Arsonists or firefighters? Affectiveness in agile software development

In this paper, we present an analysis of more than 500K comments from open-source repositories of software systems developed using agile methodologies. Our aim is to empirically determine how developers interact with each other under certain psychological conditions generated by politeness, sentiment and emotion expressed within developers' comments. Developers involved in an open-source projects do not usually know each other; they mainly communicate through mailing lists, chat, and tools such as issue tracking systems. The way in which they communicate a ects the development process and the productivity of the people involved in the project. We evaluated politeness, sentiment and emotions of comments posted by agile developers and studied the communication ow to understand how they interacted in the presence of impolite and negative comments (and vice versa). Our analysis shows that \ re ghters" prevail. When in presence of impolite or negative comments, the probability of the next comment being impolite or negative is 13% and 25%, respectively; ANGER however, has a probability of 40% of being followed by a further ANGER comment. The result could help managers take control the development phases of a system, since social aspects can seriously a ect a developer's productivity. In a distributed agile environment this may have a particular resonance

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Low adherence with antihypertensives in actual practice: the association with social participation – a multilevel analysis

BACKGROUND: Low adherence is a key factor in explaining impaired effectiveness and efficiency in the pharmacological treatment of hypertension. However, little is known about which factors determine low adherence in actual practice. The purpose of this study is to examine whether low social participation is associated with low adherence with antihypertensive medication, and if this association is modified by the municipality of residence. METHODS: 1288 users of antihypertensive medication were identified from The Health Survey in Scania 2000, Sweden. The outcome was low adherence with antihypertensives during the last two weeks. Multilevel logistic regression with participants at the first level and municipalities at the second level was used for analyses of the data. RESULTS: Low social participation was associated with low adherence with antihypertensives during the last two weeks (OR = 2.05, 95% CI: 1.05–3.99), independently of low educational level. However, after additional adjustment for poor self-rated health and poor psychological health, the association between low social participation and low adherence with antihypertensives during the last two weeks remained but was not conclusive (OR = 1.80, 95% CI: 0.90–3.61). Furthermore, the association between low social participation and low adherence with antihypertensives during the last two weeks varied among municipalities in Scania (i.e., cross-level interaction). CONCLUSION: Low social participation seems to be associated with low adherence with antihypertensives during the last two weeks, and this association may be modified by the municipality of residence. Future studies aimed at investigating health-related behaviours in general and low adherence with medication in particular might benefit if they consider area of residence

A Multilevel Analysis of the Impact of Socio-Structural and Environmental Influences on Condom Use Among Female Sex Workers

This study uses multilevel analysis to examine individual, organizational and community levels of influence on condom use among female commercial sex workers (FSW) in the Philippines. A randomized controlled study involving 1,382 female commercial sex workers assigned to three intervention groups consisting of peer education, managerial training, combined peer and managerial intervention and a usual care control group was conducted. The results of the multilevel analysis show that FSWs who work in establishments with condom use rules tend to have a higher level of condom use (β = .70, P < 0.01). Among the different intervention groups, the combined peer and managerial intervention had the largest effect on condom use (β = 1.30, P < 0.01) compared with the usual care group. Using a three-level hierarchical model, we found that 62% of the variation lies within individuals, whereas 24% and 14% of the variation lies between establishments, and communities, respectively. Standard errors were underestimated when clustering of the FSWs in the different establishments and communities were not taken into consideration. The results demonstrate the importance of using multilevel analysis for community-based HIV/AIDS intervention programs to examine individual, establishment and community effects

How does our motor system determine its learning rate?

Motor learning is driven by movement errors. The speed of learning can be quantified by the learning rate, which is the proportion of an error that is corrected for in the planning of the next movement. Previous studies have shown that the learning rate depends on the reliability of the error signal and on the uncertainty of the motor system’s own state. These dependences are in agreement with the predictions of the Kalman filter, which is a state estimator that can be used to determine the optimal learning rate for each movement such that the expected movement error is minimized. Here we test whether not only the average behaviour is optimal, as the previous studies showed, but if the learning rate is chosen optimally in every individual movement. Subjects made repeated movements to visual targets with their unseen hand. They received visual feedback about their endpoint error immediately after each movement. The reliability of these error-signals was varied across three conditions. The results are inconsistent with the predictions of the Kalman filter because correction for large errors in the beginning of a series of movements to a fixed target was not as fast as predicted and the learning rates for the extent and the direction of the movements did not differ in the way predicted by the Kalman filter. Instead, a simpler model that uses the same learning rate for all movements with the same error-signal reliability can explain the data. We conclude that our brain does not apply state estimation to determine the optimal planning correction for every individual movement, but it employs a simpler strategy of using a fixed learning rate for all movements with the same level of error-signal reliability

A whey protein-based multi-ingredient nutritional supplement stimulates gains in lean body mass and strength in healthy older men: A randomized controlled trial

Protein and other compounds can exert anabolic effects on skeletal muscle, particularly in conjunction with exercise. The objective of this study was to evaluate the efficacy of twice daily consumption of a protein-based, multi-ingredient nutritional supplement to increase strength and lean mass independent of, and in combination with, exercise in healthy older men. Forty-nine healthy older men (age: 73 ± 1 years [mean ± SEM]; BMI: 28.5 ± 1.5 kg/m2) were randomly allocated to 20 weeks of twice daily consumption of either a nutritional supplement (SUPP; n = 25; 30 g whey protein, 2.5 g creatine, 500 IU vitamin D, 400 mg calcium, and 1500 mg n-3 PUFA with 700 mg as eicosapentanoic acid and 445 mg as docosahexanoic acid); or a control (n = 24; CON; 22 g of maltodextrin). The study had two phases. Phase 1 was 6 weeks of SUPP or CON alone. Phase 2 was a 12 week continuation of the SUPP/CON but in combination with exercise: SUPP + EX or CON + EX. Isotonic strength (one repetition maximum [1RM]) and lean body mass (LBM) were the primary outcomes. In Phase 1 only the SUPP group gained strength (Σ1RM, SUPP: +14 ± 4 kg, CON: +3 ± 2 kg, P < 0.001) and lean mass (LBM, +1.2 ± 0.3 kg, CON: -0.1 ± 0.2 kg, P < 0.001). Although both groups gained strength during Phase 2, upon completion of the study upper body strength was greater in the SUPP group compared to the CON group (Σ upper body 1RM: 119 ± 4 vs. 109 ± 5 kg, P = 0.039). We conclude that twice daily consumption of a multi-ingredient nutritional supplement increased muscle strength and lean mass in older men. Increases in strength were enhanced further with exercise training

Obesity prevalence in a cohort of women in early pregnancy from a neighbourhood perspective

<p>Abstract</p> <p>Background</p> <p>The evidence of an association between neighbourhood deprivation and overweight is established for different populations. However no previous studies on neighbourhood variations in obesity in pregnant women were found. In this study we aimed to determine whether obesity during early pregnancy varied by neighbourhood economic status.</p> <p>Methods</p> <p>A register based study on 94,323 primiparous pregnant women in 586 Swedish neighbourhoods during the years 19922001. Multilevel technique was used to regress obesity prevalence on socioeconomic individual-level variables and the neighbourhood economic status. Five hundred and eighty-six neighbourhoods in the three major cities of Sweden, Stockholm, Göteborg and Malmö, during 19922001, were included. The majority of neighbourhoods had a population of 4 00010 000 inhabitants.</p> <p>Results</p> <p>Seven per cent of the variation in obesity prevalence was at the neighbourhood level and the odds of being obese were almost doubled in poor areas.</p> <p>Conclusion</p> <p>Our findings supports a community approach in the prevention of obesity in general and thus also in pregnant women.</p

Current understanding of the human microbiome

Author Posting. © The Author(s), 2018. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Medicine 24 (2018): 392–400, doi:10.1038/nm.4517.Our understanding of the link between the human microbiome and disease, including obesity, inflammatory bowel disease, arthritis and autism, is rapidly expanding. Improvements in the throughput and accuracy of DNA sequencing of the genomes of microbial communities associated with human samples, complemented by analysis of transcriptomes, proteomes, metabolomes and immunomes, and mechanistic experiments in model systems, have vastly improved our ability to understand the structure and function of the microbiome in both diseased and healthy states. However, many challenges remain. In this Review, we focus on studies in humans to describe these challenges, and propose strategies that leverage existing knowledge to move rapidly from correlation to causation, and ultimately to translation.Many of the studies described here in our laboratories were supported by the NIH, NSF, DOE, and the Alfred P. Sloan Foundation.2018-10-1