Project description:Research software is often developed with expedience as a core development objective because experimental results, but not the software, are specified and resourced as a project output. While such code can help find answers to specific research questions, it may lack longevity and flexibility to make it reusable. We reimplemented BoneJ, our software for skeletal biology image analysis, to address design limitations that put it at risk of becoming unusable. We improved the quality of BoneJ code by following contemporary best programming practices. These include separation of concerns, dependency management, thorough testing, continuous integration and deployment, source code management, code reviews, issue and task ticketing, and user and developer documentation. The resulting BoneJ2 represents a generational shift in development technology and integrates with the ImageJ2 plugin ecosystem.
Project description:Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs (GPs) on call categorization tasks using natural calls. We then tested categorization by the model and GPs using temporally and spectrally altered calls. Both the model and GPs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, the model predicted about 50% of the variance in GP behavior. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.
Project description:Natural biological systems are selected by evolution to continue to exist and evolve. Evolution likely gives rise to complicated systems that are difficult to understand and manipulate. Here, we redesign the genome of a natural biological system, bacteriophage T7, in order to specify an engineered surrogate that, if viable, would be easier to study and extend. Our initial design goals were to physically separate and enable unique manipulation of primary genetic elements. Implicit in our design are the hypotheses that overlapping genetic elements are, in aggregate, nonessential for T7 viability and that our models for the functions encoded by elements are sufficient. To test our initial design, we replaced the left 11,515 base pairs (bp) of the 39,937 bp wild-type genome with 12,179 bp of engineered DNA. The resulting chimeric genome encodes a viable bacteriophage that appears to maintain key features of the original while being simpler to model and easier to manipulate. The viability of our initial design suggests that the genomes encoding natural biological systems can be systematically redesigned and built anew in service of scientific understanding or human intention.
Project description:BackgroundRecent policy reforms encourage quality improvement (QI) innovations in primary care, but practitioners lack clear guidance regarding spread inside organizations.PurposeWe designed this study to identify how large organizations can facilitate intraorganizational spread of QI innovations.Methodology/approachWe conducted ethnographic observation and interviews in a large, multispecialty, community-based medical group that implemented three QI innovations across 10 primary care sites using a new method for intraorganizational process development and spread. We compared quantitative outcomes achieved through the group's traditional versus new method, created a process model describing the steps in the new method, and identified barriers and facilitators at each step.FindingsThe medical group achieved substantial improvement using its new method of intraorganizational process development and spread of QI innovations: standard work for rooming and depression screening, vaccine error rates and order compliance, and Pap smear error rates. Our model details nine critical steps for successful intraorganizational process development (set priorities, assess the current state, develop the new process, and measure and refine) and spread (develop support, disseminate information, facilitate peer-to-peer training, reinforce, and learn and adapt). Our results highlight the importance of utilizing preexisting organizational structures such as established communication channels, standardized roles, common workflows, formal authority, and performance measurement and feedback systems when developing and spreading QI processes inside an organization. In particular, we detail how formal process advocate positions in each site for each role can facilitate the spread of new processes.Practice implicationsSuccessful intraorganizational spread is possible and sustainable. Developing and spreading new QI processes across sites inside an organization requires creating a shared understanding of the necessary process steps, considering the barriers that may arise at each step, and leveraging preexisting organizational structures to facilitate intraorganizational process development and spread.
Project description:BackgroundBioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software.MethodologyA systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software.ResultsThe findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers.ConclusionsWhile strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.
Project description:The existence of a shared classification system is essential to knowledge production, transfer, and sharing. Studies of knowledge classification, however, rarely consider the fact that knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery. This neglect is problematic whenever information about categorical membership is itself used to evaluate the quality of the items that the category contains. The main objective of this paper is to show that the effects of category membership depend on the position that a category occupies in the hierarchical knowledge classification system of Wikipedia-an open knowledge production and sharing platform taking the form of a freely accessible on-line encyclopedia. Using data on all English-language Wikipedia articles, we examine how the position that a category occupies in the classification hierarchy affects the attention that articles in that category attract from Wikipedia editors, and their evaluation of quality of the Wikipedia articles. Specifically, we show that Wikipedia articles assigned to coarse-grained categories (i. e., categories that occupy higher positions in the hierarchical knowledge classification system) garner more attention from Wikipedia editors (i. e., attract a higher volume of text editing activity), but receive lower evaluations (i. e., they are considered to be of lower quality). The negative relation between attention and quality implied by this result is consistent with current theories of social categorization, but it also goes beyond available results by showing that the effects of categorization on evaluation depend on the position that a category occupies in a hierarchical knowledge classification system.
Project description:Today's computational researchers are expected to be highly proficient in using software to solve a wide range of problems ranging from processing large datasets to developing personalized treatment strategies from a growing range of options. Researchers are well versed in their own field, but may lack formal training and appropriate mentorship in software engineering principles. Two major themes not covered in most university coursework nor current literature are software testing and software optimization. Through a survey of all currently available Comprehensive R Archive Network packages, we show that reproducible and replicable software tests are frequently not available and that many packages do not appear to employ software performance and optimization tools and techniques. Through use of examples from an existing R package, we demonstrate powerful testing and optimization techniques that can improve the quality of any researcher's software.
Project description:Efficient recognition of odorous objects universally shapes animal behavior and is crucial for survival. To distinguish kin from nonkin, mate from nonmate and food from nonfood, organisms must be able to create meaningful perceptual representations of odor qualities and categories. It is currently unknown where and in what form the brain encodes information about odor quality. By combining functional magnetic resonance imaging (fMRI) with multivariate (pattern-based) techniques, we found that spatially distributed ensemble activity in human posterior piriform cortex (PPC) coincides with perceptual ratings of odor quality, such that odorants with more (or less) similar fMRI patterns were perceived as more (or less) alike. We did not observe these effects in anterior piriform cortex, amygdala or orbitofrontal cortex, indicating that ensemble coding of odor categorical perception is regionally specific for PPC. These findings substantiate theoretical models emphasizing the importance of distributed piriform templates for the perceptual reconstruction of odor object quality.
Project description:Research suggests the relationship between time pressure and software quality to be more complex than presumed. While software developers can adjust their output to improve observed performance at the expense of software quality, the latter has been found to increase with time pressure in case of work-pace dependent incentives. An untested, but widely disseminated game-theoretical model seeks to resolve this contradiction and hypothesizes that high rates of time pressure avoid so-called 'shortcuts', which occur in the form of imperfections induced by developers to meet unrealistically tight deadlines. We conduct two laboratory experiments to empirically test this model for the first time. Our results corroborate the model with regard to its suggestion that shortcuts can be reduced if developers perceive unrealistic deadlines as ever-present. However, we also show that the actual critical probability of unrealistic deadlines-the point at which shortcut taking is drastically reduced-is above the theoretical one. Although final conclusions on the impact of time pressure on software quality remain to be drawn, our results suggest that-considering the contingencies of our study-time pressure helps in striving for quality in software projects.
Project description:OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. The objective of this study was to apply machine learning techniques to automatically identify high-quality, content-specific articles for one time period in internal medicine and compare their performance with previous Boolean-based PubMed clinical query filters of Haynes et al. DESIGN The selection criteria of the ACP Journal Club for articles in internal medicine were the basis for identifying high-quality articles in the areas of etiology, prognosis, diagnosis, and treatment. Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machines were applied to identify these articles. MEASUREMENTS The machine learning models were compared in each category with each other and with the clinical query filters using area under the receiver operating characteristic curves, 11-point average recall precision, and a sensitivity/specificity match method. RESULTS In most categories, the data-induced models have better or comparable sensitivity, specificity, and precision than the clinical query filters. The polynomial support vector machine models perform the best among all learning methods in ranking the articles as evaluated by area under the receiver operating curve and 11-point average recall precision. CONCLUSION This research shows that, using machine learning methods, it is possible to automatically build models for retrieving high-quality, content-specific articles using inclusion or citation by the ACP Journal Club as a gold standard in a given time period in internal medicine that perform better than the 1994 PubMed clinical query filters.