chemotaxis assay with nematodes Steinernema feltiae, S. carpocapsae, Heterorhabditis bacteriophora, Phasmarhabditis papillosa, and Oscheius myriophilus. Root pests had a negative effect on the content of photosynthetic pigments in the leaves of infested plants, indicating that they reacted to the presence of reactive oxygen species (ROS). Using lettuce as a model plant, we recognized the ascorbate–glutathione system as a redox hub in defense response against wireworms and analyzed its role in root-exudate-mediated chemotaxis of nematodes. Infected plants also demonstrated increased levels of volatile 2,4-nonadienal. Entomopathogenic nematodes (EPNs, S. feltiae, S. carpocapsae, and H. bacteriophora) proved to be more mobile than parasitic nematodes O. myriophilus and P. papillosa towards chemotaxis compounds. Among them, 2,4-nonadienal repelled all tested nematodes. Most exudates that are involved in belowground tritrophic interactions remain unknown, but an increasing effort is being made in this field of research. Understanding more of these complex interactions would not only allow a better understanding of the rhizosphere but could also offer ecologically sound alternatives in the pest management of agricultural systems. Full article

Phytopathogens are well known for their devastating activity that causes worldwide significant crop losses. However, their exploitation for crop welfare is relatively unknown. Here, we show that the microbial volatile organic compound (mVOC) profile of the bacterial phytopathogen, Erwinia amylovora, enhances Arabidopsis thaliana shoot and root growth. GC-MS head-space analyses revealed the presence of typical microbial volatiles, including 1-nonanol and 1-dodecanol. E. amylovora mVOCs triggered early signaling events including plasma transmembrane potential Vm depolarization, cytosolic Ca²⁺ fluctuation, K⁺-gated channel activity, and reactive oxygen species (ROS) and nitric oxide (NO) burst from few minutes to 16 h upon exposure. These early events were followed by the modulation of the expression of genes involved in plant growth and defense responses and responsive to phytohormones, including abscisic acid, gibberellin, and auxin (including the efflux carriers PIN1 and PIN3). When tested, synthetic 1-nonanol and 1-dodecanol induced root growth and modulated genes coding for ROS. Our results show that E. amylovora mVOCs affect A. thaliana growth through a cascade of early and late signaling events that involve phytohormones and ROS. Full article