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Draft Genome Series associated with Dickeya sp. Isolates B16 (NIB Unces 2098) and S1 (NIB Unces 2099) Leading to Soft Decompose regarding Phalaenopsis Orchids.
3-9] per predicted body weight; median positive end-expiratory pressure (PEEP) was 5 [3 to 5] cmH20. Planned recruitment manoeuvres were used in the 6.9% of patients. No differences in ventilator settings were found among the sub-groups. PPCs occurred in 81 patients (10.3%). Duration of anaesthesia (odds ratio, 1.295 [95% confidence interval 1.067 to 1.572]; p = 0.009) and higher age for the brain group (odds ratio, 0.000 [0.000 to 0.189]; p = 0.031), but not intraoperative ventilator settings were independently associated with development of PPCs. CONCLUSIONS Neurosurgical patients are ventilated with low VT and low PEEP, while recruitment manoeuvres are seldom applied. Intraoperative ventilator settings are not associated with PPCs.BACKGROUND With the development of next generation sequencing (NGS) technology and genotype imputation methods, statistical methods have been proposed to test a set of genomic variants together to detect if any of them is associated with the phenotype or disease. In practice, within the set, there is an unknown proportion of variants truly causal or associated with the disease. There is a demand for statistical methods with high power in both dense and sparse scenarios, where the proportion of causal or associated variants is large or small respectively. RESULTS We propose a new association test - weighted Adaptive Fisher (wAF) that can adapt to both dense and sparse scenarios by adding weights to the Adaptive Fisher (AF) method we developed before. Using simulation, we show that wAF enjoys comparable or better power to popular methods such as sequence kernel association tests (SKAT and SKAT-O) and adaptive SPU (aSPU) test. We apply wAF to a publicly available schizophrenia dataset, and successfully detect thirteen genes. Among them, three genes are supported by existing literature; six are plausible as they either relate to other neurological diseases or have relevant biological functions. CONCLUSIONS The proposed wAF method is a powerful disease-variants association test in both dense and sparse scenarios. Both simulation studies and real data analysis indicate the potential of wAF for new biological findings.BACKGROUND Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. METHODS In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. RESULTS All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. CONCLUSIONS Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.BACKGROUND Non-synonymous mutations altering tumor suppressor genes and oncogenes are widely studied. However, synonymous mutations, which do not alter the protein sequence, are rarely investigated in melanoma genome studies. METHODS We explored the role of somatic synonymous mutations in melanoma samples from TCGA (The Cancer Genome Atlas). The pathogenic synonymous mutation and neutral synonymous mutation data were used to assess the significance of pathogenic synonymous mutations in melanoma likely to affect genetic regulatory elements using Fisher's exact test. Poisson distribution probabilities of each gene were used to mine the genes with multiple potential functional synonymous mutations affecting regulatory elements. RESULTS Concentrating on five types of genetic regulatory functions, we found that the mutational patterns of pathogenic synonymous mutations are mostly involved in exonic splicing regulators in near-splicing sites or inside DNase I hypersensitivity sites or non-optimal codon. Moreover, the sites of miRNA binding alteration exhibit a significantly lower rate of evolution than other sites. Finally, 12 genes were hit by recurrent potentially functional synonymous mutations, which showed statistical significance in the pathogenic mutations. WP1130 Among them, nine genes (DNAH5, ADCY8, GRIN2A, KSR2, TECTA, RIMS2, XKR6, MYH1, SCN10A) have been reported to be mutated in melanoma, and other three genes (SLC9A2, CASR, SLC8A3) have a great potential to impact melanoma. CONCLUSION These findings confirm the functional consequences of somatic synonymous mutations in melanoma, emphasizing the significance of research in future studies.BACKGROUND Elucidating molecular mechanisms that are altered during HIV-1 infection may provide a better understanding of the HIV-1 life cycle and how it interacts with infected T-cells. One such mechanism is alternative splicing (AS), which has been studied for HIV-1 itself, but no systematic analysis has yet been performed on infected T-cells. We hypothesized that AS patterns in infected T-cells may illuminate the molecular mechanisms underlying HIV-1 infection and identify candidate molecular markers for specifically targeting infected T-cells. METHODS We downloaded previously published raw RNA-seq data obtained from HIV-1 infected and non-infected T-cells. We estimated percent spliced in (PSI) levels for each AS exon, then identified differential AS events in the infected cells (FDR  0.1). We performed functional gene set enrichment analysis on the genes with differentially expressed AS exons to identify their functional roles. In addition, we used RT-PCR to validate differential alternative splicing events in cyclin T1 (CCNT1) as a case study.
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