Identification and validation of critical genes with prognostic value in gastric cancer

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality rates. Molecular targeted therapies, including those targeting human epidermal factor receptor 2 (HER2), have proven to be effective in clinical treatment. However, better identification and description of tumor-promoting genes in GC is still necessary for antitumor therapy.Methods: Gene expression and clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Last absolute shrinkage and selection operator (LASSO) Cox regression were applied to build a prognostic model, the Prognosis Score. Functional enrichment and single-sample gene set enrichment analysis (ssGSEA) were used to explore potential mechanisms. Western blotting, RNA interference, cell migration, and wound healing assays were used to detect the expression and function of myosin light chain 9 (MYL9) in GC.Results: A four-gene prognostic model was constructed and GC patients from TCGA and meta-GEO cohorts were stratified into high-prognosis score groups or low-prognosis score groups. GC patients in the high-prognosis score group had significantly poorer overall survival (OS) than those in the low-prognosis score groups. The GC prognostic model was formulated as PrognosisScore = (0.06 × expression of BGN) - (0.008 × expression of ATP4A) + (0.12 × expression of MYL9) - (0.01 × expression of ALDH3A1). The prognosis score was identified as an independent predictor of OS. High expression of MYL9, the highest weighted gene in the prognosis score, was correlated with worse clinical outcomes. Functional analysis revealed that MYL9 is mainly associated with the biological function of epithelial-mesenchymal transition (EMT). Knockdown of MYL9 expression inhibits migration of GC cells in vitro.Conclusion: We found that PrognosisScore is potential reliable prognostic marker and verified that MYL9 promotes the migration and metastasis of GC cells

BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation

We present a large-scale video subtitle translation dataset, BigVideo, to facilitate the study of multi-modality machine translation. Compared with the widely used How2 and VaTeX datasets, BigVideo is more than 10 times larger, consisting of 4.5 million sentence pairs and 9,981 hours of videos. We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation. To better model the common semantics shared across texts and videos, we introduce a contrastive learning method in the cross-modal encoder. Extensive experiments on the BigVideo show that: a) Visual information consistently improves the NMT model in terms of BLEU, BLEURT, and COMET on both Ambiguous and Unambiguous test sets. b) Visual information helps disambiguation, compared to the strong text baseline on terminology-targeted scores and human evaluation. Dataset and our implementations are available at https://github.com/DeepLearnXMU/BigVideo-VMT.Comment: Accepted to ACL 2023 Finding

Diversity in gut bacterial community of school-age children in Asia

10.1038/srep08397Scientific Reports

Clearing Model for Day-ahead Market Considering Hydraulic Coupling Relationship of Multi-operator Cascade Hydropower Stations

There is a hydraulic coupling relationship between the upstream and downstream of cascade hydropower stations, and they usually belong to different stakeholders. Traditional clearing mechanism may lead to the mismatch between the bid-winning power and the actual power generated by the downstream power stations, and the low-cost downstream power stations may lose some chance to generate, resulting in damage to social welfare. This paper analyzes the problems faced by multi-operator cascade hydropower stations participating in the day-ahead market. By deducing the generation coupling relationship between upstream and downstream cascade hydropower stations, it is found that the output of downstream hydropower stations can be divided into four parts: fixed part, adjustable part, the part coupled with the output of the upstream power station, the part coupled with the output of the upstream power station and the adjustable output. At last, day-ahead market clearing mechanism and settlement mechanism for downstream power stations to participate in bidding are proposed

Arsenic Occurrence and Cycling in the Aquatic Environment: A Comparison between Freshwater and Seawater

Owing to the toxicity and adverse effects of arsenic on human health, its levels in aquatic environments are among the most serious threats to humans globally. To improve our understanding of its occurrence and cycling in aquatic environments, herein we review the concentration, speciation, and distribution of arsenic in freshwater, seawater, and sediments. Many natural processes, such as rock weathering and geothermal activities, contribute to the background arsenic concentrations in the natural environment, whereas metal mining and smelting are anthropogenic sources of arsenic in the water. The high solubility and mobility of arsenic in aquatic environments affects its global cycling. Furthermore, the biological processes in the aquatic environment are discussed, especially the possible microbe-mediated reactions of arsenic in sediments. In addition, various environmental factors, such as redox conditions, pH, and salinity, which influence the transformation of arsenic species, are summarized. Finally, the differences between freshwater and seawater with reference to the concentration as well as speciation and distribution patterns of arsenic are addressed. This review provides deep insights into arsenic occurrence and cycling between freshwater and seawater aquatic environments, which can more accurately distinguish the risks of arsenic in different water environments, and provides theoretical guidance for the prevention and control of arsenic risks

Research on nonstroke dementia screening and cognitive function prediction model for older people based on brain atrophy characteristics

Abstract Background Brain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable. Method We acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains. Results The extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu. Conclusion For nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder

Molecular epidemiology and microbiological characteristics of Cryptococcus gattii VGII isolates from China.

Cryptococcus gattii (C. gattii) is a fungal pathogen that once caused an outbreak of cryptococcosis on Vancouver Island, and had spread worldwide, while few data were available in China. In this study, seven clinical isolates of C. gattii VGII were collected from 19 hospitals, Multi-locus Sequence Typing (MLST) analysis and whole-genome sequencing (WGS) was performed, combined with published data for phylogenetic analysis. In addition, in vitro antifungal susceptibility testing, phenotypic analysis, and in vivo virulence studies were performed, subsequently, histopathological analysis of lung tissue was performed. C.gattii VGII infected patients were mainly immunocompetent male, and most of them had symptoms of central nervous system (CNS) involvement. MLST results showed that isolates from China exhibited high genetic diversity, and sequence type (ST) 7 was the major ST among the isolates. Some clinical isolates showed a close phylogenetic relationship with strains from Australia and South America. All clinical isolates did not show resistance to antifungal drugs. In addition, there was no correlation between virulence factors (temperature, melanin production, and capsule size) and virulence while in vivo experiments showed significant differences in virulence among strains. Lung fungal burden and damage to lung tissue correlated with virulence, and degree of damage to lung tissue in mice may highlight differences in virulence. Our work seeks to provide useful data for molecular epidemiology, antifungal susceptibility, and virulence differences of C. gattii VGII in China

Image1_Identification and validation of critical genes with prognostic value in gastric cancer.TIF

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality rates. Molecular targeted therapies, including those targeting human epidermal factor receptor 2 (HER2), have proven to be effective in clinical treatment. However, better identification and description of tumor-promoting genes in GC is still necessary for antitumor therapy.Methods: Gene expression and clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Last absolute shrinkage and selection operator (LASSO) Cox regression were applied to build a prognostic model, the Prognosis Score. Functional enrichment and single-sample gene set enrichment analysis (ssGSEA) were used to explore potential mechanisms. Western blotting, RNA interference, cell migration, and wound healing assays were used to detect the expression and function of myosin light chain 9 (MYL9) in GC.Results: A four-gene prognostic model was constructed and GC patients from TCGA and meta-GEO cohorts were stratified into high-prognosis score groups or low-prognosis score groups. GC patients in the high-prognosis score group had significantly poorer overall survival (OS) than those in the low-prognosis score groups. The GC prognostic model was formulated as PrognosisScore = (0.06 × expression of BGN) - (0.008 × expression of ATP4A) + (0.12 × expression of MYL9) - (0.01 × expression of ALDH3A1). The prognosis score was identified as an independent predictor of OS. High expression of MYL9, the highest weighted gene in the prognosis score, was correlated with worse clinical outcomes. Functional analysis revealed that MYL9 is mainly associated with the biological function of epithelial-mesenchymal transition (EMT). Knockdown of MYL9 expression inhibits migration of GC cells in vitro.Conclusion: We found that PrognosisScore is potential reliable prognostic marker and verified that MYL9 promotes the migration and metastasis of GC cells.</p