Patterns Of Covariation Research Articles

Does the development environment cause the pace of life to change in a rainforest lizard?

Life history theory emphasizes that finite resources result in allocation trade-offs among the competing interests of self-maintenance, growth, reproduction, and survival. Environmental conditions, particularly during development, can influence these life history trade-offs, leading to the coupling of physiological and behavioural traits with life history strategies. Thus, populations may vary in the pattern of trait covariation, clustering along a fast-slow continuum, termed the extended pace-of-life syndrome (POLS) theory. We aimed to test how variation in ecological conditions influence life history trade-offs and their association with behaviour and physiology by comparing captive bred and wild-collected southern rainforest sunskink (Lampropholis similis). The captive bred skinks were the offspring of the wild-caught skinks, and all tests were conducted in the laboratory. We found that the groups differed, on average, in growth rate, body condition, thermal preferences, sprint performance, and activity. Counter to our expectation, wild-caught skinks exhibited a faster pace of life relative to captive-bred skinks despite experiencing more challenging environmental conditions. Furthermore, life history trade-offs were not detected, nor were traits correlated to form the syndrome. Studies are needed to identify the proximate mechanisms causing life history trade-offs and how they lead to the coupling, or decoupling, of physiological and behavioural traits. Such information will provide vital insight into how ecological forces drive the evolution of traits.

Innovative Statistical Model Uncover Effective Herbal Medicines Among Personalized Treatment Plans in Persian Medicine: A Small-Scale Study in Type 2 Diabetes.

Objectives: In holistic medicine, developing personalized treatment plans is challenging due to the multitude of possible therapy combinations. This study introduces the use of a statistical approach to identify the most effective herbal medicines prescribed in Persian medicine (PM) in a small-scale sample of patients with type 2 diabetes mellitus (T2DM). Methods: This prospective observational cohort study was conducted with 47 patients with T2DM referred to Behesht Clinic in Tehran, Iran. A physician prescribed individualized PM treatment for T2DM and related systemic issues. The fasting blood sugar (FBS) level of each patient was recorded at initial and two follow-up visits, with visit intervals and treatment modifications determined by patient health status. Patients who completed two follow-up visits were included in the final analysis (n = 27). Data were analyzed using R software. A general linear model was assumed for the mean response, along with an exponential covariance pattern model, to manage irregularly timed measurements. Results: Two fitted models showed that, after adjusting for confounders, the use of the "Diabetes Capsule" significantly reduced the average FBS by 17.14 mmol/L (p = 0.046). For each unit increase in the consumption of "Diabetes Capsule" or "Hab-e-Amber Momiai," the average FBS decreased by 15.22 mmol/L (p = 0.015) and 14.14 mmol/L (p = 0.047), respectively. Conclusion: It is possible to observe which medications are most effective, even when treatments are applied in a holistic and personalized fashion. Preliminary studies such as these may identify promising products for testing in clinical trials conducted under standardized conditions, to inform initial choices for future personalized treatments.

Bayesian hierarchical model for bias-correcting climate models

Abstract. Climate models, derived from process understanding, are essential tools in the study of climate change and its wide-ranging impacts. Hindcast and future simulations provide comprehensive spatiotemporal estimates of climatology that are frequently employed within the environmental sciences community, although the output can be afflicted with bias that impedes direct interpretation. Post-processing bias correction approaches utilise observational data to address this challenge, although they are typically criticised for not being physically justified and not considering uncertainty in the correction. This paper proposes a novel Bayesian bias correction framework that robustly propagates uncertainty and models underlying spatial covariance patterns. Shared latent Gaussian processes are assumed between the in situ observations and climate model output, with the aim of partially preserving the covariance structure from the climate model after bias correction, which is based on well-established physical laws. Results demonstrate added value in modelling shared generating processes under several simulated scenarios, with the most value added for the case of sparse in situ observations and smooth underlying bias. Additionally, the propagation of uncertainty to a simulated final bias-corrected time series is illustrated, which is of key importance to a range of stakeholders, such as climate scientists engaged in impact studies, decision-makers trying to understand the likelihood of particular scenarios and individuals involved in climate change adaption strategies where accurate risk assessment is required for optimal resource allocation. This paper focuses on one-dimensional simulated examples for clarity, although the code implementation is developed to also work on multi-dimensional input data, encouraging follow-on real-world application studies that will further validate performance and remaining limitations. The Bayesian framework supports uncertainty propagation under model adaptations required for specific applications, providing a flexible approach that increases the scope of data assimilation tasks more generally.

Covariation of phonological features in Standardised Scottish English

AbstractScottish English has characteristic phonological features including rhoticity, the /ʍ/‐/w/ contrast and the lack of the nurse merger. However, recent studies have found ongoing changes in Scottish English phonology such as the gradual loss of rhoticity, the merging of /ʍ/‐/w/ and a partial merger of the nurse vowels. This paper investigates possible covariation between these features across speakers of Standardised Scottish English. The speech of 30 speakers taken from ICE‐Scotland was analysed for their realisations of the nurse lexical set, <wh‐> and rhotics. Underlying structural covariation among these variables was investigated using Principal Components Analysis. The results show covariation between the overall rate of the traditional /ʍ/ variant and rhoticity but not with the realisation of nurse. More generally, Standardised Scottish English appears to comprise a range of individual covariation patterns that combine more traditional and more modern pronunciation variants to varying degrees.

'Drifting' Buchnera genomes track the microevolutionary trajectories of their aphid hosts.

Evolution of Buchnera-aphid host symbioses is often studied among species at macroevolutionary scales. Investigations within species offer a different perspective about how eco-evolutionary processes shape patterns of genetic variation at microevolutionary scales. Our study leverages new and publicly available whole-genome sequencing data to study Buchnera-aphid host evolution in Myzus persicae, the peach potato aphid, a globally invasive and polyphagous pest. Across 43 different asexual, clonally reproducing isofemale strains, we examined patterns of genomic covariation between Buchnera and their aphid host and considered the distribution of mutations in protein-coding regions of the Buchnera genome. We found Buchnera polymorphisms within aphid strains, suggesting the presence of genetically different Buchnera strains within the same clonal lineage. Genetic distance between pairs of Buchnera samples was positively correlated to genetic distance between their aphid hosts, indicating shared evolutionary histories. However, there was no segregation of genetic variation for both M. persicae and Buchnera with plant host (Brassicaceae and non-tobacco Solanaceae) and no associations between genetic and geographic distance at global or regional spatial scales. Abundance patterns of non-synonymous mutations were similar to synonymous mutations in the Buchnera genome, and both mutation classes had similar site frequency spectra. We hypothesize that a predominance of neutral processes results in the Buchnera of M. persicae to simply 'drift' with the evolutionary trajectory of their aphid hosts. Our study presents a unique microevolutionary characterization of Buchnera-aphid host genomic covariation across multiple aphid clones. This provides a new perspective on the eco-evolutionary processes generating and maintaining polymorphisms in a major pest aphid species and its obligate primary endosymbiont.

Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder

Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD. Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability. These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD. National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application.

Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.

Spatial distribution of physicochemical parameters and drinking and irrigation water quality indices in the Jhelum River, Pakistan.

Sustainable management of river systems is a serious concern, requiring vigilant monitoring of water contamination levels that could potentially threaten the ecological community. This study focused on the evaluation of water quality in the Jhelum River (JR), Azad Jammu and Kashmir, and northern Punjab, Pakistan. To achieve this, 60 water samples were collected from various points within the JR Basin (JRB) and subjected to a comprehensive analysis of their physicochemical parameters. The study findings indicated that the concentrations of physicochemical parameters in the JRB water remained within safety thresholds for both drinking and irrigation water, as established by the World Health Organization and Pakistan Environmental Protection Agency. These physicochemical parameters refer to various chemical and physical characteristics of the water that can have implications for both human health (drinking water) and agricultural practices (irrigation water). The spatial variations throughout the river course distinguished between the upstream, midstream, and downstream sections. Specifically, the downstream section exhibited significantly higher values for physicochemical parameters and a broader range, highlighting a substantial decline in its quality. Significant disparities in mean values and ranges were evident, particularly in the case of nitrates and total dissolved solids, when the downstream section was compared with its upstream and midstream counterparts. These variations indicated a deteriorating downstream water quality profile, which is likely attributable to a combination of geological and anthropogenic influences. Despite the observed deterioration in the downstream water quality, this study underscores that the JRB within the upper Indus Basin remains safe and suitable for domestic and agricultural purposes. The JRB was evaluated for various irrigation water quality indices. The principal component analysis conducted in this study revealed distinct covariance patterns among water quality variables, with the first five components explaining approximately 79% of the total variance. Recommending the continued utilization of the JRB for irrigation, we advocate for the preservation and enhancement of water quality in the downstream regions.

Abnormality in Peripheral and Brain Iron Contents and the Relationship with Grey Matter Volumes in Major Depressive Disorder.

Major depressive disorder (MDD) is a prevalent mental illness globally, yet its etiology remains largely elusive. Recent interest in the scientific community has focused on the correlation between the disruption of iron homeostasis and MDD. Prior studies have revealed anomalous levels of iron in both peripheral blood and the brain of MDD patients; however, these findings are not consistent. This study involved 95 MDD patients aged 18-35 and 66 sex- and age-matched healthy controls (HCs) who underwent 3D-T1 and quantitative susceptibility mapping (QSM) sequence scans to assess grey matter volume (GMV) and brain iron concentration, respectively. Plasma ferritin (pF) levels were measured in a subset of 49 MDD individuals and 41 HCs using the Enzyme-linked immunosorbent assay (ELISA), whose blood data were simultaneously collected. We hypothesize that morphological brain changes in MDD patients are related to abnormal regulation of iron levels in the brain and periphery. Multimodal canonical correlation analysis plus joint independent component analysis (MCCA+jICA) algorithm was mainly used to investigate the covariation patterns between the brain iron concentration and GMV. The results of "MCCA+jICA" showed that the QSM values in bilateral globus pallidus and caudate nucleus of MDD patients were lower than HCs. While in the bilateral thalamus and putamen, the QSM values in MDD patients were higher than in HCs. The GMV values of these brain regions showed a significant positive correlation with QSM. The GMV values of bilateral putamen were found to be increased in MDD patients compared with HCs. A small portion of the thalamus showed reduced GMV values in MDD patients compared to HCs. Furthermore, the region of interest (ROI)-based comparison results in the basal ganglia structures align with the outcomes obtained from the "MCCA+jICA" analysis. The ELISA results indicated that the levels of pF in MDD patients were higher than those in HCs. Correlation analysis revealed that the increase in pF was positively correlated with the iron content in the left thalamus. Finally, the covariation patterns obtained from "MCCA+jICA" analysis as classification features effectively differentiated MDD patients from HCs in the support vector machine (SVM) model. Our findings indicate that elevated peripheral ferritin in MDD patients may disrupt the normal metabolism of iron in the brain, leading to abnormal changes in brain iron levels and GMV.

Variation in Seed Morphological Traits Affects the Dispersal Strategies of Chromolaena odorata Following Invasion.

Seed germination and dispersal have an important impact on the establishment and spread of invasive plants. Understanding the extent of intraspecific seed trait variations can enhance our understanding of how invasive plants respond to environmental change after introduction and help predict the dynamic of invasive species under future environmental conditions. However, less attention has been given to the variation in seed traits within species as opposed to among species. We compared seed production, seed morphological traits, dispersal ability, and seedling performance of Chromolaena odorata from 10 introduced populations in Asia and 12 native populations in America in a common garden. The results showed that range (introduced vs. native) and climate affected these traits. Compared with the native population, the introduced populations had higher seed numbers per capitula, lighter seeds, and higher potential dispersal ability seeds (lower terminal velocity) but lower germination rates and seedling lengths. Climatic clines in seed numbers per capitula and pappus length were observed; however, the clines in pappus length differed between the introduced and native populations. Trait covariation patterns were also different between both ranges. In the native populations, there was a trade-off between seed numbers per capitula and seed mass, while this relationship was not found for the introduced populations. These results indicate that C. odorata alters the ecological strategy of seed following invasion, which facilitates its establishment and fast dispersal and contributes to successful invasion in the introduced ranges.

Diversity of Intraspecific Patterns of Brain Region Size Covariation in Fish.

Traits often do not evolve in isolation or vary independently of other traits. Instead, they can be affected by covariation, both within and across species. However, the importance of within-species trait covariation and, critically, the degree to which it varies between species has yet to be thoroughly studied. Brain morphology is a trait of great ecological and behavioral importance, with regions that are hypothesized to vary in size based on behavioral and cognitive demands. Sizes of brain regions have also been shown to covary with each other across various taxa. Here, we test the degree to which covariation in brain region sizes within species has been conserved across 10 teleost fish species. These 10 species span five orders, allowing us to examine how phylogenetic proximity influences similarities in intraspecific trait covariation. Our results showed a trend that similar patterns of brain region size covariation occur in more closely related species. Interestingly, there were certain brain region pairs that showed similar levels of covariation across all species regardless of phylogenetic distance, such as the telencephalon and optic tectum, while others, such as the olfactory bulb and the hypothalamus, varied more independently. Ultimately, the patterns of brain region covariation shown here suggest that evolutionary mechanisms or constraints can act on specific brain regions independently, and that these constraints can change over evolutionary time.

Intraspecific leaf trait variation of the heterophyllous amphibious plant Luronium natans: a trait-based study

AbstractHeterophylly, a type of intraspecific trait variation (ITV), is commonly observed in aquatic and amphibious plants. In this study, we focused on Luronium natans (L.) Raf., as a model species to investigate ITV, specifically looking at variations within different leaf types. The objective was to test general hypotheses regarding ITV across various leaf traits. We collected 210 leaves from the homogeneous environments of softwater lakes in northwestern Poland to measure six leaf traits: leaf area (LA), leaf dry weight (LDW), specific leaf area (SLA), leaf dry mass content (LDMC), circularity, and leaf width/length ratio (shape traits). These leaves were categorized into three types based on the shoot: heterophyllous shoots with floating leaves (HF), heterophyllous shoots with submerged leaves (HS), and submerged rosettes with linear leaves (S). Significant differences were observed in LA and LDMC within these leaf types and in SLA between HF and S types. ITV within each shoot type exhibited a wide range of variation, particularly in LA, LDW, SLA, and LDMC, with shape traits displaying stronger variability than functional traits. Our results indicate lower ITV of functional leaf traits within the same heterophyllous shoots compared to fully submerged leaves in a rosette form. We also noted negative correlations between SLA-LDMC and LA-circularity trait pairs, suggesting a variation–covariation pattern in leaf traits with minimal influence from water environmental factors.

Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarchical organization from the subcortex-limbic system to the ventral neocortex and then to the dorsal neocortex. Based on 424 unrelated, qualified healthy subjects from the Human Connectome Project, we found that vTSNs could sensitively represent sex differences, especially for connections in the subcortex-limbic system and between the subcortex-limbic system and the ventral neocortex. Moreover, a multivariate variance component model revealed that vTSNs could explain a significant proportion of inter-subject behavioral variance in cognition (80.0 %) and motor functions (63.4 %). Finally, using 494 healthy adults (aged 19–80 years old) from the Southwest University Adult Lifespan Dataset, the Spearman correlation identified a significant association between aging and vTSN strength, especially within the subcortex-limbic system and between the subcortex-limbic system and the dorsal neocortex. In summary, our proposed vTSN is robust in uncovering individual variability and neurobiological brain processes, which can serve as biologically plausible measures for linking biological processes and human behavior.

Neuromodulatory subcortical nucleus integrity is associated with white matter microstructure, tauopathy and APOE status

The neuromodulatory subcortical nuclei within the isodendritic core (IdC) are the earliest sites of tauopathy in Alzheimer’s disease (AD). They project broadly throughout the brain’s white matter. We investigated the relationship between IdC microstructure and whole-brain white matter microstructure to better understand early neuropathological changes in AD. Using multiparametric quantitative magnetic resonance imaging we observed two covariance patterns between IdC and white matter microstructure in 133 cognitively unimpaired older adults (age 67.9 ± 5.3 years) with familial risk for AD. IdC integrity related to 1) whole-brain neurite density, and 2) neurite orientation dispersion in white matter tracts known to be affected early in AD. Pattern 2 was associated with CSF concentration of phosphorylated-tau, indicating AD specificity. Apolipoprotein-E4 carriers expressed both patterns more strongly than non-carriers. IdC microstructure variation is reflected in white matter, particularly in AD-affected tracts, highlighting an early mechanism of pathological development.

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Effect of dupilumab on asthma and aeroallergen sensitization in pediatric atopic dermatitis patients: Results of the BioDay registry.

Atopic dermatitis (AD) is frequently associated with asthma and allergic rhinitis (AR). Dupilumab is an effective treatment for pediatric AD, although the effect on atopic comorbidities in pediatric AD patients is limited. To investigate the prevalence of asthma and AR in pediatric AD patients starting dupilumab treatment and to evaluate the effect of dupilumab on these comorbidities. This study included pediatric AD patients (aged 3-17 years) treated with dupilumab between 2019 and 2023. Patients were screened at baseline by a pulmonologist for the presence of asthma and AR. Screening included evaluation of medical history and current symptoms, spirometry (including Forced Expiratory Volume in 1 s (FEV1)), Fractional exhaled Nitric Oxide (FeNO), and measurement of aeroallergen-specific IgE levels. In patients diagnosed with comorbid asthma and/or AR, measurements were repeated at weeks 16 and 52. Spirometry measurements, FeNO, and aeroallergen-specific IgE levels during treatment were analyzed using a covariance pattern model. Eighty-four patients were included. Asthma was diagnosed in 50 patients (59.5%) and AR in 72 patients (85.7%). Baseline FeNO levels were elevated in both patients with (29.0 ppb (95% CI 22.0-54.0)) and without asthma (26.0 ppb (95% CI 22.0-30.0)). During treatment, FeNO levels decreased (p < .001) and FEV1 scores increased (p < .001) in patients with asthma. In patients with asthma and/or AR, all aeroallergen-specific IgE levels decreased between 61.3% and 89.1% at 52 weeks of treatment. One year of dupilumab treatment, primarily indicated for AD, resulted in a significant improvement in comorbid asthma and a profound decrease in aeroallergen-specific IgE levels in patients with asthma and/or AR.

Culm Morphological Analysis in Moso Bamboo Reveals the Negative Regulation of Internode Diameter and Thickness by Monthly Precipitation.

The neglect of Moso bamboo's phenotype variations hinders its broader utilization, despite its high economic value globally. Thus, this study investigated the morphological variations of 16 Moso bamboo populations. The analysis revealed the culm heights ranging from 9.67 m to 17.5 m, with average heights under the first branch ranging from 4.91 m to 7.67 m. The total internode numbers under the first branch varied from 17 to 36, with internode lengths spanning 2.9 cm to 46.4 cm, diameters ranging from 5.10 cm to 17.2 cm, and wall thicknesses from 3.20 mm to 33.3 mm, indicating distinct attributes among the populations. Furthermore, strong positive correlations were observed between the internode diameter, thickness, length, and volume. The coefficient of variation of height under the first branch showed strong positive correlations with several parameters, indicating variability in their contribution to the total culm height. A regression analysis revealed patterns of covariation among the culm parameters, highlighting their influence on the culm height and structural characteristics. Both the diameter and thickness significantly contribute to the internode volume and culm height, and the culm parameters tend to either increase or decrease together, influencing the culm height. Moreover, this study also identified a significant negative correlation between monthly precipitation and the internode diameter and thickness, especially during December and January, impacting the primary thickening growth and, consequently, the internode size.

Impact of smart technology use on sleep quality in individuals with autism spectrum disorder: a mixed-methods investigation

BackgroundSleep disturbances are common among individuals with autism spectrum disorder (ASD) and can have a negative impact on their daily functioning and core symptoms. As the use of smart technologies continues to rise, it is crucial to understand how these devices affect the sleep quality of individuals with ASD.AimThe objective of this study was to examine the relationship between the use of smart technology and sleep quality in individuals with ASD.MethodsA mixed-methods approach was employed, combining both quantitative and qualitative data collection techniques. A sample of 83 individuals with ASD, aged between 8 and 25 years, assessed their sleep quality using the Pittsburgh Sleep Quality Index. Additionally, information regarding patterns of smart technology use and relevant covariates was collected. Correlation and regression analyses were conducted to analyze the data. Furthermore, semi-structured interviews were conducted with a subset of participants and their caregivers.ResultsSignificant positive correlations were found between poorer sleep quality scores and total screen time (r = 0.42, p &amp;lt; 0.001), pre-bedtime technology use (r = 0.51, p &amp;lt; 0.001), gaming (r = 0.32, p = 0.003), and social media use (r = 0.29, p = 0.008). Pre-bedtime technology use was a significant predictor of poorer sleep quality (β = 0.32, p = 0.006), even after controlling for age, gender, and ASD severity.ConclusionThe findings of this study emphasize the significant associations between the use of smart technology, particularly before bedtime, and poorer sleep quality in individuals with ASD. These results underscore the importance of developing evidence-based interventions and guidelines to promote healthy sleep habits and mitigate the negative effects of technology exposure in this population.

Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children.

The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.

DCCA: detecting differential covariation patterns between two types of high-throughput omics data.

The advent of multimodal omics data has provided an unprecedented opportunity to systematically investigate underlying biological mechanisms from distinct yet complementary angles. However, the joint analysis of multi-omics data remains challenging because it requires modeling interactions between multiple sets of high-throughput variables. Furthermore, these interaction patterns may vary across different clinical groups, reflecting disease-related biological processes. We propose a novel approach called Differential Canonical Correlation Analysis (dCCA) to capture differential covariation patterns between two multivariate vectors across clinical groups. Unlike classical Canonical Correlation Analysis, which maximizes the correlation between two multivariate vectors, dCCA aims to maximally recover differentially expressed multivariate-to-multivariate covariation patterns between groups. We have developed computational algorithms and a toolkit to sparsely select paired subsets of variables from two sets of multivariate variables while maximizing the differential covariation. Extensive simulation analyses demonstrate the superior performance of dCCA in selecting variables of interest and recovering differential correlations. We applied dCCA to the Pan-Kidney cohort from the Cancer Genome Atlas Program database and identified differentially expressed covariations between noncoding RNAs and gene expressions. The R package that implements dCCA is available at https://github.com/hwiyoungstat/dCCA.