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Large sample size (N) is seen as a key criterion in judging the replicability of psychological research, a phenomenon we refer to as the N-Heuristic. This heuristic has led to the incentivization of fast, online, non-behavioral studies—to the potential detriment of psychological science. While large N should in principle increase statistical power and thus the replicability of effects, in practice it may not. Large-N studies may have other attributes that undercut their power or validity. Consolidating data from all systematic, large-scale attempts at replication (N = 307 original-replication study pairs), we find that the original study's sample size did not predict its likelihood of being replicated (rs = -0.02, p = 0.741), even with study design and research area controlled. By contrast, effect size emerged as a substantial predictor (rs = 0.21, p < 0.001), which held regardless of the study's sample size. N may be a poor predictor of replicability because studies with larger N investigated smaller effects (rs = -0.49, p < 0.001). Contrary to these results, a survey of 215 professional psychologists, presenting them with a comprehensive list of methodological criteria, found sample size to be rated as the most important criterion in judging a study's replicability. Our findings strike a cautionary note with respect to the prioritization of large N in judging the replicability of psychological science. [ABSTRACT FROM AUTHOR]

Background: Outcome selection is a critically important aspect of clinical trial design. Alive-and-ventilator free days is an outcome measure commonly used in critical care clinical trials, but has not been fully explored in resuscitation science. Methods: A simulation study was performed to explore approaches to the definition and analysis of alive-and-ventilator free days in cardiac arrest populations. Data from an in-hospital cardiac arrest observational cohort and from the Pragmatic Airway Resuscitation Trial were used to inform and conduct the simulations and validate approaches to alive-and-ventilator free days measurement and analysis. Findings: Alive-and-ventilator-free days is a flexible outcome measure in cardiac arrest populations. An approach to alive-and-ventilator free days that assigns -1 days when return of spontaneous circulation is not achieved provides a wider distribution of the outcome and improves statistical power. The optimal approach to the analysis of alive-and-ventilator free days varies based on the expected impact of the intervention under study on rates of return of spontaneous circulation, survival, and ventilator-free survival. Conclusions: Alive-and-ventilator free days adds to the armamentarium of clinical trialists in the field of resuscitation science. [ABSTRACT FROM AUTHOR]

Background: Traditional uvulectomy is widely practiced in Africa especially in sub-Saharan countries including Ethiopia. Limited Studies conducted in different times and areas of the world have shown that the prevalence of uvulectomy and its associated factors are varied from country to country. There is limited evidence to determine the prevalence and reasons of malpractice in Ethiopia. Therefore, this study aimed to assess the prevalence of traditional uvulectomy, and its associated factors among parents who had children aged less than 6 months. Methods: A community-based cross-sectional mixed study design was conducted among 630 participants selected by a systematic sampling technique. Data were collected using a pretested interviewer-administered questionnaire. The collected data were entered into Epi-data, and analyzed by using STATA version 14software. Descriptive statistics were computed and presented using tables, figures and texts. Factors associated with the prevalence of uvulectomy were selected for multiple logistic regressions at the probability value (p-value) of less than 0.2 in the X2- analysis. Statistically significant associated factors were identified at the probability value (p-value) less than 0.05 and adjusted OR (AOR) with a 95% CI. A total of 10 individual depth interviewees and 5 key informants were included using purposive sampling techniques. For the qualitative study, and thematic content analysis was employed to analyze the data, which were transcribed, translated, coded, screened, thematized, analyzed, synthesized, and finally supplemented the quantitative finding. Results: The prevalence of traditional uvulectomy was 84.60% (CI: 81.77%n—87.42%). Being rural residence (adjusted OR = 2.08, CI = 1.06–4.12), mothers aged 30 to 34 years (adjusted OR = 2.9, CI = 1.28–6.60), having no antenatal care visit (adjusted OR = 3.91, CI = 1.33–11.47), having no bad obstetric history (adjusted OR = 2.11, CI = 1.12–3.98), having no postnatal care visit (adjusted OR = 3.88, CI = 1.36–11.12) and mothers' poor attitude (adjusted OR = 3.32, CI = 2.01–5.47) were statistically significant associated factors of traditional uvulectomy. Seeking traditional uvulectomy, lack of information and third-party pressure were the main themes of the qualitative study that support the findings of the quantitative study. Conclusion: The prevalence of traditional uvulectomy was high. Being rural residents, mothers aged 30 to 34 years, having no antenatal care visit, having no complications of obstetric history, having no postnatal care visit and mothers with poor attitude were statistically significant associated factors of traditional uvulectomy. Lack of information about uvulectomy and third-party pressure was the reason for seeking traditional uvulectomy. [ABSTRACT FROM AUTHOR]

Introduction: A number of seroprevalence studies in Zambia document the extent of spread of acute SARS-CoV-2 infection, yet knowledge gaps still exist on symptoms and conditions that continue or develop after acute COVID-19 (long COVID). This is an important gap given the estimated prevalence of long COVID in other African countries. We assessed factors associated with long COVID at the initial visit to a post-acute COVID-19 (PAC-19) clinic and longitudinally among a cohort of patients with ≥2 review visits. Methods: We implemented a cross-sectional and longitudinal analysis of PAC-19 clinic patients from Aug-2020 to Jan-2023. The study outcome was long COVID; defined as the presence of new, relapsing, or persistent COVID-19 symptoms that interfere with the ability to function at home or work. Explanatory variables were demographic and clinical characteristics of patients which included sex, age group, presence of new onset medical conditions, presence of pre-existing comorbidities, vaccination status and acute COVID-19 episode details. We fitted logistic and mixed effects regression models to assess for associated factors and considered statistical significance at p<0.05. Results: Out of a total 1,359 PAC-19 clinic patients in the cross-sectional analysis, 548 (40.3%) patients with ≥2 PAC-19 clinic visits were in the longitudinal analysis. Patients' median age was 53 (interquartile range [IQR]: 41–63) years, 919 (67.6%) were hospitalized for acute COVID-19, and of whom 686 (74.6%) had severe acute COVID-19. Overall, 377 (27.7%) PAC-19 clinic patients had long COVID. Patients with hospital length of stay ≥15 days (adjusted odds ratio [aOR]: 5.37; 95% confidence interval [95% CI]: 2.99–10.0), severe acute COVID-19 (aOR: 3.22; 95% CI: 1.68–6.73), and comorbidities (aOR:1.50; 95% CI: 1.02–2.21) had significantly higher chance of long COVID. Longitudinally, long COVID prevalence significantly (p<0.001) declined from 75.4% at the initial PAC-19 visit to 26.0% by the final visit. The median follow-up time was 7 (IQR: 4–12) weeks. Conclusion: Factors associated with long COVID in Zambia were consistent both cross-sectionally at the initial visit to PAC-19 clinics and longitudinally across subsequent review visits. This highlights the importance of ongoing monitoring and tailored interventions for patients with comorbidities and severe COVID-19 to mitigate the long-term impacts of COVID-19. [ABSTRACT FROM AUTHOR]

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design. Author summary: Using limited data to predict how biological systems respond to combinations of perturbations is important for better understanding of biological systems and for practical applications. Here we present a new method, structure-augmented regression, that efficiently learns biological response landscapes from sparse measurements. Our method exploits the property that many biological response landscapes have distinct lower-dimensional structures, which can be learned first to assist the subsequent quantitative predictions. We demonstrate that our algorithm outperforms existing algorithms on simulated and experimental data of various biological systems and dimensions. We further exploit the learned structure by suggesting new experiments that refines the learned structure to further improve the prediction accuracy. By integrating machine learning with experimental design, our approach has implications for the predictive control of biological systems. [ABSTRACT FROM AUTHOR]

This research examines how anchoring bias affects managers' multi-dimensional evaluations of supplier performance, supplier selection, and the effectiveness of two debiasing techniques. We consider the supplier past performance in one performance dimension as the anchor and investigate whether and how this anchor would have a knock-on effects on evaluating a supplier's performance in other dimensions. We conducted two online experimental studies (Study 1, sample size = 104 and Study 2, sample size = 408). Study 1 adopts a 2 x 1 (high anchor vs. low anchor) between-subjects factorial experimental design, and Study 2 is a 3 (debiasing: no, consider-the-opposite, mental-mapping) x 2 (high anchor vs. low anchor) between-subjects factorial design. The results from Studies 1 and 2 suggest that when a supplier has received a low evaluation score in one dimension in the past, participants assign the same supplier lower scores in the other dimensions compared to a supplier that has obtained a high score in the past. We also find that anchoring has a knock-on effect on how likely participants are to choose the same supplier in the future. Our findings highlight the asymmetric effectiveness of 'consider-the-opposite' and 'mental-mapping' debiasing techniques. This research is the first study that examines how anchoring bias managers' evaluations in a multi-dimensional setting and its knock-on effects. It also explores the effectiveness of two low-cost debiasing techniques. A crucial practical implication is that suppliers' exceptionally good or disappointing past performance affects the judgement of supply managers. Hence, managers should use consider-the-opposite or mental-mapping techniques to debias the effect of high and low anchors, respectively. [ABSTRACT FROM AUTHOR]

Background: Analysis of omics data that contain multidimensional biological and clinical information can be complex and make it difficult to deduce significance of specific biomarker factors. Methods: We explored the utility of propensity score matching (PSM), a statistical technique for minimizing confounding factors and simplifying the examination of specific factors. We tested two datasets generated from cohorts of colorectal cancer (CRC) patients, one comprised of immunohistochemical analysis of 12 protein markers in 544 CRC tissues and another consisting of RNA-seq profiles of 163 CRC cases. We examined the efficiency of PSM by comparing pre- and post-PSM analytical results. Results: Unlike conventional analysis which typically compares randomized cohorts of cancer and normal tissues, PSM enabled direct comparison between patient characteristics uncovering new prognostic biomarkers. By creating optimally matched groups to minimize confounding effects, our study demonstrates that PSM enables robust extraction of significant biomarkers while requiring fewer cancer cases and smaller overall patient cohorts. Conclusion: PSM may emerge as an efficient and cost-effective strategy for multiomic data analysis and clinical trial design for biomarker discovery. [ABSTRACT FROM AUTHOR]

Background: Juvenile antisocial behavior can have long-lasting and devastating effects for juveniles themselves, victims, and society. Evidence-based treatment is vital. Forensic Outpatient Systemic Therapy (Forensische Ambulante Systeem Therapie; FAST) is a promising treatment for juveniles showing severe antisocial behavior including aggression, (domestic) violence, and delinquent behavior. FAST has a flexible intensity and length, addresses individual and systemic risk and protective factors, and is responsive to the abilities of the client (system), intervention characteristics all considered crucial for effective treatment. The current study will investigate whether FAST is effective in reducing aggression of the juvenile, reaching client formulated subgoals, and improving family functioning. Processes of change will be examined, as well as mediation by reaching client formulated subgoals and improved family functioning. Methods: A Multiple Case Experimental Design (MCED) with an ABC design will be performed (A = baseline, B = intervention, and C = follow-up). Juveniles with primary aggression and/or anger problems (N = 15) and their caregiver(s) will be recruited. Data collection will consist of self-report questionnaires and case file analysis. Participants fill out frequent short self-report questionnaires (twice a week during phase A, every other week during phase B, and every week during phase C) and two main questionnaires at the start of the intervention and immediately after intervention end, thereby covering a period of 5 to 11 months. Both visual and statistical analyses will be performed. Discussion: This study will generate robust knowledge and inform clinical practice on the effectiveness, processes of change, and mediating mechanisms of FAST, aiming to improve the treatment of future families within youth forensic care. Trial registration: This trial was registered at ClinicalTrials.gov on 28/08/2023, protocol ID 60-63600-98-1138a. [ABSTRACT FROM AUTHOR]

Antimicrobial de-escalation refers to reducing the spectrum of antibiotics used in treating bacterial infections. This strategy is widely recommended in many antimicrobial stewardship programs and is believed to reduce patients' exposure to broad-spectrum antibiotics and prevent resistance. However, the ecological benefits of de-escalation have not been universally observed in clinical studies. This paper conducts computer simulations to assess the ecological effects of de-escalation on the resistance prevalence of Pseudomonas aeruginosa—a frequent pathogen causing nosocomial infections. Synthetic data produced by the models are then used to estimate the sample size and study period needed to observe the predicted effects in clinical trials. Our results show that de-escalation can reduce colonization and infections caused by bacterial strains resistant to the empiric antibiotic, limit the use of broad-spectrum antibiotics, and avoid inappropriate empiric therapies. Further, we show that de-escalation could reduce the overall super-infection incidence, and this benefit becomes more evident under good compliance with hand hygiene protocols among health care workers. Finally, we find that any clinical study aiming to observe the essential effects of de-escalation should involve at least ten arms and last for four years—a size never attained in prior studies. This study explains the controversial findings of de-escalation in previous clinical studies and illustrates how mathematical models can inform outcome expectations and guide the design of clinical studies. [ABSTRACT FROM AUTHOR]

Practical courses mimic experimental research and may generate valuable data. Yet, data that is generated by students during a course is often lost as there is no centrally organized collection and storage of the data. The loss of data prevents its reuse. To provide access to these data, I present an approach that I call studentsourcing. It collects, aggregates, and reuses data that is generated by students in a practical course on cell biology. The course runs annually, and I have recorded the data that was generated by >100 students over 3 years. Two use cases illustrate how the data can be aggregated and reused either for the scientific record or for teaching. As the data is obtained by different students, in different groups, over different years, it is an excellent opportunity to discuss experimental design and modern data visualization methods such as the superplot. The first use case demonstrates how the data can be presented as an online, interactive dashboard, providing real-time data of the measurements. The second use case shows how central data storage provides a unique opportunity to get precise quantitative data due to the large sample size. Both use cases illustrate how data can be effectively aggregated and reused. [ABSTRACT FROM AUTHOR]

Automated decision-making gains traction, prompting discussions on regulation with calls for human oversight. Understanding how human involvement affects the acceptance of algorithmic recommendations and the accuracy of resulting decisions is vital. In an online experiment (N = 292), for a prediction task, participants choose a recommendation stemming either from an algorithm or another participant. In a between-subject design, we varied if the prediction was delegated completely or if the recommendation could be adjusted. 66% of times, participants preferred to delegate the decision to an algorithm over an equally accurate human. The preference for an algorithm increased by 7 percentage points if participants could monitor and adjust the recommendations. Participants followed algorithmic recommendations more closely. Importantly, they were less likely to intervene with the least accurate recommendations. Hence, in our experiment the human-in-the-loop design increases the uptake but decreases the accuracy of the decisions. [ABSTRACT FROM AUTHOR]

Background: Montelukast has shown potential as a candidate treatment for dengue. This study aimed to evaluate the efficacy and safety of montelukast in preventing dengue with warning signs. Methods: This multicenter, randomized, double-blind, placebo-controlled trial enrolled adult participants with NS1 antigenemia in Thailand. The participants were randomly assigned to receive either oral montelukast (10 mg) or a placebo for 10 days or until all symptoms resolved. Results: Between January 2021 and June 2023, 358 participants were enrolled and randomly assigned (1:1) to receive either montelukast or placebo. The incidence rate of warning signs in the montelukast group and the placebo group were 9.5% and 7.8% per day, respectively. There was no difference between the two groups (HR 1.36; 95%CI 0.94–1.96, P = 0.105). No statistically significant differences were observed in the incidence rate of severe dengue, hemoconcentration, thrombocytopenia, admission, or recovery from dengue. Neither dengue shock, nor mortality occurred. The montelukast group exhibited a decreased incidence rate of transaminase elevations (0.7% vs 1.4% per day, HR: 0.48, 95%CI 0.25–0.90, P = 0.023). Conclusion: Oral montelukast does not reduce the incidence of warning signs among patients with dengue. Nevertheless, the observed decrease in transaminase elevations warrants further investigation to evaluate the potential effect of montelukast. Clinical Trials Registration: Clinicaltrials.gov, NCT04673422, registered on 9 December 2020 Author summary: Although there is no specific treatment for dengue to date, montelukast shows promise as a potential therapeutic agent. An open-label study previously showed a 71% reduction in dengue shock among patients with dengue who were administered montelukast. However, concerns were raised regarding the clinical study's lack of a power analysis or predefined statistical difference. Because of its affordability, relatively safe profile, and widespread availability, exploring its role in preventing dengue complications holds great significance. This study is a double-blind clinical trial with pragmatic design assessing the efficacy of montelukast in dengue. In contrast to the previous open-label study, montelukast does not reduce the incidence of warning signs among patients with dengue. Nevertheless, a reduction in the incidence of transaminase elevations was observed. In conclusion, montelukast should not be used for the prevention of warning signs in patients with dengue. However, the observed decrease in transaminase elevations warrants further investigation to evaluate the potential effect of montelukast. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of our study was to determine the order in which science and health researchers read scientific papers, their reasons for doing so and the perceived difficulty and perceived importance of each section. Study design and setting: An online survey open to science and health academics and researchers distributed via existing research networks, X (formerly Twitter), and LinkedIn. Results: Almost 90% of respondents self-declared to be experienced in reading research papers. 98.6% of the sample read the abstract first because it provides an overview of the paper and facilitates a decision on continuing to read on or not. Seventy-five percent perceived it to be the easiest to read and 62.4% perceived it to be very important (highest rank on a 5-point Likert scale). The majority of respondents did not read a paper in the IMRAD (Introduction, Methods, Results And Discussion) format. Perceived difficulty and perceived importance influenced reading order. Conclusion: Science and health researchers do not typically read scientific and health research papers in IMRAD format. The more important a respondent perceives a section to be, the more likely they are to read it. The easier a section is perceived, the more likely it will be read. We present recommendations to those teaching the skill of writing scientific papers and reports. [ABSTRACT FROM AUTHOR]

Data from the marriage of paleomagnetism and archaeology (archaeomagnetism) are the backbone of attempts to create geomagnetic field models for ancient times. Paleointensity experimental design has been the focus of intensive efforts and the requirements and shortcomings are increasingly well understood. Some archaeological materials have excellent age control from inscriptions, which can be tied to a given decade or even a specific year in some cases. In this study, we analyzed fired mud bricks used for the construction of the Ishtar Gate, the entrance complex to the ancient city of Babylon in Southern Mesopotamia. We were able to extract reliable intensity data from all three phases of the gate, the earliest of which includes bricks inscribed with the name of King Nebuchadnezzar II (605 to 562 BCE). These results (1) add high quality intensity data to a region relatively unexplored so far (Southern Mesopotamia), (2) contribute to a better understanding of paleosecular variation in this region, and the development of an archaeomagnetic dating reference for one of the key regions in the history of human civilizations; (3) demonstrate the potential of inscribed bricks (glazed and unglazed), a common material in ancient Mesopotamia, to archaeomagnetic studies; and (4) suggest that the gate complex was constructed some time after the Babylonian conquest of Jerusalem, and that there were no substantial chronological gaps in the construction of each consecutive phase. The best fit of our data (averaging 136±2.1 ZAm2) with those of the reference curve (the Levantine Archaeomagnetic Curve) is 569 BCE. [ABSTRACT FROM AUTHOR]

Medical Respite Programs (MRPs) characterize a care model that has been developed to address the health care and social needs of persons experiencing homelessness by providing post-acute hospital care in a safe environment. Although this model has been shown to reduce hospitalizations, improve health outcomes and increase access to health services, prior studies of MRP programs and outcomes have been limited to individual sites and may not generalize to the population of individuals receiving MRP care. This study protocol describes a mixed method design to collect organizational, provider, and patient-level data from a sample of MRPs. [ABSTRACT FROM AUTHOR]

Power analysis currently dominates sample size determination for experiments, particularly in grant and ethics applications. Yet, this focus could paradoxically result in suboptimal study design because publication biases towards studies with the largest effects can lead to the overestimation of effect sizes. In this Essay, we propose a paradigm shift towards better study designs that focus less on statistical power. We also advocate for (pre)registration and obligatory reporting of all results (regardless of statistical significance), better facilitation of team science and multi-institutional collaboration that incorporates heterogenization, and the use of prospective and living meta-analyses to generate generalizable results. Such changes could make science more effective and, potentially, more equitable, helping to cultivate better collaborations. Statistical power analysis currently dominates experimental design. In this Essay, the authors argue that we should move away from the current focus on power analysis and instead encourage smaller scale studies and collaborative projects. [ABSTRACT FROM AUTHOR]

Background: Healthcare-associated acquisition and transmission of nontuberculous mycobacteria (NTM) among people with cystic fibrosis (pwCF) has been described, and remains a concern for both patients and providers. This report describes the design of a prospective observational study utilizing the standardized epidemiologic investigation toolkit for healthcare-associated links in transmission of NTM among pwCF. Methods: This is a parallel multi-site study of pwCF who have infections with respiratory NTM isolates and receive healthcare within a common CF Care Center. Participants have a history of one or more NTM positive airway cultures and have been identified as having NTM infections suggestive of a possible outbreak within a single Center, based on NTM isolate genomic analysis. Participants are enrolled in the study over a 3-year period. Primary endpoints are identification of shared healthcare-associated source(s) among pwCF in a Center, identification of healthcare environmental dust and water biofilm NTM isolates that are genetically highly-related to respiratory isolates, and identification of common home of residence watersheds among pwCF infected with clustered isolates. Secondary endpoints include characterization of healthcare-associated transmission and/or acquisition modes and settings as well as description of incidence and prevalence of healthcare-associated environmental NTM species/subspecies by geographical region. Discussion: We hypothesize that genetically highly-related isolates of NTM among pwCF cared for at the same Center may arise from healthcare sources including patient-to-patient transmission and/or acquisition from health-care environmental dust and/or water biofilms. This study design utilizes a published, standardized, evidence-based epidemiologic toolkit to facilitate confidential, independent healthcare-associated NTM outbreak investigations within CF Care Centers. This study will facilitate real-time, rapid detection and mitigation of healthcare-associated NTM outbreaks to reduce NTM risk, inform infection prevention and control guidelines, and characterize the prevalence and origin of NTM outbreaks from healthcare-associated patient-to-patient transmission and/or environmental acquisition. This study will systematically characterize human disease causing NTM isolates from serial collection of healthcare environmental dust and water biofilms and define the most common healthcare environmental sources harboring NTM biofilms. Trial registration: ClinicalTrials.gov NCT05686837. [ABSTRACT FROM AUTHOR]

Background: Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intelligence (AI) in the field of endoscopy, specifically in diagnosing colorectal lesions. The research was conducted under the supervision of experienced professionals and trainees. Methods: EMBASE, PubMed, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI) database, and other potential databases were surveyed for articles related to the EC with AI published before September 2023. RevMan (5.40), Stata (14.0), and R software (4.1.0) were used for statistical assessment. Studies that measured the accuracy of EC using AI for colorectal lesions were included. Two authors independently assessed the selected studies and their extracted data. This included information such as the country, literature, total study population, study design, characteristics of the fundamental study and control groups, sensitivity, number of samples, assay methodology, specificity, true positives or negatives, and false positives or negatives. The diagnostic accuracy of EC by AI was determined by a bivariate random-effects model, avoiding a high heterogeneity effect. The ANOVA model was employed to determine the more effective approach. Results: A total of 223 studies were reviewed; 8 articles were selected that included 2984 patients (4241 lesions) for systematic review and meta-analysis. AI assessed 4069 lesions; experts diagnosed 3165 and 5014 by trainees. AI demonstrated high accuracy, sensitivity, and specificity levels in detecting colorectal lesions, with values of 0.93 (95% CI: 0.90, 0.95) and 0.94 (95% CI: 0.73, 0.99). Expert diagnosis was 0.90 (95% CI: 0.85, 0.94), 0.87 (95% CI: 0.78, 0.93), and trainee diagnosis was 0.74 (95% CI: 0.67, 0.79), 0.72 (95% CI: 0.62, 0.80). With the EC by AI, the AUC from SROC was 0.95 (95% CI: 0.93, 0.97), therefore classified as excellent category, expert showed 0.95 (95% CI: 0.93, 0.97), and the trainee had 0.79 (95% CI: 0.75, 0.82). The superior index from the ANOVA model was 4.00 (1.15,5.00), 2.00 (1.15,5.00), and 0.20 (0.20,0.20), respectively. The examiners conducted meta-regression and subgroup analyses to evaluate the presence of heterogeneity. The findings of these investigations suggest that the utilization of NBI technology was correlated with variability in sensitivity and specificity. There was a lack of solid evidence indicating the presence of publishing bias. Conclusions: The present findings indicate that using AI in EC can potentially enhance the efficiency of diagnosing colorectal abnormalities. As a valuable instrument, it can enhance prognostic outcomes in ordinary EC procedures, exhibiting superior diagnostic accuracy compared to trainee-level endoscopists and demonstrating comparability to expert endoscopists. The research is subject to certain constraints, namely a limited number of clinical investigations and variations in the methodologies used for identification. Consequently, it is imperative to conduct comprehensive and extensive research to enhance the precision of diagnostic procedures. [ABSTRACT FROM AUTHOR]

In view of the problem that removing abnormal plants in breeding rape requires a large amount of labor and is inefficient, combined with the planting requirements of breeding rape, a shovel-tooth end-effector was designed, and a shovel-tooth removal test bench was built. A simulation model based on MBD (Multibody Dynamics)-DEM (Discrete Element Method) coupling was constructed. Then we conducted a Box-Behnken test with four factors and three levels. Taking the angle of soil penetration, speed of soil penetration, depth of soil penetration and speed of shovel-tooth gathering as the test factors, the soil penetration force and shovel-tooth gathering force as the test indicators. The mathematical regression model between test indicators and test factor was established. After optimizing the parameters of the model, the best combination of parameters with low soil penetration force and low shovel-tooth gathering force was obtained: angle of soil penetration of 84°, speed of soil penetration of 9 cm/s, depth of soil penetration of 8cm, and speed of shovel-tooth gathering of 6 cm/s. The simulation model was validated by field experiments. The average soil penetration force and average shovel-tooth gathering force of the three groups of pull-out tests were 34.8 N and 763.0 N, respectively. The removal rates were 96%, 92%, and 94%, all greater than 90%, indicating that the removal effect of the shovel-tooth end-effector was good, and the parameters were reasonably designed. The results can serve as reference for the design of rape abnormal plants removal device and the operation of MBD-DEM coupling simulation end-effector. [ABSTRACT FROM AUTHOR]

There is a great deal of indirect evidence suggesting that people with facial difference (FD) may be dehumanized. This research aimed to provide direct evidence of the dehumanization of people with FD based on the stigmatizing reactions they elicit. More precisely, previous findings revealed that the specific way people with FD are looked upon is related to the feelings of disgust they elicit. Since disgust fosters dehumanization, our aim was to confirm the modified pattern of visual attention towards people with FD and to determine whether it was also related to humanness perception. For that purpose, a preregistered eye-tracking study (N = 97) using a former experimental design extended to humanity attributions was conducted. This research replicates findings showing that the face of people with FD is explored differently in comparison with other human faces. However, the hypothesis that people with FD were given fewer humanity attributions was not supported. Therefore, the hypothesis of a "dehumanizing gaze" towards people with FD–beyond humanity-related attributions–is discussed in light of these findings. [ABSTRACT FROM AUTHOR]

Motivation: Our study aimed to identify biologically relevant transcription factors (TFs) that control the expression of a set of co-expressed or co-regulated genes. Results: We developed a fully automated pipeline, Motif Over Representation Analysis (MORA), to detect enrichment of known TF binding motifs in any query sequences. MORA performed better than or comparable to five other TF-prediction tools as evaluated using hundreds of differentially expressed gene sets and ChIP-seq datasets derived from known TFs. Additionally, we developed EnsembleTFpredictor to harness the power of multiple TF-prediction tools to provide a list of functional TFs ranked by prediction confidence. When applied to the test datasets, EnsembleTFpredictor not only identified the target TF but also revealed many TFs known to cooperate with the target TF in the corresponding biological systems. MORA and EnsembleTFpredictor have been used in two publications, demonstrating their power in guiding experimental design and in revealing novel biological insights. [ABSTRACT FROM AUTHOR]

Background: Adaptive thermogenesis, defined as the decrease in the energy expenditure components beyond what can be predicted by changes in body mass stores, has been studied as a possible barrier to weight loss and weight maintenance. Intermittent energy restriction (IER), using energy balance refeeds, has been pointed out as a viable strategy to reduce adaptive thermogenesis and improve weight loss efficiency (greater weight loss per unit of energy deficit), as an alternative to a continuous energy restriction (CER). Following a randomized clinical trial design, the BREAK Study aims to compare the effects of IER versus CER on body composition and in adaptive thermogenesis, and understand whether participants will successfully maintain their weight loss after 12 months. Methods: Seventy-four women with obesity and inactive (20–45 y) will be randomized to 16 weeks of CER or IER (8x2 weeks of energy restriction interspersed with 7x1 week in energy balance). Both groups will start with 2 weeks in energy balance before energy restriction, followed by 16 weeks in energy restriction, then 8 weeks in energy balance and finally a 12-month weight maintenance phase. Primary outcomes are changes in fat-mass and adaptive thermogenesis after weight loss and weight maintenance. Secondary outcomes include weight loss, fat-free mass preservation, alterations in energy expenditure components, and changes in hormones (thyroid function, insulin, leptin, and cortisol). Discussion: We anticipate that The BREAK Study will allow us to better understand adaptive thermogenesis during weight loss and weight maintenance, in women with obesity. These findings will enable evidence-based decisions for obesity treatment. Trial registration: ClinicalTrials.gov: NCT05184361. [ABSTRACT FROM AUTHOR]

Background: Plague is a zoonotic disease that, despite affecting humans for more than 5000 years, has historically been the subject of limited drug development activity. Drugs that are currently recommended in treatment guidelines have been approved based on animal studies alone–no pivotal clinical trials in humans have yet been completed. As a result of the sparse clinical research attention received, there are a number of methodological challenges that need to be addressed in order to facilitate the collection of clinical trial data that can meaningfully inform clinicians and policy-makers. One such challenge is the identification of clinically-relevant endpoints, which are informed by understanding the clinical characterisation of the disease–how it presents and evolves over time, and important patient outcomes, and how these can be modified by treatment. Methodology/Principal findings: This systematic review aims to summarise the clinical profile of 1343 patients with bubonic plague described in 87 publications, identified by searching bibliographic databases for studies that meet pre-defined eligibility criteria. The majority of studies were individual case reports. A diverse group of signs and symptoms were reported at baseline and post-baseline timepoints–the most common of which was presence of a bubo, for which limited descriptive and longitudinal information was available. Death occurred in 15% of patients; although this varied from an average 10% in high-income countries to an average 17% in low- and middle-income countries. The median time to death was 1 day, ranging from 0 to 16 days. Conclusions/Significance: This systematic review elucidates the restrictions that limited disease characterisation places on clinical trials for infectious diseases such as plague, which not only impacts the definition of trial endpoints but has the knock-on effect of challenging the interpretation of a trial's results. For this reason and despite interventional trials for plague having taken place, questions around optimal treatment for plague persist. Author summary: Plague is an infectious disease that, despite affecting humans for more than 5000 years, has historically been the subject of limited drug development activity. In fact, the drugs currently used to treat plague have been approved based on experimental data alone–no major clinical trials have yet been completed that demonstrate the efficacy and safety of one treatment over another in humans. A major barrier to accomplishing this is that few research studies have taken place to date that can meaningfully inform the design of a clinical trial. We conducted this systematic review to gather and summarise all the existing information on the clinical profile of plague patients to understand whether sufficient information exists on which to design informative clinical trials that would provide clinicians and policy-makers with the information needed to make treatment decisions for patients. This study however found that, based on the existing literature, there is insufficient data that can be used to develop methodologies for future trials. Either time must be invested in to collecting robust clinical data or innovative trial designs need to be identified that can simultaneously collect much-needed observational data while also evaluating much-needed interventions. [ABSTRACT FROM AUTHOR]

The article titled "Expression of Concern: An integrated view on society readiness and initial reaction to COVID-19: A study across European countries" raises concerns about the statistical analyses and conclusions of a study on the societal reaction to the COVID-19 outbreak in European countries. The study was conducted during the early stages of the pandemic when data were limited, which influenced the study design decisions. The concerns include limited variables used, lack of robust statistical analysis, and potential biases in the dataset and study design. The authors partially addressed the concerns but not all have been fully resolved. The study's findings should be interpreted with caution due to the limitations associated with available data and the early phase of the pandemic outbreak. [Extracted from the article]

Spatial navigation research in humans increasingly relies on experiments using virtual reality (VR) tools, which allow for the creation of highly flexible, and immersive study environments, that can react to participant interaction in real time. Despite the popularity of VR, tools simplifying the creation and data management of such experiments are rare and often restricted to a specific scope—limiting usability and comparability. To overcome those limitations, we introduce the Virtual Navigation Toolbox (VNT), a collection of interchangeable and independent tools for the development of spatial navigation VR experiments using the popular Unity game engine. The VNT's features are packaged in loosely coupled and reusable modules, facilitating convenient implementation of diverse experimental designs. Here, we depict how the VNT fulfils feature requirements of different VR environments and experiments, guiding through the implementation and execution of a showcase study using the toolbox. The presented showcase study reveals that homing performance in a classic triangle completion task is invariant to translation velocity of the participant's avatar, but highly sensitive to the number of landmarks. The VNT is freely available under a creative commons license, and we invite researchers to contribute, extending and improving tools using the provided repository. [ABSTRACT FROM AUTHOR]

To solve the problem of low efficiency of manual harvesting of green soybeans and lack of adaptable harvesters, in this study, a brushing-type green soybean harvester was designed. The comb-brushing type green soybean pod harvesting equipment is composed of a front-mounted separation drum, a full-width material delivery mechanism, a negative pressure cleaning system, and a stalk-pod separation system. Based on the operation requirements of the front-mounted brushing-type detachment drum, the drum parameters, parameters of comb arrangement, and structural parameters of the comb, the force analysis in detachment was performed. By taking the pod detachment rate and damage rate as the response indexes, the rotational speed of the drum, the travel speed of the device, and teeth distance as influencing factors, a three-factor five-level orthogonal rotary combination test was carried out by the software Design-Expert. By establishing mathematical regression models for various influencing factors and evaluation indicators, conducting variance analysis and significance analysis on the response indicators of each factor, the optimal parameters were obtained at a rotational speed of teeth of 397.36 rpm/min, minimum axial teeth distance of 4.8 mm and travel speed of the device of 0.5 m/s. Field test results showed that, under the optimal parameter combination, the pod detachment rate was 94%, the damage rate was 3.04%, the harvesting efficiency was greater than 0.187 hm2/h, and impurity content was less than 7.8%, all of which met the design and usage requirements. The research results can provide a reference for the design of soybean harvesters. [ABSTRACT FROM AUTHOR]

In our prior studies, human participants were required to generate long sequences of targeted hand movement when task difficulty varied between conditions, and where full vision of the hand and target was always available. The movement amplitude—that is, the actual distance travelled—for each movement was measured; consecutive movement amplitude values were formed into time series; then, the time series were submitted to spectral analysis. As task difficulty increased, there was a pink-to-white-noise shift in movement amplitude time-series structure. Those changes could be attributed to a difficulty-induced increase in the need to engage visual feedback processes, which maintain accurate guidance of the hand to the target. The current study was designed to provide a more direct test of the hypothesis that difficulty-induced increases in visual feedback processing modulate movement amplitude time-series structure. To that end, we examined cyclical aiming performance under four unique conditions created from the crossing of two index of difficulty (2 and 5 bits) and two visual feedback (visual feedback and no-visual feedback) conditions. That allowed us to examine how variations in visual feedback quality might influence difficulty-induced changes in time-series structure. In the visual feedback condition, we predicted that the increase in difficulty should result in a pink-to-white-noise shift in time-series structure. If that expected shift resulted from increased engagement of visual feedback processing, then in the no-visual feedback condition—where visual feedback processing was disabled—we should observe a strengthened pink-noise time-series structure that does not change with the increase in difficulty. The current results confirmed those predictions. That provides further support for the hypothesis that engagement of closed-loop visual feedback processing modulates movement amplitude time-series structure. [ABSTRACT FROM AUTHOR]

The dynamic nature of movement and muscle activation emphasizes the importance of a sound experimental design. To ensure that an experiment determines what we intend, the design must be carefully evaluated. Before analyzing data, it is imperative to limit the number of outliers, biases, and skewness. In the present study, a simple center-out experiment was performed by 16 healthy volunteers. The experiment included three load conditions, two preparatory delays, two perturbations, and four targets placed along a diagonal path on a 2D plane. While the participants performed the tasks, the activity of seven arm muscles were monitored using surface electromyography (EMG). Principal component analysis (PCA) was used to evaluate the study design, identify muscle synergies, and assess the effects of individual quirks. With PCA, we can identify the trials that trigger stretch reflexes and pinpoint muscle synergies. The posterior deltoid, triceps long head, and brachioradialis were engaged when targets were in the direction of muscle shortening and the perturbation was applied in the opposite direction. Similarly, the pectoralis and anterior deltoid were engaged when the targets were in the direction of muscle shortening and the perturbation was applied in the opposite direction. The stretch reflexes were not triggered when the perturbation brought the hand in the direction of, or into the target, except if the muscle was pre-loaded. The use of PCA was also proven valuable when evaluating participant performance. While individual quirks are to be expected, failure to perform trials as expected can adversely affect the study results. [ABSTRACT FROM AUTHOR]

Quantitatively summarizing results from a collection of primary studies with meta-analysis can help answer ecological questions and identify knowledge gaps. The accuracy of the answers depends on the quality of the meta-analysis. We reviewed the literature assessing the quality of ecological meta-analyses to evaluate current practices and highlight areas that need improvement. From each of the 18 review papers that evaluated the quality of meta-analyses, we calculated the percentage of meta-analyses that met criteria related to specific steps taken in the meta-analysis process (i.e., execution) and the clarity with which those steps were articulated (i.e., reporting). We also re-evaluated all the meta-analyses available from Pappalardo et al. [1] to extract new information on ten additional criteria and to assess how the meta-analyses recognized and addressed non-independence. In general, we observed better performance for criteria related to reporting than for criteria related to execution; however, there was a wide variation among criteria and meta-analyses. Meta-analyses had low compliance with regard to correcting for phylogenetic non-independence, exploring temporal trends in effect sizes, and conducting a multifactorial analysis of moderators (i.e., explanatory variables). In addition, although most meta-analyses included multiple effect sizes per study, only 66% acknowledged some type of non-independence. The types of non-independence reported were most often related to the design of the original experiment (e.g., the use of a shared control) than to other sources (e.g., phylogeny). We suggest that providing specific training and encouraging authors to follow the PRISMA EcoEvo checklist recently developed by O'Dea et al. [2] can improve the quality of ecological meta-analyses. [ABSTRACT FROM AUTHOR]

The purpose of this study was to establish best ways of improving household soybean processing and utilization in selected districts in the Eastern Province of Zambia. This was a concurrent triangulation study design, nested with a cross sectional survey and barrier analysis. Up to 1,237 households and 42 key informants participated in the quantitative and qualitative studies respectively. Quantitative data was analysed using Stata MP 15 software (StataCorp, College Station, TX, USA). NVIVO QSR10 software (QSRInt, Melbourne Australia) was used to organize qualitative data which was later analysed thematically. In this study whole soybean processing and utilization in eastern province was at 48%. However, accessibility to soybean for household consumption throughout the year was negligible (0.29%). Based on the food systems an interplay of factors influenced soybean processing and utilization. In the food environment, a ready-made Textured Soya Protein mainly imported [1,030/1237(83%)] and a milled whole soybean-maize blend AOR 816.37; 95%CI 110.83 to 6013.31 were preferred. Reports of labour intensity, hard to cook properties, coarse milling and beany flavour with associated anti-nutrients negatively influenced whole soybean utilization. In the enabling environment, soybean production AOR 4.47; 95%CI 2.82 to 7.08 increased the chances of utilization. Lack of inputs, poor access to affordable credit and lack of ingredients were deleterious to utilization. Low coverage of existing projects and poor access to technologies were other adverse factors. Among the Socioeconomic factors, a higher social hierarchy shown by owning a bed AOR 1.75; 95%CI 1.22 to 2.49, belonging to the Chewa community AOR 1.16; 95%CI 1.08 to 0 1.25, gender of household head particularly male AOR 1.94; 95%CI 1.21 to 3.13, off farm income and livestock ownership were supportive to soybean utilization. Unfavourable factors were; belonging to any of the districts under study AOR 0.76; 95%CI 0.58 to 0.98, lack of knowledge (55.65%), low involvement of the male folks AOR 0.47; 95%CI 0.30 to 0.73 and belonging to a female headed household AOR 1.94; 95%CI 1.21 to 3.13. Age, time and household size constraints as well as unreliable soybean output markets, lack of land, poor soils in some wards and poor soybean value chain governance were other negative factors. Immediately in the food environment there is need to boost milling of whole soybean while strengthening cooking demonstrations, correct processing, incorporation of soybean in the local dishes and conducting acceptability tests. In the enabling environment, there should be access to inputs, affordable credit facilities and subsidized mineral fertilisers. Post-harvest storage, collective action with full scale community involvement and ownership should be heightened. Socioeconomic approaches should target promotion of soybean processing and utilization among all ethnic groups, participation of male folks and female headed households as well as advocating for increased nutrition sensitive social protection. In the medium or long term, capacity building, market development, import substitution agreements, creation of new products, development of cottage industries, information exchange and inter district trade as well as more public-private partnerships and more local private sector players should be bolstered. Lastly farm diversification should be supported. [ABSTRACT FROM AUTHOR]

Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behavior of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically-constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it can outperform existing machine learning methods in the prediction of certain experimentally measured species abundance and metabolite concentrations. Further, we present a closed-loop, Bayesian experimental design algorithm to guide data collection by selecting experimental conditions that simultaneously maximize information gain and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions. Author summary: The functions performed by microbiomes hold tremendous promise to address grand challenges facing society ranging from improving human health to promoting plant growth. To design their properties, flexible computational models that can predict the temporally changing behaviors of microbiomes in response to key environmental parameters are needed. When considering a bottom-up design of microbiomes, the number of possible communities grows exponentially with the number of organisms and environmental factors, which makes it challenging to navigate the microbiome function landscape. To overcome these challenges, we present a physically-constrained machine learning model for microbiomes and a closed-loop, Bayesian experimental design framework to efficiently navigate the space of possible communities and environmental factors. [ABSTRACT FROM AUTHOR]

This paper explores the dark core's role in an employee's evaluations of coworkers electronic multitasking behaviors. Using an experimental vignette design collected via Amazon's Mechanical Turk (N = 485), we demonstrate that employees high in the dark core report higher turnover intentions and more interpersonal conflict, regardless of the multitasking behavior relevance. A three-way interaction between multitasking relevance, perceived intentionality, and the dark core when predicting turnover intentions emerged. Perceived coworker intentions played the largest role in impacting turnover and interpersonal conflict. Implications for theory and practice are discussed below. [ABSTRACT FROM AUTHOR]

In modern engineering application, enough high cycle bending fatigue strength is the necessary factor to provide the basic safety security for the application of the crankshaft in automobile engines (both diesel and gasoline types). At present, this parameter is usually obtained through the standard bending fatigue experiment process, which is time consuming and expensive. In this paper, a new accelerated crankshaft bending fatigue experiment was proposed step by step. First the loading procedure was quickened through the prediction of the residual fatigue life based on the UKF (unscented Kalman filtering algorithm). Then the accuracy of the predictions was improved based on the modified sampling range and the theory of fracture mechanics. Finally the statistical analysis method of the fatigue limit load was performed based on the above predictions. The main conclusion of this paper is that the proposed accelerated bending fatigue experiment can save more than 30% of the bending fatigue experiment period and provide nearly the same fatigue limit load analysis result. In addition, compared with the particle filtering algorithm method, the modified UKF can provide much higher accuracy in predicting the residual bending fatigue life of the crankshaft, which makes this method more superior to be applied in actual engineering. [ABSTRACT FROM AUTHOR]

There are about 29 strokes per 100,000 people, annually, in the Kingdom of Saudi Arabia (KSA). These patients require long-term rehabilitation services to enhance recovery and independence in the community. Currently there are limited long-term rehabilitation services in KSA and research is needed to establish pathways for provision of community-based rehabilitation (CBR). To develop effective new CBR models, understanding the experiences and needs of stroke patients in KSA who have undergone poststroke care services is essential. This study aims to gain insight into stroke patients' needs after their discharge from rehabilitation centres in Saudi Arabia. An interpretive phenomenological analysis (IPA) study was undertaken using semi-structured interviews. Participants were eligible if they had a stroke, completed their in-hospital rehabilitation sessions and had been discharged within the past three years. Semi-structured interviews were conducted using interview guides. Transcripts were translated and analysed using interpretive phenomenological analysis. Twenty-four (15 males and 9 females) participants were recruited from two hospitals in KSA. The key findings suggested that patients experienced limited community rehabilitation services postdischarge unless they were financially able to pay for private therapy. Coping barriers including Medical, Psychological, Social, and Financial and facilitators including Faith, Recovery, Social support and leisure were identified. Participants suggested strategies to improve services within hospital and community for rehabilitation, needs of staff, access to services and ongoing care. Further work is required to develop, implement and evaluate a community rehabilitation intervention that includes education, and self-management elements to support stroke survivors in the community in KSA. [ABSTRACT FROM AUTHOR]

Thigmotaxis is an innate predator avoidance behaviour of rodents. To gain insight into how injury and disease models, and analgesic drug treatments affect thigmotaxis, we performed a systematic review and meta-analysis of studies that assessed thigmotaxis in the open field test. Systematic searches were conducted of 3 databases in October 2020, March and August 2022. Study design characteristics and experimental data were extracted and analysed using a random-effects meta-analysis. We also assessed the correlation between thigmotaxis and stimulus-evoked limb withdrawal. This review included the meta-analysis of 165 studies We report thigmotaxis was increased in injury and disease models associated with persistent pain and this increase was attenuated by analgesic drug treatments in both rat and mouse experiments. Its usefulness, however, may be limited in certain injury and disease models because our analysis suggested that thigmotaxis may be associated with the locomotor function. We also conducted subgroup analyses and meta-regression, but our findings on sources of heterogeneity are inconclusive because analyses were limited by insufficient available data. It was difficult to assess internal validity because reporting of methodological quality measures was poor, therefore, the studies have an unclear risk of bias. The correlation between time in the centre (type of a thigmotactic metric) and types of stimulus-evoked limb withdrawal was inconsistent. Therefore, stimulus-evoked and ethologically relevant behavioural paradigms should be viewed as two separate entities as they are conceptually and methodologically different from each other. [ABSTRACT FROM AUTHOR]

Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the observations and the unknown parameters. However, the applicability of BAL to experiments is limited as it requires performing high-dimensional integrations and optimizations in real time. Current methods are either too time consuming, or only applicable to specific models. Here, we propose an Efficient Sampling-Based Bayesian Active Learning (ESB-BAL) framework, which is efficient enough to be used in real-time biological experiments. We apply our method to the problem of estimating the parameters of a chemical synapse from the postsynaptic responses to evoked presynaptic action potentials. Using synthetic data and synaptic whole-cell patch-clamp recordings, we show that our method can improve the precision of model-based inferences, thereby paving the way towards more systematic and efficient experimental designs in physiology. Author summary: Optimizing the design of an experiment is a critical problem in biology. However, most experiments still rely on suboptimal designs, which may not yield sufficient information about the studied system. Consequently, such experiments often require more observations to reach a certain result. An efficient theoretical framework to alleviate this issue is called Optimal Experiment Design (OED), in which experimental protocols are selected to reduce the uncertainty of inferred parameters. However, the applicability of OED methods to actual experiments is limited: they often require computations which are too long for sequential experiments, and do not generalize to different models. Here, we developed a method called Efficient Sampling-Based Bayesian Active Learning (ESB-BAL), and apply it to the problem of estimating the parameters of a chemical synapse from its evoked postsynaptic currents. After each new observation, the optimal next stimulation time can be computed using ESB-BAL. Using recordings in cerebellar brain slices, we show that our method is fast enough to be used in real-time biological experiments and can significantly reduce the uncertainty of inferred parameters. Our method can be readily used by experimentalists via a simple interface. Moreover, our proposed solution is general enough to be applicable to different experimental settings. [ABSTRACT FROM AUTHOR]

Objective: To describe a methodological approach to explore the concordance of recommendations across guidelines and its application to the case of the WHO recommendations on chest imaging for the diagnosis and management of COVID-19. Study design and setting: We followed a methodological approach applied to a case study that included: defining the 'reference guideline' (i.e., the WHO guidance) and the 'reference recommendations'; searching for 'related guidelines' and identifying 'related recommendations'; constructing the PICO for the recommendations; assessing the matching of the PICO of each related recommendation to the PICO corresponding reference recommendation; and assessing the concordance between the PICO-matching recommendations. Results: We identified a total of 89 related recommendations from 22 related guidelines. Out of the 89 related recommendations, 43 partly matched and 1 entirely matched one of the reference recommendations, and out of these, 8 were concordant with one of the reference recommendations. When considering the seven reference recommendations, they had a median of 12 related recommendations (range 3–17), a median of 7 PICO-matching recommendations (range 0–13), and a median of 1 concordant recommendation (range 0–4). Conclusion: Following a detailed methodological approach, we were able to explore the concordance between our reference recommendations and related recommendations from other guidelines. A relatively low percentage of recommendations was concordant. [ABSTRACT FROM AUTHOR]

Ecological dynamics are strongly influenced by the relationship between prey density and predator feeding behavior—that is, the predatory functional response. A useful understanding of this relationship requires us to distinguish between competing models of the functional response, and to robustly estimate the model parameters. Recent advances in this topic have revealed bias in model comparison, as well as in model parameter estimation in functional response studies, mainly attributed to the quality of data. Here, we propose that an adaptive experimental design framework can mitigate these challenges. We then present the first practical demonstration of the improvements it offers over standard experimental design. Our results reveal that adaptive design can efficiently identify the preferred functional response model among the competing models, and can produce much more precise posterior distributions for the estimated functional response parameters. By increasing the efficiency of experimentation, adaptive experimental design will lead to reduced logistical burden. [ABSTRACT FROM AUTHOR]

Introduction: Antimicrobials are consumed among patients globally, but in developing and middle-income countries, these drugs can be obtained without a prescription from pharmacies and licensed drug stores due to inadequate regulation in the pharmaceutical sector. This study aimed to assess antimicrobial dispensing practices in medicine sales outlets (i.e., pharmacies and licensed drug stores) to provide quantitative evidence for policy discussions to enhance patient safety and care quality in Ghana's pharmaceutical industry. Method: The data for this study were obtained from a cross-sectional survey conducted in the Greater Accra region between July and August 2022. The survey was conducted through interviewer-administered questionnaires, and 200 staff members from medicine sales outlets were randomly selected using a two-stage cluster and random sampling technique. The maximum difference experiment model, rooted in random utility theory, was used to analyze their antimicrobial dispensing practices. Result: We found that medicine sales outlet staffs were highly concerned about following the drug act and not dispensing antimicrobials without a prescription, and usually refer a patient to get a prescription from a doctor if the patient has complications (like high fever, generalized malaise, fatigue as symptoms, sinusitis). Stronger concerns were also observed for medicine outlet staff not dispensing antimicrobials without a prescription if the patient is pediatric or geriatric with a severe infection. They also evaluated patients and dispensed antimicrobials based on symptoms, not their age or gender. However, they tended not to dispense antibiotics if the patient had a mild fever and requested it without a prescription. Conclusion: Our results provide insight into the need for a national surveillance system for monitoring antimicrobial prescribing and dispensing practices at medicine sales outlets. Therefore, the selection of antimicrobials for treating infectious diseases may be informed by evidence-based antimicrobial prescribing and dispensing surveillance data and will help policymakers to know the pattern of commonly consumed antimicrobials in the medicine sales outlets. [ABSTRACT FROM AUTHOR]

The intricate dependency structure of biological "omics" data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures. Author summary: With advances in high-throughput omics platforms coupled with a reduction in associated costs, we increasingly see intervention studies generating extensive time-series of measurements simultaneously capturing changes across many dimensions. However, in order to derive meaningful information from such data, we must take into account the high-dimensionality, the interrelatedness of outcomes, the experimental design, the temporal dependency, as well as the subject-to-subject variability. Analytical tools that are able to account for all of these properties, in particular approaches that incorporate the temporal dependencies and the corresponding between-subject variability, are needed to make efficient use of such data. Here, we introduce novel methodology to quantify the temporal dependency and its subject-to-subject variability in high-dimensional, frequently sampled time-series data from longitudinal intervention studies. Our approach provides a systematic way to quantify and summarize multivariate outcomes in longitudinal intervention studies while accounting for the study design, the temporal dependency, and its between-subject variability. We demonstrate the effectiveness of this approach in a simulation study as well as on a metabolomics dataset. [ABSTRACT FROM AUTHOR]

Background: There is limited data on antibiotic treatment in hospitalized neonates in low- and middle-income countries (LMICs). We aimed to describe patterns of antibiotic use, pathogens, and clinical outcomes, and to develop a severity score predicting mortality in neonatal sepsis to inform future clinical trial design. Methods and findings: Hospitalized infants <60 days with clinical sepsis were enrolled during 2018 to 2020 by 19 sites in 11 countries (mainly Asia and Africa). Prospective daily observational data was collected on clinical signs, supportive care, antibiotic treatment, microbiology, and 28-day mortality. Two prediction models were developed for (1) 28-day mortality from baseline variables (baseline NeoSep Severity Score); and (2) daily risk of death on IV antibiotics from daily updated assessments (NeoSep Recovery Score). Multivariable Cox regression models included a randomly selected 85% of infants, with 15% for validation. A total of 3,204 infants were enrolled, with median birth weight of 2,500 g (IQR 1,400 to 3,000) and postnatal age of 5 days (IQR 1 to 15). 206 different empiric antibiotic combinations were started in 3,141 infants, which were structured into 5 groups based on the World Health Organization (WHO) AWaRe classification. Approximately 25.9% (n = 814) of infants started WHO first line regimens (Group 1—Access) and 13.8% (n = 432) started WHO second-line cephalosporins (cefotaxime/ceftriaxone) (Group 2—"Low" Watch). The largest group (34.0%, n = 1,068) started a regimen providing partial extended-spectrum beta-lactamase (ESBL)/pseudomonal coverage (piperacillin-tazobactam, ceftazidime, or fluoroquinolone-based) (Group 3—"Medium" Watch), 18.0% (n = 566) started a carbapenem (Group 4—"High" Watch), and 1.8% (n = 57) a Reserve antibiotic (Group 5, largely colistin-based), and 728/2,880 (25.3%) of initial regimens in Groups 1 to 4 were escalated, mainly to carbapenems, usually for clinical deterioration (n = 480; 65.9%). A total of 564/3,195 infants (17.7%) were blood culture pathogen positive, of whom 62.9% (n = 355) had a gram-negative organism, predominantly Klebsiella pneumoniae (n = 132) or Acinetobacter spp. (n = 72). Both were commonly resistant to WHO-recommended regimens and to carbapenems in 43 (32.6%) and 50 (71.4%) of cases, respectively. MRSA accounted for 33 (61.1%) of 54 Staphylococcus aureus isolates. Overall, 350/3,204 infants died (11.3%; 95% CI 10.2% to 12.5%), 17.7% if blood cultures were positive for pathogens (95% CI 14.7% to 21.1%, n = 99/564). A baseline NeoSep Severity Score had a C-index of 0.76 (0.69 to 0.82) in the validation sample, with mortality of 1.6% (3/189; 95% CI: 0.5% to 4.6%), 11.0% (27/245; 7.7% to 15.6%), and 27.3% (12/44; 16.3% to 41.8%) in low (score 0 to 4), medium (5 to 8), and high (9 to 16) risk groups, respectively, with similar performance across subgroups. A related NeoSep Recovery Score had an area under the receiver operating curve for predicting death the next day between 0.8 and 0.9 over the first week. There was significant variation in outcomes between sites and external validation would strengthen score applicability. Conclusion: Antibiotic regimens used in neonatal sepsis commonly diverge from WHO guidelines, and trials of novel empiric regimens are urgently needed in the context of increasing antimicrobial resistance (AMR). The baseline NeoSep Severity Score identifies high mortality risk criteria for trial entry, while the NeoSep Recovery Score can help guide decisions on regimen change. NeoOBS data informed the NeoSep1 antibiotic trial (ISRCTN48721236), which aims to identify novel first- and second-line empiric antibiotic regimens for neonatal sepsis. Trial registration: ClinicalTrials.gov, (NCT03721302). James Neal Russell and colleagues report a global neonatal sepsis observational cohort study (NeoOBS) exploring patterns of antibiotic use, pathogens and prediction of mortality in hospitalised neonates and young infants with sepsis. Author summary: Why was this study done?: Increasing trends in antimicrobial resistance (AMR) disproportionately affect neonates and young infants with sepsis in LMIC settings and undermine the effectiveness of WHO-recommended antibiotics. Despite this, longitudinal data on antibiotic management strategies and outcomes of hospitalized neonates and young infants with sepsis in low- and middle-income country (LMIC) settings are extremely limited, impeding the design of robust antibiotic trials. There is limited data to define risk stratification, inclusion, and escalation criteria in trials of sepsis in hospitalized neonates and young infants. What did the researchers do and find?: In this large global, prospective, hospital-based observational study across 4 continents, including LMIC settings, there was a high mortality among infants with culture positive sepsis (almost 1 in 5), and a significant burden of antibiotic resistance. This study highlights wide variations in standard of care (SOC) for sepsis in neonates and young infants, with more than 200 different antibiotic combinations, significant divergence from WHO-recommended regimens, and frequent switching of antibiotics. A NeoSep Severity Score that defined patterns of mortality risk at baseline was developed from 4 non-modifiable and 6 modifiable factors that are feasible to measure across a range of LMIC hospital contexts. A NeoSep Recovery Score including the same modifiable factors (with the addition of cyanosis) predicted mortality on the following day during treatment. What do these findings mean?: These data demonstrate that patterns of routine antibiotic use are now markedly divergent from global guidance. There is an urgent need for large pragmatic randomized controlled trials to address optimal empiric first- and second-line antibiotic treatment strategies in LMIC hospital settings with a significant AMR burden. The wide range of multiple antibiotic regimens routinely used as SOC suggests the need for novel trial designs. The NeoSep Severity Score and NeoSep Recovery Score informed inclusion and escalation criteria in the NeoSep1 antibiotic trial (ISRCTN48721236) that aims to identify novel first- and second-line empiric antibiotic regimens for neonatal sepsis. [ABSTRACT FROM AUTHOR]

Gene editing characterization with currently available tools does not always give precise relative proportions among the different types of gene edits present in an edited bulk of cells. We have developed CRISPR-Analytics, CRISPR-A, which is a comprehensive and versatile genome editing web application tool and a nextflow pipeline to give support to gene editing experimental design and analysis. CRISPR-A provides a robust gene editing analysis pipeline composed of data analysis tools and simulation. It achieves higher accuracy than current tools and expands the functionality. The analysis includes mock-based noise correction, spike-in calibrated amplification bias reduction, and advanced interactive graphics. This expanded robustness makes this tool ideal for analyzing highly sensitive cases such as clinical samples or experiments with low editing efficiencies. It also provides an assessment of experimental design through the simulation of gene editing results. Therefore, CRISPR-A is ideal to support multiple kinds of experiments such as double-stranded DNA break-based engineering, base editing (BE), primer editing (PE), and homology-directed repair (HDR), without the need of specifying the used experimental approach. Author summary: Precision and accuracy are paramount for genome editing usage in sensitive applications such as clinical. This field requires reliable and traceable results to ensure the safety and viability of the resulting genomic modifications. To go in this direction, we have developed a computational platform that can simulate and analyze genome editing outcomes comprehensively and in a versatile manner. Our computational pipeline and web application, CRISPR-Analytics (CRISPR-A), expands the functionality of currently available tools. The added robustness of our tool makes it perfect for analyzing sensitive data, such as clinical samples or experiments with low editing efficiencies. [ABSTRACT FROM AUTHOR]

Binaural beats are an auditory phenomenon that occurs when two tones of different frequencies, which are presented separately to each ear, elicit the sensation of a third tone oscillating at the difference frequency of the two tones. Binaural beats can be perceived in the frequency range of about 1–30 Hz, a range that coincides with the main human EEG frequency bands. The brainwave entrainment hypothesis, which assumes that external stimulation at a certain frequency leads to the brain's electrocortical activity oscillating at the same frequency, provides the basis for research on the effects of binaural beat stimulation on cognitive and affective states. Studies, particularly in more applied fields, usually refer to neuroscientific research demonstrating that binaural beats elicit systematic changes in EEG parameters. At first glance, however, the available literature on brainwave entrainment effects due to binaural beat stimulation appears to be inconclusive at best. The aim of the present systematic review is, thus, to synthesize existing empirical research. A sample of fourteen published studies met our criteria for inclusion. The results corroborate the impression of an overall inconsistency of empirical outcomes, with five studies reporting results in line with the brainwave entrainment hypothesis, eight studies reporting contradictory, and one mixed results. What is to be noticed is that the fourteen studies included in this review were very heterogeneous regarding the implementation of the binaural beats, the experimental designs, and the EEG parameters and analyses. The methodological heterogeneity in this field of study ultimately limits the comparability of research outcomes. The results of the present systematic review emphasize the need for standardization in study approaches so as to allow for reliable insight into brainwave entrainment effects in the future. [ABSTRACT FROM AUTHOR]

Objective: The All of Us Research Program collects data from multiple information sources, including health surveys, to build a national longitudinal research repository that researchers can use to advance precision medicine. Missing survey responses pose challenges to study conclusions. We describe missingness in All of Us baseline surveys. Study design and setting: We extracted survey responses between May 31, 2017, to September 30, 2020. Missing percentages for groups historically underrepresented in biomedical research were compared to represented groups. Associations of missing percentages with age, health literacy score, and survey completion date were evaluated. We used negative binomial regression to evaluate participant characteristics on the number of missed questions out of the total eligible questions for each participant. Results: The dataset analyzed contained data for 334,183 participants who submitted at least one baseline survey. Almost all (97.0%) of the participants completed all baseline surveys, and only 541 (0.2%) participants skipped all questions in at least one of the baseline surveys. The median skip rate was 5.0% of the questions, with an interquartile range (IQR) of 2.5% to 7.9%. Historically underrepresented groups were associated with higher missingness (incidence rate ratio (IRR) [95% CI]: 1.26 [1.25, 1.27] for Black/African American compared to White). Missing percentages were similar by survey completion date, participant age, and health literacy score. Skipping specific questions were associated with higher missingness (IRRs [95% CI]: 1.39 [1.38, 1.40] for skipping income, 1.92 [1.89, 1.95] for skipping education, 2.19 [2.09–2.30] for skipping sexual and gender questions). Conclusion: Surveys in the All of Us Research Program will form an essential component of the data researchers can use to perform their analyses. Missingness was low in All of Us baseline surveys, but group differences exist. Additional statistical methods and careful analysis of surveys could help mitigate challenges to the validity of conclusions. [ABSTRACT FROM AUTHOR]

The public perception of viruses has historically been negative. We are now at a stage where the development of tools to study viruses is at an all-time high, but society's perception of viruses is at an all-time low. The literature regarding viral interactions has been skewed towards negative (i.e., pathogenic) symbioses, whereas viral mutualisms remain relatively underexplored. Viral interactions with their hosts are complex and some non-pathogenic viruses could have potential benefits to society. However, viral research is seldom designed to identify viral mutualists, a gap that merits considering new experimental designs. Determining whether antagonisms, mutualisms, and commensalisms are equally common ecological strategies requires more balanced research efforts that characterize the full spectrum of viral interactions. Viral interactions with their hosts are complex and can be beneficial, yet the public perception of viruses is generally negative. In this Essay, the authors argue that new experimental designs are required to characterize the full spectrum of viral interactions, including mutualism and commensalism. [ABSTRACT FROM AUTHOR]

Oaks (Quercus spp.) are widespread hardwood trees in the Northern Hemisphere and of high ecological, economic, and social values. Optimal experimental design of genetic trials is essential for accurate estimates of genetic parameters and improving the genetic merit of breeding stock. Here, we evaluate the use of post hoc row-column factors combined with spatial adjustment to improve genetic analyses of parents and individual trees in field progeny tests of plantation hardwoods, using cherrybark oak (Quercus pagoda Raf.) as an example. For tree height, post hoc incomplete blocking reduced ~14% more of the within-block environmental variance compared to the randomized complete block design (RCBD) model. Incomplete blocking also improved the heritability estimates for height by 7% to 14% compared to the original RCBD model. No clinal trend for growth breeding values was identified due to provenances. Our approach warrants the initial selection for height as early as age ~10 based on its moderate narrow-sense heritability of 0.2; however, diameter and volume need longer evaluation times. The post hoc incomplete blocking is more efficient and promising to improve the genetic analysis of Q. pagoda to minimize the environmental heterogeneity influences. Adjusting competition and spatial effects, including the distance principal components and autoregressive residual structure notably improves the model fit based on the observed reductions in AICs and BICs. Employing our approach is promising for hardwood genetic improvement in the southern USA. [ABSTRACT FROM AUTHOR]

In order to improve the hardware configuration and interaction mode of the fish tank system and realize the diversification of client functions, the purpose of real-time remote monitoring and management is achieved. A set of IoT intelligent fish tank system composed of sensor unit, signal processing unit and wireless transmission unit was designed. The system improves the algorithm of the data collected by the sensor, and proposes an improved first-order lag average filtering algorithm. The system uses composite collection information, intelligent processing, chart data analysis and other methods to transmit the processed data to the cloud server through the WIFI communication module. An APP is designed on the remote monitoring and control end, and a visual data interface of the smart fish tank is made, and the user can modify the environmental parameters conducive to the biological survival inside the fish tank through the APP, it brings great convenience to the family fish tank, and the test shows that the system network is stable and fast in response, and the overall purpose of the intelligent fish tank system is achieved. [ABSTRACT FROM AUTHOR]

We present a study design and baseline results to establish the impact of interventions on peri-urban water access, security and quality in Kasai Oriental province of the Democratic Republic of the Congo. In standard development practice, program performance is tracked via monitoring and evaluation frameworks of varying sophistication and rigor. Monitoring and evaluation, while usually occurring nearly concurrently with program delivery, may or may not measure parameters that can identify performance with respect to the project's overall goals. Impact evaluations, often using tightly controlled trial designs and conducted over years, challenge iterative program evolution. This study will pilot an implementation science impact evaluation approach in the areas immediately surrounding 14 water service providers, at each surveying 100 randomly-selected households and conducting water quality assessments at 25 randomly-selected households and five water points every three months. We present preliminary point-of-collection and point-of-use baseline data. This study is utilizing a variety of short- and medium-term monitoring and impact evaluation methods to provide feedback at multiple points during the intervention. Rapid feedback monitoring will assess the continuity of water services, point-of-consumption and point-of-collection microbial water quality, household water security, household measures of health status, ability and willingness to pay for water and sanitation service provision, and service performance monitoring. Long-term evaluation will focus on the use of qualitative comparative analysis whereby we will investigate the combination of factors that lead to improved water access, security and quality. [ABSTRACT FROM AUTHOR]

This study calls into question the default computation of the disposition effect that uses the average purchase price as a reference point. We show, through a lab experiment, that the reference price of participants can change depending on the experimental behavioral design that is used. Our results show that for the control group, different reference prices do not show significant differences in the computation of the disposition effect, thus supporting the use of the average purchase price as a reference. However, this is not the case for participants in the treatment group. With the addition of experimental treatment concerning the disclosure of the final balance of the participants, the reference prices to compute the disposition effect showed statistically significant differences. The need to display their results caused these participants to use the first purchase price as a reference point when selling their assets. [ABSTRACT FROM AUTHOR]

This study conducted two experiments to investigate the extraction of semantic preview information from the parafovea in Tibetan reading. In Experiment 1, a single-factor (preview type: identical vs. semantically related vs. unrelated) within-subject experimental design was used to investigate whether there is a parafoveal semantic preview effect (SPE) in Tibetan reading. Experiment 2 used a 2 (contextual constraint: high vs. low) × 3 (preview type: identical vs. semantically related vs. unrelated) within-subject experimental design to investigate the influence of contextual constraint on the parafoveal semantic preview effect in Tibetan reading. Supporting the E-Z reader model, the experimental results showed that in Tibetan reading, readers could not obtain semantic preview information from the parafovea, and contextual constraint did not influence this process. However, comparing high- and low-constrained contexts, the latter might be more conducive to extracting semantic preview information from the parafovea. [ABSTRACT FROM AUTHOR]