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During the dormant period of peach trees in winter, flower buds exhibit weak cold resistance and are susceptible to freezing at low temperatures. Understanding the physiological and molecular mechanisms underlying the response of local peach buds to low-temperature adversity is crucial for ensuring normal flowering, fruiting, and yield. In this study, the experimental materials included the conventional cultivar ‘Xia cui’ (XC) and the cold-resistant local resources ‘Ding jiaba’ (DJB) peach buds. The antioxidant enzyme activity, levels of malondialdehyde (MDA), proline (Pro), and hydrogen peroxide content (H2O2) were determined in peach buds at different dormancy periods. Transcriptome sequencing was performed at three dormancy stages: the dormancy entry stage (FD), deep dormancy release stage (MD), and dormancy release stage (RD). Additionally, transcriptome sequencing was conducted to analyze gene expression profiles during these stages. Our findings revealed that compared with XC cultivars, DJB peach buds exhibited decreased MDA and H2O2 contents but increased superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities as well as Pro content during the dormancy period. These findings suggest that cold-resistant cultivars possess significantly stronger antioxidant capacity than conventional cultivars under low-temperature stress. A total of 10,168 differential genes were annotated through transcriptome sequencing. Among them, 4975 were up-regulated while 5193 were down-regulated. The differentially expressed genes associated with low-temperature response in peach buds are primarily enriched in plant hormone signal transduction pathway and phenylpropane synthesis pathway. Key differentially expressed genes related to cold resistance include ARF2, GH3, and SAPK2, and differentially expressed transcription factors mainly belong to the AP2/ERF-ERF, bHLH, and C2H2 families. This study provides a theoretical foundation for understanding the key genes involved.

Introduction Hangzhou Public Places Smoking Control Regulations (2019) have been gradually adopted, which explicitly stipulate that smoking is strictly prohibited in the outdoor areas of educational and healthcare institutions for minors. However, there are few studies reporting the exposure to secondhand smoke (SHS) in outdoor public places for minors in the urban area of Hangzhou City. Methods We aimed to assess the exposure to SHS in public spaces frequented by minors using on-site observations and questionnaires. In this cross-sectional study, the area was divided into core and non-core areas based on the spatial distribution and development process of the city. The core areas included the West Lake commercial district, extending to the Qiantang River, while non-core areas were located beyond this radius. Using stratified random sampling, 30 public places in each area were selected as observation sites. On-site observations measured SHS exposure and smoking control, and questionnaires were administered to 6 individuals at each site. The results were compared between the two investigation methods. Results Among the 57 valid observation points, 24.6% (14/57) did not display any no-smoking signs. Outdoor SHS exposure rate from on-site observation P1 (observing someone smoking or smelling tobacco smoke), on-site observation P2 (observing someone smoking or smelling tobacco smoke or seeing cigarette butts) and questionnaire survey P3, were 59.6% (95% CI: 45.7–72.2), 91.1% (95% CI: 79.7–96.7) and 41.0% (95% CI: 35.5–46.7), respectively. Conclusions The outdoor SHS exposure in areas frequented by minors in the urban district of Hangzhou City remains high, coupled with a lack of awareness of SHS risks among underage individuals. Therefore, controlling outdoor SHS exposure in these key areas is a critical public health issue in Hangzhou, requiring further tobacco control efforts. On-site observation is an important and supplementary research method to investigate outdoor SHS exposure, especially to describe the SHS exposure of focus areas.

Ammonia-N stress is a significant environmental factor that adversely affects the health and productivity of aquaculture species. This study investigates the effects of ammonia-N stress on the shrimp Penaeus monodon through a combination of biochemical, histological, transcriptomic, and metabolomic analyses. Shrimp were exposed to ammonia-N stress for 12 and 96 hours, and key markers of oxidative stress, nitrogen metabolism, immune response, and overall health were assessed. The results showed that prolonged ammonia-N exposure causes significant hepatopancreatic damage, including atrophy and deformation. Transcriptomic analysis revealed significant changes in gene expression related to apoptosis, immune response, and key metabolic pathways, with particular emphasis on the disruption of innate immune signaling and defense mechanisms. Metabolomic analysis identified disruptions in nucleotide turnover, antioxidant defenses, and fundamental metabolic processes. These findings suggest that ammonia-N stress induces a multifaceted stress response in shrimp, involving oxidative stress, immune activation, and metabolic disturbances. Understanding these immune-related and metabolic mechanisms provides valuable insights into the molecular responses of crustaceans to environmental stress, laying the foundation for assessing the ecological risk of ammonia-N and identifying potential immunological biomarkers for monitoring and mitigating its adverse effects in aquaculture systems.

The complexity of the relationship between climate variables including temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI) arises from the complex interaction between these factors. NDVI is a widely used index to analyze the characteristics of vegetation cover, including its dynamic patterns. It is a crucial parameter for examining vegetation stability, which is vital for ensuring sustainable food production. This study aims to develop a global-scale NDVI forecasting model based on deep learning algorithms that consider climate variables. The model was trained using three years of global data, including NDVI, temperature, precipitation, and soil moisture. The results of this study demonstrate the effectiveness of the deep learning model for forecasting NDVI. The model accurately predicted NDVI values, as evidenced by the high coefficient of determination (R2) values and the negligible average disparity between predicted and observed NDVI values. The study conducted an analysis of the model’s performance both temporally and spatially. The performance of the model was examined for each month and the overall performance of the model for months presented as the model’s temporal performance overall. Additionally, the model’s performance was analyzed at different latitudes, categorized as mid-latitude and low-latitude performance. The temporal analysis of the model demonstrated an overall R2 value of 0.85 and an RMSE of 0.096. Meanwhile, the spatial analysis of the model showed that it performed well at low-latitude, with an R2 value of 0.84 and an RMSE of 0.098, and at mid-latitude, with an R2 value of 0.82 and an RMSE of 0.095. This suggests that the model’s forecasted NDVI values showed a small average difference compared to actual values in both temporal and spatial analyses. Overall, the study supports the idea that deep learning models can effectively forecast NDVI using climate variables across various geographical zones and throughout different months of the year.

The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation.

The study aimed to evaluate the cold tolerance of various peach cultivars under diverse low-temperature conditions (−5, −10, −15, −20, −25, −30, and −35 °C). A comprehensive assessment of their responses to cold was performed by integrating LT50 values with membership functions and evaluating local adaptability among the selected peach cultivars. The findings revealed that as temperatures dropped, electrical conductivity (REC), malondialdehyde (MDA), and hydrogen peroxide (H2O2) levels initially rose, then fell, and subsequently increased once more. Soluble sugar (SS) and soluble protein (SP) concentrations peaked at −25 °C and showed a significant negative correlation with semi-lethal temperature (LT50). The expression of free proline varied among different samples. Combining physiological analyses with field adaptation correlation assessments, it was found that ‘Ziyan Ruiyang’ exhibited a relatively low LT50 value of −29.67 °C and a membership function degree of 0.76, suggesting robust field adaptation abilities. At the same time, ‘Ganlu Shumi’ demonstrated stable trends in H2O2 and MDA levels, maintaining them at relatively low concentrations; it also had the lowest LT50 value, the highest membership function score, and the highest survival rate. Consequently, this cultivar could be a valuable resource for enhancing cold resistance under low-temperature stress. In summary, by correlating LT50 values with membership functions and observing local adaptability in these peach cultivars, we have established reliable data that can serve as a basis for identifying potential cross-breeding parents to develop new cold-resistant varieties.

Fenneropenaeus merguiensis is one of the largest species of penaeid shrimp. It has a wide distribution of germplasm resources in the South China Sea and the southeastern coastal regions of China, yet its germplasm characteristics remain insufficiently understood. Therefore, we conducted analyses of basic nutritional components, amino acids, fatty acids, antioxidant indices, and genetic diversity in three F. merguiensis populations (FmRP, FmSZ, FmSY). The results showed a significant difference in ash content, with FmSZ having the highest ash content (1.77 g/100 g) (p < 0.05). A total of 17 amino acids were detected, and FmSZ exhibited higher concentrations of most amino acids, especially essential amino acids (p < 0.05). The analysis of fatty acid composition revealed significant differences between the populations, with FmSY potentially having a nutritional advantage overall (p < 0.05). In terms of antioxidant capacity and genetic diversity, there were no significant differences in total antioxidant capacity (T-AOC), catalase (CAT), or superoxide dismutase (SOD) among the populations (p > 0.05). However, genetic analysis indicated that FmSZ had the lowest inbreeding coefficient and relatively higher genetic diversity. In conclusion, among the three F. merguiensis populations, FmSZ has relatively higher nutritional quality and genetic diversity, and F. merguiensis is a high-quality aquatic product worth promoting. The analysis and characterization of various aspects of F. merguiensis from the three locations provide important information and data support for germplasm resources and genetic breeding efforts.

Peach bud differentiation is commercially significant for fruit production. Bud differentiation in peach production is closely linked to chilling requirements. This study investigates the mechanisms of flower bud differentiation in peach varieties L12 and N1 under varying chilling requirements by comparing paraffin sections, hormone content changes, and transcriptomes during four chilling hours and the same physiological stage. At 400 chilling hours, significant changes in flower bud differentiation were observed. During this period, the hormone levels of auxin and gibberellin reached their peak, while abscisic acid levels were at their lowest. This finding indicates that 400 chilling hours has a significant regulatory effect on flower bud development. Transcriptome analysis revealed that 4719 differentially expressed genes were identified in the flower bud differentiation of L12-4 and N1-4, and 2717 differentially expressed genes of L12-8 and N1-2, many of which are involved in IAA, GA, and ABA signal transduction pathways. In N1, the differentially expressed genes AUX/IAA, SAUR, and DELLA were significantly higher than in L12, whereas genes associated with the ABA synthesis pathway, such as PYL2, PYL8, and SRK2A, remained at the lowest level. This study provides a crucial molecular basis for understanding the regulation of plant hormones and their effects on flower bud development under varying chilling hours.

Detecting binary changes in co-registered bitemporal hyperspectral images (HSIs) using deep learning methods is challenging due to the high dimensionality of spectral data and significant variations between images. To address this challenge, previous approaches often used dimensionality reduction methods separately from the change detection network, leading to less accurate results. In this study, we propose an end-to-end fully connected adversarial network (EFC-AdvNet) for binary change detection, which efficiently reduces the dimensionality of bitemporal HSIs and simultaneously detects changes between them. This is accomplished by extracting critical spectral features at the pixel level through a self-spectral reconstruction (SSR) module working in conjunction with an adversarial change detection (Adv-CD) module to effectively delineate changes between bitemporal HSIs. The SSR module employs a fully connected autoencoder for hyperspectral dimensionality reduction and spectral feature extraction. By integrating the encoder segment of the SSR module with the change detection network of the Adv-CD module, we create a generator that directly produces highly accurate change maps. This joint learning approach enhances both feature extraction and change detection capabilities. The proposed network is trained using a comprehensive loss function derived from the concurrent learning of the SSR and Adv-CD modules, establishing EFC-AdvNet as a robust end-to-end network for hyperspectral binary change detection. Experimental evaluations of EFC-AdvNet on three public hyperspectral datasets demonstrate that joint learning between the SSR and Adv-CD modules improves the overall accuracy (OA) by 5.44%, 10.43%, and 7.52% for the Farmland, Hermiston, and River datasets, respectively, compared with the separate learning approach.