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Institution

San Diego State University

EducationSan Diego, California, United States
About: San Diego State University is a education organization based out in San Diego, California, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 12418 authors who have published 27950 publications receiving 1192375 citations. The organization is also known as: SDSU & San Diego State College.


Papers
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Journal ArticleDOI
TL;DR: A subset of the published research on mood-congruent memory in normal nondepressed, subclinically depressed, clinically depressed, induced depressed, and induced elated persons is examined with meta-analytic techniques.

671 citations

Journal ArticleDOI
09 Mar 2011-PLOS ONE
TL;DR: DeconSeq is a robust framework for the rapid, automated identification and removal of sequence contamination in longer-read datasets (150 bp mean read length) and allows scientists to automatically detect and efficiently remove unwanted sequence contamination from their datasets while eliminating critical limitations of current methods.
Abstract: High-throughput sequencing technologies have strongly impacted microbiology, providing a rapid and cost-effective way of generating draft genomes and exploring microbial diversity. However, sequences obtained from impure nucleic acid preparations may contain DNA from sources other than the sample. Those sequence contaminations are a serious concern to the quality of the data used for downstream analysis, causing misassembly of sequence contigs and erroneous conclusions. Therefore, the removal of sequence contaminants is a necessary and required step for all sequencing projects. We developed DeconSeq, a robust framework for the rapid, automated identification and removal of sequence contamination in longer-read datasets (150 bp mean read length). DeconSeq is publicly available as standalone and web-based versions. The results can be exported for subsequent analysis, and the databases used for the web-based version are automatically updated on a regular basis. DeconSeq categorizes possible contamination sequences, eliminates redundant hits with higher similarity to non-contaminant genomes, and provides graphical visualizations of the alignment results and classifications. Using DeconSeq, we conducted an analysis of possible human DNA contamination in 202 previously published microbial and viral metagenomes and found possible contamination in 145 (72%) metagenomes with as high as 64% contaminating sequences. This new framework allows scientists to automatically detect and efficiently remove unwanted sequence contamination from their datasets while eliminating critical limitations of current methods. DeconSeq's web interface is simple and user-friendly. The standalone version allows offline analysis and integration into existing data processing pipelines. DeconSeq's results reveal whether the sequencing experiment has succeeded, whether the correct sample was sequenced, and whether the sample contains any sequence contamination from DNA preparation or host. In addition, the analysis of 202 metagenomes demonstrated significant contamination of the non-human associated metagenomes, suggesting that this method is appropriate for screening all metagenomes. DeconSeq is available at http://deconseq.sourceforge.net/.

670 citations

Journal ArticleDOI
TL;DR: It is shown that phage-to-bacteria ratios were increased, relative to the adjacent environment, on all mucosal surfaces sampled, ranging from cnidarians to humans, and that this increase in phage abundance is mucus dependent and protects the underlying epithelium from bacterial infection.
Abstract: Mucosal surfaces are a main entry point for pathogens and the principal sites of defense against infection. Both bacteria and phage are associated with this mucus. Here we show that phage-to-bacteria ratios were increased, relative to the adjacent environment, on all mucosal surfaces sampled, ranging from cnidarians to humans. In vitro studies of tissue culture cells with and without surface mucus demonstrated that this increase in phage abundance is mucus dependent and protects the underlying epithelium from bacterial infection. Enrichment of phage in mucus occurs via binding interactions between mucin glycoproteins and Ig-like protein domains exposed on phage capsids. In particular, phage Ig-like domains bind variable glycan residues that coat the mucin glycoprotein component of mucus. Metagenomic analysis found these Ig-like proteins present in the phages sampled from many environments, particularly from locations adjacent to mucosal surfaces. Based on these observations, we present the bacteriophage adherence to mucus model that provides a ubiquitous, but non–host-derived, immunity applicable to mucosal surfaces. The model suggests that metazoan mucosal surfaces and phage coevolve to maintain phage adherence. This benefits the metazoan host by limiting mucosal bacteria, and benefits the phage through more frequent interactions with bacterial hosts. The relationships shown here suggest a symbiotic relationship between phage and metazoan hosts that provides a previously unrecognized antimicrobial defense that actively protects mucosal surfaces.

670 citations

Journal ArticleDOI
TL;DR: The NEWS and NEWS-A possess adequate levels of factorial and criterion validity and aesthetics, mixed destinations, and residential density were associated with walking for recreation.
Abstract: Purpose:The aim of this study was to examine the factorial and criterion validity of the Neighborhood Environment Walkability Scale (NEWS) and to develop an abbreviated version (NEWS-A).Methods:A stratified two-stage cluster sample design was used to recruit 1286 adults. The sample was drawn

668 citations

Journal ArticleDOI
TL;DR: Text messages might prove to be a productive channel of communication to promote behaviors that support weight loss in overweight adults.
Abstract: Background: To our knowledge, no studies have evaluated whether weight loss can be promoted in overweight adults through the use of an intervention that is largely based on daily SMS (Short Message Service: text) and MMS (Multimedia Message Service: small picture) messages transmitted via mobile phones. Objective: This paper describes the development and evaluation of a text message-based intervention designed to help individuals lose or maintain weight over 4 months. Methods: The study was a randomized controlled trial, with participants being exposed to one of the following two conditions, lasting 16 weeks: (1) receipt of monthly printed materials about weight control; (2) an intervention that included personalized SMS and MMS messages sent two to five times daily, printed materials, and brief monthly phone calls from a health counselor. The primary outcome was weight at the end of the intervention. A mixed-model repeated-measures analysis compared the effect of the intervention group to the comparison group on weight status over the 4-month intervention period. Analysis of covariance (ANCOVA) models examined weight change between baseline and 4 months after adjusting for baseline weight, sex, and age. Results: A total of 75 overweight men and women were randomized into one of the two groups, and 65 signed the consent form, completed the baseline questionnaire, and were included in the analysis. At the end of 4 months, the intervention group (n = 33) lost more weight than the comparison group (?1.97 kg difference, 95% CI ?0.34 to ?3.60 kg, P = .02) after adjusting for sex and age. Intervention participants' adjusted average weight loss was 2.88 kg (3.16%). At the end of the study, 22 of 24 (92%) intervention participants stated that they would recommend the intervention for weight control to friends and family. Conclusions: Text messages might prove to be a productive channel of communication to promote behaviors that support weight loss in overweight adults. Trial Registration: Clinicaltrials.gov NCT00415870; http://clinicaltrials.gov/ct2/show/NCT00415870 (Archived by WebCite at http://www.webcitation.org/5dnolbkFt) [J Med Internet Res 2009;11(1):e1]

665 citations


Authors

Showing all 12533 results

NameH-indexPapersCitations
David R. Williams1782034138789
James F. Sallis169825144836
Steven Williams144137586712
Larry R. Squire14347285306
Murray B. Stein12874589513
Robert Edwards12177574552
Roberto Kolter12031552942
Jack E. Dixon11540847201
Sonia Ancoli-Israel11552046045
John D. Lambris11465148203
Igor Grant11379155147
Kenneth H. Nealson10848351100
Mark Westoby10831659095
Eric Courchesne10724041200
Marc A. Schuckit10664343484
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202345
2022168
20211,596
20201,535
20191,454
20181,262