Invasive fungal infections acquired in the hospital have progressively emerged as an important cause of life-threatening infection. In particular, airborne fungi in hospitals are considered critical pathogens of hospital-associated infections. To identify the causative airborne microorganisms, high-volume air samplers were utilized for collection, and species identification was performed using a culture-based method and DNA sequencing analysis with the Illumina MiSeq and HiSeq 2000 sequencing systems. Few bacteria were grown after cultivation in blood agar. However, using microbiome sequencing, the relative abundance of fungi, Archaea species, bacteria and viruses was determined. The distribution characteristics of fungi were investigated using heat map analysis of four departments, including the Respiratory Intensive Care Unit, Intensive Care Unit, Emergency Room and Outpatient Department. The prevalence of Aspergillus among fungi was the highest at the species level, approximately 17% to 61%, and the prevalence of Aspergillus fumigatus among Aspergillus species was from 34% to 50% in the four departments. Draft genomes of microorganisms isolated from the hospital environment were obtained by sequence analysis, indicating that investigation into the diversity of airborne fungi may provide reliable results for hospital infection control and surveillance.
A recently introduced type of neural network called maxout has worked well in many domains. In this paper, we propose to apply maxout for acoustic models in speech recognition. The maxout neuron picks the maximum value within a group of linear pieces as its activation. This nonlinearity is a generalization to the rectified nonlinearity and has the ability to approximate any form of activation functions. We apply maxout networks to the Switchboard phone-call transcription task and evaluate the performances under both a 24-hour low-resource condition and a 300-hour core condition. Experimental results demonstrate that maxout networks converge faster, generalize better and are easier to optimize than rectified linear networks and sigmoid networks. Furthermore, experiments show that maxout networks reduce underfitting and are able to achieve good results without dropout training. Under both conditions, maxout networks yield relative improvements of 1.1-5.1% over rectified linear networks and 2.6-14.5% over sigmoid networks on benchmark test sets.
SIRT1 exerts protective effects against endothelial cells dysfunction, inflammation and atherosclerosis, indicating an important role on myocardial infarction (MI) pathogenesis. Nonetheless, the effects of SIRT1 variants on MI risk remain poorly understood. Here we aimed to investigate the influence of SIRT1 polymorphisms on individual susceptibility to MI. Genotyping of three tagSNPs (rs7069102, rs3818292 and rs4746720) in SIRT1 gene was performed in a Chinese Han population, consisting of 287 MI cases and 654 control subjects. In a logistic regression analysis, we found that G allele of rs7069102 had increased MI risk with odds ratio (OR) of 1.57 [95% confidence interval (CI) = 1.15–2.16, Bonferroni corrected P (Pc) = 0.015] after adjustment for conventional risk factors compared to C allele. Similarly, the combined CG/GG genotypes was associated with the increased MI risk (OR = 1.64, 95% CI = 1.14–2.35, Pc = 0.021) compared to the CC genotype. Further stratified analysis revealed a more significant association with MI risk among younger subjects (≤ 55 years old). Consistent with these results, the haplotype rs7069102G-rs3818292A-rs4746720T containing the rs7069102 G allele was also associated with the increased MI risk (OR = 1.41, 95% CI = 1.09–1.84, Pc = 0.040). However, we did not detect any association of rs3818292 and rs4746720 with MI risk. Our study provides the first evidence that the tagSNP rs7069102 and haplotype rs7069102G-rs3818292A-rs4746720T in SIRT1 gene confer susceptibility to MI in the Chinese Han population.
A group of circulating microRNAs (miRNAs) have been implicated in the pathogenesis of Parkinson’s disease. However, a comprehensive study of the interactions between pathogenic miRNAs and their downstream Parkinson’s disease (PD)-related target genes has not been performed. Here, we identified the miRNA expression profiles in the plasma and circulating exosomes of Parkinson’s disease patients using next-generation RNA sequencing. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses showed that the miRNA target genes were enriched in axon guidance, neurotrophin signaling, cellular senescence, and the Transforming growth factor-β (TGF-β), mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT) and mechanistic target of rapamycin (mTOR) signaling pathways. Furthermore, a group of aberrantly expressed miRNAs were selected and further validated in individual patient plasma, human neural stem cells (NSCs) and a rat model of PD. More importantly, the full scope of the regulatory network between these miRNAs and their PD-related gene targets in human neural stem cells was examined, and the findings revealed a similar but still varied downstream regulatory cascade involving many known PD-associated genes. Additionally, miR-23b-3p was identified as a novel direct regulator of alpha-synuclein, which is possibly the key component in PD. Our current study, for the first time, provides a glimpse into the regulatory network of pathogenic miRNAs and their PD-related gene targets in PD. Moreover, these PD-associated miRNAs may serve as biomarkers and novel therapeutic targets for PD.
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