Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory elements.

The discovery of cancer driver mutations is a fundamental goal in cancer research. While many cancer driver mutations have been discovered in the protein-coding genome, research into potential cancer drivers in the non-coding regions showed limited success so far. Here, we present a novel comprehensive framework Dr.Nod for detection of non-coding cis-regulatory candidate driver mutations that are associated with dysregulated gene expression using tissue-matched enhancer-gene annotations. Applying the framework to data from over 1500 tumours across eight tissues revealed a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer driver genes. An overarching conclusion that emerges is that the non-coding driver mutations contribute to cancer by significantly altering transcription factor binding sites, leading to upregulation of tissue-matched oncogenes and down-regulation of tumour-suppressor genes. Interestingly, more than half of the detected cancer-promoting non-coding regulatory driver mutations are over 20 kb distant from the cancer-associated genes they regulate. Our results show the importance of tissue-matched enhancer-gene maps, functional impact of mutations, and complex background mutagenesis model for the prediction of non-coding regulatory drivers. In conclusion, our study demonstrates that non-coding mutations in enhancers play a previously underappreciated role in cancer and dysregulation of clinically relevant target genes.

Effectiveness of abdominal sandbag training in enhancing diaphragm muscle function and exercise tolerance in patients with chronic respiratory failure.

BACKGROUND: Chronic respiratory failure is a common cause of ventilator dependence in the intensive care unit (ICU). The causes of chronic respiratory failure include primary disease or complications, such as ICU-acquired weakness. Traditional practice requires patients to remain immobile and bedridden; however, recent evidence suggests that early adequate exercise promotes recovery without increasing risks. In this study, we explored the efficacy of planned progressive abdominal sandbag training in promoting the successful withdrawal of patients with chronic respiratory failure from mechanical ventilation. METHODS: This study was conducted between April 2019 and November 2020. Patients were recruited and divided into two groups: abdominal sandbag training group and control group (no training). The training group participated in a 3-month daily pulmonary rehabilitation program, which involved a 30-min session of progressive sandbag loading on the upper abdomen as a form of diaphragmatic resistant exercise. The pressure support level of the ventilator was adjusted to maintain a tidal volume of 8 mL/kg. To investigate the effect of abdominal sandbag training on patients with chronic respiratory failure, we compared tidal volume, shallow breathing index, maximum respiratory pressure, and diaphragm characteristics between the training and control groups. RESULTS: This study included 31 patients; of them, 17 (54.8 %) received abdominal sandbag training and 14 (45.2 %) did not. No significant between-group difference was found in baseline characteristics. Compared with the control group, the training group exhibited considerable improvements in ventilation-related parameters (p < 0.001): the tidal volume markedly increased (p = 0.012), rapid shallow breathing index declined (p = 0.016), and maximum respiratory pressure increased (p < 0.001) in the training group. The diaphragm motion value (p = 0.048) and diaphragm thickness (p = 0.041) were greater in the training group than in the control group. Nine patients (52.9 %) in the training group were removed from the ventilator compared with 1 (7.1 %) in the control group (p = 0.008). CONCLUSION: Abdominal sandbag training may be beneficial for patients dependent on a ventilator. The training improves the function of the diaphragm muscle, thereby increasing tidal volume and reducing the respiratory rate and rapid shallow breathing index, thus facilitating withdrawal from ventilation. This training approach may also improve the thickness and motion of the diaphragm and the rate of ventilator detachment.

Development of the Computerized Adaptive Test of Motor Development (MD-CAT) Adopting Multidimensional Rasch Analysis.

OBJECTIVE: This study aimed to develop the Computerized Adaptive Test of Motor Development (MD-CAT) in preschool children based on multidimensional Rasch analysis. DESIGN: A retrospective study with cross-sectional design. SETTING: A medical center. PARTICIPANTS: A total of 1738 children (N=1738). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: MD-CAT. RESULTS: Multidimensional Rasch analysis was used to develop the item bank of the MD-CAT. The item bank of the MD-CAT contained 74 items, with 44 and 30 items, respectively, for the subscales of gross and fine motor skills. High correlation existed between the 2 subscales (r=0.96). Three stopping rules were set for the MD-CAT: (1) the person reliability achieved 0.95 or the limited reliability increase by <0.01; (2) at least 3 items were assessed in each dimension; and (3) the number of items used for assessment reached 16. Based on the 3 stopping rules, the MD-CAT had high correlations with its total test length (r=0.87-0.98 for the 2 dimensions), indicating sufficient construct validity. The MD-CAT also had adequate diagnostic validity (area under the curve=0.72-0.93) and efficiency (an average of 3-6 items used for the assessment). CONCLUSIONS: The MD-CAT has high precision and efficiency, good construct validity, and high diagnostic validity. The results of our study indicate that the MD-CAT can be useful in clinical practice and in research as a diagnostic measure.

Anti-Tn Monoclonal Antibody Ameliorates Hyperoxia-Induced Kidney Injury by Suppressing Oxidative Stress and Inflammation in Neonatal Mice.

The Tn antigen, an N-acetylgalactosamine structure linked to serine or threonine, has been shown to induce high-specificity, high-affinity anti-Tn antibodies in mice. Maternal immunization with the Tn vaccine increases serum anti-Tn antibody titers and attenuates hyperoxia-induced kidney injury in neonatal rats. However, immunizing mothers to treat neonatal kidney disease is clinically impractical. This study is aimed at determining whether anti-Tn monoclonal antibody treatment ameliorates hyperoxia-induced kidney injury in neonatal mice. Newborn BALB/c mice were exposed to room air (RA) or normobaric hyperoxia (85% O2) for 1 week. On postnatal days 2, 4, and 6, the mice were injected intraperitoneally with PBS alone or with anti-Tn monoclonal antibodies at 25 µg/g body weight in 50 µL phosphate-buffered saline (PBS). The mice were divided into four study groups: RA + PBS, RA + anti-Tn monoclonal antibody, O2 + PBS, and O2 + anti-Tn monoclonal antibody. The kidneys were excised for histology, oxidative stress, cytokine, and Western blot analyses on postnatal day 7. The O2 + PBS mice exhibited significantly higher kidney injury scores, 8-hydroxy-2'-deoxyguanosine (8-OHdG) and nuclear factor-κB (NF-κB) expression, and cytokine levels than did the RA + PBS mice or RA + anti-Tn mice. Anti-Tn monoclonal antibody treatment reduced kidney injury and cytokine levels to normoxic levels. The attenuation of kidney injury was accompanied by a reduction of oxidative stress and NF-κB expression. Therefore, we propose that anti-Tn monoclonal antibody treatment ameliorates hyperoxia-induced kidney injury by suppressing oxidative stress and inflammation in neonatal mice.

Motivation to run measured by progressive ratio tests: Failure to support the addiction hypothesis for rats.

When laboratory rats are given repeated access to an activity wheel, the amount that they run steadily increases. This suggests an analogy with drug dependency in animals and humans, in that this is marked by both increasing intakes of the drug and increasing motivation to obtain the drug (craving). This analogy was examined by measuring motivation to obtain an opportunity to run using a progressive ratio (PR) schedule, whereby the number of lever presses required to release a brake on an activity wheel was increased progressively. Each of two experiments included two groups of rats that differed in running experience. In Experiment 1, both groups were given 17 wheel-running sessions before they were given the PR test, with sessions for the short group lasting only 30 min, while those for the long group lasted 4.5 hrs. In Experiment 2, both groups were given 3-hr wheel sessions, with the short group given only four such sessions and the medium groups given 12 such sessions prior to their PR test. In both experiments, the PR tests revealed that motivation to run was greater when the rats had not had an opportunity to run for at least 24 hrs prior to the test than when they had run the previous day. However, neither experiment produced evidence that motivation to run increased with the amount of previous running. Given only limited support for the analogy between running and drug addiction, steady increases in running may instead reflect circadian adaptation and/or increases in fitness.

Developing a novel algorithm for comparing cluster patterns in networks on journal articles during and after COVID-19: Bibliometric analysis.

BACKGROUND: Cluster analysis is vital in bibliometrics for deciphering large sets of academic data. However, no prior research has employed a cluster-pattern algorithm to assess the similarities and differences between 2 clusters in networks. The study goals are 2-fold: to create a cluster-pattern comparison algorithm tailored for bibliometric analysis and to apply this algorithm in presenting clusters of countries, institutes, departments, authors (CIDA), and keywords on journal articles during and after COVID-19. METHODS: We analyzed 9499 and 5943 articles from the Journal of Medicine (Baltimore) during and after COVID-19 in 2020 to 2021 and 2022 to 2023, sourced from the Web of Science (WoS) Core Collection. Follower-leading clustering algorithm (FLCA) was compared to other 8 counterparts in cluster validation and effectiveness and a cluster-pattern-comparison algorithm (CPCA) was developed using the similarity coefficient, collaborative maps, and thematic maps to evaluate CIDA cluster patterns. The similarity coefficients were categorized as identical, similar, dissimilar, or different for values above 0.7, between 0.5 and 0.7, between 0.3 and 0.5, and below 0.3, respectively. RESULTS: Both stages displayed similar trends in annual publications and average citations, although these trends are decreasing. The peak publication year was 2020. Similarity coefficients of cluster patterns in these 2 stages for CIDA entities and keywords were 0.73, 0.35, 0.80, 0.02, and 0.83, respectively, suggesting the existence of identical patterns (>0.70) in countries, departments, and keywords plus, but dissimilar (<0.5) and different patterns (<0.3) found in institutes and 1st and corresponding authors, during and after COVID-19. CONCLUSIONS: This research effectively created and utilized CPCA to analyze cluster patterns in bibliometrics. It underscores notable identical patterns in country-/department-/keyword based clusters, but dissimilar and different in institute-/author- based clusters, between these 2 stages during and after COVID-19, offering a framework for future bibliographic studies to compare cluster patterns beyond just the CIDA entities, as demonstrated in this study.

Identifying authorial roles in research: A Kano model-based bibliometric analysis for the Journal of Medicine (Baltimore) 2023.

The landscape of research roles within academic journals often remains uncharted territory, with authorial contributions frequently reduced to linear hierarchies (e.g., professor and assistant professor). The Kano model, traditionally used in customer satisfaction research, offers a nuanced framework for identifying the multifaceted roles of authors in scholarly publications. This study utilizes the Kano model to dissect and categorize the roles of authors in the medicine field. To conform to the hypothesis, China is the research leader while the US is the research collaborator, as reflected in the publications of the journal of Medicine (Baltimore) in the year 2023. We conducted a comprehensive bibliometric analysis of all research articles published in the journal of Medicine (Baltimore) in 2023. The Kano model was applied to classify authors into 5 categories reflective of their research roles: followers, leaders, partners, contributors, and collaborators. Data on author publications and co-authorship networks with multi-author rates (MARs) were analyzed to assign Kano categories based on the authorship positions of first and corresponding authors. Descriptive statistics and network analysis tools were used to interpret the data, including radar plots, geographical maps, and Kano diagrams. The analysis covered 1976 articles, uncovering a complex network of author roles that extends beyond the conventional binary distinction of lead and supporting authors (i.e., leading, and following researchers). A research leader in China and a collaborator in the US were conformed to support the hypothesis, based on their publications (1148 vs 51) and MARs (12.20% vs 19.61%). The Kano classification was visually adapted to classify authors (or entities) into 5 categories. The combined choropleth and geographical network maps were illustrated to identify author roles in research briefly. The Kano model serves as an effective tool for uncovering the diverse contributions of authors in medical research. By moving beyond the lead and follower dichotomy, this study highlights the intricate ecosystem of authorial roles, emphasizing the importance of each in advancing knowledge within the field of medicine. Future application of the Kano model could foster a more collaborative and inclusive recognition of contributions across various disciplines.

Analyzing collaboration and impact: A bibliometric review of four highly published authors' research profiles on collaborative maps.

The concept of impact beam plots (IBPs) has been introduced in academia as a means to profile individual researchers. Despite its potential, there has been a lack of comprehensive analysis that evaluates the research profiles of highly published authors through the lens of collaborative maps. This study introduces a novel approach, the rating scale for research profiles (RSRP), to create collaborative maps for prolific authors. The initial hypothesis posited that each of the research profiles would attain a grade A, necessitating empirical verification. This research employed collaborative maps to analyze the publication patterns of authors using the Web of Science database, focusing on co-authorship patterns and the impact of their scholarly work. The study relied on various bibliometric indicators, such as publication count, citation metrics, h-index, and co-authorship networks, to provide a detailed assessment of the contributions made by each author in their field. Additionally, authors' IBPs were generated and assessed alongside collaborative maps, using a grading scale ranging from A (excellent) to F (lacking any articles as first or corresponding author). The analysis confirmed that all 4 research profiles achieved a grade A, with their centroids located in the third quadrant, indicating a high level of scholarly impact. The h-indexes for the authors were found to be 38, 51, 53, and 59, respectively. Notably, Dr Tseng from Taiwan showed a distinct pattern, with a significant number of solo-authored publications in the second quadrant, in contrast to the other 3 authors who demonstrated a greater emphasis on collaboration, as evidenced by their positioning in the first quadrant. The study successfully demonstrates that RSRP and IBPs can be effectively used to analyze and profile the research output of highly published authors through collaborative maps. The research confirms the initial hypothesis that all 4 profiles would achieve a grade A, indicating an excellent level of scholarly impact and a strong presence in their respective fields. The utility of collaborative maps can be applied to bibliometric indicators in assessing the contributions and impact of scholars in the academic community.

Development of mobile CAT for patient feedback on pediatric consultations based on Rasch analysis of online techniques.

The Rasch Rating Scale Model (RSM) is widely used in questionnaire analysis, providing insights into how individuals respond to item-level stimuli. Existing software for Rasch RSM parameter estimation, while powerful, often presents a steep learning curve. An accessible online tool can greatly benefit novice users, particularly students and clinicians, by simplifying the analytical process. This study introduces an online tool, an intuitive online RSM analysis tool designed to facilitate questionnaire data analysis for applied researchers, students, and clinicians. The online tool employs the joint maximum likelihood method for estimation, yielding estimates, standard errors (SE), and fit statistics iteratively. A unique feature of the tool is its ability to visualize estimates on Google Maps with an opacity setting of 0, enhancing data interpretation through a user-friendly interface. This study outlines the estimation process and key features, employing data from 200 proxy participants who answered 20 5-point questions regarding doctor-patient and doctor-family interactions in pediatric consultations. Mobile computerized adaptive testing (CAT) was employed. The online tool offers 5 essential visual displays often utilized in Rasch analyses, including the Wright Map, KIDMAP, category probability curve, performance plot, and differential item functioning (DIF) graph. DIF analysis revealed that 2 items, concerning the doctor attentiveness and empathy toward the child illness, exhibited differences in female proxy participants' responses, indicating lower satisfaction with pediatricians. The online tool emerges as a user-friendly and efficient RSM analysis tool with notable advantages for newcomers, improving data visualization and comprehension. Its capacity to pinpoint key areas of concern, such as gender-related satisfaction disparities among proxy participants, enhances its utility in questionnaire analysis. The online tool holds promise as a valuable resource for researchers, students, and clinicians seeking accessible Rasch analysis solutions.

Evaluating the dependability of reference-driven citation forecasts amid the COVID-19 pandemic: A bibliometric analysis across diverse journals.

BACKGROUND: The journal impact factor significantly influences research publishing and funding decisions. With the surge in research due to COVID-19, this study investigates whether references remain reliable citation predictors during this period. METHODS: Four multidisciplinary journals (PLoS One, Medicine [Baltimore], J. Formos. Med. Assoc., and Eur. J. Med. Res.) were analyzed using the Web of Science database for 2020 to 2022 publications. The study employed descriptive, predictive, and diagnostic analytics, with tools such as 4-quadrant radar plots, univariate regressions, and country-based collaborative maps via the follower-leading cluster algorithm. RESULTS: Six countries dominated the top 20 affiliations: China, Japan, South Korea, Taiwan, Germany, and Brazil. References remained strong citation indicators during the COVID-19 period, except for Eur. J. Med. Res. due to its smaller sample size (n = 492) than other counterparts (i.e., 41,181, 12,793, and 1464). Three journals showed higher network density coefficients, suggesting a potential foundation for reference-based citation predictions. CONCLUSION: Despite variations among journals, references effectively predict article citations during the COVID-19 era, underlining the importance of network density. Future studies should delve deeper into the correlation between network density and citation prediction.

Assessing ChatGPT's Capability for Multiple Choice Questions Using RaschOnline: Observational Study.

BACKGROUND: ChatGPT (OpenAI), a state-of-the-art large language model, has exhibited remarkable performance in various specialized applications. Despite the growing popularity and efficacy of artificial intelligence, there is a scarcity of studies that assess ChatGPT's competence in addressing multiple-choice questions (MCQs) using KIDMAP of Rasch analysis-a website tool used to evaluate ChatGPT's performance in MCQ answering. OBJECTIVE: This study aims to (1) showcase the utility of the website (Rasch analysis, specifically RaschOnline), and (2) determine the grade achieved by ChatGPT when compared to a normal sample. METHODS: The capability of ChatGPT was evaluated using 10 items from the English tests conducted for Taiwan college entrance examinations in 2023. Under a Rasch model, 300 simulated students with normal distributions were simulated to compete with ChatGPT's responses. RaschOnline was used to generate 5 visual presentations, including item difficulties, differential item functioning, item characteristic curve, Wright map, and KIDMAP, to address the research objectives. RESULTS: The findings revealed the following: (1) the difficulty of the 10 items increased in a monotonous pattern from easier to harder, represented by logits (-2.43, -1.78, -1.48, -0.64, -0.1, 0.33, 0.59, 1.34, 1.7, and 2.47); (2) evidence of differential item functioning was observed between gender groups for item 5 (P=.04); (3) item 5 displayed a good fit to the Rasch model (P=.61); (4) all items demonstrated a satisfactory fit to the Rasch model, indicated by Infit mean square errors below the threshold of 1.5; (5) no significant difference was found in the measures obtained between gender groups (P=.83); (6) a significant difference was observed among ability grades (P<.001); and (7) ChatGPT's capability was graded as A, surpassing grades B to E. CONCLUSIONS: By using RaschOnline, this study provides evidence that ChatGPT possesses the ability to achieve a grade A when compared to a normal sample. It exhibits excellent proficiency in answering MCQs from the English tests conducted in 2023 for the Taiwan college entrance examinations.

Instruction on the Scientific Method Provides (Some) Protection Against Illusions of Causality.

People tend to overestimate the efficacy of an ineffective treatment when they experience the treatment and its supposed outcome co-occurring frequently. This is referred to as the outcome density effect. Here, we attempted to improve the accuracy of participants' assessments of an ineffective treatment by instructing them about the scientific practice of comparing treatment effects against a relevant base-rate, i.e., when no treatment is delivered. The effect of these instructions was assessed in both a trial-by-trial contingency learning task, where cue administration was either decided by the participant (Experiments 1 & 2) or pre-determined by the experimenter (Experiment 3), as well as in summary format where all information was presented on a single screen (Experiment 4). Overall, we found two means by which base-rate instructions influence efficacy ratings for the ineffective treatment: 1) When information was presented sequentially, the benefit of base-rate instructions on illusory belief was mediated by reduced sampling of cue-present trials, and 2) When information was presented in summary format, we found a direct effect of base-rate instruction on reducing causal illusion. Together, these findings suggest that simple instructions on the scientific method were able to decrease participants' (over-)weighting of cue-outcome coincidences when making causal judgements, as well as decrease their tendency to over-sample cue-present events. However, the effect of base-rate instructions on correcting illusory beliefs was incomplete, and participants still showed illusory causal judgements when the probability of the outcome occurring was high. Thus, simple textual information about assessing causal relationships is partially effective in influencing people's judgements of treatment efficacy, suggesting an important role of scientific instruction in debiasing cognitive errors.

Uncertainty-modulated attentional capture: Outcome variance increases attentional priority.

Our prior experiences shape the way that we prioritize information from the environment for further processing, analysis, and action. We show in three experiments that this process of attentional prioritization is critically modulated by the degree of uncertainty in these previous experiences. Participants completed a visual search task in which they made a saccade to a target to earn a monetary reward. The color of a color-singleton distractor in the search array signaled the reward outcome(s) that were available, with different degrees of variance (uncertainty). Participants were never required to look at the colored distractor, and doing so would slow their response to the target. Nevertheless, across all experiments, participants were more likely to look at distractors associated with high outcome variance versus low outcome variance. This pattern was observed when all distractors had equal expected value (Experiment 1), when the difference in variance was opposed by a difference in expected value (i.e., the high-variance distractor had a low expected value, and vice versa: Experiment 2), and when high- and low-variance distractors were paired with the maximum-value outcome on an equal proportion of trials (Experiment 3). Our findings demonstrate that experience of prediction error plays a fundamental role in guiding "attentional exploration," wherein priority is driven by the potential for a stimulus to reduce future uncertainty through a process of learning, as opposed to maximizing current information gain. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prediction of Neurodevelopmental Disorders Based on De Novo Coding Variation.

The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an approach for the accurate prediction of NDDs with very low false positive rate (FPR) using de novo coding variation for a small subset of cases. We use a shallow neural network that integrates de novo likely gene-disruptive and missense variants, measures of gene constraint, and conservation information to predict a small subset of NDD cases at very low FPR and prioritizes NDD risk genes for future clinical study.

Using chord diagrams to explore article themes in 100 top-cited articles citing Hirsch's h-index since 2005: A bibliometric analysis.

BACKGROUND: The h-index is increasingly being used as a measure of individual research achievement (IRA). More than 4876 citing articles have been published and indexed in Web of Science. The articles citing the h-index that have made the greatest contribution to scientific academics are still unknown. It is also unclear which subject categories (SCs) can be classified based on their keywords. METHODS: These 4976 citing articles have been collected from the Web of Science since 2005. SCs were classified using chord diagrams to visualize their associations of SCs and documents in 100 top-cited articles (T100hciting). In addition to chord diagrams, 6 visualizations were used to illustrate study results: choropleth maps were used to depict the geographical distribution of publications across countries, network diagrams were created by using coword analysis, box plots were created to complement the network diagrams, Sankey diagrams highlighted the 5 most important elements in each article entity, the dot plot was used for displaying T100hciting, and a radar plot was used to present the top 10 high-IRA elements of countries, institutes, departments, and authors based on category, journal impact factor, authorship, and L-index scores. RESULTS: A coword cluster analysis indicates that the majority of articles come from the US (918, 18%) and China (603, 12%), the top 2 SCs are h-index and bibliometric analysis, and the top 5 countries account for 55% in T100hciting, such as the US (25%), Spain (10%), Netherlands (9%), China (6%), and Belgium (5%). In T100hciting, 4 SCs are included, namely, the h-index (72%), bibliometric analysis (24%), physics & multidisciplinary (3%), and infectious diseases (1%). CONCLUSION: A total of 7 visualizations were used to display the results in this study. Chord diagrams are suggested as a tool for future bibliographical studies to classify SCs Future bibliometrics with chord diagrams should not be limited to the topic of h-index-citing articles, as we did in this study.

A leading author of meta-analysis does not have a dominant contribution to research based on the CJAL score: Bibliometric analysis.

BACKGROUND: There have been nearly 200 thousand meta-analysis articles indexed by web of science (WoS) since 2013. To date, a bibliometric analysis of leading authors of meta-analyses that contribute to the field has not been conducted. Analyzing trend patterns in article citations and comparing individual research achievements (IRAs) are required following the extraction of meta-analysis articles. Using trend analysis, this study aims to verify the hypotheses that; The leading author has a dominant research achievement and; Recent articles that deserve worth reading can be identified. METHODS: In the WoS collection, we identified the top 20 authors with the most articles related to meta-analysis. Using coword analysis, 2882 articles were collected to cluster author collaborations and identify the top 3 authors with the highest weighted centrality degrees. Based on the CJAL (category, journal raking by impact factor, authorship, and L-index on article citation) score and absolute advantage coefficient (AAC), we compared the IRAs and identified the author who dominated the field significantly beyond the next 2 authors. In WoS collection, coword analysis was used to highlight the characteristics of research domains for the top authors contributing to meta-analyses. The selection of articles that deserve reading is based on a temporal heatmap. RESULTS: The top 2 authors were Young-Ho Lee (South Korea), Patompong Ungprasert (U.S.), and Brendon Stubbs (US) with CJAL scores of 240.71, 230.99, and 240.71, respectively. Based on the weak dominance coefficient (AAC = 0.49 < 0.50), it is evident that the leading meta-analysis author does not possess a significant dominant position over the next 2 leading authors in IRAs. Coword analysis was used to illustrate the characteristics of the 3 authors research domains. The 3 articles worth reading were selected based on a trend analysis of the last 4 years using the temporal heatmap. CONCLUSION: A coword analysis of meta-analysis studies identified 3 leading authors. There was no evidence that 1 author possessed a dominant position due to the lower AAC (=0.49 < 0.50) for the leading author. As we have demonstrated in this study, the CJAL score and the AAC can be applied to many bibliographical studies in the future.

The ascendancy of research in acronyms related to COVID-19 displayed on a growth-share matrix (GSM): Bibliometric analysis.

BACKGROUND: The acronym COVID, which stands for coronavirus disease, has become one of the most infamous acronyms in the world since 2020. An analysis of acronyms in health and medical journals has previously found that acronyms have become more common in titles and abstracts over time (e.g., DNA and human immunodeficiency virus are the most common acronyms). However, the trends in acronyms related to COVID remain unclear. It is necessary to verify whether the dramatic rise in COVID-related research can be observed by visualizations. The purpose of this study was to display the acronym trends in comparison through the use of temporal graphs and to verify that the COVID acronym has a significant edge over the other 2 in terms of research dominance. METHODS: An analysis of the 30 most frequently used acronyms related to COVID in PubMed since 1950 was carried out using 4 graphs to conduct this bibliometric analysis, including line charts, temporal bar graphs (TBGs), temporal heatmaps (THM), and growth-share matrices (GSM). The absolute advantage coefficient (AAC) was used to measure the dominance strength for COVID acronym since 2020. COVID's AAC trend was expected to decline over time. RESULTS: This study found that COVID, DNA, and human immunodeficiency virus have been the most frequently observed research acronyms since 2020, followed by computed tomography and World Health Organization; although there is no ideal method for displaying acronym trends over time, researchers can utilize the GSM to complement traditional line charts, TBGs, and THMs, as shown in this study; and COVID has a significant edge over the other 2 in terms of research dominance by ACC (≥0.67), but COVID's AAC trend has declined (e.g., AACs 0.83, 0.80, and 0.69) since 2020. CONCLUSIONS: It is recommended that the GSM complement traditional line charts, TBGs, and THMs in trend analysis, rather than being restricted to acronyms in future research. This research provides readers with the AAC to understand how research dominates its counterparts, which will be useful for future bibliometric analyses.

Inhibitory Learning with Bidirectional Outcomes: Prevention Learning or Causal Learning in the Opposite Direction?

Influential models of causal learning assume that learning about generative and preventive relationships are symmetrical to each other. That is, a preventive cue directly prevents an outcome from occurring (i.e., "direct" prevention) in the same way a generative cue directly causes an outcome to occur. However, previous studies from our lab have shown that many participants do not infer a direct prevention causal structure after feature-negative discrimination (A+/AB-) with a unidirectional outcome (Lee & Lovibond, 2021). Melchers et al. (2006) suggested that the use of a bidirectional outcome that can either increase or decrease from baseline, encourages direct prevention learning. Here we test an alternative possibility that a bidirectional outcome encourages encoding of a generative relationship in the opposite direction, where B directly causes a decrease in the outcome. Thus, previous evidence of direct prevention learning using bidirectional outcomes may instead be explained by some participants inferring an "Opposite Causal" structure. In two experiments, participants did indeed report an opposite causal structure. In Experiment 1, these participants showed the lowest outcome predictions when B was combined with a novel cause in a summation test, and lowest outcome predictions when B was presented alone. In Experiment 2, B successfully blocked learning to a novel cue that was directly paired with a reduction in the outcome, and this effect was strongest among participants who endorsed an Opposite Causal structure. We conclude that previous evidence of direct prevention learning using bidirectional outcomes may be a product of excitatory rather than inhibitory learning.

Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study.

BACKGROUND: Dengue fever (DF) is a significant public health concern in Asia. However, detecting the disease using traditional dichotomous criteria (i.e., absent vs present) can be extremely difficult. Convolutional neural networks (CNNs) and artificial neural networks (ANNs), due to their use of a large number of parameters for modeling, have shown the potential to improve prediction accuracy (ACC). To date, there has been no research conducted to understand item features and responses using online Rasch analysis. To verify the hypothesis that a combination of CNN, ANN, K-nearest-neighbor algorithm (KNN), and logistic regression (LR) can improve the ACC of DF prediction for children, further research is required. METHODS: We extracted 19 feature variables related to DF symptoms from 177 pediatric patients, of whom 69 were diagnosed with DF. Using the RaschOnline technique for Rasch analysis, we examined 11 variables for their statistical significance in predicting the risk of DF. Based on 2 sets of data, 1 for training (80%) and the other for testing (20%), we calculated the prediction ACC by comparing the areas under the receiver operating characteristic curve (AUCs) between DF + and DF- in both sets. In the training set, we compared 2 scenarios: the combined scheme and individual algorithms. RESULTS: Our findings indicate that visual displays of DF data are easily interpreted using Rasch analysis; the k-nearest neighbors algorithm has a lower AUC (<0.50); LR has a relatively higher AUC (0.70); all 3 algorithms have an almost equal AUC (=0.68), which is smaller than the individual algorithms of Naive Bayes, LR in raw data, and Naive Bayes in normalized data; and we developed an app to assist parents in detecting DF in children during the dengue season. CONCLUSION: The development of an LR-based APP for the detection of DF in children has been completed. To help patients, family members, and clinicians differentiate DF from other febrile illnesses at an early stage, an 11-item model is proposed for developing the APP.

Retardation of acquisition after conditioned inhibition and latent inhibition training in human causal learning.

Inhibitory stimuli are slow to acquire excitatory properties when paired with the outcome in a retardation test. However, this pattern is also seen after simple nonreinforced exposure: latent inhibition. It is commonly assumed that retardation would be stronger for a conditioned inhibitor than for a latent inhibitor, but there is surprisingly little empirical evidence comparing the two in either animals or humans. Thus, retardation after inhibitory training could in principle be attributable entirely to latent inhibition. We directly compared the speed of excitatory acquisition after conditioned inhibition and matched latent inhibition training in human causal learning. Conditioned inhibition training produced stronger transfer in a summation test, but the two conditions did not differ substantially in a retardation test. We offer two explanations for this dissociation. One is that learned predictiveness attenuated the latent inhibition that otherwise would have occurred during conditioned inhibition training, so that retardation in that condition was primarily due to inhibition. The second explanation is that inhibitory learning in these experiments was hierarchical in nature, similar to negative occasion-setting. By this account, the conditioned inhibitor was able to negatively modulate the test excitor in a summation test, but was no more retarded than a latent inhibitor in its ability to form a direct association with the outcome. (PsycInfo Database Record (c) 2023 APA, all rights reserved).