Power transformer oil temperature prediction based on empirical mode decomposition-bidirectional long short-term memory

Power transformers are crucial components of power transmission and transformation networks. Their operational status has a direct impact on the reliability of power supply systems. As such, the security and stability of power systems depend heavily on the state of transformers within them. The oil temperature of a transformer is a critical indicator of its working condition. Accurately and rapidly predicting transformer oil temperature is therefore of significant practical importance for ensuring the safe and effective operation of power systems. To address this prediction problem, this article proposes a transformer oil temperature prediction method based on empirical mode decomposition-bidirectional long short-term memory (EMD-BiLSTM). The time series of oil temperature is first cleaned before being processed. Next, the EMD algorithm is used to decompose the time series into relatively stable components. The BiLSTM neural network is then utilized to predict the complex nonlinear long-term series. The proposed method is evaluated using the open data set Electricity Transformer Temperature (ETT)-small. Experimental results show that the EMD-BiLSTM model outperforms traditional LSTM, BiLSTM, EMD-BP, and Wavelet Transform-Bidirectional Long Short-Term Memory (WT-BiLSTM) methods, demonstrating that it is an effective and accurate prediction method for transformer oil temperature

dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder

Abstract Background Autism spectrum disorders (ASD) are hereditary, heterogeneous and biologically complex neurodevelopmental disorders. Individual studies on gene expression in ASD cannot provide clear consensus conclusions. Therefore, a systematic review to synthesize the current findings from brain tissues and a search tool to share the meta-analysis results are urgently needed. Methods Here, we conducted a meta-analysis of brain gene expression profiles in the current reported human ASD expression datasets (with 84 frozen male cortex samples, 17 female cortex samples, 32 cerebellum samples and 4 formalin fixed samples) and knock-out mouse ASD model expression datasets (with 80 collective brain samples). Then, we applied R language software and developed an interactive shared and updated database (dbMDEGA) displaying the results of meta-analysis of data from ASD studies regarding differentially expressed genes (DEGs) in the brain. Results This database, dbMDEGA ( https://dbmdega.shinyapps.io/dbMDEGA/ ), is a publicly available web-portal for manual annotation and visualization of DEGs in the brain from data from ASD studies. This database uniquely presents meta-analysis values and homologous forest plots of DEGs in brain tissues. Gene entries are annotated with meta-values, statistical values and forest plots of DEGs in brain samples. This database aims to provide searchable meta-analysis results based on the current reported brain gene expression datasets of ASD to help detect candidate genes underlying this disorder. Conclusion This new analytical tool may provide valuable assistance in the discovery of DEGs and the elucidation of the molecular pathogenicity of ASD. This database model may be replicated to study other disorders

A novel deletion in KRT75L4 mediates the frizzle trait in a Chinese indigenous chicken

Abstract Background Highly diversified in morphology and structure, feathers have evolved into various forms. Frizzle feathers, which result from a developmental defect of the feather, are observed in several domestic chicken breeds. The frizzle phenotype is consistent with incomplete dominance of a major gene, but the molecular mechanisms that underlie this phenotype remain obscure. Kirin, a Chinese indigenous chicken breed that originated in the Guangdong province, is famous for its frizzle feathers. The KRT75 gene is considered as the dominant gene responsible for the frizzle trait in several chicken breeds, but this is not the case in the Kirin breed. Thus, the objective of our study was to investigate the genomic region and mutation responsible for this phenotype in this particular breed. Results A resource population was produced by crossing Kirin and Huaixiang chickens to produce F1 and F2 generations. DNA samples from 75 frizzle feather and normal feather individuals were sequenced with double-digest genotyping by sequencing (dd-GBS). After the detection of 525,561 high-quality variants, a genome-wide association analysis was carried out and the gene responsible for the frizzle phenotype was localized within the type II α-keratin cluster on chromosome 33. Sanger sequencing was used to screen for mutations in the exons of five genes of this type II α-keratin cluster. A 15-bp deletion in exon 3 of KRT75L4 that showed complete segregation with the frizzle phenotype was detected within the F2 population. Transcriptome sequencing demonstrated that KRT75L4 was expressed but that the transcript was shorter in Kirin than in Huaixiang chickens. In addition, by using Sanger sequencing, we were able to confirm that the deletion was in complete linkage with frizzle feathers. Conclusions A deletion in the KRT75L4 gene is responsible for the frizzle feather phenotype in the Kirin chicken. The identification of this mutation, which causes a developmental defect of avian integument appendages, will improve our understanding of the mechanisms that are involved in feather formation

Additional file 1: Table S1. of dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder

Brain samples of cortex included in the meta-analysis. (DOC 93 kb

Additional file 2: Table S2. of dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder

Brain samples of cerebellum included in the meta-analysis. (DOC 52 kb