Omnibus Test Research Articles

The development and assessment of early cardinal-number concepts

Number-recognition tasks, such as the how-many task, involve set-to-word mapping, and number-creation tasks, such as the give-n task, entail word-to-set mapping. The present study involved comparing sixty 3-year-olds’ performance on the two tasks with collections of one to three items over three time points about 3 weeks apart. Inconsistent with the sparse evidence indicating equivalent task performance, an omnibus test indicated that success differed significantly by task (and set size but not by time). A follow-up analysis indicated that the hypothesis that success emerges first on the how-many task was, in general, significantly superior to the hypothesis of simultaneous development. It further indicated the how-many-first hypothesis was superior to a give-n-first hypothesis for sets of three. A theoretical implication is that set-to-word mapping appears to develop before word-to-set mapping, especially in the case of three. A methodological implication is that the give-n task may underestimate a key aspect of children’s cardinal understanding of small numbers. Another is that the traditional give-n task, which requires checking an initial response by one-to-one counting, confounds pre-counting and counting competencies.

A comparative study to alternatives to the log-rank test

BackgroundStudies to compare the survival of two or more groups using time-to-event data are of high importance in medical research. The gold standard is the log-rank test, which is optimal under proportional hazards. As the latter is no simple regularity assumption, we are interested in evaluating the power of various statistical tests under different settings including proportional and non-proportional hazards with a special emphasis on crossing hazards. This challenge has been going on for many years now and multiple methods have already been investigated in extensive simulation studies. However, in recent years new omnibus tests and methods based on the restricted mean survival time appeared that have been strongly recommended in biometric literature. MethodsThus, to give updated recommendations, we perform a vast simulation study to compare tests that showed high power in previous studies with these more recent approaches. We thereby analyze various simulation settings with varying survival and censoring distributions, unequal censoring between groups, small sample sizes and unbalanced group sizes. ResultsOverall, omnibus tests are more robust in terms of power against deviations from the proportional hazards assumption. ConclusionWe recommend considering the more robust omnibus approaches for group comparison in case of uncertainty about the underlying survival time distributions.

Plastic waste knowledge of households towards a sustainable environment

Plastic waste in Malaysia has become a serious problem since it affects not only the environment but wildlife animals and marine life too. These may cause by irresponsible people in dealing with their plastic waste management. Therefore, this study is conducted mainly to analyse the knowledge, attitude, awareness, and behaviours of households in Pasir Gudang towards plastic waste management in separating plastic waste before its disposal. A questionnaire was distributed among households who live in this area. The Binary Logistic Regression is used to analyse the knowledge, attitude, awareness, and behaviour of households towards plastic waste management. The results show that only the behaviours of households affect the plastic waste management in their households (p-value=0.04). The Hosmer-Lemeshow test indicates the model has a good fit for the data (p-value=0.480). Other than that, the Omnibus test (p-value=0.03) shows predictors have significant effects and gave improvement to the model. The Classification Table shows the accuracy of 67.4% of the households who separated plastic waste before its disposal. It is suggested to improve the result by adding more respondents to provide insight information on the independent variables, analyse further using other approach such as decision tree and parallelly acknowledge others on the importance of recycling plastic waste.

Clinical and biochemical parameters associated with substance-induced psychotic disorder: which differences between alcohol, cannabis and psychostimulants

IntroductionAccording to DSM V, substance-induced psychotic disorder is a mental health condition in which the onset of psychotic symptoms can be traced to the use of a psychotropic substance. The pathogenesis of this disease is still poorly understood; current literature traces its causes back to genetic predisposition and early traumatic events (i.e. child abuse).ObjectivesThe present study aims to identify specific clinical features and biochemical markers which could be addressed as predictors for the long-term prognosis of this disease. Moreover, we aim to identify specific correlations between the clinical phenotype and the underlying substance abuse, in order to allow the early start of a tailored treatment.MethodsBetween 2020 and 2022 we recruited 218 patients referring to the Policlinico Hospital in Milan and the San Gerardo Hospital in Monza, Italy. All the patients were diagnosed with substance induced psychotic disorder: 31 reported alcohol abuse (14,2%), 71 psichostimulants (32,6%), 116 cannabis, (53,2%). For each patients, we collected demographic data, medical records and a comprehensive psychometric assessment (GAF, PANSS, BPRS, Modified Sad Person Scale-MOAS). Furthermore, we collected a blood sample for dosing Na+, K+, Na+/K+, hemogram with formula and platelets, glucose , urea, creatinine, uric acid, transaminases, γGT, bilirubin, plasma proteins, albumin, LDH, CPK, PCHE, cholesterol, HDL, LDL, Tg, TSH.ResultsChi squared test (χ²) has been used to compare qualitative variables between the 3 subgrous (alcohol-, psychostimulants- and cannabis-induced psychotic syndromes) (fig.1). One way ANOVA test has been used to compare quantitative variables between the same 3 subgroups (fig.2). After removing one of the subgropus (alcohol-induced psychotic symptoms), the same analysis have been repeated. Significant variables have been included in a binary logistic regression model in order to confirm their validity as predictors for cannabis- and psychostimulants-induced psychotic disorders (fig 3). Finally we performed Omnibus test and Hosmer-Lemeshow test in order to verify the validity of these regression models.Image:Image 2:Image 3:ConclusionsFor each considered subgroups, we indentified the following features. Alcohol induced psychotic syndrome: higher age of onset and age of hospital admission, higher cholesterol and hurea levels, , high comorbidity with medical conditions anxiety/depression, low social functioning, higher suicidal risk;, higher hospitalization rate. Cannabis induced psychotic syndrome: higher hemoglobin and albumin levels, more severe psychiatric symtoms (BPRS), higher smoking rates. Psychostimulants induced psychotic syndrome: higher multi-drug abuse risk. We could assume that according to this consideration the treatment protocols for each of these subgroups should be tailored according to their specific features.Disclosure of InterestNone Declared

Digestive plants in a Mapuche community of the Patagonian steppe: multidimensional variables that affect their knowledge and use

Background: In a rural Mapuche community of the Patagonian steppe (Chubut, Argentina), we analyzed how the use and popularity of digestive plants are affected by their aroma, the presence of dual medicinal-edible uses, their digestive versatility, biogeographical origin and accessibility. Methods: Free listing and in-depth interviews were conducted with 25 collaborators, complemented with participant observation and walks in the field. Quantitative indices, non-parametric methods (Binomial and Chi-square tests, p < 0, 05) and generalized linear models (GLM, Omnibus test, p < 0, 05) were performed. Results: Local collaborators use 61 digestive plants, including a similar number of native and exotic species. Most are used for only one type of digestive use, are very accessible and have aroma. There are as many species with dual use as without dual use. Exotic species generally have more aroma than native ones. Only exotic species are highly accessible. Species with dual uses are mainly used as condiments. Digestive-only species could or could not have aroma. The popularity of species is only affected by their digestive versatility and aroma, mainly for exotic ones. Conclusions: Local pharmacopoeia reflects aspects that account for its cultural resilience, including a high richness of native and exotic digestive plants, many of which are used for specific ailments. The species have been selected from the joint evaluation of biophysical and sociocultural variables that influence their knowledge and forms of use. These variables and perceptions would also allow diversification in response to changes, forming part of a bio-cultural legacy unique to Patagonia. Keywords: Mapuche medicinal flora, Patagonia, popularity, cultural resilience.

Associations between self-reported sleep patterns and health, cognition and amyloid measures: results from the Wisconsin Registry for Alzheimer's Prevention.

Previous studies suggest associations between self-reported sleep problems and poorer health, cognition, Alzheimer's disease pathology and dementia-related outcomes. It is important to develop a deeper understanding of the relationship between these complications and sleep disturbance, a modifiable risk factor, in late midlife, a time when Alzheimer's disease pathology may be accruing. The objectives of this study included application of unsupervised machine learning procedures to identify distinct subgroups of persons with problematic sleep and the association of these subgroups with concurrent measures of mental and physical health, cognition and PET-identified amyloid. Dementia-free participants from the Wisconsin Registry for Alzheimer's Prevention (n = 619) completed sleep questionnaires including the Insomnia Severity Index, Epworth Sleepiness Scale and Medical Outcomes Study Sleep Scale. K-means clustering analysis identified discrete sleep problem groups who were then compared across concurrent health outcomes (e.g. depression, self-rated health and insulin resistance), cognitive composite indices including episodic memory and executive function and, in a subset, Pittsburgh Compound B PET imaging to assess amyloid burden. Significant omnibus tests (P < 0.05) were followed with pairwise comparisons. Mean (SD) sample baseline sleep assessment age was 62.6 (6.7). Cluster analysis identified three groups: healthy sleepers [n = 262 (42.3%)], intermediate sleepers [n = 229 (37.0%)] and poor sleepers [n = 128 (20.7%)]. All omnibus tests comparing demographics and health measures across sleep groups were significant except for age, sex and apolipoprotein E e4 carriers; the poor sleepers group was worse than one or both of the other groups on all other measures, including measures of depression, self-reported health and memory complaints. The poor sleepers group had higher average body mass index, waist-hip ratio and homeostatic model assessment of insulin resistance. After adjusting for covariates, the poor sleepers group also performed worse on all concurrent cognitive composites except working memory. There were no differences between sleep groups on PET-based measures of amyloid. Sensitivity analyses indicated that while different clustering approaches resulted in different group assignments for some (predominantly the intermediate group), between-group patterns in outcomes were consistent. In conclusion, distinct sleep characteristics groups were identified with a sizable minority (20.7%) exhibiting poor sleep characteristics, and this group also exhibited the poorest concurrent mental and physical health and cognition, indicating substantial multi-morbidity; sleep group was not associated with amyloid PET estimates. Precision-based management of sleep and related factors may provide an opportunity for early intervention that could serve to delay or prevent clinical impairment.

A 3-minute recumbent stepper test in chronic stroke.

Persons with stroke often have difficulty achieving target heart rate (HR) during graded exercise testing (GXT), which is known to limit test sensitivity for detecting clinically relevant cardiac conditions. A novel Recumbent Stepper 3-minute (RS 3Min) "all out" test may increase sensitivity of stress testing after stroke. To determine the feasibility of adding the RS 3Min test after GXT among persons after stroke. A within-participant, nonrandomized, repeated measures design. Rehabilitation research laboratory and cardiovascular stress laboratory PARTICIPANTS: Fifteen participants with chronic stroke (56.7 ± 9.6 years; 6.4 ± 4.3 years post stroke; 8 male). All participants randomly completed (1) a symptom-limited treadmill GXT and (2) a symptom-limited RS GXT followed by RS 3Min critical power test. HR, ratings of perceived exertion, oxygen consumption, respiratory exchange ratio, and power output measured continuously during each test. Blood pressure measured every 2 minutes and or immediately post exercise. P value set at p < .05 from omnibus test for a significant difference among protocols. The RS 3Min test had a significantly higher rate of achieving target HR compared to the RS GXT (9/14 vs 4/14, p=.02) and was not significantly different from the treadmill GXT (9/14 vs 5/14, p=.09). Minimum power output during the RS 3Min was significantly higher than peak power output during the RS GXT (110 ± 41 W vs. 84 ± 22 W, p=.02) with 12/15 participants reaching a VO2 plateau. Although additional studies with randomized designs are needed, a novel RS 3Min "all out" test appears to be a promising method for enhancing test sensitivity in cardiovascular screening after stroke, while providing a potentially valid measure of critical power.

Generalized Linear Modeling of Crashes on Urban Road Links

As crash data have distinctive behavior like over-dispersion, researchers have used statistical methods to deal with this unique behavior of crash data specifically. This study employed generalized linear modeling techniques to develop the model. It was assumed that the accident counts followed negative-binomial distribution, and the link function was chosen to be the log link function. Negative-binomial modeling technique was chosen over Poisson distribution because it is the most used technique by many researchers as crash data may encounter over-dispersion. The accident data set showed greater variability between its variance and mean. The accident frequency distribution is shown in this study that it is highly skewed, with a very high number of road segments registering zero accidents. Negative binomial distribution was chosen over Poisson distribution after comparing Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC). The method is widely applied to count data. Twenty-two parameters were estimated in the model. Since p &lt; 0.05 in the omnibus test, the null hypothesis is rejected, which indicates that the model is reasonably fit. The strongest variables in the model were witnessed to be the length of the links, number of lanes, average daily traffic, bus lane, number of buses and trolleys, and HGVs.

Incremental prognostic value of left atrial and biventricular feature tracking in dilated cardiomyopathy: a long-term study

BackgroundDespite the use of cardiovascular magnetic resonance (CMR) feature tracking (FT) imaging to detect myocardial deformation, the optimal strain index in dilated cardiomyopathy (DCM) is unclear. This study aimed to determine whether atrial and biventricular strains can provide the greatest or joint incremental prognostic value in patients with DCM over a long follow-up period. MethodsFour hundred-twelve DCM patients were included retrospectively. Comprehensive clinical evaluation and imaging investigations were obtained, including measurements of CMR-FT derived left atrial (LA) reservoir, conduit, booster strain (εs, εe, εa); left ventricular (LV) and right ventricular (RV) global longitudinal, radial, circumferential strain (GLS, GRS, GCS). All patients were followed up for major adverse cardiac events (MACE) including all-cause mortality, heart transplantation, and implantable cardioverter defibrillator discharge. The predictors of MACE were examined with univariable and multivariable Cox regression analysis. Subsequently, nested Cox regression models were built to evaluate the incremental prognostic value of strain parameters. The incremental predictive power of strain parameters was assessed by Omnibus tests, and the model performance and discrimination were evaluated by Harrell C-index and integrated discrimination improvement (IDI) analysis. Patient survival was illustrated by Kaplan–Meier curves and differences were evaluated by log-rank test. ResultsDuring a median follow-up of 5.0 years, MACE were identified in 149 (36%) patients. LAεe, LVGLS, and RVGLS were the most predictive strain parameters for MACE (AUC: 0.854, 0.733, 0.733, respectively). Cox regression models showed that the predictive value of LAεe was independent from and incremental to LVGLS, RVGLS, and baseline variables (HR 0.74, 95% CI 0.68–0.81, P < 0.001). In reclassification analysis, the addition of LAεe provided the best discrimination of the model (χ2 223.34, P < 0.001; C-index 0.833; IDI 0.090, P < 0.001) compared with LVGLS and RVGLS models. Moreover, LAεe with a cutoff of 5.3% further discriminated the survival probability in subgroups of patients with positive LGE or reduced LVEF (all log-rank P < 0.001). ConclusionLAεe provided the best prognostic value over biventricular strains and added incremental value to conventional clinical predictors for patients with DCM.

Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks.

As biobanks become increasingly popular, access to genotypic and phenotypic data continues to increase in the form of precomputed summary statistics (PCSS). Widespread accessibility of PCSS alleviates many issues related to biobank data, including that of data privacy and confidentiality, as well as high computational costs. However, questions remain about how to maximally leverage PCSS for downstream statistical analyses. Here we present a novel method for testing the association of an arbitrary number of single nucleotide variants (SNVs) on a linear combination of phenotypes after adjusting for covariates for common multimarker tests (e.g., SKAT, SKAT-O) without access to individual patient-level data (IPD). We validate exact formulas for each method, and demonstrate their accuracy through simulation studies and an application to fatty acid phenotypic data from the Framingham Heart Study.

Accommodating multiple potential normalizations in microbiome associations studies

BackgroundMicrobial communities are known to be closely related to many diseases, such as obesity and HIV, and it is of interest to identify differentially abundant microbial species between two or more environments. Since the abundances or counts of microbial species usually have different scales and suffer from zero-inflation or over-dispersion, normalization is a critical step before conducting differential abundance analysis. Several normalization approaches have been proposed, but it is difficult to optimize the characterization of the true relationship between taxa and interesting outcomes. ResultsTo avoid the challenge of picking an optimal normalization and accommodate the advantages of several normalization strategies, we propose an omnibus approach. Our approach is based on a Cauchy combination test, which is flexible and powerful by aggregating individual p values. We also consider a truncated test statistic to prevent substantial power loss. We experiment with a basic linear regression model as well as recently proposed powerful association tests for microbiome data and compare the performance of the omnibus approach with individual normalization approaches. Experimental results show that, regardless of simulation settings, the new approach exhibits power that is close to the best normalization strategy, while controling the type I error well. ConclusionsThe proposed omnibus test releases researchers from choosing among various normalization methods and it is an aggregated method that provides the powerful result to the underlying optimal normalization, which requires tedious trial and error. While the power may not exceed the best normalization, it is always much better than using a poor choice of normalization.

Divided-and-combined omnibus test for genetic association analysis with high-dimensional data.

Advances in biologic technology enable researchers to obtain a huge amount of genetic and genomic data, whose dimensions are often quite high on both phenotypes and variants. Testing their association with multiple phenotypes has been a hot topic in recent years. Traditional single phenotype multiple variant analysis has to be adjusted for multiple testing and thus suffers from substantial power loss due to ignorance of correlation across phenotypes. Similarity-based method, which uses the trace of product of two similarity matrices as a test statistic, has emerged as a useful tool to handle this problem. However, it loses power when the correlation strength within multiple phenotypes is middle or strong, for some signals represented by the eigenvalues of phenotypic similarity matrix are masked by others. We propose a divided-and-combined omnibus test to handle this drawback of the similarity-based method. Based on the divided-and-combined strategy, we first divide signals into two groups in a series of cut points according to eigenvalues of the phenotypic similarity matrix and combine analysis results via the Cauchy-combined method to reach a final statistic. Extensive simulations and application to a pig data demonstrate that the proposed statistic is much more powerful and robust than the original test under most of the considered scenarios, and sometimes the power increase can be more than 0.6. Divided-and-combined omnibus test facilitates genetic association analysis with high-dimensional data and achieves much higher power than the existing similarity based method. In fact, divided-and-combined omnibus test can be used whenever the association analysis between two multivariate variables needs to be conducted.

Actigraphic Wake after Sleep Onset and Symptom Severity Correspond with Rumination in Trauma-Exposed Individuals.

Rumination and worry are forms of repetitive negative thinking (RNT) commonly associated with internalizing psychopathologies, although less is known about RNT in trauma-exposed individuals with internalizing psychopathologies. Separate lines of research show RNT also plays a role in problematic sleep, which is frequently experienced after trauma exposure. To address gaps in the literature, the current study examines the impact of sleep and symptoms on RNT in trauma-exposed participants. A transdiagnostic sample of 46 unmedicated treatment-seeking trauma-exposed participants completed standard measures of rumination and worry, as well as clinical measures that assessed posttraumatic stress, depression, and anxiety severity. Actigraphic sleep variables were sleep duration, wake after sleep onset (WASO), and sleep efficiency. Sleep and clinical measures were submitted to multiple regression analyses with rumination and worry as dependent variables. The regression results showed that rumination was significantly explained by WASO and posttraumatic stress symptom (PTSS) severity, and the omnibus test was significant. Depression, anxiety, and other estimates of sleep were not significant. No significant results emerged for worry. Preliminary findings suggest that PTSS and WASO, an index of fragmented sleep, may contribute to rumination, but not worry, in trauma-exposed individuals. Longitudinal studies are needed to determine potential causal relationships.

Differential neural response to psychoanalytic intervention techniques during structural interviewing: A single-case analysis using EEG.

Previous studies detected changes in the electroencephalographic (EEG) signal as an effect of psychoanalytic interventions. However, no study has investigated neural correlates of specific psychoanalytic interventions in the EEG power spectrum yet. In the present case study, we contrasted three types of interventions (clarification, confrontation, and interpretation) and a neutral control condition during a structural psychoanalytic interview conducted while EEG was recorded. A 27-year-old male patient diagnosed with major depressive disorder and borderline personality disorder with recurrent suicidal and self-injurious behavior underwent a structural interview while recording EEG. Two independent experts selected by consensus the characteristic episodes of the four conditions (clarification, confrontation, interpretation, and neutral control) within the interview, which were included in the EEG analyses. Fast Fourier transformation (FFT) was applied to subsegments of the intervention type to analyze the EEG power spectra. Alpha and beta power from central, frontal, and parietal sites were considered in linear mixed-effects models with segments as a random factor with maximum-likelihood estimates due to the lack of balance in the length of the interview segments. The interventions "interpretation" and "confrontation" showed a significantly lower alpha power compared with the control condition in the central electrodes. In the frontal and parietal sites of the alpha power and all beta power sites, the omnibus tests (full model/model without intervention) and comparisons relative to control conditions showed no significant overall result or failed significance after alpha error correction. Incisive interventions, such as confrontation with discrepancies and interpretation of unconscious intrapsychic conflicts, may have provoked temporary emotional lability, leading to a change in psychic processing akin to interference from external stimuli. This conclusion is consistent with the finding that interpretations, which are potentially the most concise interventions, had the strongest effects on alpha power. Using EEG during therapeutic psychoanalytic intervention techniques might be a helpful tool to evaluate differential responses to the psychotherapeutic process on a neural level. However, this single-case result has to be replicated in a larger sample and does not allow generalizations.

Somatic Super-Enhancer Epigenetic Signature for Overall Survival Prediction in Patients with Breast Invasive Carcinoma.

To analyze genome-wide super-enhancers (SEs) methylation signature of breast invasive carcinoma (BRCA) and its clinical value. Differential methylation sites (DMS) between BRCA and adjacent tissues from The Cancer Genome Atlas (TCGA) database were identified by using ChAMP package in R software. Super-enhancers were identified sing ROSE software. Overlap analysis was used to assess the potential DMS in SEs region. Feature selection was performed by Cox regression and least absolute shrinkage and selection operator (LASSO) algorithm based on TCGA training cohort. Prognosis model validation was performed in TCGA training cohort, TCGA validation cohort, and gene expression omnibus (GEO) test cohort. The gene ontology and KEGG analysis revealed that SEs target genes were significantly enriched in cell-migration-associated processes and pathways. A total of 83 654 DMS were identified between BRCA and adjacent tissues. Around 2397 DMS in SEs region were identified by overlap study and used to feature selection. By using Cox regression and LASSO algorithm, 42 features were selected to develop a clinical prediction model (CPM). Both training (TCGA) and validation cohorts (TCGA and GEO) show that the CPM has ideal discrimination and calibration. The CPM based on DMS at SE regions has ideal discrimination and calibration, which combined with tumor node metastasis (TNM) stage could improve prognostication, and thus contribute to individualized medicine.

Clinical evaluation of the pain predictors among temporomandibular joint disorders patients with full dentition and free-end extensions: An Analytical Cross-Sectional Study

Introduction: We aimed to evaluate the possible role of the age, occlusion type, type of dentition (full dentition or free-end extensions), and type of temporomandibular disorders (TMD) to predict the presence of pain. Methods: Subjects were selected from volunteer male TMD patients with one partially edentulous jaw from the Baghdad city in 2022. Pain was assessed via the Visual Analogue Scale (VAS). Angle’s and Kennedy’s classifications were employed to assess occlusion and partially edentulous jaw conditions. TMD was assessed using Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) for Clinical and Research Applications. Relationship between pain as ordinal dependent variable and other predictor variables was assessed via ordinal logistic regression using SPSS 26. Results: 240 subjects were assessed for eligibility and 180 TMD patients (mean age 41.1 ± 0.46) were included in the study. The omnibus test showed that the model outperforms the null model (p < 0.001). Disc displacement with reduction (odds ratio: 0.09) and Kennedy’s Class I (odds ratio: 0.42) were statistically significant inverse predictors for pain (p < 0.05). Age (p = 0.66) and Angle’s occlusion type (p = 0.91) were not significant predictors for pain. Conclusion: Probability of pain decreased in disc displacement with reduction and Kennedy’s Class I.

Indigenous and improved adaptation technologies in response to climate change adaptation and barriers among smallholder farmers in the East Wollega Zone of Oromia, Ethiopia

Agriculture is the foundation of economy for Ethiopia. The majority of people in the study area rely on rain-fed subsistence agriculture for their livelihood. As a result, smallholder farmers were more susceptible to climate change due to their reliance on a single rain season and lack of climate change adaptation technologies. The sustainability of food crop production, which employs the majority of the labor force in the nation, is threatened by climate change. The study's objectives were to look at indigenous and improved adaptation technologies in reaction to local climate change effects and barriers to adaptation among smallholder farmers. A closed-ended, semi-structured questionnaire was used to gather the data from 400 smallholder farmer household heads. An analysis of the variables influencing farmers' decisions regarding climate change adaptation was conducted using binary logistic regression model. The fitness and accuracy of the model were examined using an omnibus test of model coefficients. The study's findings showed that absence of crop insurance, a lack of training and demonstration, a lack of non-farm employment, a lack of weather forecasting and early warning, shortage of finance, the high price of farm inputs, and reliance on a single rain season were the main barriers preventing farmers from adapting to climate change. The most popular indigenous adaptation techniques used by smallholder farmers included mixed cropping of local variety, reducing social and religious rituals, mixed farming (local crop and livestock), and reducing the amount of food consumed. The primary improved adaptation strategies used by the farmers included the use of chemical fertilizers, various agrochemicals, and soil and water conservation methods. The binary logistic regression model's findings showed that access to extension services by household heads significantly and favorably influenced farmers' decisions to scale back social and religious rituals, reduce food consumption, sell livestock, and adopt improved mixed farming as a means of adapting to climate change. Farm experience significantly influenced the farmers’ decision to practice improved mixed farming; access to the market significantly affected farmers’ decision on soil and water conservation practices; and the age of the respondents significantly influenced the farmers’ decision to practice improved crop variety as a response to climate change adaptation. The study's conclusions showed that a number of factors limited farmers' attempts to adapt to climate change. The study recommends that decisions regarding adaptation plans should concentrate on reducing these barriers.

Evaluation of rock mass permeability along a borehole by integrating the techniques of geological features and logistic regression: a case study in Taiwan

When the budget for in situ hydraulic tests is constrained, cost-effective approaches for determining the variations in hydraulic conductivity along a borehole remain enticing for helping design and planning of groundwater-related engineering systems. This study proposes a practical method with probabilistic outputs for fulfilling engineering concerns. First, 474 sets of hydrogeological investigation and hydraulic test data of fractured rock masses in most of the watersheds of mountainous areas of Taiwan were collected. Then, seven geological indices [rock quality design (RQD), depth index (DI), gouge content design (GCD), lithology permeability index (LPI), fracture density (FD), fracture width (FW), and groundwater velocity (GV)] significantly correlated with rock mass permeability were identified. Third, using logistic regression analysis, the seven indices were used as explanatory variables, and the hydraulic conductivity was utilized as an outcome variable (its threshold value is 1 × 10−6 m/s) for developing prediction models of high groundwater potential zones along a borehole. All indices passed the collinearity test, indicating no collinearity between the indices. To make the prediction models proposed more flexible in practical applications, a total of 127 combinations based on the combination selection of seven explanatory variables were explored to develop various prediction models. Through the validation of the Hosmer–Lemeshow test, Omnibus test, and Wald test for all developed models, only 77 models were statistically significant. The 77 models were evaluated by three measures of Nagelkerke R2, SR (success rate), and AUC (area under the curve) to further understand the prediction performance of each model. The results show that the accuracy of the prediction model is positively correlated with the number of geological indices used. When all seven geological indices are used, Nagelkerke R2, SR, and AUC can reach the best values, which are 0.83, 91.6%, and 0.976, respectively. In conclusion, the prediction model developed by combining geological indices with logistic regression analysis can provide a more efficient way of understanding the hydrogeological conditions of a borehole site.

EXOME-WIDE STUDY IDENTIFIED 12 PLEIOTROPIC LOCI ASSOCIATED WITH ALZHEIMER’S AND CARDIOVASCULAR DISEASE RISKS

Abstract Health of brain, heart and blood vessels are closely connected, which means that Alzheimer’s (AD) and cardiovascular (CVD) diseases may have overlapping etiologies including genetic component. Most of previous genetic association studies considered different traits (AD, CVD, and their risk factors) separately. The analysis of pleiotropic predisposition to these traits may shed light on the trait-specific mechanisms in protection against AD. We carried out pair-wise pleiotropic exome-wide association study (~250K common genetic variants) in predisposition to AD and each of 17 traits in a sample of 118K individuals from UK biobank. The analysis included seven qualitative traits (CVD, coronary-heart disease, type 2 diabetes, stroke, myocardial infarction, heart failure and hypertension), as well as their 10 risk factors (blood glucose, body-mass index, height, weight, 4 lipid traits, systolic and diastolic blood pressure). Fisher’s method and omnibus test were used in pleiotropic analyses. In addition to the APOE-TOMM40 locus, the analysis identified 12 genetic loci in which genetic polymorphisms demonstrated significant associations with AD at p≤5×10-4 and pleiotropic associations at genome-wide level, p≤5×10-8. The identified genes are involved in processes of phosphorylation; regulation of cell growth, differentiation, and brain development; signal transduction and neurotransmission; mitochondrial and cytoskeleton organization; regulation of gene expression, apoptotic processes, and adaptive immune response. Our results provide novel evidence supporting the hypothesis of complex pleiotropic mechanisms contributing to the development and progression of AD and provide more insights into understanding of underlying biological functions and regulatory mechanisms behind these effects.

Multigroup analysis of more than two groups in PLS-SEM: A review, illustration, and recommendations

Multigroup analysis (MGA) in partial least squares structural equation modeling (PLS-SEM) has grown considerably in the past few years in many different research fields, particularly in the business area. However, a close examination of MGA in PLS-SEM articles revealed much less research that compared more than two groups. Furthermore, research applying MGA in PLS-SEM with more than two groups has several constraints. For instance, most researchers need clarification about using either the omnibus test of group differences (OTG) or non-parametric distance-based tests (NDT) for an overall difference across the groups. Moreover, they do not handle family-wise error when comparing more than two groups, nor do they check for measurement invariance. This article uses an empirical illustration to fully understand multigroup analysis with more than two groups, providing valuable guidelines and comprehensible recommendations for researchers applying PLS-MGA.