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High spatial resolution remote sensing images contain rich information, it is therefore very important to study their semantic segmentation. Traditional machine learning methods appear low accuracy and efficiency when used for segmenting high-resolution remote sensing images. In recent years, the deep learning method has developed rapidly and has become the mainstream method of image semantic segmentation. Some scholars have introduced SegNet, Deeplabv3+, U-Net and other neural networks into remote sensing image semantic segmentation, but these networks have only limited effect in remote sensing image semantic segmentation. This paper improves the U-Net network for semantic segmentation of remote sensing images. Firstly, an improved convolutional attention module channel interaction and spatial group attention module (CISGAM) is embedded in the feature extraction stage of the U-Net network, so that the network can obtain more effective features; secondly, a residual module is used in the decoding layer to replace the ordinary convolutional layer to avoid the degradation of the model. In addition, we use an attention pyramid pooling module (APPM) with CISGAM to connect the encoder and decoder of U-Net to enhance the network's extraction of multi-scale features. Finally, experiments are carried out on the UC Merced dataset with 0.3m resolution and the GID dataset with 1m resolution. Compared with the original networks such as U-Net and Deeplabv3+, the mean intersection over union （MIoU) of our method on the UCM dataset has increased by 14.56% and 8.72%, and the mean pixel accuracy （MPA) has increased by 12.71% and 8.24%, respectively. In the classification results on the GID dataset, the classification accuracy of waters, buildings and other objects has also been greatly improved. Compared with the original CBAM and PPM, the CISGAM and APPM also achieve certain performance improvement. The experimental results show that the feasibility and robustness of the model is stronger than traditional networks, and it can improve the accuracy of semantic segmentation of high-resolution remote sensing images through stronger feature extraction capabilities, hence providing a new approach for intelligent interpretation of high-resolution remote sensing images.(高空间分辨率（高分）遥感影像中存在海量信息，因此对高分影像的语义分割研究十分重要。传统机器学习方法的语义分割精度和效率均不高，近年来，深度学习方法迅速发展，逐渐成为影像语义分割领域的常用方法，已有研究将SegNet、Deeplabv3+、U-Net等神经网络引入遥感影像语义分割，但效果有限。考虑高分影像的特性，对用于遥感影像语义分割的U-Net网络进行了改进。首先，在U-Net网络特征提取过程中使用通道交互空间组注意力模块（channel interaction and spatial group attention module，CISGAM），使得网络能够获取更多有效特征。其次，在编码过程中将普通卷积层变换为残差模块，并在U-Net的编码器和解码器之间用加入了CISGAM的注意力金字塔池化模块（attention pyramid pooling module，APPM）连接，以加强网络对多尺度特征的提取。最后，在0.3 m分辨率的UC Merced数据集和1 m分辨率的GID数据集上进行实验，与U-Net、Deeplabv3+等原始网络相比，在UC Merced数据集上的平均交并比（mean intersection over union，MIoU）分别提升了14.56%和8.72%，平均像素准确率（mean pixel accuracy，MPA）分别提升了12.71%和8.24%。在GID数据集的分割结果中，水体、建筑物等地物的综合分割精度大幅提升，在平均分割精度上，CISGAM和APPM较常用的CBAM和PPM有一定提升。实验结果表明，加入CISGAM和APPM的网络可行性与鲁棒性均较传统网络强，其较强的特征提取能力有利于提升高分辨率遥感影像语义分割的精度，为高分辨率遥感影像智能解译提供新方案。)

BackgroundHuman Cub and Sushi Multiple Domains 1 (CSMD1) is a novel candidate tumor-suppressor gene that codes for multiple domains, including complement regulatory and adhesion proteins, and has recently been shown to have alterations in multiple cancers. We investigated CSMD1 in esophageal squamous cell carcinoma (ESCC) by performing an integrated analysis on somatic copy number alterations (CNAs), including copy-number gain or loss, allelic imbalance (AI), loss of heterozygosity (LOH), and the expressions of mRNA and its target miRNAs on specimens from the same patients with ESCC.Results(i) Two-thirds of ESCC patients had all three types of alterations studied-somatic DNA alterations in 70%, and abnormal expressions of CSMD1 RNA in 69% and in target miRNAs in 66%; patterns among these alterations were complex. (ii) In total, 97% of 888 CSMD1 SNPs studied showed somatic DNA alterations, with most located near exons 4-11, 24-25, 39-40, 55-56, and 69-70. (iii) In total, 68% of SNPs with a CNA were correlated with expression of CSMD1. (iv) A total of 33 correlations between non-coding SNPs and expression of CSMD1 target miRs were found.ConclusionsOur results indicate that the CSMD1 gene may play a role in ESCC through complex patterns of DNA alterations and RNA and miRNA expressions. Alterations in some somatic SNPs in non-coding regions of CSMD1 appear to influence expression of this gene and its target miRNAs.

Achieving full‐color emission with just two emitters presents a significant challenge. Two N‐heterocyclic carbene metallacycles (NHC‐M; M ═ Ag, Au) featuring a tetraphenylethene core, combining covalent and coordination bonds are synthesized to restrict rotation within the NHC‐M‐BC (BC = bicyclic) metallacycles and greatly enhanced their quantum yields. The enhancement is accomplished by adjusting the CH3CN/H2O solvent mixture, allowing emission tuning from blue to green for NHC‐Ag‐BC. Further diversification of the emission spectrum, including access to high‐quality white light (CIE coordinates 0.33, 0.34), is facilitated through the addition of sulforhodamine B via fluorescence resonance energy transfer (FRET). The compatibility of NHC‐Ag‐BC with agarose gel extends its applicability to UV‐LEDs chromic coatings, as well as information encryption and anti‐counterfeiting materials. The results underscore the viability of dual‐fluorophore systems for achieving full‐color emission and highlight the potential for developing versatile, multi‐colored functional materials. [ABSTRACT FROM AUTHOR]

The obscure puffer (Takifugu obscurus) is a popular cultured species and accounts for around 50% of the total pufferfish production in China. A hybrid puffer was generated by crossing a female obscure puffer with a male tiger puffer (T. rubripes). Its growth model has not been developed and the genetic basis underlying its growth superiority has not yet been fully investigated. In this study, the growth model and morphological traits of the hybrid puffer were explored. The results indicated that the hybrid puffer exhibited a significant growth advantage compared to the obscure puffer. There were also significant differences in their morphological traits. We conducted genotyping-by-sequencing (GBS) on hybrid and obscure puffer groups, identifying 215,288 high-quality single nucleotide polymorphisms (SNPs) on 22 chromosomes. Subsequently, a total of 13 growth-related selection regions were identified via a combination of selection signatures and a genome-wide association study (GWAS); these regions were mainly located on chromosomes 10 and 22. Ultimately, the screened regions contained 13 growth-related genes, including itgav, ighv3-43, ighm, atp6v1b2, pld1, xmrk, inhba, dsp, dsg2, and dsc2, which regulate growth through a variety of pathways. Taken together, the growth models and candidate genes used in this study will aid our understanding of production characteristics and the genetic basis of growth rates. The hybrid will also be of great significance for the genome-assisted breeding of pufferfish in the future. [ABSTRACT FROM AUTHOR]

Helicobacter pylori (Hp) is the primary cause of gastric cancer but we know little of its relative abundance and other microbes in the stomach, especially at the time of gastric cancer diagnosis. Here we characterized the taxonomic and derived functional profiles of gastric microbiota in two different sets of gastric cancer patients, and compared them with microbial profiles in other body sites. Paired non-malignant and tumor tissues were sampled from 160 gastric cancer patients with 80 from China and 80 from Mexico. The 16S rRNA gene V3-V4 region was sequenced using MiSeq platform for taxonomic profiles. PICRUSt was used to predict functional profiles. Human Microbiome Project was used for comparison. We showed that Hp is the most abundant member of gastric microbiota in both Chinese and Mexican samples (51 and 24%, respectively), followed by oral-associated bacteria. Taxonomic (phylum-level) profiles of stomach microbiota resembled oral microbiota, especially when the Helicobacter reads were removed. The functional profiles of stomach microbiota, however, were distinct from those found in other body sites and had higher inter-subject dissimilarity. Gastric microbiota composition did not differ by Hp colonization status or stomach anatomic sites, but did differ between paired non-malignant and tumor tissues in either Chinese or Mexican samples. Our study showed that Hp is the dominant member of the non-malignant gastric tissue microbiota in many gastric cancer patients. Our results provide insights on the gastric microbiota composition and function in gastric cancer patients, which may have important clinical implications.

With the daily use of liquid cargoes such as crude oil and their derivatives, the global transportation of liquid cargoes has developed rapidly. Liquid cargoes are mainly transported via tankers and pipelines. In the liquid terminal, the handling operations and internal transportation operations are conducted using oil transfer arms and pipelines, and the pipeline path of the cargo is selected using valves. The number of times a valve opens and closes and the length of pipeline paths are the main factors that affect handling time and cost. In addition, different types of valves have different operating costs and levels of operating energy consumption. At this stage, most of the valve selection work is still manually completed, which consumes a lot of time and generates high labor costs, and the actual operation efficiency is low. In this paper, the cargo unloading pipeline path is the main research object, the problem of oil transfer arms–valves–pipeline (PAVP) is proposed, and a dual-objective model is established, accounting for total time in port and the unloading cost of the vessel. An NSGA-II-Dijkstra hybrid algorithm is employed to solve the PAVP, and the improved algorithm (INIIDA) is designed to improve the solution speed via an adaptive dynamic probability based on the Pareto level and heaps in the shortest path. The results show that the INIIDA could better address the PAVP than the NSGA-II-Dijkstra hybrid algorithm. Innovative fusion algorithms are employed to improve the efficiency of port operations. [ABSTRACT FROM AUTHOR]