Correlation Patterns Research Articles

Variability, Correlation Patterns and Principal Component Analysis (PCA) for Seed Yield and Contributing Traits in Castor (Ricinus communis L.)

Variability, Correlation Patterns and Principal Component Analysis (PCA) for Seed Yield and Contributing Traits in Castor (Ricinus communis L.)

Addressing spatiotemporal signal variations in pair correlation function analysis

Fluorescence correlation spectroscopy (FCS) is a cornerstone technique in optical microscopy to measure, for example, the concentration and diffusivity of fluorescent emitters and biomolecules in solution. The application of FCS to complex biological systems, however, is fraught with inherent intricacies that impair the interpretation of correlation patterns. Critical among these intricacies are temporal variations beyond diffusion in the quantity, intensity, and spatial distribution of fluorescent emitters. These variations introduce distortions into correlated intensity data, thus compromising the accuracy and reproducibility of the analysis. This issue is accentuated in imaging-based approaches such as pair correlation function (pCF) analysis due to their broader regions of interest compared with point-detector-based approaches. Despite ongoing developments in FCS, attention to systems characterized by a spatiotemporal-dependent probability distribution function (ST-PDF) has been lacking. To address this knowledge gap, we developed a new analytical framework for ST-PDF systems that introduces a dual-timescale model function within the conventional pCF analysis. Our approach selectively differentiates the signals associated with rapid processes, such as particle diffusion, from signals stemming from spatiotemporal variations in the distribution of fluorescent emitters occurring at extended delay timescales. To corroborate our approach, we conducted proof-of-concept experiments on an ST-PDF system, wherein the, initially, uniform distribution of fluorescent microspheres within a microfluidic channel changes into a localized accumulation of microspheres over time. Our framework is offering a comprehensive solution for investigating various phenomena such as biomolecular binding, sedimentation, and particle accumulation.

Scoring story recall for individual differences research: Central details, peripheral details, and automated scoring.

Story recall is an episodic memory paradigm that is popular among researchers interested in the effects of aging, disease, and/or injury on memory functioning; it is less popular among individual-differences researchers studying neurotypical young adults. One reason differential psychologists may favor other episodic memory paradigms is that the prospect of scoring story recall is daunting, as it typically requires manually scoring hundreds or thousands of freely recalled narratives. In this study, I investigated two questions related to scoring story recall for individual differences research. First, whether there is anything to gain by scoring story recall for memory of central and peripheral details or if a single score is sufficient. Second, I investigated whether scoring can be automated using computational methods-namely, BERTScore and GPT-4. A total of 235 individuals participated in this study. At the latent variable level, central and peripheral factors were highly correlated (r = .99), and the two factors correlated with external factors (viz., fluid intelligence, crystallized intelligence, and working memory capacity) similarly. Regarding automated scoring, both BERTScore and GPT-4 derived scores were strongly correlated with manually derived scores (r ≥ .97); additionally, factors estimated from the various scoring methods all showed a similar pattern of correlations with the external factors. Thus, differential psychologists may be able to streamline scoring by disregarding detail type and by using automated approaches. Further research is needed, particularly of the automated approaches, as both BERTScore and GPT-4 derived scores were occasionally leptokurtic while manual scores were not.

ChatGPT Goes to College: Exploring Student Perspectives on Artificial Intelligence in the Classroom

Background The emergence of artificial intelligence (AI) in higher education has sparked numerous discussions about its implications. ChatGPT, a prominent AI conversational model, has attracted significant attention for its ability to generate essays and formulate responses. Objective The current study sought to explore how and why students are using ChatGPT, and to examine their perceptions about ChatGPT and academic integrity. Method Students were surveyed about the frequency and motivation for ChatGPT use and their views on ChatGPT and academic misconduct. Exploratory factor analyses were conducted to examine patterns of correlations between each of the measures. Results Students primarily use ChatGPT for gathering information, motivated by its value and convenience rather than hedonic reasons, and can correctly identify academically unethical uses of the tool as cheating. Conclusion The current study presents comprehensive data on college students’ ChatGPT usage patterns, attitudes, and perceptions of cheating behavior. The outcomes of this research provide insight into how college students are currently interacting with AI tools. Teaching Implications Our findings offer practical insights for universities developing AI policies in the classroom, contributing to the ongoing discourse on AI’s role in higher education by providing accurate information about ChatGPT’s pervasiveness in academia.

Violent attitudes in Portugal and Canada: Measurement invariance and psychometric properties of the Evaluation of Violence Questionnaire.

Theory and evidence suggest that attitudes toward violence are relevant for the explanation, prediction, and reduction of violent behavior. The purpose of the present study was to adapt a measure of violent attitudes-the Evaluation of Violence Questionnaire (EVQ)-for use in Portugal, test the cross-country equivalence, and test the validity of both versions. We found the expected one-factor structure, high internal consistency, and cross-country measurement invariance for the Portuguese and original EVQ with men in Portugal (N = 320) and Canada (N = 298). We also found the expected pattern of correlations with measures of more versus less theoretically relevant constructs: both versions of the EVQ showed the strongest correlations with overall aggression and reactive aggression; slightly lower correlations with proactive aggression; negative correlations with self-control; and the smallest correlations with self-esteem. Our results support the equivalence, reliability, and validity of the Portuguese and original versions of the EVQ.

Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture

Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.

Correlates of multidimensional sleep in premenopausal women: The BioCycle study

PurposeTo identify sleep dimensions (characteristics) that co-occur in premenopausal women. The second aim was to examine associations between multiple dimensions of sleep and a set of demographic, lifestyle, and health correlates. The overarching goal was to uncover patterns of poor-sleep correlates that might inform interventions to improve sleep health of women in this age group. MethodsThe BioCycle Study included 259 healthy women aged 18–44y recruited between 2005 and 2007 from Western New York. Participants reported sleep data through daily diaries and questionnaires that were used to create five sleep health dimensions (duration, variability, timing, latency, and continuity). We used multivariate analysis – canonical correlation methods – to identify links among dimensions of sleep health and patterns of demographic, psychological, and occupational correlates. ResultsTwo distinct combinations of sleep dimensions were identified. The first - primarily determined by low variability in nightly sleep duration, low variability in bedtime (timing), greater nocturnal awakening, and less sleep onset latency – was distinguished from the second – primarily determined by sleep duration.The first combination of sleep dimensions was associated with older age and higher parity, fewer depressive symptoms, and higher stress level. The second combination of sleep dimensions was associated with perception of longer sleep duration as optimal, lower parity, not engaging in shift work, older age, lower stress level, higher prevalence of depressive symptoms, and White race. ConclusionAmong premenopausal women, we demonstrated distinct patterns of sleep dimensions that co-occur and vary by demographic, health, and lifestyle correlates. These findings shed light on the correlates of sleep health vulnerabilities among young women.

A statistic of the subseasonal forecast skill windows of 2-meter air temperature

The forecast skill on subseasonal time scale is currently limited, inhibiting its effective application broadly. Under some special conditions, the subseasonal-seasonal (S2S) forecast skill would be increased intermittently, namely, the forecast opportunity window. The identification of such forecast windows can increase the credibility of subseasonal prediction results, providing final users the opportunity to make the appropriate decision. To identify the subseasonal forecast window of 2-m air temperature (T2m), based on the S2S multi-model prediction results, this study evaluated the pattern correlation coefficient between the third-week T2m forecasted by S2S models and the observation. When the forecast skills of the majority of models reach the prescribed threshold, this period is defined as a forecast skill window. In this way, the subseasonal forecast skill windows of T2m over different regions of the global lands are identified. From the perspective of seasonal distribution, the forecast skill windows over almost all the continents appear more in boreal winter, while the forecast skill windows of the Australian continent mainly appear in boreal summer. Significant differences can be found in the occurrence frequency of forecast windows during different phases of ENSO. During El Niño events, forecast windows appear more frequently over North America, Asia, and South America, especially during winter-spring from January to April. From the T2m spatial pattern during the window periods, the forecast skill windows have some relevance among several continents, and the windows over the whole global land mainly correspond to those over Asia, Europe, and North America. Deep investigation of the physical mechanism behind the forecast skill windows helps understand the sources of predictability and improves the skill of subseasonal prediction.

Analysis of early-pregnancy metabolome in early- and late-onset gestational diabetes reveals distinct associations with maternal overweight.

It is not known whether the early-pregnancy metabolome differs in patients with early- vs late-onset gestational diabetes mellitus (GDM) stratified by maternal overweight. The aims of this study were to analyse correlations between early-pregnancy metabolites and maternal glycaemic and anthropometric characteristics, and to identify early-pregnancy metabolomic alterations that characterise lean women (BMI <25 kg/m2) and women with overweight (BMI ≥25 kg/m2) with early-onset GDM (E-GDM) or late-onset GDM (L-GDM). We performed a nested case-control study within the population-based prospective Early Diagnosis of Diabetes in Pregnancy cohort, comprising 210 participants with GDM (126 early-onset, 84 late-onset) and 209 normoglycaemic control participants matched according to maternal age, BMI class and primiparity. Maternal weight, height and waist circumference were measured at 8-14 weeks' gestation. A 2 h 75 g OGTT was performed at 12-16 weeks' gestation (OGTT1), and women with normal results underwent repeat testing at 24-28 weeks' gestation (OGTT2). Comprehensive metabolomic profiling of fasting serum samples, collected at OGTT1, was performed by untargeted ultra-HPLC-MS. Linear models were applied to study correlations between early-pregnancy metabolites and maternal glucose concentrations during OGTT1, fasting insulin, HOMA-IR, BMI and waist circumference. Early-pregnancy metabolomic features for GDM subtypes (participants stratified by maternal overweight and gestational timepoint at GDM onset) were studied using linear and multivariate models. The false discovery rate was controlled using the Benjamini-Hochberg method. In the total cohort (n=419), the clearest correlation patterns were observed between (1) maternal glucose concentrations and long-chain fatty acids and medium- and long-chain acylcarnitines; (2) maternal BMI and/or waist circumference and long-chain fatty acids, medium- and long-chain acylcarnitines, phospholipids, and aromatic and branched-chain amino acids; and (3) HOMA-IR and/or fasting insulin and L-tyrosine, certain long-chain fatty acids and phospholipids (q<0.001). Univariate analyses of GDM subtypes revealed significant differences (q<0.05) for seven non-glucose metabolites only in overweight women with E-GDM compared with control participants: linolenic acid, oleic acid, docosapentaenoic acid, docosatetraenoic acid and lysophosphatidylcholine 20:4/0:0 abundances were higher, whereas levels of specific phosphatidylcholines (P-16:0/18:2 and 15:0/18:2) were lower. However, multivariate analyses exploring the early-pregnancy metabolome of GDM subtypes showed differential clustering of acylcarnitines and long-chain fatty acids between normal-weight and overweight women with E- and L-GDM. GDM subtypes show distinct early-pregnancy metabolomic features that correlate with maternal glycaemic and anthropometric characteristics. The patterns identified suggest early-pregnancy disturbances of maternal lipid metabolism, with most alterations observed in overweight women with E-GDM. Our findings highlight the importance of maternal adiposity as the primary target for prevention and treatment.

Seasonal plasticity in sympatric Bicyclus butterflies in a tropical forest where temperature does not predict rainfall

AbstractWhile variation in temperature appears to be the main environmental cue for plasticity in adult traits in many species of Mycalesina, relying on temperature would result in a mismatch between adult phenotype and environment in some regions. We measured phenotypes of six species of Bicyclus butterflies (Nymphalidae: Satyrinae: Mycalesina) in a humid tropical forest with two rainy seasons per year and modest unimodal seasonal temperature variation, such that temperature does not predict rainfall and butterflies can reproduce year‐round. The butterflies showed subtle temporal variation in body size and relative eyespot size, while relative androconia length was robust to temporal environmental variation. After higher temperatures, body size tended be smaller, and relative eyespot size was larger for some species‐eyespot combinations. This indicates that these butterflies follow the “hotter is smaller” rule, and show developmental plasticity in eyespot size that is typical in this clade. Eyespot sizes tended to be correlated with each other, except Cu1 in B. auricruda and some eyespots that always remained very small. Androconia length was not related to eyespot size. This pattern of correlations suggests conserved cue‐use and shared mechanisms for eyespot size using both temperature and rainfall‐related cues, with some exceptions.

Hourly impact of urban features on the spatial distribution of land surface temperature: A study across 30 cities

Global warming and the urban heat island effect exacerbate excessive heat in urban environments, adversely impacting human health. Effective urban planning can mitigate these effects by influencing land surface temperature (LST). While previous studies have examined the influence of urban features on LST across seasons and between day and night, detailed hour-by-hour comparisons remain unexplored. This study addresses this gap by analyzing the hourly impacts of various urban features on LST in 30 U.S. cities using geostationary weather satellite data. We employed cloud-based analytics and machine learning techniques to aggregate data from thousands of images, identifying the relative importance and correlation curve of each urban feature on LST at each hour. Our findings revealed two distinct correlation patterns: dynamic daytime patterns with significant hourly variability and stable nocturnal patterns with minimal hourly differences. These results demonstrate that sunlight intensity greatly affects the correlation between urban features and LST. Urban planners should therefore consider broader patterns rather than focusing on specific hours. These insights provide valuable guidance for landscape and urban planners in developing strategies for climate adaptation and heatwave mitigation, contributing to the growing body of literature on sustainable cities.

Mapping the associations among task complexity, emotionality, and linguistic complexity in L2 writing

This study explored the relationship between task complexity, textual emotionality, and linguistic complexity in second language (L2) writing. Fifty-eight L2 English learners performed simple and complex versions of an argumentative writing task manipulated along with resource-­directing variables. The essays were first analysed for textual emotionality with sentiment analysis and then examined using a set of lexical and syntactic complexity and cohesiveness indices. Regarding emotionality, the complex task elicited more positive and negative emotions than the simple task. Further, the complex task led to greater lexical complexity and cohesiveness than the simple task, but the two tasks were not statistically different from each other in terms of syntactic complexity features. Varied patterns of correlations were observed between emotionality metrics and indices of different dimensions of linguistic complexity in the two tasks. Our findings focusing on the intersections of cognitive, socio-emotive, and linguistic components suggest that different task complexity manipulations could generate varying cognitive demands, affecting L2 written production. These findings provide theoretical and pedagogical implications for TBLT researchers and L2 writing practitioners.

Soil-Microbial Interactions in Rice-Loach-Shrimp Integrated Farming: Multivariate Analysis of Ecological Intensification

Ecological intensification of agriculture is crucial for sustainable food production, yet the complex soil-microbial interactions in integrated farming systems remain poorly understood. This five-year study compared a Rice-Loach-Shrimp (RLS) integrated system with conventional Rice Monoculture (RM) using advanced multivariate techniques, including correlation analysis, Principal Component Analysis, Random Forest modeling, and Structural Equation Modeling. The RLS system exhibited higher values for key soil health indicators compared to RM, although the overall correlation patterns were similar. Stronger correlations among soil organic matter, enzyme activities, and microbial populations were observed in RLS, suggesting a more tightly coupled soil ecosystem. Available phosphorus and soil organic matter were identified as primary drivers of microbial community structure. The RLS system showed a slightly higher mean pH and weaker correlations between pH and other indicators compared to RM, indicating potential altered biogeochemical processes. Complex cascading effects among soil properties, enzyme activities, and microbial populations were revealed in RLS. These soil health benefits persisted throughout the study period, demonstrating the potential of RLS as an effective ecological intensification strategy for sustainable rice production.

Interdecadal variability of winter and spring Eurasian snow depth and its impact on Eastern China precipitation

Empirical orthogonal function (EOF) and correlation analyses were employed to investigate the winter and spring snow depth in Eurasia and its relationship with Eastern China precipitation based on the observed and reanalyzed data from 1980 to 2016. The results show that the winter and spring snow cover in Eurasia not only highlights a decreasing trend due to global warming (the first EOF mode, its variance accounted for 24.4% and 22.6% of the total variance) but also exhibits notable interdecadal variation (the second EOF mode, its variance accounted for 10.2% and 11.5% of the total variance). The second EOF mode of winter snow depth in Eurasia is characterized by a west-east dipole pattern. It was observed that the spatial correlation pattern between the EOF2 of Eurasian snow depth and summer precipitation in China closely resembles the meridional quadrupole structure of the third EOF mode of summer precipitation in China. This pattern is characterized by excessive rainfall in Northeast China and the lower-middle reaches of the Yangtze River, and less rainfall over the Yellow River basin and southern China. The EOF mode of spring snow depth not only reflects the declining trend but also regulates precipitation in Eastern China. The possible mechanisms by which snow depth causes changes in soil moisture and subsequently affects atmospheric circulation are then explored from the perspective of the hydrological effects of snow cover. Decreased (Increased) snow depth in Eurasia during the winter and spring directly leads to diminished (increased) soil moisture while increasing (decreasing) net radiation and sensible heat flux at the surface. The meridional distribution of surface temperature also exhibits a dipole pattern, leading to enhanced subtropical westerly jet in the upper troposphere. The Eurasian snow cover anomalies pattern triggered an anomalous mid-latitude Eurasian wave train, which strengthened significantly in the Western Siberian Plain. It then splits into two branches, one continuing to propagate eastward at high latitudes and the other shifting towards East Asia, thereby impacting precipitation in Eastern China. This work indicates that the second EOF mode of Eurasian snow cover can impact the precipitation variability in Eastern China during the same period and in summer on an interdecadal scale.

An Empirical Examination of the Relationship Between Emotion Regulation and Emotional Labor

The expression of contextually appropriate emotions in the workplace is critical to fostering effective interpersonal interactions. What constitutes an appropriate emotional expression is determined by the display rules an employee perceives. Within the emotional labor framework, the management of emotional expression at work (i.e., ensuring alignment with display rules) occurs through the engagement in two primary strategies by employees. These are known as surface acting and deep acting. Despite theoretical efforts to synthesize these strategies with the broader emotion regulation framework and its strategies of expressive suppression and cognitive reappraisal, no empirical examination of their relationship exists. The present study aimed to investigate this empirical relationship to provide clarity on the extent to which these constructs (i.e., strategies) are unique across frameworks. A second aim was to assess whether method bias could explain any overlap between these constructs. A total of 800 participants (Mage = 22.4 years, 78.8% female) who worked across a range of service industries completed measures of emotion regulation and emotional labor under two conditions designed to manipulate the presence of method bias (i.e., varying the order of item administration). Participants also completed the DASS-21, a measure of affective symptom severity. Using multigroup analysis, the results indicated that analogous latent constructs (cognitive reappraisal and deep acting; expressive suppression and surface acting) yielded significant, small-to-moderate correlations, and that correlation coefficients were invariant regardless of how items were administered. The pattern of correlations with affective symptoms also differed across constructs. Together, the limited correlations between the analogous strategies, and the differential associations with affective symptoms, suggest a relative independence between these constructs. Findings carry theoretical and practical implications across research and clinical settings.

Capacitance Evaluation of Metallized Polypropylene Film Capacitors Considering Cumulative Self-Healing Damage

Self-healing (SH) in metallized polypropylene film capacitors (MPPFCs) can lead to irreversible damage to electrode and dielectric structures, resulting in capacitance loss and significant stability degradation, especially under cumulative SH conditions. To enhance the reliability assessment of MPPFCs post-SH, this study conducted SH experiments on MPPFCs, examined the damage patterns of the electrodes and dielectric films, and proposed a novel capacitance evaluation method for MPPFCs under cumulative SH conditions. The results reveal that with increasing SH voltage, the number of dielectric layers experiencing single SH breakdowns rises, SH energy significantly escalates, and the loss area of the electrode due to high-temperature evaporation expands. Under cumulative SH conditions, the number of SH events is linearly correlated with the total number of SH-breakdown film layers and shows an exponential decay with the average single SH energy. By utilizing a Support Vector Machine (SVM) to classify the SH condition and damage features within the capacitor based on the correlation and distribution patterns of SH feature parameters, this study introduces an advanced method for evaluating the capacitance of MPPFCs under cumulative SH conditions. This method promises to improve the predictive maintenance and reliability of power electronic systems utilizing MPPFCs.

Emotional and Cognitive "Route" in Decision-Making Process: The Relationship between Executive Functions, Psychophysiological Correlates, Decisional Styles, and Personality.

Studies on decision-making have classically focused exclusively on its cognitive component. Recent research has shown that a further essential component of decisional processes is the emotional one. Indeed, the emotional route in decision-making plays a crucial role, especially in situations characterized by ambiguity, uncertainty, and risk. Despite that, individual differences concerning such components and their associations with individual traits, decisional styles, and psychophysiological profiles are still understudied. This pilot study aimed at investigating the relationship between individual propensity toward using an emotional or cognitive information-processing route in decision-making, EEG and autonomic correlates of the decisional performance as collected via wearable non-invasive devices, and individual personality and decisional traits. Participants completed a novel task based on realistic decisional scenarios while their physiological activity (EEG and autonomic indices) was monitored. Self-report questionnaires were used to collect data on personality traits, individual differences, and decisional styles. Data analyses highlighted two main findings. Firstly, different personality traits and decisional styles showed significant and specific correlations, with an individual propensity toward either emotional or cognitive information processing for decision-making. Secondly, task-related EEG and autonomic measures presented a specific and distinct correlation pattern with different decisional styles, maximization traits, and personality traits, suggesting different latent profiles.

Characteristics of intestinal microbiota parameters in young people with metabolic syndrome

Objective. To study the features of the colon microbiota, as well as the associations of microbial representatives with anthropometric, anamnestic and biochemical parameters in young patients with metabolic syndrome. Materials and methods. 118 young people took part in a single-center, one stage, controlled study. 87 of them were diagnosed with obesity, and 31 people with normal body weight formed the control group (“C”). 87 obese patients were divided into 2 groups: “MS-” which consisted of 43 people (49.4%), and “MS+” including 44 people (50.6%) with metabolic syndrome. When stratifying the groups, the NCEP ATP III criteria were used. Blood for biochemical test was taken from all the participants, and the condition of the colon microbiota was assessed using polymerase chain reaction («Colonoflor-16 (premium)»). The Microsoft Excel 2010 and IBM SPSS Statistics 26.0 application software package were used for statistical calculations. The results were evaluated as statistically significant at a level of p0.05. Results. In the MS+ group Fusobacterium nucleatum (Fusobacteriaceae family) was detected statistically significantly more often than in individuals from group “C” (40.5%). Differences in the bacterial composition of the intestinal microbiota between two groups of obese people were revealed: in the “MS+” group there was a significant decrease in bacteria of the genus Bifidobacterium (Bifidobacteriaceae family), Prevotella (Prevotellaceae family) and Faecalibacterium prausnitzii (Ruminococcaceae family) (p0.05). In addition, correlation patterns between the species and generic composition of the microbiota on the one hand and age, BMI, waist circumference, hip circumference, breastfeeding duration, indicators of carbohydrate (glucose, insulin, HOMA-IR index) and lipid (total cholesterol, triglycerides, low-density lipoproteins, very low-density lipoproteins, high-density lipoproteins) metabolism, CRP on the other hand have been established. Conclusions. The colon microbiota in obese patients is characterized by proinflammatory changes. For the metabolically unhealthy phenotype of obesity these changes are most characteristic. It is clear that further research is needed to determine the mechanisms underlying the influence of bacterial-fungal associations on metabolism in obese individuals, as these mechanisms are likely to play a key role in the development of metabolic diseases.

Multivariate associations between neuroanatomy and cognition in unmedicated and medicated individuals with schizophrenia

Previous studies that focused on univariate correlations between neuroanatomy and cognition in schizophrenia identified some inconsistent findings. Moreover, antipsychotic medication may impact the brain-behavior profiles in affected individuals. It remains unclear whether unmedicated and medicated individuals with schizophrenia would share common neuroanatomy-cognition associations. Therefore, we aimed to investigate multivariate neuroanatomy-cognition relationships in both groups. A sample of 59 drug-naïve individuals with first-episode schizophrenia (FES) and a sample of 115 antipsychotic-treated individuals with schizophrenia were finally included. Multivariate modeling was conducted in the two patient samples between multiple cognitive domains and neuroanatomic features, such as cortical thickness (CT), cortical surface area (CSA), and subcortical volume (SV). We observed distinct multivariate correlational patterns between the two samples of individuals with schizophrenia. In the FES sample, better performance in token motor, symbol coding, and verbal fluency tests was associated with greater thalamic volumes but lower CT in the prefrontal and anterior cingulate cortices. Two significant multivariate correlations were identified in antipsychotic-treated individuals: 1) worse verbal memory performance was related to smaller volumes for the most subcortical structures and smaller CSA mainly in the temporal regions and inferior parietal lobule; 2) a lower symbol coding test score was correlated with smaller CSA in the right parahippocampal gyrus but greater volume in the right caudate. These multivariate patterns were sample-specific and not confounded by imaging quality, illness duration, antipsychotic dose, or psychopathological symptoms. Our findings may help to understand the neurobiological basis of cognitive impairments and the development of cognition-targeted interventions.

A comparison of antibiotic resistance genes and mobile genetic elements in wild and captive Himalayan vultures.

As the most widely distributed scavenger birds on the Qinghai-Tibetan Plateau, Himalayan vultures (Gyps himalayensis) feed on the carcasses of various wild and domestic animals, facing the dual selection pressure of pathogens and antibiotics and are suitable biological sentinel species for monitoring antibiotic resistance genes (ARGs). This study used metagenomic sequencing to comparatively investigate the ARGs and mobile genetic elements (MGEs) of wild and captive Himalayan vultures. Overall, the resistome of Himalayan vultures contained 414 ARG subtypes resistant to 20 ARG types, with abundances ranging from 0.01 to 1,493.60 ppm. The most abundant resistance type was beta-lactam (175 subtypes), followed by multidrug resistance genes with 68 subtypes. Decreases in the abundance of macrolide-lincosamide-streptogramin (MLS) resistance genes were observed in the wild group compared with the zoo group. A total of 75 genera (five phyla) of bacteria were predicted to be the hosts of ARGs in Himalayan vultures, and the clinical (102 ARGs) and high-risk ARGs (35 Rank I and 56 Rank II ARGs) were also analyzed. Among these ARGs, twenty-two clinical ARGs, nine Rank I ARG subtypes, sixteen Rank II ARG subtypes were found to differ significantly between the two groups. Five types of MGEs (128 subtypes) were found in Himalayan vultures. Plasmids (62 subtypes) and transposases (44 subtypes) were found to be the main MGE types. Efflux pump and antibiotic deactivation were the main resistance mechanisms of ARGs in Himalayan vultures. Decreases in the abundance of cellular protection were identified in wild Himalayan vultures compared with the captive Himalayan vultures. Procrustes analysis and the co-occurrence networks analysis revealed different patterns of correlations among gut microbes, ARGs, and MGEs in wild and captive Himalayan vultures. This study is the first step in describing the characterization of the ARGs in the gut of Himalayan vultures and highlights the need to pay more attention to scavenging birds.