Empirical Analysis of Factors Affecting Confirmation Bias Levels of Software Engineers

Confirmation bias is defined as the tendency of people to seek evidence that verifies a hypothesis rather than seeking evidence to falsify it. Due to the confirmation bias, defects may be introduced in a software product during requirements analysis, design, implementation and/or testing phases. For instance, testers may exhibit confirmatory behavior in the form of a tendency to make the code run rather than employing a strategic approach to make it fail. As a result, most of the defects that have been introduced in the earlier phases of software development may be overlooked leading to an increase in software defect density. In this paper, we quantify confirmation bias levels in terms of a single derived metric. However, the main focus of this paper is the analysis of factors affecting confirmation bias levels of software engineers. Identification of these factors can guide project managers to circumvent negative effects of confirmation bias, as well as providing guidance for the recruitment and effective allocation of software engineers. In this empirical study, we observed low confirmation bias levels among participants with logical reasoning and hypothesis testing skills

Acute Responses in Agonists of uEGF to Moderate-Intensity and High-Intensity Interval Exercise in Mid-Spectrum CKD

Urine epidermal growth factor (uEGF) is a novel biomarker utilized in assessing renal health in various renal diseases, specifically chronic kidney disease (CKD). uEGF promotes multiple intracellular pathways, stimulating renal cell growth, survival, and replication. uEGF production is activated by multiple agonists that bind to the uEGF receptor. Aerobic exercise initiates the upregulation of several of these agonists to increase the production of uEGF. Depending on the mode and intensity of aerobic exercise, uEGF agonists may activate differently in CKD populations. PURPOSE: To determine the influence of an acute bout of steady-state exercise (SSE) and high-intensity interval exercise (HIIE) on concentrations of uEGF agonists (serum insulin-like growth factor 1 (IGF-1), angiotensin II receptor type 1 (AGTR-1), and transforming growth factor beta 1 (TGF-β1)) in mid-spectrum CKD. METHODS: Twenty participants (n = 6 men; n = 14 women; age 62.0 + 9.9 yr; weight 80.9 + 16.2 kg; body fat 37.3 + 8.5% of weight; VO2max 19.4 + 4.7 ml/kg/min) completed 30 min of SSE at 65% VO2reserve or HIIE by treadmill walking (90% and 20% of VO2reserve in 3:2 min ratio) in a randomized crossover design. Both exercise conditions averaged ~ 65% VO2reserve. Blood and urine samples were obtained under standardized conditions just before, 1hr, and 24hrs after exercise. uEGF (ng/mL), serum IGF-1 (ng/mL), AGTR-1 (ng/mL), and TGF-β1 (pg/mL) responses were analyzed using 2 (condition) by 3 (sample point) repeated measures ANOVAs and Pearson Correlations. RESULTS: Serum IGF-1 and AGTR-1 increased 1hr and 24hr post-exercise in both exercise conditions; however, statistical significance was not achieved (p = 0.28 and p = 0.09). Similarly, serum TGF-β1 decreased at 24hrs in both exercise conditions but statistically remained unaltered (p = 0.42). IGF-1 was significantly correlated to uEGF in both conditions at all three-time points (p = 0.03), while AGTR-1 was significantly correlated to uEGF at 1hr in HIIE. uEGF findings were previously reported in ACSM abstract (DOI: 10.1249/01.mss.0000560710.72569.11). CONCLUSION: Agonists of uEGF remained unaltered following an acute bout of SSE and HIIE in mid-spectrum CKD. Further research is needed to understand better uEGF response activation to aerobic exercise in mid-spectrum CKD

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Dudley Knox Library Survey of Distance Learning Students

Two files: Report and presentation (with findings and background data).How can the Naval Postgraduate School's (NPS) Dudley Knox Library (DKL) better serve the needs of distance learning students? The Fall 2011 NPS Survey Research Methods class (OA4109) devised, conducted and analyzed results of a survey of distance learning students on behalf of DKL. The study succeeded in meeting three objectives: 1) Evaluate the need for library services; 2) Determine if needs vary among various distance learning curricula; and 3) Solicit feedback on the usability of the DKL website

Evolutionary dynamics of the accessory genome of Listeria monocytogenes.

Listeria monocytogenes, a foodborne bacterial pathogen, is comprised of four phylogenetic lineages that vary with regard to their serotypes and distribution among sources. In order to characterize lineage-specific genomic diversity within L. monocytogenes, we sequenced the genomes of eight strains from several lineages and serotypes, and characterized the accessory genome, which was hypothesized to contribute to phenotypic differences across lineages. The eight L. monocytogenes genomes sequenced range in size from 2.85-3.14 Mb, encode 2,822-3,187 genes, and include the first publicly available sequenced representatives of serotypes 1/2c, 3a and 4c. Mapping of the distribution of accessory genes revealed two distinct regions of the L. monocytogenes chromosome: an accessory-rich region in the first 65° adjacent to the origin of replication and a more stable region in the remaining 295°. This pattern of genome organization is distinct from that of related bacteria Staphylococcus aureus and Bacillus cereus. The accessory genome of all lineages is enriched for cell surface-related genes and phosphotransferase systems, and transcriptional regulators, highlighting the selective pressures faced by contemporary strains from their hosts, other microbes, and their environment. Phylogenetic analysis of O-antigen genes and gene clusters predicts that serotype 4 was ancestral in L. monocytogenes and serotype 1/2 associated gene clusters were putatively introduced through horizontal gene transfer in the ancestral population of L. monocytogenes lineage I and II

Clade membership plot of individual genes plotted against the genome of <i>L. monocytogenes</i> F2365.

<p>The order of genome rings is listed in the circle center, with F2365 being the outermost ring. The 7 outermost rings represent lineage I (serotype 4b and 1/2b), the next three rings represent lineage III and lineage IV strains (serotype 4a and 4c), and the last 11 rings represent lineage II strains (serotype 1/2a, 1/2c, and 3a). Clade membership of the individual genes is indicated by color; blue indicates lineage II, red indicates lineage I, and gray is unresolved membership. The two O-antigen gene clusters are highlighted in green and yellow. Genes in these clusters found in serotype 1/2b lineage I cluster phylogenetically with orthologs found in lineage II clade.</p