Other affiliations: State University of New York System
Bio: Wei Zheng is an academic researcher from University at Buffalo. The author has contributed to research in topics: Microbiome & Energy consumption. The author has an hindex of 11, co-authored 14 publications receiving 292 citations. Previous affiliations of Wei Zheng include State University of New York System.
TL;DR: This study standardized and tested an efficient, reliable, and straightforward workflow for the amplification, library construction, and sequencing of the 16S V1–V3 hypervariable region using the new 2 × 300 MiSeq platform, and shows that the experimental protocol accurately measures true bacterial community composition.
Abstract: Currently, taxonomic interrogation of microbiota is based on amplification of 16S rRNA gene sequences in clinical and scientific settings. Accurate evaluation of the microbiota depends heavily on the primers used, and genus/species resolution bias can arise with amplification of non-representative genomic regions. The latest Illumina MiSeq sequencing chemistry has extended the read length to 300 bp, enabling deep profiling of large number of samples in a single paired-end reaction at a fraction of the cost. An increasingly large number of researchers have adopted this technology for various microbiome studies targeting the 16S rRNA V3–V4 hypervariable region. To expand the applicability of this powerful platform for further descriptive and functional microbiome studies, we standardized and tested an efficient, reliable, and straightforward workflow for the amplification, library construction, and sequencing of the 16S V1–V3 hypervariable region using the new 2 × 300 MiSeq platform. Our analysis involved 11 subgingival plaque samples from diabetic and non-diabetic human subjects suffering from periodontitis. The efficiency and reliability of our experimental protocol was compared to 16S V3–V4 sequencing data from the same samples. Comparisons were based on measures of observed taxonomic richness and species evenness, along with Procrustes analyses using beta(β)-diversity distance metrics. As an experimental control, we also analyzed a total of eight technical replicates for the V1–V3 and V3–V4 regions from a synthetic community with known bacterial species operon counts. We show that our experimental protocol accurately measures true bacterial community composition. Procrustes analyses based on unweighted UniFrac β-diversity metrics depicted significant correlation between oral bacterial composition for the V1–V3 and V3–V4 regions. However, measures of phylotype richness were higher for the V1–V3 region, suggesting that V1–V3 offers a deeper assessment of population diversity and community ecology for the complex oral microbiota. This study provides researchers with valuable experimental evidence for the selection of appropriate 16S amplicons for future human oral microbiome studies. We expect that the tested 16S V1–V3 framework will be widely applicable to other types of microbiota, allowing robust, time-efficient, and inexpensive examination of thousands of samples for population, phylogenetic, and functional crossectional and longitutidal studies.
••30 Jun 2014
TL;DR: This study builds four versions of a previously proposed linear power-throughput model for WiFi active power/energy consumption based on parameters readily available to smartphone app developers and evaluates its accuracy under a variety of scenarios which have not been considered in previous studies.
Abstract: We conduct the first detailed measurement study of the properties of a class of WiFi active power/energy consumption models based on parameters readily available to smartphone app developers. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. Using a large dataset and an 802.11n-equipped smartphone, we build four versions of a previously proposed linear power-throughput model, which allow us to explore the fundamental trade off between accuracy and simplicity. We study the properties of the model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios which have not been considered in previous studies. Our study shows that the model works well in a number of scenarios but its accuracy drops with high throughput values or when tested on different hardware. We further show that a non-linear model can greatly improve the accuracy in these two cases.
TL;DR: Among older women, taxonomic differences in subgingival microbiome composition and diversity were observed in relation to clinical periodontal disease measures and potential differences in bacterial subspecies (oligotypes) and their function were also identified in periodonta disease compared with health.
Abstract: Understanding of the oral microbiome in relation to periodontal disease in older adults is limited. The composition and diversity of the subgingival microflora and their oligotypes in health and levels of periodontal disease were investigated in this study on older postmenopausal women. The 16S rRNA gene was sequenced using the Illumina MiSeq platform in 1,206 women aged 53 to 81 y. Presence and severity of periodontal disease were defined by Centers for Disease Control and Prevention/American Academy of Periodontology criteria. Composition of the microbiome was determined by 16S rRNA amplicon sequencing and the abundance of taxa described by the centered log2-ratio (CLR) transformed operational taxonomic unit (OTU) values. Differences according to periodontal disease status were determined by analysis of variance with Bonferroni correction. Bacteria oligotypes associated with periodontal disease and health were determined by minimum entropy decomposition and their functions estimated in silico using PICRUSt. Prevalence of none/mild, moderate, and severe periodontal disease was 25.1%, 58.3%, and 16.6%, respectively. Alpha diversity of the microbiome differed significantly across the 3 periodontal disease categories. β-Diversity differed between no/mild and severe periodontal disease, although considerable overlap was noted. Of the 267 bacterial species identified at ≥0.02% abundance, 56 (20.9%) differed significantly in abundance according to periodontal disease status. Significant linear correlations for pocket depth and clinical attachment level with bacterial amounts were observed for several taxa. Of the taxa differing in abundance according to periodontal disease status, 53% had multiple oligotypes appearing to differ between none/mild and severe periodontal disease. Among older women, taxonomic differences in subgingival microbiome composition and diversity were observed in relation to clinical periodontal disease measures. Potential differences in bacterial subspecies (oligotypes) and their function were also identified in periodontal disease compared with health.
TL;DR: The first measurement study of 802.11n power consumption in smartphones is reported, which has significant implications in the design of energy efficient rate adaptation algorithms for the next generation of wireless cards for desktop/laptop computers.
Abstract: We report the first measurement study of 802.11n power consumption in smartphones. Using a popular 802.11n-enabled smartphone and an 802.11n wireless testbed, we evaluate the power and energy consumption on the phone for a variety of configurations including different MAC bitrates, frame sizes, and channel conditions. We contrast our results against recent studies using 802.11n wireless cards for desktop/laptop computers. Our findings have significant implications in the design of energy efficient rate adaptation algorithms for the next generation of 802.11n-enabled smartphones.
TL;DR: The basic idea is to use a deep neural network to learn an explicit embedding function based on a small training dataset to project sequences into an embedding space so that the mean square error between alignment distances and pairwise distances defined in theembedding space is minimized.
Abstract: Motivation Sequence analysis is arguably a foundation of modern biology. Classic approaches to sequence analysis are based on sequence alignment, which is limited when dealing with large-scale sequence data. A dozen of alignment-free approaches have been developed to provide computationally efficient alternatives to alignment-based approaches. However, existing methods define sequence similarity based on various heuristics and can only provide rough approximations to alignment distances. Results In this article, we developed a new approach, referred to as SENSE (SiamEse Neural network for Sequence Embedding), for efficient and accurate alignment-free sequence comparison. The basic idea is to use a deep neural network to learn an explicit embedding function based on a small training dataset to project sequences into an embedding space so that the mean square error between alignment distances and pairwise distances defined in the embedding space is minimized. To the best of our knowledge, this is the first attempt to use deep learning for alignment-free sequence analysis. A large-scale experiment was performed that demonstrated that our method significantly outperformed the state-of-the-art alignment-free methods in terms of both efficiency and accuracy. Availability and implementation Open-source software for the proposed method is developed and freely available at https://www.acsu.buffalo.edu/∼yijunsun/lab/SENSE.html. Supplementary information Supplementary data are available at Bioinformatics online.
TL;DR: A systematic review and meta-analyses of stool microbiome profiles in preterm infants to discern and describe microbial dysbiosis prior to the onset of NEC revealed differences in microbial profiles by study and the target region of the 16S rRNA gene (V1-V3 or V3-V5).
Abstract: Necrotizing enterocolitis (NEC) is a catastrophic disease of preterm infants, and microbial dysbiosis has been implicated in its pathogenesis. Studies evaluating the microbiome in NEC and preterm infants lack power and have reported inconsistent results. Our objectives were to perform a systematic review and meta-analyses of stool microbiome profiles in preterm infants to discern and describe microbial dysbiosis prior to the onset of NEC and to explore heterogeneity among studies. We searched MEDLINE, PubMed, CINAHL, and conference abstracts from the proceedings of Pediatric Academic Societies and reference lists of relevant identified articles in April 2016. Studies comparing the intestinal microbiome in preterm infants who developed NEC to those of controls, using culture-independent molecular techniques and reported α and β-diversity metrics, and microbial profiles were included. In addition, 16S ribosomal ribonucleic acid (rRNA) sequence data with clinical meta-data were requested from the authors of included studies or searched in public data repositories. We reprocessed the 16S rRNA sequence data through a uniform analysis pipeline, which were then synthesized by meta-analysis. We included 14 studies in this review, and data from eight studies were available for quantitative synthesis (106 NEC cases, 278 controls, 2944 samples). The age of NEC onset was at a mean ± SD of 30.1 ± 2.4 weeks post-conception (n = 61). Fecal microbiome from preterm infants with NEC had increased relative abundances of Proteobacteria and decreased relative abundances of Firmicutes and Bacteroidetes prior to NEC onset. Alpha- or beta-diversity indices in preterm infants with NEC were not consistently different from controls, but we found differences in taxonomic profiles related to antibiotic exposure, formula feeding, and mode of delivery. Exploring heterogeneity revealed differences in microbial profiles by study and the target region of the 16S rRNA gene (V1-V3 or V3-V5). Microbial dysbiosis preceding NEC in preterm infants is characterized by increased relative abundances of Proteobacteria and decreased relative abundances of Firmicutes and Bacteroidetes. Microbiome optimization may provide a novel strategy for preventing NEC.
TL;DR: The siamese neural network architecture is described, and its main applications in a number of computational fields since its appearance in 1994 are outlined, including the programming languages, software packages, tutorials, and guides that can be practically used by readers to implement this powerful machine learning model.
Abstract: Similarity has always been a key aspect in computer science and statistics. Any time two element vectors are compared, many different similarity approaches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correlation coefficient, Spearman's rank correlation coefficient, and others). But if the comparison has to be applied to more complex data samples, with features having different dimensionality and types which might need compression before processing, these measures would be unsuitable. In these cases, a siamese neural network may be the best choice: it consists of two identical artificial neural networks each capable of learning the hidden representation of an input vector. The two neural networks are both feedforward perceptrons, and employ error back-propagation during training; they work parallelly in tandem and compare their outputs at the end, usually through a cosine distance. The output generated by a siamese neural network execution can be considered the semantic similarity between the projected representation of the two input vectors. In this overview we first describe the siamese neural network architecture, and then we outline its main applications in a number of computational fields since its appearance in 1994. Additionally, we list the programming languages, software packages, tutorials, and guides that can be practically used by readers to implement this powerful machine learning model.
TL;DR: This review selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets to assist bioinformatics users in employing suitable clustering tool effectively to analyze big sequencing data.
Abstract: Sequence clustering is a basic bioinformatics task that is attracting renewed attention with the development of metagenomics and microbiomics. The latest sequencing techniques have decreased costs and as a result, massive amounts of DNA/RNA sequences are being produced. The challenge is to cluster the sequence data using stable, quick and accurate methods. For microbiome sequencing data, 16S ribosomal RNA operational taxonomic units are typically used. However, there is often a gap between algorithm developers and bioinformatics users. Different software tools can produce diverse results and users can find them difficult to analyze. Understanding the different clustering mechanisms is crucial to understanding the results that they produce. In this review, we selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets. Our aim is to assist bioinformatics users in employing suitable clustering tools effectively to analyze big sequencing data. Related data, codes and software tools were accessible at the link http://lab.malab.cn/∼lg/clustering/.
TL;DR: HSIH could be transferred by fecal microbiota transplantation, indicating the pivotal roles of intestinal flora in hSIH development, and a novel mechanism different from inflammation/immunity by which intestinal flora regulated BP is revealed.
Abstract: Rationale: High-salt diet is one of the most important risk factors for hypertension. Intestinal flora has been reported to be associated with high salt–induced hypertension (hSIH). However, the de...