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We generalize Levene's test for variance (scale) heterogeneity between k groups for more complex data, when there are sample correlation and group membership uncertainty. Following a two-stage regression framework, we show that least absolute deviation regression must be used in the stage 1 analysis to ensure a correct asymptotic $X_{k - 1}^2/(k\\, - \\,\\,1)$ distribution of the generalized scale (gS) test statistic. We then show that the proposed gS test is independent of the generalized location test, under the joint null hypothesis of no mean and no variance heterogeneity. Consequently, we generalize the recently proposed joint location-scale (gJLS) test, valuable in settings where there is an interaction effect but one interacting variable is not available. We evaluate the proposed method via an extensive simulation study and two genetic association application studies.

Research into dogs' olfactory ability is growing rapidly. However, generalising based on scientific results is challenging, because research has been typically conducted on a few specially trained subjects of a few breeds tested in different environmental conditions. We investigated the effects of temperature and humidity (outdoors), age, test location, sex, neutering status, and repeated testing (outdoors and indoors) on the olfactory performance of untrained family dogs (N = 411) of various breeds. We employed the Natural Detection Task with three difficulty levels, from which we derived two performance metrics: Top Level and Success Score. Temperature (0–25 °C) and humidity (18–90%) did not affect olfactory performance. Young adult dogs surpassed other age groups in reaching the Top Level. Sex and neutering status showed no discernible influence on Top Level and Success Score. Dogs performed better in both metrics when tested indoors compared to outdoors. In the test–retest procedure no significant learning effect was observed. We confirmed on untrained companion dogs that olfactory performance declines with age and rejected some factors that have been previously hypothesised to significantly affect dogs’ olfactory success. The influence of the testing environment was notable, emphasising the need to consider various factors in understanding dogs' olfactory capabilities.

Memory and mood deficits are the enduring brain-related symptoms in Gulf War illness (GWI). Both animal model and epidemiological investigations have indicated that these impairments in a majority of GW veterans are linked to exposures to chemicals such as pyridostigmine bromide (PB, an antinerve gas drug), permethrin (PM, an insecticide) and DEET (a mosquito repellant) encountered during the Persian Gulf War-1. Our previous study in a rat model has shown that combined exposures to low doses of GWI-related (GWIR) chemicals PB, PM, and DEET with or without 5-min of restraint stress (a mild stress paradigm) causes hippocampus-dependent spatial memory dysfunction in a water maze test (WMT) and increased depressive-like behavior in a forced swim test (FST). In this study, using a larger cohort of rats exposed to GWIR-chemicals and stress, we investigated whether the memory deficiency identified earlier in a WMT is reproducible with an alternative and stress free hippocampus-dependent memory test such as the object location test (OLT). We also ascertained the possible co-existence of hippocampus-independent memory dysfunction using a novel object recognition test (NORT), and alterations in mood function with additional tests for motivation and depression. Our results provide new evidence that exposure to low doses of GWIR-chemicals and mild stress for 4 weeks causes deficits in hippocampus-dependent object location memory and perirhinal cortex-dependent novel object recognition memory. An open field test performed prior to other behavioral analyses revealed that memory impairments were not associated with increased anxiety or deficits in general motor ability. However, behavioral tests for mood function such as a voluntary physical exercise paradigm and a novelty suppressed feeding test (NSFT) demonstrated decreased motivation levels and depression. Thus, exposure to GWIR-chemicals and stress causes both hippocampus-dependent and hippocampus-independent memory impairments as well as mood dysfunction in a rat model.

This article is concerned with the high-dimensional location testing problem. For high-dimensional settings, traditional multivariate-sign-based tests perform poorly or become infeasible since their Type I error rates are far away from nominal levels. Several modifications have been proposed to address this challenging issue and shown to perform well. However, most of modified sign-based tests abandon all the correlation information, and this results in power loss in certain cases. We propose a projection weighted sign test to utilize the correlation information. Under mild conditions, we derive the optimal direction and weights with which the proposed projection test possesses asymptotically and locally best power under alternatives. Benefiting from using the sample-splitting idea for estimating the optimal direction, the proposed test is able to retain type-I error rates pretty well with asymptotic distributions, while it can be also highly competitive in terms of robustness. Its advantage relative to existing methods is demonstrated in numerical simulations and a real data example.

Background Adverse psychiatric symptoms caused by cannabis are a significant concern, and Δ9-tetrahydrocannabinol (THC) has been identified as a key contributor to these symptoms. THC binds to cannabinoid type 1 receptors (CB1Rs), which are abundant in the brain and associated with cognition. The prefrontal cortex (PFC) is crucial for cognitive functions. However, the functions of CB1Rs in the PFC in cognition processes remain unclear. Here, we injected arachidonylcyclopropylamide (ACPA), a CB1Rs agonist, into the PFC of male C57BL/6J mice via the cannula and conducted cognitive tests, including the novel object recognition test and object location test (OLT). Results These tests assessed memory in three stages: acquisition, consolidation, and retrieval. ACPA was administered immediately before each stage, and its intra-PFC administration specifically impaired memory acquisition in the OLT. In addition, in vivo microdialysis revealed that ACPA reduced extracellular GABA levels within the PFC. Next, we produced an adeno-associated virus with a glutamic acid decarboxylase promoter and an hM3Dq-encording chemogenic activator to activate GABAergic neurons in the PFC. Subsequently, deschloroclozapine (DCZ), an hM3Dq agonist, restored the memory acquisition impaired by ACPA. Conclusion Our findings suggest that CB1Rs in the PFC are involved in memory acquisition through the regulation of GABA release, offering new insights into how cannabis use lead to cognitive impairment.

The accurate delineation of mega-environments (MEs) and the rigorous evaluation of test locations are critical for optimizing regional variety trial schemes, particularly when addressing unbalanced datasets from multi-year, multi-location wheat (Triticum aestivum L.) trials. This study aimed at refining the regional wheat trial framework in the Huanghuai Winter Wheat Region (HWWR) of China using an integrated BLUP-GGE biplot approach, which combines best linear unbiased prediction (BLUP) values with genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to account for temporal variability and experimental error. We systematically evaluated the BLUP-GGE biplot approach, focusing on its goodness of fit and its ability to resolve inter-location relationships. We further assessed test location representativeness, discriminating ability, and overall desirability via the BLUP-GGE biplot, and contrasted ME delineation outcomes between the traditional “which-won-where” polygon method and the test location clustering-based approach. The BLUP-GGE biplot explained 72.9% of total phenotypic variation, with all location vectors displaying positive correlations (maximum angle = 88.8°), confirming the ecological homogeneity of the target region and yielding robust evaluation results. Based on the ideal tester view, Puyang was identified as the most desirable location, followed by Zhumadian, Shangqiu, and Huixian, while Lianyungang and Suqian exhibited relatively poor comprehensive performance. MEs delineated by the “which-won-where” method showed strong inter-ME correlations and insufficient differentiation, whereas the location clustering-based method markedly enhanced inter-ME discrimination (maximum vector angle > 60°), stably partitioning the HWWR into three distinct MEs with clear cultivar–ME interaction patterns: ME1 (Lianyungang, Suqian, Fuyang, Suzhou, Guoyang, Huixian, Huai’an, Xinmaqiao, Huayin, and Yangling), ME2 (Luoyang, Xinxiang, Zhumadian, Shangqiu, Puyang, and Luohe), and ME3 (Baoji, Xuzhou, Yuanyang, Sheyang, and Xingyang). This study confirms the superiority of the BLUP-GGE biplot for analyzing unbalanced multi-year multi-environment trial data and validates a robust clustering strategy for ME delineation. The findings provide a scientific basis for optimizing wheat regional trial systems and facilitating precise cultivar deployment in the HWWR, and offer a reference for analogous studies on other crops or ecological regions.

The present study investigated the effects of the evaluation environment and sample number on liking ratings within the same testing session. It comprised two experiments that determined consumer taste ratings of the following food products: (1) almond beverage and (2) vegan ramen, as rated by 322 and 287 Korean consumers, respectively. Consumers tasted each food product under either laboratory or home-used test conditions. Additionally, three levels of sample numbers were established for evaluation (almond beverage test: 1, 2, and 4; vegan ramen test: 1, 3, and 5) in each test condition. A target sample was selected for each of the two food products to directly ascertain the effects of the evaluation environment and sample number on the liking ratings. The results revealed that during the same evaluation session, the sample number affected the liking ratings of the target sample more than the testing location. Moreover, the sample number effect was product item dependent, that is, no significant change was noted in the liking ratings of the target almond beverage sample according to sample number, whereas significant differences were observed in the liking ratings of the target vegan ramen sample. Furthermore, the sample number effect was more prominent under laboratory test conditions than under home-used test conditions probably due to the serving order effect driven by hedonic contrast, carry over effect, and sensory specific satiety. The findings demonstrate that home-used tests should be recommended over laboratory tests when measuring the liking of a small number of multiple sample food items with high flavor complexity.

The SARS-CoV-2 pandemic has resulted in an unprecedented level of worldwide testing for epidemiologic and diagnostic purposes, and due to the extreme need for tests, the gold-standard Reverse Transcription Polymerase Chain Reaction (RT-PCR) testing capacity has been unable to meet the overall worldwide testing demand. Consequently, although the current literature has shown the sensitivity of rapid antigen tests (RATs) to be inferior to RT-PCR, RATs have been implemented on a large scale without solid data on performance. This study will compare analytical and clinical sensitivities and specificities of 50 lateral flow- or laboratory-based RATs and 3 strand invasion-based amplification (SIBA)-RT-PCR tests from 30 manufacturers to RT-PCR testing of samples obtained from the deep oropharynx. In addition, the study will compare sensitivities and specificities of the included RATs as well as RT-PCR on clinical samples obtained from the deep oropharynx, the anterior nasal cavity, saliva, the deep nasopharynx, and expired air to RT-PCR on deep oropharyngeal samples. In the prospective part of the study, 200 individuals found SARS-CoV-2 positive and 200 individuals found SARS-CoV-2 negative by routine RT-PCR testing will be retested with each RAT, applying RT-PCR as the reference method. In the retrospective part of the study, 304 deep oropharyngeal cavity swabs divided into 4 groups based on RT-PCR quantification cycle (Cq) levels will be tested with each RAT. The results will be reported in several papers with different aims. The first paper will report retrospective (analytical sensitivity, overall and stratified into different Cq range groups) and prospective (clinical sensitivity) data for RATs, with RT-PCR as the reference method. The second paper will report results for RAT based on anatomical sampling location. The third paper will compare different anatomical sampling locations by RT-PCR testing. The fourth paper will focus on RATs that rely on central laboratory testing. Tests from 4 different manufacturers will be compared for analytical performance data on retrospective deep oropharyngeal swab samples. The fifth paper will report the results of 4 RATs applied both as professional use and as self-tests. The last paper will report the results from 2 breath tests in the study. A comparison of sensitivity and specificity between RATs will be conducted using the McNemar test for paired samples and the chi-squared test for unpaired samples. Comparison of the positive predictive value (PPV) and negative predictive value (NPV) between RATs will be performed by the bootstrap test, and 95% CIs for sensitivity, specificity, PPV, and NPV will be calculated as bootstrap CIs. The study will compare the sensitivities of a large number of RATs for SARS-CoV-2 to with those of RT-PCR and will address whether lateral flow-based RATs differ significantly from laboratory-based RATs. The anatomical test locations for both RATs and RT-PCR will also be compared. ClinicalTrials.gov NCT04913116; https://clinicaltrials.gov/ct2/show/NCT04913116. DERR1-10.2196/35706.

How many test locations and replications are needed in crop variety trials is a question every plant breeder has to ask. Simple formulas were developed to estimate the optimum number of replicates and test locations. The optimum number of replicates in a trial was estimated by the formula N r = 3 σ ϵ 2 / σ g 2 , where σ g 2 and σ ϵ 2 are the variance components for genotypic main effect and experimental error in the trial, respectively. The optimum number of test locations for a target region was estimated by N e = 1 + 3 σ ge 2 σ g 2 , where σ g 2 and σ ge 2 are the variance components for genotypic main effect and genotype-by-location interaction, respectively. These formulas were applied to data from the oat registration trials conducted in eastern Canada in 2006–2012. The optimum number of replicates within a trial at the Ottawa site was estimated to be fewer than three for all traits considered. The estimated optimum number of test locations for the whole eastern Canada was fewer than four for all traits except for grain yield and lodging scores. For grain yield, the estimated optimum number of test locations was 20, twice as many as the actual test locations used. Mega-environment analysis using a “GGL+GGE biplot” revealed two distinct subregions in eastern Canada. Analysis within mega-environment revealed that the number of test locations actually used was close to optimal for one mega-environment but severely inadequate for the other.

Phase-II statistical process control (SPC) procedures are designed to detect a change in distribution when a possibly never-ending stream of observations is collected. Several techniques have been proposed to detect a shift in location vector when each observation consists of multiple measurements. These procedures require the user to make assumptions about the distribution of the process readings, to assume that process parameters are known, or to collect a large training sample before monitoring the ongoing process for a change in distribution. We propose a nonparametric procedure for multivariate phase-II statistical process control designed to detect shifts in location vector that relaxes these requirements based on an approximately distribution free multivariate test statistic. This procedure may not be appropriate for some multivariate distributions with unusual dependence structure between vector components. A diagnostic tool that can be used if a historical sample of data is available is provided to assist user in determining if the proposed procedure is appropriate for a given application.