Zhoukou City Academy of Agricultural Sciences

The transportation sector powered by fossil fuels is a significant contributor to PM2.5 pollution. Systematic evaluation of traffic-related PM2.5 on acute coronary syndrome (ACS) onset is not yet characterized, and the health benefits of reduced traffic-related PM2.5 and evidence-based mitigation strategies are currently absent. We investigated the association of ACS onset with short-term traffic-related PM2.5 exposure in a nationwide time-stratified case-crossover study, using 627,828 ACS cases extracted from the China Cardiovascular Association. We evaluated the risks of ACS onset associated with traffic-related PM2.5, calculated the attributable onsets, and then conducted cluster analysis using the K-means algorithm to identify priority cities for emission reduction. We found a robust association between traffic-related PM2.5 and ACS onset, i.e., an increased risk of 2.39% (95% CI: 1.79–3.00%) for same-day (lag 0) PM2.5 per 10 μg/m3 increase. The greatest health benefits due to a 1 μg/m3 PM2.5 reduction were achieved by traffic-related PM2.5, reaching 10,267 (95%CI: 4503–16,063) onsets per year. In addition, three obvious categories were clustered, and traffic-polluted cities were highlighted (cluster 2, not belonging to megacities), with high emissions and concentrations of traffic-related PM2.5. Therefore, intensified efforts to mitigate traffic-related PM2.5 emissions should be promoted, and the prioritization of emission reduction strategies in these traffic-polluted cities is imperative for safeguarding public health.

Ventricular arrhythmias (VA) are among the most prevalent and clinically significant cardiac arrhythmias. Conventional detection methodologies predominantly employ supervised learning approaches that depend on precisely annotated training datasets. However, the morphological similarity between VA waveforms and noise artifacts poses a significant challenge for traditional algorithms in discriminating these clinically distinct categories. In this paper, we propose a novel anomaly detection framework named PHVA (Pseudo-data and Hard-sample mining for VA detection) based on one-class anomaly detection, where only normal ECGs are used for training, while pseudo-anomalies are generated through self-supervised modules. These modules, including pseudo-anomaly generation and hard-sample mining, provide supervisory signals without requiring abnormal labels. Specifically, we introduce a physiology-aware synthetic ECG generation method that captures the beat morphology and waveform characteristics of VA while incorporating realistic noise simulations based on conventional noise models. These pseudo-annotated signals are then used to refine the decision boundary of a time-frequency hypersphere, constructed from normal ECG features in both temporal and spectral domains. Additionally, we employ triplet-loss-based hard-sample mining to improve the model's discriminative power for ventricular fibrillation detection. Extensive experiments on three public ECG datasets demonstrate that our proposed method PHVA achieves superior overall performance, outperforming state-of-the-art anomaly detection baselines by up to 8.7% in AUC (the Area Under a Receiver Operating Characteristic Curve). We share an online repository containing the code and data processing scripts https://github.com/Augustgaoshaochen/PHVA.

To enhance electron donor utilization efficiency for advanced nitrogen removal from low carbon-to-nitrogen (C/N) ratio wastewater, polyvinyl alcohol-sodium acetate (PVA-SA) fillers embedded with powdered activated carbon (PAC) were developed and...

A wavelet-based local and global feature fusion network (LAGN) is proposed for low-light image enhancement, aiming to enhance image details and restore colors in dark areas. This study focuses on addressing three key issues in low-light image enhancement: Enhancing low-light images using LAGN to preserve image details and colors; extracting image edge information via wavelet transform to enhance image details; and extracting local and global features of images through convolutional neural networks and Transformer to improve image contrast. Comparisons with state-of-the-art methods on two datasets verify that LAGN achieves the best performance in terms of details, brightness, and contrast.

Sepsis and its associated microcirculatory and endothelial dysfunction leading to organ failure remained the primary causes of morbidity and mortality in intensive care units. Fluid resuscitation served as the cornerstone of early supportive therapy. This study aimed to evaluate the impact of human albumin (HA) versus crystalloid-based resuscitation on microcirculation, capillary leakage, and endothelial function in sepsis patients with hypoalbuminemia who are not severely ill. This retrospective cohort study included sepsis patients admitted to the same hospital between January 2022 and December 2024. Patients were divided into two groups based on the type of fluid resuscitation received: HA or crystalloids. Key parameters assessed included microcirculatory parameters, markers of capillary leakage, endothelial function, inflammatory markers, and albumin function within 72 hours, with organ support outcomes and clinical indicators evaluated at 4 weeks. The study included 223 patients (crystalloid group: n = 108; HA group: n = 115). The HA group showed superior microcirculatory perfusion at 72 hours (microvascular flow index: 2.87 vs. 2.73, P = 0.003; tissue oxygen saturation: 80.14% vs. 78.32%, P = 0.002), reduced capillary leakage (albumin leakage: 30.35% vs. 33.16%, P = 0.006), and improved endothelial function at 72 hours (nitric oxide: 25.83 vs . 24.56 μmol/L, P = 0.003). The incidence of acute kidney injury was lower (14.78% vs. 25.93%, P = 0.038), and intensive care unit stay duration was shorter (7.57 vs. 8.31 days, P = 0.006). Albumin treatment independently reduced the risk of microcirculatory deterioration ( P < 0.001). In hypoalbuminemic sepsis patients who are not severely ill, HA resuscitation provided multitarget benefits by improving microcirculation, endothelial integrity, and organ outcomes.

Background: Ethylene is one of the most important plant hormones. ACC oxidase (ACO) plays a vital role in ethylene synthesis and responses to biotic and abiotic stresses in plants. However, its function in Triticum urartu remains unclear. This study aims to systematically identify the members of the TuACO gene family to elucidate its response characteristics and functions under biotic and abiotic stresses. Methods: Through homologous alignment, phylogenetic evolution analysis, and investigations of gene structure and promoter cis-elements, a total of eight TuACO genes were identified in the T. urartu genome based on their homology to OsACO and AtACO protein sequences. Results: These genes were classified into five ACO subfamilies and distributed across chromosomes 1A, 4A, 5A, 6A, and 7A. TuACO gene families contained 0–3 introns and 1–4 exons. The protein sequence contains 10 different conservative motifs. QRT-PCR expression analysis revealed that the transcript levels of TuACO5a, TuACO5b, and TuACO3a were significantly upregulated at 6 and 24 h after infection with powdery mildew, a biotic stress. Under boron deficiency, an abiotic stress, the expression of TuACO6 and TuACO1b increased, whereas the expression of TuACO5b and TuACO3b was markedly induced under high-boron conditions. Conclusions: These results demonstrate that TuACO genes exhibit functional diversification in response to biotic and abiotic stresses, which lays the foundation for elucidating their gene functions.

Sweet sorghum has a high tolerance to toxic metals, but its response to soil amendments in Cd-polluted soils remains underexplored. Here, we compared the effects of biochar and hydroxyapatite (HAP) at different doses (0, 0.5%, and 1%, w/w) on the growth, mineral nutrition, stress tolerance, and phytoremediation efficiency of sweet sorghum grown in an agricultural soil heavily polluted by Cd (20.80 mg/kg). In most cases, both amendments greatly increased plant biomass and nutrient uptake of N, P, and K, and enhanced the activities of antioxidative enzymes (peroxidase and superoxide dismutase) in leaves. They also increased soil pH and reduced soil available Cd concentrations, confirming their liming effects and immobilization of Cd. Unexpectedly, both HAP and biochar significantly increased Cd concentration and uptake in the shoots and roots, contradicting prior findings. Shoot Cd uptake was increased by 96% and 57% by 0.5% and 1% biochar, respectively. Root Cd uptake was increased by 50% and 64% by 1% HAP and 0.5% biochar, respectively. Overall, both HAP and biochar improved sweet sorghum growth and mitigated Cd toxicity, and particularly, decreased soil Cd availability while enhancing shoot and root Cd accumulation, indicating a promising strategy for the phytoextraction and phytostabilization of Cd-polluted soils.

This study is dedicated to exploring the application of deep learning technology in soybean single-row seeding devices, aiming to achieve integrated and precise control of the seeding process. By comprehensively analyzing the seeding requirements and designing the corresponding system architecture, this study uses the long short-term memory network (LSTM) model to predict and optimize seeding parameters. Experimental results show that the proposed model can significantly reduce errors in sowing depth and spacing and improve sowing accuracy. In addition, the effectiveness and generalization ability of the model are verified by detailed analysis of the model's performance in the training and testing phases. Despite limitations such as dataset size and diversity, high computational resource requirements, and model interpretability, this study provides strong evidence for the application of deep learning technology in precision agriculture and provides suggestions for the direction of future related research.