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Empirical studies, often in the form of controlled experiments, have been widely adopted in software engineering research as a way to evaluate the merits of new software engineering tools. However, controlled experiments involving human participants actually using new tools are still rare, and when they are conducted, some have serious validity concerns. Recent research has also shown that many software engineering researchers view this form of tool evaluation as too risky and too difficult to conduct, as they might ultimately lead to inconclusive or negative results. In this paper, we aim both to help researchers minimize the risks of this form of tool evaluation, and to increase their quality, by offering practical methodological guidance on designing and running controlled experiments with developers. Our guidance fills gaps in the empirical literature by explaining, from a practical perspective, options in the recruitment and selection of human participants, informed consent, experimental procedures, demographic measurements, group assignment, training, the selecting and design of tasks, the measurement of common outcome variables such as success and time on task, and study debriefing. Throughout, we situate this guidance in the results of a new systematic review of the tool evaluations that were published in over 1,700 software engineering papers published from 2001 to 2011.

Background Cervicovaginal inflammation has been linked to negative reproductive health outcomes including the acquisition of HIV, other sexually transmitted infections, and cervical carcinogenesis. While changes to the vaginal microbiome have been linked to genital inflammation, the molecular relationships between the functional components of the microbiome with cervical immunology in the reproductive tract are understudied, limiting our understanding of mucosal biology that may be important for reproductive health. Results In this study, we used a multi’-omics approach to profile cervicovaginal samples collected from 43 Canadian women to characterize host, immune, functional microbiome, and metabolome features of cervicovaginal inflammation. We demonstrate that inflammation is associated with lower amounts of L. crispatus and higher levels of cervical antigen-presenting cells (APCs). Proteomic analysis showed an upregulation of pathways related to neutrophil degranulation, complement, and leukocyte migration, with lower levels of cornified envelope and cell-cell adherens junctions. Functional microbiome analysis showed reductions in carbohydrate metabolism and lactic acid, with increases in xanthine and other metabolites. Bayesian network analysis linked L. crispatus with glycolytic and nucleotide metabolism, succinate and xanthine, and epithelial proteins SCEL and IVL as major molecular features associated with pro-inflammatory cytokines and increased APCs. Conclusions This study identified key molecular and immunological relationships with cervicovaginal inflammation, including higher APCs, bacterial metabolism, and proteome alterations that underlie inflammation. As APCs are involved in HIV transmission, parturition, and cervical cancer progression, further studies are needed to explore the interactions between these cells, bacterial metabolism, mucosal immunity, and their relationship to reproductive health. BGqtALm3-LNazbdZNFi1WR Video Abstract

“In what circumstances would you want this AI to make decisions on your behalf?” We have been investigating how to enable a user of an Artificial Intelligence‐powered system to answer questions like this through a series of empirical studies, a group of which we summarize here. We began the series by (a) comparing four explanation configurations of saliency explanations and/or reward explanations. From this study we learned that, although some configurations had significant strengths, no one configuration was a clear “winner.” This result led us to hypothesize that one reason for the low success rates Explainable AI (XAI) research has in enabling users to create a coherent mental model is that the AI itself does not have a coherent model. This hypothesis led us to (b) build a model‐based agent, to compare explaining it with explaining a model‐free agent. Our results were encouraging, but we then realized that participants' cognitive energy was being sapped by having to create not only a mental model, but also a process by which to create that mental model. This realization led us to (c) create such a process (which we term After‐Action Review for AI or “AAR/AI”) for them, integrate it into the explanation environment, and compare participants' success with AAR/AI scaffolding vs without it. Our AAR/AI studies' results showed that AAR/AI participants were more effective assessing the AI than non‐AAR/AI participants, with significantly better precision and significantly better recall at finding the AI's reasoning flaws. \"In what circumstances would you want this Artificial Intelligence (AI) to make decisions on your behalf?\" This paper summarizes multiple empirical investigations of how to enable a user of an AI‐powered system to answer questions like this.

[...]participants had varied levels of experience with our departmental MDR process. Informatics research must focus on strategies to incorporate well-designed EHR technology into the process of MDR. [...]it is necessary to establish an evidence-based paradigm to incorporate the use of technology with behavioral strategies to enhance multidisciplinary clinical communication and care coordination for effective discharge planning.

This study aimed at investigating the relative roles of the duration versus intensity of exercise on the metabolic adaptations in vastus lateralis to short-term (10 day) aerobic-based cycle training. Healthy males with a peak aerobic power ( ) of 46.0 ± 2.0 ml kg −1  min −1 were assigned to either a 30-min ( n  = 7) or a 60-min ( n  = 8) duration performed at two different intensities (with order randomly assigned), namely moderate (M) and heavy (H), corresponding to 70 and 86 % , respectively. No change ( P  > 0.05) in was observed regardless of the training program. Based on the metabolic responses to prolonged exercise (60 % ), both M and H and 30 and 60 min protocols displayed less of a decrease ( P  < 0.05) in phosphocreatine (PCr) and glycogen (Glyc) and less of an increase ( P  < 0.05) in free adenosine diphosphate (ADP f ), free adenosine monophosphate (AMP f ), inosine monophosphate (IMP) and lactate (La). Training for 60 min compared with 30 min resulted in a greater protection ( P  < 0.05) of ADP f, AMP f , PCr and Glyc during exercise, effects that were not displayed between M and H. The reduction in both and RER ( P  < 0.05) observed during submaximal exercise did not depend on training program specifics. These findings indicate that in conjunction with our earlier study (Green et al., Eur J Appl Physiol, 2012b ), a threshold exists for duration rather than intensity of aerobic exercise to induce a greater training impact in reducing metabolic strain.

This study investigated the hypothesis that the duration of aerobic-based cycle exercise would affect the adaptations in substrate and metabolic regulation that occur in vastus lateralis in response to a short-term (10 day) training program. Healthy active but untrained males ( n  = 7) with a peak aerobic power ( ) of 44.4 ± 1.4 ml kg −1  min −1 participated in two different training programs with order randomly assigned (separated by ≥2 weeks). The training programs included exercising at a single intensity designated as light (L) corresponding to 60 % , for either 30 or 60 min. In response to a standardized task (60 % ), administered prior to and following each training program, L attenuated the decrease ( P  < 0.05) in phosphocreatine and the increase ( P  < 0.05) in free adenosine diphosphate and free adenosine monophosphate but not lactate. These effects were not altered by daily training duration. In the case of muscle glycogen, training for 60 versus 30 min exaggerated the increase ( P  < 0.05) that occurred, an effect that extended to both rest and exercise concentrations. No changes were observed in measured during progressive exercise to fatigue or in and RER during submaximal exercise with either training duration. These findings indicate that reductions in metabolic strain, as indicated by a more protected phosphorylation potential, and higher glycogen reserves, can be induced with a training stimulus of light intensity applied for as little as 30 min over 10 days. Our results also indicate that doubling the duration of daily exercise at L although inducing increased muscle glycogen reserves did not result in a greater metabolic adaptation.

Millions of people program to support their work but don't call themselves programmers. The field of end-user software engineering is concerned with helping these people create reliable, dependable, and reusable programs, without distracting them from their primary tasks. This special issue of IEEE Software presents a selection of research from this field, providing a glimpse of some of the exciting advances made in past 10 years of research and development.

The AgAID Institute is a National AI Research Institute focused on developing AI solutions for specialty crop agriculture. Specialty crops include a variety of fruits and vegetables, nut trees, grapes, berries, and different types of horticultural crops. In the United States, the specialty crop industry accounts for a multibillion dollar industry with over 300 crops grown just along the U.S. west coast. Specialty crop agriculture presents several unique challenges: they are labor‐intensive, are easily impacted by weather extremities, and are grown mostly on irrigated lands and hence are dependent on water. The AgAID Institute aims to develop AI solutions to address these challenges, particularly in the face of workforce shortages, water scarcity, and extreme weather events. Addressing this host of challenges requires advancing foundational AI research, including spatio‐temporal system modeling, robot sensing and control, multiscale site‐specific decision support, and designing effective human–AI workflows. This article provides examples of current AgAID efforts and points to open directions to be explored.

Objective: Most previous studies that have investigated the relation between abortion and mental illness have presented mixed findings. We examined the relation between abortion, mental disorders, and suicidality using a US nationally representative sample. Methods: Data came from the National Comorbidity Survey Replication (n = 3310 women, aged 18 years and older). The World Health Organization-Composite International Diagnostic Interview was used to assess mental disorders based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria and lifetime abortion in women. Multiple logistic regression analyses were employed to examine associations between abortion and lifetime mood, anxiety, substance use, eating, and disruptive behaviour disorders, as well as suicidal ideation and suicide attempts. We calculated the percentage of respondents whose mental disorder came after the first abortion. The role of violence was also explored. Population attributable fractions were calculated for significant associations between abortion and mental disorders. Results: After adjusting for sociodemographics, abortion was associated with an increased likelihood of several mental disorders—mood disorders (adjusted odds ratio [AOR] ranging from 1.75 to 1.91), anxiety disorders (AOR ranging from 1.87 to 1.91), substance use disorders (AOR ranging from 3.14 to 4.99), as well as suicidal ideation and suicide attempts (AOR ranging from 1.97 to 2.18). Adjusting for violence weakened some of these associations. Forall disorders examined, less than one-half of women reported that their mental disorder had begun after the first abortion. Population attributable fractions ranged from 5.8% (suicidal ideation) to 24.7% (drug abuse). Conclusions: Our study confirms a strong association between abortion and mental disorders. Possible mechanisms of this relation are discussed.

The objective of this paper is to provide an overview of the recent developments in muscle physiology and biochemistry in general, and with respect to chronic obstructive pulmonary disease (COPD) specifically. As a way of illustration, we have presented data on the remodeling that occurs in vastus lateralis in two patients with COPD (COPD #1, forced expiratory volume in one second/forced vital capacity [FEV(1)/FVC] = 63%; COPD #2, FEV(1)/FVC = 41%) exhibiting differences in muscle wasting as compared to healthy controls (CON; FEV(1)/FVC = 111 +/- 2.2%, n = 4). Type I fibers percentages were lower in both COPD #1 (16.7) and COPD #2 (24.9) compared to CON (57.3 +/- 5.2). Cross sectional area of the type I fibers of the patients ranged between 65%-68% of CON and for the type II subtypes (IIA, IIAX, IIX) between 74% and 89% (COPD #1) and 17%-32% (COPD #2). A lower number of capillary contacts were observed for all fiber types in COPD #1 but not COPD #2. Lower concentrations of adenosine triphosphate (ATP) (24%-26%) and phosphocreatine (18%-20%), but not lactate occurred in COPD. In contrast to COPD #1, who displayed normal glucose transporter content, GLUT1 and GLUT4 were only 71% and 54%, respectively of CON in COPD #2. Lower monocarboxylate contents were found for MCT1 in both COPD #1 (63%) and COPD #2 (41%) and for MCT4 (78%) in COPD #1. Maximal oxidative enzyme activities (V(max)) for COPD #2 ranged between 37% (succinic dehydrogenase) and 70% (cytochrome C oxidase) of CON. For the cytosolic enzymes, V(max) ranged between 89% (hexokinase) to 31% (pyruvate kinase) of CON. Depressions were also observed in V(max) of the Na(+)-K(+)-ATPase for COPD #1 (66% of CON) but not COPD #2 (92% of CON) while V(max) of the Ca(2+)-ATPase was near normal in COPD #1 (84% CON). It is concluded that disturbances can occur in muscle to a wide range of excitation, contraction and metabolic processes in COPD.