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Building segmentation of high-resolution remote sensing images using deep learning effectively reduces labor costs, but still faces the key challenges of effectively modeling cross-scale contextual relationships and preserving fine spatial details. Current Transformer-based approaches demonstrate superior long-range dependency modeling, but still suffer from the problem of progressive information loss during hierarchical feature encoding. Therefore, this study proposed a new semantic segmentation network named SegTDformer to extract buildings in remote sensing images. We designed a Dynamic Atrous Attention (DAA) fusion module that integrated multi-scale features from Transformer, constructing an information exchange between global information and local representational information. Among them, we introduced the Shift Operation module and the Self-Attention module, which adopt a dual-branch structure to respectively capture local spatial dependencies and global correlations, and perform weight coupling to achieve highly complementary contextual information fusion. Furthermore, we fused triplet attention with depth-wise separable convolutions, reducing computational requirements and mitigating potential overfitting scenarios. We benchmarked the model on three different datasets, including Massachusetts, INRIA, and WHU, and the results show that the model consistently outperforms existing models. Notably, on the Massachusetts dataset, the SegTDformer model achieved benchmark in mIoU, F1-score, and Overall Accuracy of 75.47%, 84.7%, and 94.61%, respectively, superseding other deep learning models. The proposed SegTDformer model exhibits enhanced precision in the extraction of urban structures from intricate environments and manifests a diminished rate of both omission and internal misclassification errors, particularly within the context of diminutive and expansive edifices.

As the carrier of small-piece logistics, urban joint distribution has frequent and complex operations, lacks systematic management and planning, and has large optimization space. Enterprises should bear the social responsibility of reducing carbon emissions in the logistics industry. Using Company M as an example, this article examines the urban joint distribution problem from a low-carbon point of view to reduce carbon emissions. By deriving the carbon emission formula, we obtain the crucial component for resolving the issue—the kilogram kilometers of distribution operation—and develop a mathematical model to minimize carbon emissions. The strategy of delayed delivery is used in distribution optimization to lower the no-load rate, and a scoring mechanism is presented to assist in determining the distribution time and location. In terms of route optimization, the problems of traditional ant colony algorithms that cannot consider distribution energy consumption, cannot deal with load limitations, and have slow iteration speeds are solved by using the introduction of minimum energy consumption, employing k-means clustering, and setting up elite ants, respectively. Finally, numerical simulations are implemented using C and Python, and the proposed optimization scheme demonstrates a 33.5% reduction in total carbon emissions compared to Company M’s original distribution model. It has been proven that the method proposed in this article has a certain effect on reducing carbon emissions from urban joint distribution.

Environment-friendly sound measures with high algal growth inhibition efficiency are required to control and eliminate CyanoHABs. This study examined the effects of protopine on growth, gene expression, and antioxidant system of the M. aeruginosa TY001 and explored possible damage mechanism. The results revealed that higher concentrations of protopine seriously inhibited the growth of M . aeruginosa . Quantitative real-time PCR analysis showed downregulated expression of stress response genes ( prx and fabZ ), and DNA repair gene ( recA ) on days 3 and 5. The activities of antioxidant enzymes were also decreased markedly, including superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD). Additionally, protopine stress can significantly increase the malondialdehyde (MDA) level in cells. In conclusion, oxidative damage and DNA damage are the main mechanisms of protopine inhibition on M. aeruginosa TY001. Our studies provide evidence that alkaloid compounds such as protopine may have a potential use value as components of aquatic management strategies.

Cultured human embryonic stem cells often acquire chromosomal abnormalities that could be detrimental in certain applications. Närvä et al . report the highest-resolution genetic analysis of these cells to date and identify genes whose expression is altered by culture-induced genetic changes. Prolonged culture of human embryonic stem cells (hESCs) can lead to adaptation and the acquisition of chromosomal abnormalities, underscoring the need for rigorous genetic analysis of these cells. Here we report the highest-resolution study of hESCs to date using an Affymetrix SNP 6.0 array containing 906,600 probes for single nucleotide polymorphisms (SNPs) and 946,000 probes for copy number variations (CNVs). Analysis of 17 different hESC lines maintained in different laboratories identified 843 CNVs of 50 kb–3 Mb in size. We identified, on average, 24% of the loss of heterozygosity (LOH) sites and 66% of the CNVs changed in culture between early and late passages of the same lines. Thirty percent of the genes detected within CNV sites had altered expression compared to samples with normal copy number states, of which >44% were functionally linked to cancer. Furthermore, LOH of the q arm of chromosome 16, which has not been observed previously in hESCs, was detected.

T helper 17 (Th17) cells protect against fungal and bacterial infections and are implicated in autoimmunity. Several long intergenic noncoding RNAs (lincRNA) are induced during Th17 differentiation, however, their contribution to Th17 differentiation is poorly understood. We aimed to characterize the function of the lincRNA Myocardial Infarction Associated Transcript (MIAT) during early human Th17 cell differentiation. We found MIAT to be upregulated early after induction of human Th17 cell differentiation along with an increase in the chromatin accessibility at the gene locus. STAT3, a key regulator of Th17 differentiation, directly bound to the MIAT promoter and induced its expression during the early stages of Th17 cell differentiation. MIAT resides in the nucleus and regulates the expression of several key Th17 genes, including IL17A, IL17F, CCR6 and CXCL13, possibly by altering the chromatin accessibility of key loci, including IL17A locus. Further, MIAT regulates the expression of protein kinase C alpha (PKCα), an upstream regulator of IL17A. A reanalysis of published single-cell RNA-seq data showed that MIAT was expressed in T cells from the synovium of RA patients. Our results demonstrate that MIAT contributes to human Th17 differentiation by upregulating several genes implicated in Th17 differentiation. High MIAT expression in T cells of RA patient synovia suggests a possible role of MIAT in Th17 mediated autoimmune pathologies.

Mosquitoes are one of the most important disease vectors from a medical viewpoint in that they transmit several diseases such as malaria, filariasis, yellow and Dengue fever. Mosquito vector control and personal protection from mosquito bites are currently the most efficient ways to prevent these diseases. Several synthetic repellents such as DEET, ethyl butylacetylaminopropionate (IR3535) and 1-(1-methylpropoxycarbonyl)-2-(2-hydroxyethyl)piperidine) (Picaridin), have been widely used to prevent humans from receiving mosquito bites. However, the use of synthetic repellents has raised several environment and health concerns. Therefore, essential oils (EOs) as natural alternatives receive our attention. In order to discover highly effective mosquito repellents from natural sources, the repellent activity of 60 commercial EOs against Ae. albopictus was screened in this study. Eight EOs including cinnamon, marjoram, lemongrass, bay, chamomile, jasmine, peppermint2, and thyme, showed a suitable repellent rate (>40%) at the tested dose of 10 μg/cm2. Then, their main constituents were analyzed by GC-MS, and the active constituents were identified. The most active compounds including cinnamaldehyde, citral and terpinen-4-ol, exhibited an 82%, 65% and 60% repellent rate, respectively. Moreover, the nanoemulsions of the three active compounds were prepared and characterized. In the arm-in-cage assay, the protection times of the nanoemulsions of cinnamaldehyde and citral were significantly extended compared with their normal solutions. This study provides several lead compounds to develop new mosquito repellents, and it suggests that nanoemulsification is an effective method for improving the duration of the activity of natural repellents.

The potential impact of nanoparticles on the environment and on human health has attracted considerable interest worldwide. The amount of transcriptomics data, in which tissues and cell lines are exposed to nanoparticles, increases year by year. In addition to the importance of the original findings, this data can have value in broader context when combined with other previously acquired and published results. In order to facilitate the efficient usage of the data, we have developed the NanoMiner web resource (http://nanominer.cs.tut.fi/), which contains 404 human transcriptome samples exposed to various types of nanoparticles. All the samples in NanoMiner have been annotated, preprocessed and normalized using standard methods that ensure the quality of the data analyses and enable the users to utilize the database systematically across the different experimental setups and platforms. With NanoMiner it is possible to 1) search and plot the expression profiles of one or several genes of interest, 2) cluster the samples within the datasets, 3) find differentially expressed genes in various nanoparticle studies, 4) detect the nanoparticles causing differential expression of selected genes, 5) analyze enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms for the detected genes and 6) search the expression values and differential expressions of the genes belonging to a specific KEGG pathway or Gene Ontology. In sum, NanoMiner database is a valuable collection of microarray data which can be also used as a data repository for future analyses.

Insulin resistance is associated adiposity, but the mechanisms are not fully understood. In this study, we aimed to identify early metabolic alterations associated with insulin resistance in normoglycemic women with varying degree of adiposity. One-hundred and ten young and middle-aged women were divided into low and high IR groups based on their median HOMA-IR (0.9 ± 0.4 vs. 2.8 ± 1.2). Body composition was assessed using DXA, skeletal muscle and liver fat by proton magnetic resonance spectroscopy, serum metabolites by nuclear magnetic resonance spectroscopy and adipose tissue and skeletal muscle gene expression by microarrays. High HOMA-IR subjects had higher serum branched-chain amino acid concentrations (BCAA) (p < 0.05 for both). Gene expression analysis of subcutaneous adipose tissue revealed significant down-regulation of genes related to BCAA catabolism and mitochondrial energy metabolism and up-regulation of several inflammation-related pathways in high HOMA-IR subjects (p < 0.05 for all), but no differentially expressed genes in skeletal muscle were found. In conclusion, in normoglycemic women insulin resistance was associated with increased serum BCAA concentrations, down-regulation of mitochondrial energy metabolism and increased expression of inflammation-related genes in the adipose tissue.

Aedes albopictus (Skuse) is a vector of several arboviruses, such as dengue, chikungunya, West Nile, and Zika viruses. At present, the use of synthetic insecticides is the main vector control strategy. However, the widespread and long-term use of insecticides has aroused several problems, including insecticide resistance, environmental pollution, and non-target species effects, thereby encouraging researchers to search for new alternatives derived from natural products. In recent decades, essential oils (EOs) as natural alternatives to control mosquitoes have received increasing attention. In the initial larvicidal activity screen, two Rutaceae plants (Citrus aurantium and Citrus paradisi) EOs were selected and evaluated for killing Ae. albopictus larvae. The LC50 values of C. aurantium and C. paradisi EOs against Ae. albopictus were 91.7 and 100.9 ppm, respectively. The main components of C. aurantium EO include diethyl o-phthalate (37.32%), limonene (10.04%), and methyl dihydrojasmonate (6.48%). The main components of C. paradisi EO include limonene (60.51%), diethyl o-phthalate (11.75%), linalool (7.90%), and styralyl acetate (6.28%). Among these main components of the two EOs, limonene showed potent larvicidal activity, with the LC50 value of 39.7 ppm. The nanoemulsions of limonene were prepared and characterized. The duration of larvicidal activity was greater in the limonene nanoemulsions than when limonene was applied in solvent. This study demonstrates that EOs of plants in family Rutaceae are a potential resource to develop new larvicides, and nanoemulsification is an effective method for improving the physicochemical properties and efficacy of natural products as larvicides.

Seasonal influenza causes mild to severe respiratory infections and significant morbidity, especially in older adults. Transcriptomic analysis in populations across multiple flu seasons has provided insights into the molecular determinants of vaccine response. Still, the metabolic changes that underlie the immune response to influenza vaccination remain poorly characterized. We performed untargeted metabolomics to analyze plasma metabolites in a cohort of younger and older subjects before and after influenza vaccination to identify vaccine‐induced molecular signatures. Metabolomic and transcriptomic data were combined to define networks of gene and metabolic signatures indicative of high and low antibody response in these individuals. We observed age‐related differences in metabolic baselines and signatures of antibody response to influenza vaccination and the abundance of α‐linolenic and linoleic acids, sterol esters, fatty‐acylcarnitines, and triacylglycerol metabolism. We identified a metabolomic signature associated with age‐dependent vaccine response, finding increased tryptophan and decreased polyunsaturated fatty acids (PUFAs) in young high responders (HRs), while fatty acid synthesis and cholesteryl esters accumulated in older HRs. Integrated metabolomic and transcriptomic analysis shows that depletion of PUFAs, which are building blocks for prostaglandins and other lipid immunomodulators, in young HR subjects at Day 28 is related to a robust immune response to influenza vaccination. Increased glycerophospholipid levels were associated with an inflammatory response in older HRs to flu vaccination. This multi‐omics approach uncovered age‐related molecular markers associated with influenza vaccine response and provides insight into vaccine‐induced metabolic responses that may help guide development of more effective influenza vaccines. Seasonal influenza causes respiratory infections and significant morbidity, especially in older adults, and response to flu vaccine remains particularly poor among this population. We performed untargeted plasma metabolomics and transcriptomics in a cohort of younger and older subjects before and after influenza vaccination. We identified age‐ and vaccine‐related molecular markers associated with vaccine response, insights which may guide development of improved influenza vaccines.