Association Test Research Articles

Mi-Mic: a novel multi-layer statistical test for microbiota-disease associations.

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

The effect of selective dry cow therapies based on two different algorithms on antimicrobial use, udder health, milk production, and culling in the absence of internal teat sealant use at dry-off.

The objective of this study was to evaluate the effect of selective dry cow therapy (SDCT) strategies based on 2 different algorithms as compared with blanket dry-cow therapy for measures of udder health, milk yield, and culling in herds not using internal teat sealant. Cows from 2 commercial farms in West Texas were randomized into 3 different groups: SDCT Algorithm 1 (ALG1; n = 455) cows treated with an intramammary antimicrobial at dry-off if somatic cell count (SCC) > 200,000 cells/mL at any Dairy Herd Improvement Association (DHIA) test date or if the cow had 2 or more cases of clinical mastitis during the enrollment lactation; SDCT Algorithm 2 (ALG2; n = 458) cows treated with an intramammary antimicrobial at dry-off if SCC >200,000 cell/mL at last test date or any case of clinical mastitis during the enrollment lactation; Control cows (CON = 447) received blanket dry cow therapy. All cows enrolled in the study did not receive an internal or external teat sealant. Data related to milk and somatic cell count linear score (LSCC) was collected monthly. Milk yield and LSCC during the first 6 mo of lactation were analyzed using repeated measures ANOVA models, while Cox's Proportional Hazards models were fitted to culling and clinical mastitis data. The farm was fitted as a random effect in all models. The percentage of cows receiving antimicrobials at dry cow was 51.3, 24.7, and 100% for ALG1, ALG2, and CON, respectively. Treatment did not influence the IMI dynamics during the dry period. Additionally, no statistical differences related to treatment were observed for LSCC and milk yield. The LSCC for ALG1, ALG2, and CON was 2.44, 2.41, and 2.26, respectively. The average milk yield for ALG1, ALG2, and CON cows was 43.2, 43.2, and 44.0 kg/d, respectively. Treatment did not affect clinical mastitis incidence and culling. The cumulative incidence of clinical mastitis was 19.6%, 19.4%, and 21.4% for ALG1, ALG2, and CON cows respectively. Additionally, the cumulative risk of death or culling was 18.5%, 17.1%, and 19.5% for ALG1, ALG2, and CON cows respectively. In conclusion, SDCT strategies led to a decrease in antimicrobial drug use at dry-off, without significantly impacting the incidence of clinical mastitis, the risk of culling, LSCC and milk yield of dairy cows. However, numerical differences in LSCC and milk yield were observed between treatment groups.

Predicting Undergraduates’ willingness to engage in dangerous e-scooter use behaviors

IntroductionThe misuse of electronic scooters (e-scooters), particularly among young adults, is increasingly being linked to hospital admissions and injuries in cities across the globe. Yet, despite growing concern about dangerous e-scooter use, there remains a dearth of research on the determinants of engaging in risky behaviors while using an e-scooter. MethodA sample of 262 Australian undergraduate students completed measures relating to the theory of planned behavior, risk perceptions, and knowledge of laws in the context of three e-scooter risk behaviors: riding over the speed limit on footpaths, riding without a helmet, and riding after consuming alcohol, as well as a risk identity implicit association test. Participants then recorded their willingness to engage in each of the three behaviors. ResultsThe model predicted a modest portion of variance in willingness to engage in each of the three e-scooter risk behaviors. Subjective norm predicted all behaviors, while attitude, risk perceptions, and perceived behavioral control each predicted two of the three behaviors. Gender had a significant total effect on willingness for each behavior, partially mediated by the theory of planned behavior constructs, while knowledge of laws had minimal effects and only on helmet use. ConclusionsSocial cognition factors show promise in explaining willingness to engage in dangerous e-scooter use and may provide an important formative basis for future research and behavior change interventions to reduce such risky behaviors.

Utilisation and barriers of social health protection program among its enrolled population of federally administrative areas, Pakistan

ObjectivesThe objective of this research is to analyse the extent of utilisation and identify the barriers faced by individuals in the Federally Administrative Area of Pakistan concerning the Social Health...

Adolescent athlete low back pain diagnoses, characteristics, and management: A retrospective chart review

ObjectivesThe aim of this study was to characterise the presentation, treatment, and management of adolescent athlete low back pain (LBP) as diagnosed in a clinical setting. The objectives were to 1) identify diagnoses associated with LBP in adolescent athletes, 2) categorise the differences in LBP diagnosis and presentation by sport, sex, BMI, and age, and 3) examine treatment and management methods of LBP in adolescent athletes. DesignRetrospective chart review. MethodsThis retrospective medical chart review was conducted in the Sports Medicine Division of Boston Children's Hospital (BCH), a tertiary paediatric academic hospital. Data were collected and analysed from 363 adolescent athletes who had experienced LBP between 2015 and 2020. Chi-squared tests for association were used to assess for associations between LBP diagnoses and age, sex, BMI, and sport. Statistical analysis was conducted using SAS software version 9.4 (SAS Institute, Cary NC). ResultsNon-specific LBP was the most common LBP diagnosis amongst 363 adolescent athletes with LBP (34 %). This was closely followed by spondylolysis (28 %). There was a higher proportion of female athletes amongst participants diagnosed with facet-joint related pain (90 %) and SI-joint related pain (89 %) compared to the proportion of female athletes amongst participants diagnosed with spondylolysis (50 %). There was a high rate of diagnostic MRI usage in this cohort. Commonly used management techniques in this cohort were diagnostic MRI, physical therapy, relative rest, and bracing. ConclusionsAn awareness of the sex-based differences in adolescent athlete LBP diagnoses may be useful for clinicians. It may be important to refine methods of diagnosis of LBP in this group, as there was a high rate of diagnostic MRI use. Future research should be directed towards the development of management guidelines specific to LBP in adolescent athletes to assist in the optimal management of this diagnosis.

Rare variant association analyses reveal the significant contribution of carbohydrate metabolic disturbance in severe adolescent idiopathic scoliosis

BackgroundAdolescent idiopathic scoliosis (AIS), the predominant genetic-influenced scoliosis, results in spinal deformities without vertebral malformations. However, the molecular aetiology of AIS remains unclear.MethodsUsing genome/exome sequencing, we studied 368 patients with...

MetaGWASmanager: a toolbox for an automated workflow from phenotypes to meta-analysis in GWAS consortia.

This article introduces the metaGWASmanager, which streamlines genome-wide association studies within large-scale meta-analysis consortia. It is a toolbox for both the central consortium analysis group and participating studies to generate homogeneous phenotypes, minimize unwanted variability from inconsistent methodologies, ensure high-quality association results, and implement time-efficient quality control workflows. The toolbox features a plug-in-based approach for customization of association testing. The metaGWASmanager toolbox has been successfully deployed in both the CKDGen and MetalGWAS Initiative consortia across hundreds of participating studies, demonstrating its effectiveness in GWAS analysis optimization by automating routine tasks and ensuring the value and reliability of association results, thus, ultimately promoting scientific discovery. We provide a simulated data set with examples for script customization so that readers can reproduce the pipeline at their convenience. GitHub: https://github.com/genepi-freiburg/metaGWASmanager.

X-linked genetic associations in sporadic thoracic aortic dissection.

The male predominance in sporadic thoracic aortic aneurysm and dissection (TAD) suggests that the X chromosome contributes to TAD, but this has not been tested. We investigated whether X-linked variation-common (minor allele frequency [MAF] ≥0.01) and rare (MAF <0.01)-was associated with sporadic TAD in three cohorts of European descent (Discovery: 364 cases, 874 controls; Replication: 516 cases, 440,131 controls, and ARIC [Atherosclerosis Risk in Communities study]: 753 cases, 2247 controls). For analysis of common variants, we applied a sex-stratified logistic regression model followed by a meta-analysis of sex-specific odds ratios. Furthermore, we conducted a meta-analysis of overlapping common variants between the Discovery and Replication cohorts. For analysis of rare variants, we used a sex-stratified optimized sequence kernel association test model. Common variants results showed no statistically significant findings in the Discovery cohort. An intergenic common variant near SPANXN1 was statistically significant in the Replication cohort (p = 1.81 × 10-8). The highest signal from the meta-analysis of the Discovery and Replication cohorts was a ZNF182 intronic common variant (p = 3.5 × 10-6). In rare variants results, RTL9 reached statistical significance (p = 5.15 × 10-5). Although most of our results were statistically insignificant, our analysis is the most comprehensive X-chromosome association analysis of sporadic TAD to date.

The Effect Of Authentic Learning Practices On To The Mental Structures Conceptual Change And Of Attitudes Of Students In Life Science Course

In this study, it is aimed to determine the effect of authentic learning practices in Life Science lesson on the conceptual change of students and their attitudes towards the course. The research was modelled with a semi-experimental design with pre-test post-test control group using quantitative methods. The research was carried out with the students of two different 3rd grade branches studying in a primary school located in the center of İnegöl District of Bursa Province in the 2022-2023 academic year. In the study, data were collected through the Personal Information Form, Word Association Test and Attitude Scale Towards Life Science Lesson. The results of the Word Association Test, which was applied to reveal the conceptual change in the mental structures of the students in authentic learning environments, were analyzed by content analysis. The data obtained in the Word Association Test were converted into concept network maps using the Breakpoint technique. In the study, t-test, which is one of the parametric tests, was used to analyze the data obtained from the attitude scale. In the research, conclusions were reached about the effect on the conceptual change of the students and their attitudes towards the course. In this context, it has been revealed that authentic learning practices carried out in the Life Science lesson improve the mental structure of the students, have a positive effect on concept change and contribute to meaningful learning. It was concluded that the authentic learning environments and activities applied positively improved the attitudes of the students towards the Life Studies lesson.

The relationship between L2 grit and vocabulary knowledge in first-year Moroccan university students

The present study aimed at examining the role of L2 grit in the vocabulary knowledge of English major freshmen in Morocco. A hypothesized model was specified in which two correlated factors of L2 grit, namely perseverance of efforts (PE) and consistency of interest (CI), contribute to vocabulary knowledge measured in terms of vocabulary size, associations, and collocations. This model was tested against data collected from 157 first-year university students. The L2 Grit Scale was used to measure students' PE and CI in language learning. The Yes/No test was used to estimate the vocabulary size of the participants across the most frequent 5000 words in English. The Word Association Test and COLLEX Test were employed to tap into students' depth of vocabulary knowledge. The assessment of the validity of the constructs indicated that the two correlated-factor model of L2 grit was acceptable, while a single-factor model of vocabulary knowledge construct excellently fit the data. Concerning the role of L2 grit, the estimates showed that both factors, PE and CI, statistically significantly contributed to students’ vocabulary knowledge. To this end, recommendations were suggested to encourage the exploration of L2 grit in the realm of language learning, specifically vocabulary learning.

“How Do We Put the Country Back Together?” Mitigating Affective Polarization Through Mediated Intergroup Contact

ABSTRACT Affective polarization presents an ongoing threat to the political and social functioning of democracies as partisans increasingly dislike and distrust opposing partisans. Because engagement with political media is one way by which citizens understand partisan dynamics, we hypothesize that exposure to positive mediated intergroup contact via an entertainment narrative may be able to attenuate feelings of affective polarization—especially through identification with a character that expresses differing political views—but also that negative intergroup contact via a political debate could exacerbate partisan tensions. This premise is tested in an experiment (N = 544) that juxtaposed real-world examples of political media using excerpts from the U.S. television comedy The Conners and the 2020 U.S. presidential debates. Two common indicators of affective polarization, a feeling thermometer and social distance, were used to measure the affective gap between Democrats and Republicans. For the purposes of our analyses, a brief implicit association test (BIAT) was also conducted to measure participants’ implicit bias toward members of the political outgroup. We find evidence that exposure to positive intergroup contact can attenuate partisan tensions, especially through identification with the character belonging to the opposing political party, but that identification with the co-partisan character can further exacerbate negative affect.

Poor statistical power in population-based association study of gene interaction

BackgroundStatistical epistasis, or “gene–gene interaction” in genetic association studies, means the nonadditive effects between the polymorphic sites on two different genes affecting the same phenotype. In the genetic association analysis of complex traits, nevertheless, the researchers haven’t found enough clues of statistical epistasis so far.MethodsWe developed a statistical model where the statistical epistasis was presented as an extra linkage disequilibrium between the polymorphic sites of different risk genes. The power of statistical test for identifying the gene–gene interaction was calculated and then compared in different hypothesis scenarios.ResultsOur results show the statistical power increases with the increasing of interaction coefficient, relative risk, and linkage disequilibrium with genetic markers. However, the power of interaction discovery is much lower than that of regular single-site association test. When rigorous criteria were employed in statistical tests, the identification of gene–gene interaction became a very difficult task. Since the criterion of significance was given to be p-value ≤ 5.0 × 10–8, the same as that of many genome-wide association studies, there is little chance to identify the gene–gene interaction in all kind of circumstances.ConclusionsThe lack of epistasis tends to be an inevitable result caused by the statistical principles of methods in the genetic association studies and therefore is the inherent characteristic of the research itself.

Fetomaternal Outcomes of Low-Risk Females Presenting with Perceived Reduced Fetal Movements at Moi Teaching and Referral Hospital, Eldoret, Kenya

Objective: To determine the fetomaternal outcomes of low-risk females presenting with perceived reduced fetal movements at Moi Teaching and Referral Hospital (MTRH), Eldoret, Kenya. Methods: This was a cross-sectional study among 133 females of gestation between 34–41 weeks presenting with a perception of reduced fetal movement, who were consecutively recruited. Pearson χ2 and Fischer’s exact tests of association were used to test the association between the non-stress test (NST) findings, biophysical profile (BPP) scores, and fetomaternal outcomes, where p≤0.05 was considered statistically significant. Results: A reactive NST finding was seen in 88 (66.2%) participants with 104 (78.2%) of all participants enrolled having a normal BPP score. Active management of the current pregnancy was offered to 89 (66.9%) of the participants, and 81 (60.9%) of the females had a vaginal delivery. Three quarters (76.7%) of the newborns had a 5-minute appearance, pulse, grimace, activity, and respiration (APGAR) score ≥7, 18 (13.5%) were resuscitated, 13 (9.8%) were admitted to the newborn unit, and four (3%) fresh stillbirths were noted. Both NST (p&lt;0.001) and BPP (p&lt;0.001) were good predictors of a 5-minute APGAR score ≥7, but poorly (p=0.086) predicted resuscitation. There was a statistically significant association between the NST findings and admission to newborn unit (p=0.034). Conclusion: A reactive NST is a good predictor of a 5-minute APGAR score greater than 7, and is associated with a reduced likelihood of both admission to the newborn unit and newborn resuscitation. Both reactive NST and normal BPP are good predictors of vaginal delivery as opposed to Caesarean delivery.

Application of Deep Learning for the Detection of Genetic Variations: Its Implementation in Classifying Alzheimer's Disease

Deep learning emerges as a promising technique, utilizing nonlinear transformations for feature extraction from high-dimensional datasets. However, its application encounters challenges in genome-wide association studies (GWAS) dealing with high-dimensional genomic data. This study introduces an innovative three-step method termed SWAT-CNN for the identification of genetic variants. This approach employs deep learning to pinpoint phenotype-related single nucleotide polymorphisms (SNPs), facilitating the development of precise disease classification models. In the first step, the entire genome undergoes division into non overlapping fragments of an optimal size. Subsequently, convolutional neural network (CNN) analysis is conducted on each fragment to identify phenotype-associated segments. The second step, employs a Sliding Window Association Test (SWAT), where CNN is utilized on the selected fragments to compute phenotype influence scores (PIS) and detect phenotype-associated SNPs based on these scores. The third step involves running CNN on all identified SNPs to construct a comprehensive classification model. Validation of the proposed approach utilized GWAS data from the Alzheimer’s disease Neuroimaging Initiative (ADNI), encompassing 981 subjects, including cognitively normal older adults (CN) and individuals with Alzheimer's disease (AD). Notably, the method successfully identified the widely recognized APOE region as the most significant genetic locus for AD. The resulting classification model exhibited an area under the curve (AUC) of 0.82, demonstrating compatibility with traditional machine learning approaches such as random forest and XGBoost. SWAT-CNN, as a groundbreaking deep learning-based genome-wide methodology, not only identified AD-associated SNPs but also presented a robust classification model for Alzheimer's disease, suggesting potential applications across diverse biomedical domains.

‘Interesting’ or ‘boring’: Modifying the implicit association task to measure implicit fire interest in an adolescent community sample

The efficacy of explicit measures in assessing fire interest is often compromised by social desirability biases, presenting a challenge for early intervention programs aimed at preventing firesetting behaviour. The current study aimed to validate a novel fire interest Implicit Association Task (IAT), as a more reliable measure of implicit fire interest in adolescents. An Australian community adolescent sample of 85 participants, aged 10–17 (M = 13.65, SD = 1.81), completed a series of questionnaires, and the novel fire interest IAT. Based on self-reports, participants were classified as firelighters (n = 52) or non-firelighters (n = 33). IAT outcomes revealed an inclination towards associating “fire” with “interesting.” Notably, firelighters, compared to non-firelighters, performed significantly quicker during hypothesis-consistent trials of the IAT where fire-images were paired with interesting-words. Moreover, a weak correlation emerged between the speed of responses in these hypothesis-consistent IAT trials and self-reported fire interest. This investigation is one of the few that examined the efficacy of implicit measures of fire interest and is the first to do so using a modified IAT. With continued refinement, the fire interest IAT could be successfully used to assist with early intervention programs aimed at preventing child firesetting behaviour.PsychINFO Code: 3230

The time course of encoding specific and gist episodic memory representations among young and older adults.

How rapidly can we encode the specifics versus the gist of episodic memories? Competing theories have opposing answers, but empirical tests are based primarily on tasks of item memory. Few studies have addressed this question with tasks measuring the binding of event components (e.g., a person and a location), which forms the core of episodic memory. None of these prior studies included older adults, whose episodic memories are less specific in nature. We addressed this critical gap by presenting face-scene pairs (e.g., an old man with a park) at various encoding presentation rates to 80 young (M = 21.83 years) and 86 older (M = 68.62 years) adults. Participants completed associative recognition tests featuring old/intact (e.g., the old man with the same park), similar (e.g., the old man with a different park), and unrelated (e.g., the old man with a kitchen) pairs. Multinomial-processing-tree model analyses revealed that young and older adults encoded each pair's gist representation more rapidly than its specific representation, supporting fuzzy-trace theory. No age-related differences in gist representations were obtained at any presentation rate, but older adults required more time to encode specific representations commensurate with those of younger adults. However, older adults' abilities to retrieve these representations were cue-dependent, as they were more susceptible than younger adults to experiencing vivid false memories of similar lures. These phantom recollections were remediated with further increases in encoding time. Thus, slower speed of encoding partially underlies age-related declines in episodic memory specificity, but retrieval mechanisms also play a role. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Intersectional race-gender stereotypes in natural language.

How are Asian and Black men and women stereotyped? Research from the gendered race and stereotype content perspectives has produced mixed empirical findings. Using BERT models pre-trained on English language books, news articles, Wikipedia, Reddit and Twitter, with a new method for measuring propositions in natural language (the Fill-Mask Association Test, FMAT), we explored the gender (masculinity-femininity), physical strength, warmth and competence contents of stereotypes about Asian and Black men and women. We find that Asian men (but not women) are stereotyped as less masculine and less moral/trustworthy than Black men. Compared to Black men and Black women, respectively, both Asian men and Asian women are stereotyped as less muscular/athletic and less assertive/dominant, but more sociable/friendly and more capable/intelligent. These findings suggest that Asian and Black stereotypes in natural language have multifaceted contents and gender nuances, requiring a balanced view integrating the gender schema theory and the stereotype content model. Exploring their semantic representations as propositions in large language models, this research reveals how intersectional race-gender stereotypes are naturally expressed in real life.

A cross-sectional study of the association of exposure to web-based streaming platforms on the psychosocial behaviour and the sleep pattern of Indian youth

Background The rapid proliferation of web-based streaming platforms has become a ubiquitous aspect of contemporary life, especially among the youth in urban areas. This study seeks to delve into the usage patterns and discern potential associations between web-based streaming platforms and the psychosocial behaviour, sleep patterns, and work and study routines of Indian youth in the Wardha District of Maharashtra and Central India. With the escalating prevalence of digital entertainment, understanding the impact on this demographic's mental well-being and lifestyle is imperative. Methods Employing a cross-sectional design, this study will involve youth aged 15-29 from urban communities and schools in the designated district. The sample size, determined by the Cochran formula, is 170 participants. A comprehensive data collection process will unfold by utilising validated assessment tools, including the Young's Internet Addiction Test, WHO (Five) Well-Being Index, Pittsburgh Sleep Quality Index, and Eysenck's Personality Inventory. Statistical analyses, encompassing descriptive statistics, association tests, and regression analyses, will be conducted using SPSS version 23 software. Expected Outcome Anticipated outcomes of this study encompass a nuanced understanding of web-based streaming platform usage patterns, unveiling potential associations with psychosocial behaviours, sleep patterns, and work and study routines among Indian youth. The results are poised to contribute valuable insights into the intricate interplay between digital entertainment and the well-being of the urban youth population. Such findings can inform future interventions and strategies to foster a balanced and healthy digital lifestyle.

Socioeconomic status, loneliness, and depression among older adults: a cross-sectional study in Spain

BackgroundThe association between socioeconomic status and depression is weaker in older adults than in younger populations. Loneliness may play a significant role in this relationship, explaining (at least partially) the attenuation of the social gradient in depression. The current study examined the relationship between socioeconomic status and depression and whether the association was affected by loneliness.MethodsA cross-sectional design involving dwelling and nursing homes residents was used. A total of 887 Spanish residents aged over 64 years took part in the study. Measures of Depression (GDS-5 Scale), Loneliness (De Jong-Gierveld Loneliness Scale), Socioeconomic Status (Education and Economic Hardship), and sociodemographic parameters were used. The study employed bivariate association tests (chi-square and Pearson’s r) and logistic regression analyses.ResultsThe percentage of participants at risk of suffering depression was significantly higher among those who had not completed primary education (45.5%) and significantly lower among those with university qualifications (16.4%) (X2 = 40.25;p <.001), and respondents who could not make ends meet in financial terms faced a higher risk of depression (X2 = 23.62;p <.001). In terms of the respondents who experienced loneliness, 57.5% were at risk of depression, compared to 19% of those who did not report loneliness (X2 = 120.04;p <.001). The logistic regression analyses showed that having university qualifications meant a 47% reduction in the risk of depression. This risk was 86% higher among respondents experiencing financial difficulties. However, when scores for the loneliness measure were incorporated, the coefficients relating to education and economic hardships ceased to be significant or were significantly reduced.ConclusionLoneliness can contribute to explaining the role played by socioeconomic inequalities in depression among older adults.

Scaling Implicit Bias Analysis across Transformer-Based Language Models through Embedding Association Test and Prompt Engineering

In the evolving field of machine learning, deploying fair and transparent models remains a formidable challenge. This study builds on earlier research, demonstrating that neural architectures exhibit inherent biases by analyzing a broad spectrum of transformer-based language models from base to x-large configurations. This article investigates movie reviews for genre-based bias, which leverages the Word Embedding Association Test (WEAT), revealing that scaling models up tends to mitigate bias, with larger models showing up to a 29% reduction in prejudice. Alternatively, this study also underscores the effectiveness of prompt-based learning, a facet of prompt engineering, as a practical approach to bias mitigation, as this technique reduces genre bias in reviews by more than 37% on average. This suggests that the refinement of development practices should include the strategic use of prompts in shaping model outputs, highlighting the crucial role of ethical AI integration to weave fairness seamlessly into the core functionality of transformer models. Despite the basic nature of the prompts employed in this research, this highlights the possibility of embracing structured prompt engineering to create AI systems that are ethical, equitable, and more responsible for their actions.