Oligotyping reveals stronger relationship of organic soil bacterial community structure with N-amendments and soil chemistry in comparison to that of mineral soil at Harvard Forest, MA, USA

The impact of chronic nitrogen amendments on bacterial communities was evaluated at Harvard Forest, Petersham, MA, USA. Thirty soil samples (3 treatments × 2 soil horizons × 5 subplots) were collected in 2009 from untreated (control), low nitrogen-amended (LN; 50 kg NH4NO3 ha-1 yr-1) and high nitrogen-amended (HN; 150 kg NH4NO3 ha-1 yr-1) plots. PCR-amplified partial 16S rRNA gene sequences made from soil DNA were subjected to pyrosequencing (Turlapati et al., 2013) and analyses using oligotyping. The parameters M (the minimum count of the most abundant unique sequence in an oligotype) and s (the minimum number of samples in which an oligotype is expected to be present) had to be optimized for forest soils because of high diversity and the presence of rare organisms. Comparative analyses of the pyrosequencing data by oligotyping and operational taxonomic unit clustering tools indicated that the former yields more refined units of taxonomy with sequence similarity of ≥99.5%. Sequences affiliated with four new phyla and 73 genera were identified in the present study as compared to 27 genera reported earlier from the same data (Turlapati et al., 2013). Significant rearrangements in the bacterial community structure were observed with N-amendments revealing the presence of additional genera in N-amended plots with the absence of some that were present in the control plots. Permutational MANOVA analyses indicated significant variation associated with soil horizon and N treatment for a majority of the phyla. In most cases soil horizon partitioned more variation relative to treatment and treatment effects were more evident for the organic (Org) horizon. Mantel test results for Org soil showed significant positive correlations between bacterial communities and most soil parameters including NH4 and NO3. In mineral soil, correlations were seen only with pH, NH4, and NO3. Regardless of the pipeline used, a major hindrance for such a study remains to be the lack of reference databases for forest soils

Computational binding mechanism of Mycobacterium tuberculosis UDP-NAG enolpyruvyl transferase (MurA) with inhibitors fosfomycin, cyclic disulfide analog RWJ-3981, pyrazolopyrimidine analog RWJ-110192, purine analog RWJ-140998, 5-sulfonoxy-anthranilic aci

Worldwide, tuberculosis (TB) remains the most frequent and important infectious disease causing morbidity and death. One-third of the world's population is infected with Mycobacterium tuberculosis (Mtb), the etiologic agent of TB. In this context, TB is in the top three, with malaria and HIV being the leading causes of death from a single infectious agent, and about two million deaths are attributable to TB annually. The bacterial enzyme MurA catalyzes the transfer of enolpyruvate from phosphoenolpyruvate (PEP) to uridine diphospho-N-acetylglucosamine (UNAG), which is the first committed step of bacterial cell wall biosynthesis. In this work, 3D structural model of Mtb-MurA enzyme has been developed, for the first time, by homology modeling and molecular dynamics simulation techniques. The model provided clear insight in its structure features, i.e. substrate binding pocket, and common docking site. Multiple sequence alignment and 3D structure model provided the putative substrate binding pocket of Mtb-MurA with respect to E.coli MurA. This analysis was helpful in identifying the binding sites and molecular function of the MurA homologue. Molecular docking study was performed on this 3D structural model, using different classes of inhibitors like fosfomycin, cyclic disulfide analog RWJ-3981, pyrazolopyrimidine analog RWJ-110192, purine analog RWJ-140998, 5-sulfonoxy-anthranilic acid derivatives T6361, T6362 and the results showed that the 5-sulfonoxyanthranilic acid derivatives is showed best interaction compared with other inhibitor, taking in to this we also design a new efficient analogs of T6361 and T6362 which are showed even better interaction with Mtb-MurA than the parental5-sulfonoxy-anthranilic acid derivatives. Further the comparative molecular electrostatic potential and cavity depth analysis of Mtb-MurA suggested several important differences in its substrate and inhibitor binding pocket. Such differences could be exploited in the future for designing of a more specific inhibitor for Mtb-MurA enzym

A phason disordered two dimensional quantum antiferromagnet

We examine a novel type of disorder in quantum antiferromagnets. Our model consists of localized spins with antiferromagnetic exchanges on a bipartite quasiperiodic structure, which is geometrically disordered in such a way that no frustration is introduced. In the limit of zero disorder, the structure is the perfect Penrose rhombus tiling. This tiling is progressively disordered by augmenting the number of random "phason flips" or local tile-reshuffling operations. The ground state remains N\'eel ordered, and we have studied its properties as a function of increasing disorder using linear spin wave theory and quantum Monte Carlo. We find that the ground state energy decreases, indicating enhanced quantum fluctuations with increasing disorder. The magnon spectrum is progressively smoothed, and the effective spin wave velocity of low energy magnons increases with disorder. For large disorder, the ground state energy as well as the average staggered magnetization tend towards limiting values characteristic of this type of randomized tilings.Comment: 5 pages, 7 figure

In silico identification of microRNAs predicted to regulate the drug metabolizing cytochrome P450 genes

OBJECTIVE: Cytochrome P450 (CYP) enzymes exhibit high interindividual variability that is not completely explained by known environmental and genetic factors. To further understand this variability, we hypothesized that microRNAs (miRNAs) may regulate CYP expression. METHODS: MiRNA identification algorithms were used to identify the miRNAs that are predicted to regulate twelve major drug metabolizing CYPs and to identify polymorphisms in CYP mRNA 3'-UTRs that are predicted to interfere with normal mRNA-miRNA interactions. RESULTS: All twelve CYPs were predicted to be targets of miRNAs. Additionally, 38 SNPs in CYP mRNA 3'-UTRs were predicted to interfere with miRNA targeting of mRNAs. These predicted miRNAs and SNPs are candidates for future in vitro studies focused on understanding the molecular regulation of these CYP genes. CONCLUSION: These in silico results provide strong support for a role of miRNA in the regulation and variability of CYP expression

SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

Sentiment Analysis has been a topic of interest for researchers due to its increasing usage by Industry. To measure end-user sentiment., there is no clear verdict on which algorithms are better in real-time scenarios. A rigorous benchmark evaluation of various algorithms running across multiple datasets and different hardware architectures is required that can guide future researchers on potential advantages and limitations. In this paper, proposed SentiMLBench is a critical evaluation of key ML algorithms as standalone classifiers, a novel cascade feature selection (CFS) based ensemble technique in multiple benchmark environments each using a different twitter dataset and processing hardware. The best trained ensemble model with CFS enhancement surpasses current state-of-the-art models, according to experimental results. In a study, though ensemble model provides good accuracy, it falls short of neural networks accuracy by 2%. ML algorithms accuracy is poor as standalone classifiers across all three studies. The supremacy of neural networks is further stamped in study three where it outperforms other algorithms in accuracy by over 10%. Graphical processing unit provide speed and higher computational power at a fraction of a cost compared to a normal processor thereby providing critical architectural insights into developing a robust expert system for sentiment analysis

Brain Tumor Segmentation Techniques: A Review

Image processing is used widely in solving a variety of problems. The important and complex phase of image processing is image segmentation. This paper provides a brief description on some of the segmentation algorithms specifically on brain tumor MR Images. Later in this paper, simple comparisons are made between the listed algorithms. This work helps in understanding some of the existing brain MR Image segmentation algorithms better

A retrospective study on endometrial patterns in abnormal uterine bleeding

Background: To determine the type of endometrial patterns of the different age categories of women who presented as a case of abnormal uterine bleeding.Methods: This is a retrospective study conducted on 105 patients who presented with abnormal uterine bleeding who underwent fractional curettage in our hospital. The data on their age, presenting complaints, and comorbidities of all the women were collected. The patterns of endometrial changes were studied and classified.Results: The most common histopathological findings were anovulatory shedding (34.3%) and irregular shedding (18.1%). The other findings include irregular ripening, papillary endocervicitis, endocervicitis, pill endometrium, atrophic endometrium, squamous metaplasia, and endometrial hyperplasia. The most common malignant change seen was endometrioid carcinoma which was seen in women over 40 years of age.Conclusions: Histopathological examination of the endometrium shows a clear-cut differentiation between physiological and malignancy changes in the endometrium. Hence, endometrial sampling is considered the golden tool for accurate analysis of the endometrium