Explore the vast range of titles available.

In recent decades, human activities have impaired the structure, function, and diversity of coastal wetland ecosystems, and there is a need for the rational planning of ecological restoration to curb wetland degradation. However, the challenge remains to quickly and accurately identify degraded wetland areas and their degradation levels. In this study, we used remote sensing interpretation data from 1980 to 2020 and the wetland degradation evaluation method based on a landscape directional succession model to quantify the spatial and temporal characteristics of wetland degradation in Jiangsu Province, China. The key findings showed that 3020.67 km2 of wetlands became degraded over the 40 years of this study, accounting for 42.74% of the total area of coastal wetlands, and that the overall degradation was mild. This degradation presented significant spatial differences, with the wetland degradation in Yancheng City observed to be more serious than that in Nantong City. Degradation mainly occurred in Sheyang County, Dafeng District, Dongtai City, and Rudong County, and the spatial distribution pattern of severe and moderate degradation, mild degradation, and non-degradation was observed from land to sea in that order. The degradation of wetlands was observed to have obvious stages, and the degradation of coastal wetlands in the study area from 1980 to 2020 showed a significant increasing trend. The comprehensive score of wetland degradation in 2020 (1.67) was 3.70 times that in 1985 (0.45), and the turning point occurred in 2000. The types of wetland degradation were dominated by the transformation of natural wetlands into construction land (coastal industry), fish farming, and arable land, as well as the invasion of exotic species. Although great efforts have been made in recent years to protect and restore coastal wetlands, the development and utilization of coastal wetland resources should be strictly controlled to achieve the goal of sustainable development in coastal areas.

This study aimed to identify therapeutic targets and signaling pathways of Capilliposide A (LC-A) for diabetic nephropathy (DN) through integrated network pharmacology and experimental validation. Using a DN mouse model induced by high-fat diet (HFD) and streptozotocin (STZ), we investigated the molecular mechanisms of LC-A. The PubChem database provided the two-dimensional structure of LC-A, with subsequent identification of LC-A/DN-associated targets through intersection analysis. Core targets were determined via protein–protein interaction (PPI) network construction, followed by gene ontology enrichment and pathway analyses. Molecular docking simulations evaluated LC-A's binding affinity to DN-related targets. C57BL/6 mice receiving HFD and STZ intraperitoneal injections were treated daily with LC-A or metformin (MET) via oral gavage for 8 weeks. Weekly monitoring included body weight and blood glucose measurements. Post-treatment assessments encompassed serum lipid profiles, renal function biomarkers, inflammatory cytokines, oxidative stress markers, and renal histopathology. Western blotting, immunohistochemistry, and immunofluorescence were performed to analyze inflammatory pathway proteins and apoptosis-related factors. Network pharmacology identified TLR4, NF-κB1, STAT3, EGFR, mTOR, and MAPK as core targets involved in inflammation, oxidative stress, and apoptosis regulation. Pathway enrichment analysis revealed critical inflammatory pathways including IL-17, TNF, and Th17 cell differentiation. Molecular docking confirmed stable binding of LC-A to TLR4, NF-κB1, Bax, and Bcl-2. In vivo experiments demonstrated that LC-A significantly reduced renal injury markers (KI, SCR, BUN), lipid profiles (TG, TC), and oxidative stress (MDA), while enhancing antioxidant enzymes (SOD, GPX, CAT) in DN mice ( P  < 0.05). LC-A effectively suppressed serum inflammatory cytokines (IL-6, TNF-α, IL-1β, MCP-1, iNOS) through NF-κB pathway modulation and apoptosis regulation ( P  < 0.05). Histopathological analysis revealed attenuated renal cortical vacuolization, tubular swelling, glomerular mesangial expansion, sclerosis, and inflammatory infiltration. These findings suggest LC-A as a promising natural nephroprotective agent for DN prevention and treatment.

The 14-3-3 genes are widely present in plants and participate in a wide range of cellular and physiological processes. In the current study, twelve 14-3-3s were identified from potato genome. According to phylogenetic evolutionary analysis, potato 14-3-3s were divided into ϵ and non-ϵ groups. Conserved motif and gene structure analysis displayed a distinct class-specific divergence between the ϵ group and non-ϵ group. Multiple sequence alignments and three-dimensional structure analysis of 14-3-3 proteins indicated all the members contained nine conservative antiparallel α-helices. The majority of 14-3-3s had transcript accumulation in each detected potato tissue, implying their regulatory roles across all stages of potato growth and development. Numerous cis-acting elements related to plant hormones and abiotic stress response were identified in the promoter region of potato 14-3-3s , and the transcription levels of these genes fluctuated to different degrees under exogenous ABA, salt and drought stress, indicating that potato 14-3-3s may be involved in different hormone signaling pathways and abiotic stress responses. In addition, eight potato 14-3-3s were shown to interact with StABI5, which further demonstrated that potato 14-3-3s were involved in the ABA-dependent signaling pathway. This study provides a reference for the identification of the 14-3-3 gene family in other plants, and provides important clues for cloning potential candidates in response to abiotic stresses in potato.

Background Potato ( Solanum tuberosum L.) is one of the world's most important crops, the cultivated potato is frost-sensitive, and low-temperature severely influences potato production. However, the mechanism by which potato responds to low-temperature stress is unclear. In this research, we apply a combination of second-generation sequencing and third-generation sequencing technologies to sequence full-length transcriptomes in low-temperature-sensitive cultivars to identify the important genes and main pathways related to low-temperature resistance. Results In this study, we obtained 41,016 high-quality transcripts, which included 15,189 putative new transcripts. Amongst them, we identified 11,665 open reading frames, 6085 simple sequence repeats out of the potato dataset. We used public available genomic contigs to analyze the gene features, simple sequence repeat, and alternative splicing event of 24,658 non-redundant transcript sequences, predicted the coding sequence and identified the alternative polyadenylation. We performed cluster analysis, GO, and KEGG functional analysis of 4518 genes that were differentially expressed between the different low-temperature treatments. We examined 36 transcription factor families and identified 542 transcription factors in the differentially expressed genes, and 64 transcription factors were found in the AP2 transcription factor family which was the most. We measured the malondialdehyde, soluble sugar, and proline contents and the expression genes changed associated with low temperature resistance in the low-temperature treated leaves. We also tentatively speculate that StLPIN10369.5 and StCDPK16 may play a central coordinating role in the response of potatoes to low temperature stress. Conclusions Overall, this study provided the first large-scale full-length transcriptome sequencing of potato and will facilitate structure–function genetic and comparative genomics studies of this important crop.

This study focuses on the main urban area of Yangzhou City and conducts a quantitative comparative analysis of traffic accessibility during normal weather and extreme precipitation conditions (typhoon) based on GPS trajectories of buses. From both temporal and spatial dimensions, it comprehensively examines the impact of extreme precipitation on bus travel speed, travel time, and the commuting range of residents in the main urban area of Yangzhou City. (1) Through the mining and analysis of multi-source heterogeneous big data (bus GPS trajectory data, bus network data, rainfall remote sensing data, and road network data), it is found that the rainstorm weather greatly affects the average speed and travel time of buses. In addition, when the intensity of heavy rainfall increases (decreases), the average bus speed and travel time exhibit varying degrees of spatio-temporal change. During the morning and evening rush hour commuting period of rainstorm weather, there are obvious differences in the accessibility change in each typical traffic community in the main urban area of Yangzhou city. In total, 90% of the overall accessibility change value is concentrated around −5 min~5 min, and the change range is concentrated around −25~10%. (2) To extract the four primary traffic districts (Lotus Pond, Slender West Lake, Jinghua City, and Wanda Plaza), we collected Points of Interest (POI) data from Amap and Baidu heat map, and a combination analysis of the employment–residence ratio model and proximity methods was employed. The result show that the rainstorm weather superimposed on the morning peak hour has different degrees of impact on the average speed of the above-mentioned traffic zones, with the most obvious impact on the Lotus Pond and the smallest impact on Wanda Plaza. Under the rainstorm weather, the traffic commute in the main urban area of Yangzhou in the morning and evening peak hour is basically normal. The results of this paper can help to quantify the impact of typhoon-rainstorm weather events on traffic commuting in order to provide a scientific basis for the traffic management department to effectively prevent traffic jams, ensure the reliability of the road network, and allow the traffic management department to more effectively manage urban traffic.

Low-temperature is one of the most severe abiotic stresses affecting the potato production as the cultivated potato (Solanum tuberosum) is frost sensitive. MicroRNAs have been identified in response to low-temperature stress in plants. Here, via high throughput sequencing, we described the profiling of low-temperature stress response to miRNA and mRNA in potato. Two small RNA and six mRNA libraries were constructed and sequenced. Exactly 294 known and 211 novel microRNAs were identified, 24 microRNAs had the higher expression quantity in Treatment group (EG) than in Control group (CG), and 80 microRNAs had the lower expression in EG than CG. A total of 3298 differentially expressed genes (DEGs) were detected, with 1629 up-regulated and 1669 down-regulated. The metabolism pathway of phenylpropanoid biosynthesis (ko00940) is the common KEGG pathway in differentially expressed miRNA and mRNA. In this pathway, StuPOD13654 and StuPOD32147 are peroxidase which mainly catalyzes the coniferyl and sinapyl alcohol to form guaiac wood lignin monomer and syringyl lignin monomer, which are controlled by stu-novel-miR28211 and stu-novel-miR43095. These indicated that microRNAs may play essential roles in the low-temperature tolerance of potato.

In recent years, the waste produced as a result of the production and consumption activities of urban residents has led to significant environmental degradation and resource wastage. This paper focuses on the research object of municipal solid waste (MSW) collection and transportation based on the concept of “sustainable development and green economy”. Firstly, this study examines the current state of urban domestic garbage collection and transportation. It analyzes the following challenges and deficiencies of the existing collection and transportation system: (1) the operating efficiency of garbage collection vehicles is low, resulting in a significant accumulation of waste on the roadside and within the community; (2) the vehicle collection and transportation routes are fixed, and there are empty vehicles running; (3) the amount of garbage on a route exceeds the vehicle’s loading capacity, which requires the vehicle to perform a second round of collection and transportation. To enhance the efficiency of urban garbage collection and transportation and minimize the collection and transportation costs, we are investigating the problem of optimizing the path for green vehicles. To comprehensively optimize the fixed cost, variable cost, and carbon emission cost incurred during vehicle operation, a vehicle routing model with time windows is established, taking into account vehicle load constraints. Carbon emission coefficient and carbon tax parameters are introduced into the model and the “fuel-carbon emission” conversion method is used to measure the carbon cost of enterprises. An improved ant colony optimization (ACO) method is proposed: (1) the introduction of a vehicle load factor improves the ant state transfer method; (2) the updated pheromone method is improved, and additional pheromone is added to both the feasible path and the path with the minimum objective function; (3) the max–min ACO algorithm is introduced to address the issue of premature convergence of the algorithm; (4) the embedding of a 2-opt algorithm further prevents the ACO algorithm from falling into the local optimum. Finally, the calculation results based on the example data demonstrate that the algorithm has a significant advantage over the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The total transportation distance determined by this algorithm is shorter than that of the GA and PSO methods, and the total cost of the scheme is 1.66% and 1.89% lower than that determined by GA and PSO, respectively. Compared to the data from the actual case, the number of vehicles required in the operation of this algorithm and model is reduced by three. Additionally, the total cost, fixed cost, and carbon emission cost incurred by the vehicles during operation were reduced by 31.2%, 60%, and 25.3% respectively. The results of this study help the station to collect and distribute waste efficiently, while also achieving the goals of energy saving, consumption reduction, and emission reduction.

Global warming, sea-level rise, and rapid urbanization all increase the risk of compound extreme weather events, presenting challenges for the operation of urban-related infrastructure, including transportation infrastructure. In this context, some questions become important. For example, what are the temporal and spatial distribution and development trends of transportation resilience when considering the impact of multilpe extreme weather events on the urban transportation system? What is the degree of loss of urban transportation resilience (UT resilience) under different extreme event intensities, and how long will it take for the entire system to restore balance? In the future, if extreme weather events become more frequent and intense, what trends will urban transportation resilience show? Considering these problems, the current monitoring methods for transportation resilience under the influence of extreme events are lacking, especially the monitoring of the temporal and spatial dynamic changes of transportation resilience under the influence of compined extreme events. The development of big data mining technology and deep learning methods for spatiotemporal predictions made the construction of spatiotemporal data sets for evaluating and predicting UT resilience-intensity indicators possible. Such data sets reveal the temporal and spatial features and evolution of UT resilience intensity under the influence of compound extreme weather events, as well as the related future change trends. They indicate the key research areas that should be focused on, namely, the transportation resilience under climate warming. This work is especially important in planning efforts to adapt to climate change and rising sea levels and is relevant to policymakers, traffic managers, civil protection managers, and the general public.

Potato (Solanum tuberosum L.) is one of the world's most important crops, the cultivated potato is frost-sensitive, and low-temperature severely influences potato production. However, the mechanism by which potato responds to low-temperature stress is unclear. In this research, we apply a combination of second-generation sequencing and third-generation sequencing technologies to sequence full-length transcriptomes in low-temperature-sensitive cultivars to identify the important genes and main pathways related to low-temperature resistance. In this study, we obtained 41,016 high-quality transcripts, which included 15,189 putative new transcripts. Amongst them, we identified 11,665 open reading frames, 6085 simple sequence repeats out of the potato dataset. We used public available genomic contigs to analyze the gene features, simple sequence repeat, and alternative splicing event of 24,658 non-redundant transcript sequences, predicted the coding sequence and identified the alternative polyadenylation. We performed cluster analysis, GO, and KEGG functional analysis of 4518 genes that were differentially expressed between the different low-temperature treatments. We examined 36 transcription factor families and identified 542 transcription factors in the differentially expressed genes, and 64 transcription factors were found in the AP2 transcription factor family which was the most. We measured the malondialdehyde, soluble sugar, and proline contents and the expression genes changed associated with low temperature resistance in the low-temperature treated leaves. We also tentatively speculate that StLPIN10369.5 and StCDPK16 may play a central coordinating role in the response of potatoes to low temperature stress. Overall, this study provided the first large-scale full-length transcriptome sequencing of potato and will facilitate structure-function genetic and comparative genomics studies of this important crop.