In-silico identification of phenotype-biased functional modules

<p>Abstract</p> <p>Background</p> <p>Phenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.</p> <p>Results</p> <p>In this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.</p> <p>Conclusion</p> <p>Thus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (<url>http://freescience.org/cs/phenotype-biased-biclusters/</url>).</p

CNN Architectures for Large-Scale Audio Classification

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on changes of latest Audio Set revision. Changed wording to fit 4 page limit with new addition

Incidental Findings on Brain MRI in People with HIV Infection

BACKGROUND: Incidental findings are a well-known complication of imaging studies done for both diagnostic and research purposes. Little is known about the rates and types of incidental findings found on brain MRI in patients with HIV infection, who may be at risk for HIV-Associated Neurocognitive Disorders (HAND). METHODS: The parent study included 108 adults with HIV infection and 125 demographically-matched uninfected controls who completed MRI and neuropsychological testing. Incidental findings were classified by the study team as vascular, neoplastic, congenital, other neurologic, or non-neurologic. Categorical measures were compared using Pearson chi-square tests; continuous measures were compared using t-tests. RESULTS: Among participants with HIV infection, 36/108 (33%) had incidental findings compared to 33/125 (26%) controls (p = 0.248). Rates of incidental findings were significantly correlated with increasing age in both participants with HIV infection (p = 0.013) and controls (p = 0.022). We found no correlation between presence of incidental findings and sex or race/ethnicity among either cohort, and no correlation with CD4 count or HAND status for the HIV-infected cohort. CONCLUSIONS: Incidental findings were common in both participants with HIV infection and controls, at higher rates than previously reported in healthy populations. There was no significant difference in prevalence between the groups