Uncovering ghost introgression through genomic analysis of a distinct eastern Asian hickory species.

Ghost introgression, or the transfer of genetic material from extinct or unsampled lineages to sampled species, has attracted much attention. However, conclusive evidence for ghost introgression, especially in plant species, remains scarce. Here, we newly assembled chromosome-level genomes for both Carya sinensis and Carya cathayensis, and additionally re-sequenced the whole genomes of 43 C. sinensis individuals as well as 11 individuals representing 11 diploid hickory species. These genomic datasets were used to investigate the reticulation and bifurcation patterns within the genus Carya (Juglandaceae), with a particular focus on the beaked hickory C. sinensis. By combining the D-statistic and BPP methods, we obtained compelling evidence that supports the occurrence of ghost introgression in C. sinensis from an extinct ancestral hickory lineage. This conclusion was reinforced through the phylogenetic network analysis and a genome scan method VolcanoFinder, the latter of which can detect signatures of adaptive introgression from unknown donors. Our results not only dispel certain misconceptions about the phylogenetic history of C. sinensis but also further refine our understanding of Carya's biogeography via divergence estimates. Moreover, the successful integration of the D-statistic and BPP methods demonstrates their efficacy in facilitating a more precise identification of introgression types.

Towards Reliable Detection of Introgression in the Presence of Among-Species Rate Variation.

The role of interspecific hybridization has recently seen increasing attention, especially in the context of diversification dynamics. Genomic research has now made it abundantly clear that both hybridization and introgression-the exchange of genetic material through hybridization and backcrossing-are far more common than previously thought. Besides cases of ongoing or recent genetic exchange between taxa, an increasing number of studies report "ancient introgression"- referring to results of hybridization that took place in the distant past. However, it is not clear whether commonly used methods for the detection of introgression are applicable to such old systems, given that most of these methods were originally developed for analyses at the level of populations and recently diverged species, affected by recent or ongoing genetic exchange. In particular, the assumption of constant evolutionary rates, which is implicit in many commonly used approaches, is more likely to be violated as evolutionary divergence increases. To test the limitations of introgression detection methods when being applied to old systems, we simulated thousands of genomic datasets under a wide range of settings, with varying degrees of among-species rate variation and introgression. Using these simulated datasets, we showed that some commonly applied statistical methods, including the D-statistic and certain tests based on sets of local phylogenetic trees, can produce false-positive signals of introgression between divergent taxa that have different rates of evolution. These misleading signals are caused by the presence of homoplasies occurring at different rates in different lineages. To distinguish between the patterns caused by rate variation and genuine introgression, we developed a new test that is based on the expected clustering of introgressed sites along the genome and implemented this test in the program Dsuite.

Cortical microstructural brain network mediates the association between personality trait of agreeableness and life satisfaction.

Personality traits are commonly regarded as relatively stable, whereas life satisfaction can fluctuate with time and circumstances, shaped by external influences and personal encounters. The correlation between personality traits and life satisfaction is well-established, yet the underlying neural mechanisms of the myelin-based microstructural brain network connecting them remain unclear. Here, we constructed individual-level whole-brain myelin microstructural networks from the MRI data of 1,043 healthy adults and performed correlation analysis to detect significant personality trait-related and life satisfaction-related subnetworks. A mediation analysis was used to verify whether the shared structural basis of personality traits and life satisfaction significantly mediated their association. The results showed that agreeableness positively correlated with life satisfaction. We identified a shared structural basis of the personality trait of agreeableness and life satisfaction. The regions comprising this overlapping network include the superior parietal lobule, inferior parietal lobule, and temporoparietal junction. Moreover, the shared microstructural connections mediate the association between the personality trait of agreeableness and life satisfaction. This large-scale neuroimaging investigation substantiates a mediation framework for understanding the microstructural connections between personality and life satisfaction, offering potential targets for assessment and interventions to promote human well-being.

Signal detection statistics of adverse drug events in hierarchical structure for matched case-control data.

The tree-based scan statistic is a data mining method used to identify signals of adverse drug reactions in a database of spontaneous reporting systems. It is particularly beneficial when dealing with hierarchical data structures. One may use a retrospective case-control study design from spontaneous reporting systems (SRS) to investigate whether a specific adverse event of interest is associated with certain drugs. However, the existing Bernoulli model of the tree-based scan statistic may not be suitable as it fails to adequately account for dependencies within matched pairs. In this article, we propose signal detection statistics for matched case-control data based on McNemar's test, Wald test for conditional logistic regression, and the likelihood ratio test for a multinomial distribution. Through simulation studies, we demonstrate that our proposed methods outperform the existing approach in terms of the type I error rate, power, sensitivity, and false detection rate. To illustrate our proposed approach, we applied the three methods and the existing method to detect drug signals for dizziness-related adverse events related to antihypertensive drugs using the database of the Korea Adverse Event Reporting System.

Summary Tests of Introgression Are Highly Sensitive to Rate Variation Across Lineages.

The evolutionary implications and frequency of hybridization and introgression are increasingly being recognized across the tree of life. To detect hybridization from multi-locus and genome-wide sequence data, a popular class of methods are based on summary statistics from subsets of 3 or 4 taxa. However, these methods often carry the assumption of a constant substitution rate across lineages and genes, which is commonly violated in many groups. In this work, we quantify the effects of rate variation on the D test (also known as ABBA-BABA test), the D3 test, and HyDe. All 3 tests are used widely across a range of taxonomic groups, in part because they are very fast to compute. We consider rate variation across species lineages, across genes, their lineage-by-gene interaction, and rate variation across gene-tree edges. We simulated species networks according to a birth-death-hybridization process, so as to capture a range of realistic species phylogenies. For all 3 methods tested, we found a marked increase in the false discovery of reticulation (type-1 error rate) when there is rate variation across species lineages. The D3 test was the most sensitive, with around 80% type-1 error, such that D3 appears to more sensitive to a departure from the clock than to the presence of reticulation. For all 3 tests, the power to detect hybridization events decreased as the number of hybridization events increased, indicating that multiple hybridization events can obscure one another if they occur within a small subset of taxa. Our study highlights the need to consider rate variation when using site-based summary statistics, and points to the advantages of methods that do not require assumptions on evolutionary rates across lineages or across genes.

Postmenopausal bleeding after COVID-19 vaccination.

BACKGROUND: There is a growing literature base regarding menstrual changes following COVID-19 vaccination among premenopausal people. However, relatively little is known about uterine bleeding in postmenopausal people following COVID-19 vaccination. OBJECTIVE: This study aimed to examine trends in incident postmenopausal bleeding diagnoses over time before and after COVID-19 vaccine introduction, and to describe cases of new-onset postmenopausal bleeding after COVID-19 vaccination. STUDY DESIGN: For postmenopausal bleeding incidence calculations, monthly population-level cohorts consisted of female Kaiser Permanente Northwest members aged ≥45 years. Those diagnosed with incident postmenopausal bleeding in the electronic medical record were included in monthly numerators. Members with preexisting postmenopausal bleeding or abnormal uterine bleeding, or who were at increased risk of bleeding due to other health conditions, were excluded from monthly calculations. We used segmented regression analysis to estimate changes in the incidence of postmenopausal bleeding diagnoses from 2018 through 2021 in Kaiser Permanente Northwest members meeting the inclusion criteria, stratified by COVID-19 vaccination status in 2021. In addition, we identified all members with ≥1 COVID-19 vaccination between December 14, 2020 and August 14, 2021, who had an incident postmenopausal bleeding diagnosis within 60 days of vaccination. COVID-19 vaccination, diagnostic procedures, and presumed bleeding etiology were assessed through chart review and described. A temporal scan statistic was run on all cases without clear bleeding etiology. RESULTS: In a population of 75,530 to 82,693 individuals per month, there was no statistically significant difference in the rate of incident postmenopausal bleeding diagnoses before and after COVID-19 vaccine introduction (P=.59). A total of 104 individuals had incident postmenopausal bleeding diagnosed within 60 days following COVID-19 vaccination; 76% of cases (79/104) were confirmed as postvaccination postmenopausal bleeding after chart review. Median time from vaccination to bleeding onset was 21 days (range: 2-54 days). Among the 56 postmenopausal bleeding cases with a provider-attributed etiology, the common causes of bleeding were uterine or cervical lesions (50% [28/56]), hormone replacement therapy (13% [7/56]), and proliferative endometrium (13% [7/56]). Among the 23 cases without a clear etiology, there was no statistically significant clustering of postmenopausal bleeding onset following vaccination. CONCLUSION: Within this integrated health system, introduction of COVID-19 vaccines was not associated with an increase in incident postmenopausal bleeding diagnoses. Diagnosis of postmenopausal bleeding in the 60 days following receipt of a COVID-19 vaccination was rare.

Using a monotonic density ratio model to increase the power of the goodness-of-fit test for logistic regression models with case-control data.

Logistic regression models are widely used in case-control data analysis, and testing the goodness-of-fit of their parametric model assumption is a fundamental research problem. In this article, we propose to enhance the power of the goodness-of-fit test by exploiting a monotonic density ratio model, in which the ratio of case and control densities is assumed to be a monotone function. We show that such a monotonic density ratio model is naturally induced by the retrospective case-control sampling design under the alternative hypothesis. The pool-adjacent-violator algorithm is adapted to solve for the constrained nonparametric maximum likelihood estimator under the alternative hypothesis. By measuring the discrepancy between this estimator and the semiparametric maximum likelihood estimator under the null hypothesis, we develop a new Kolmogorov-Smirnov-type statistic to test the goodness-of-fit for logistic regression models with case-control data. A bootstrap resampling procedure is suggested to approximate the p $$ p $$ -value of the proposed test. Simulation results show that the type I error of the proposed test is well controlled and the power improvement is substantial in many cases. Three real data applications are also included for illustration.

Beading plot: a novel graphics for ranking interventions in network evidence.

BACKGROUND: Network meta-analysis is developed to compare all available treatments; therefore it enriches evidence for clinical decision-making, offering insights into treatment effectiveness and safety when faced with multiple options. However, the complexity and numerous treatment comparisons in network meta-analysis can challenge healthcare providers and patients. The purpose of this study aimed to introduce a graphic design to present complex rankings of multiple interventions comprehensively. METHODS: Our team members developed a "beading plot" to summary probability of achieving the best treatment (P-best) and global metrics including surface under the cumulative ranking curve (SUCRA) and P-score. Implemented via the "rankinma" R package, this tool summarizes rankings across diverse outcomes in network meta-analyses, and the package received an official release on the Comprehensive R Archive Network (CRAN). It includes the `PlotBead()` function for generating beading plots, which represent treatment rankings among various outcomes. RESULTS: Beading plot has been designed based on number line plot, which effectively displays collective metrics for each treatment across various outcomes. Order on the -axis is derived from ranking metrics like P-best, SUCRA, and P-score. Continuous lines represent outcomes, and color-coded beads signify treatments. CONCLUSION: The beading plot is a valuable graphic that intuitively displays treatment rankings across diverse outcomes, enhancing reader-friendliness and aiding decision-making in complex network evidence scenarios. While empowering clinicians and patients to identify optimal treatments, it should be used cautiously, alongside an assessment of the overall evidence certainty.

Nonlinear Regression Modelling: A Primer with Applications and Caveats.

Use of nonlinear statistical methods and models are ubiquitous in scientific research. However, these methods may not be fully understood, and as demonstrated here, commonly-reported parameter p-values and confidence intervals may be inaccurate. The gentle introduction to nonlinear regression modelling and comprehensive illustrations given here provides applied researchers with the needed overview and tools to appreciate the nuances and breadth of these important methods. Since these methods build upon topics covered in first and second courses in applied statistics and predictive modelling, the target audience includes practitioners and students alike. To guide practitioners, we summarize, illustrate, develop, and extend nonlinear modelling methods, and underscore caveats of Wald statistics using basic illustrations and give key reasons for preferring likelihood methods. Parameter profiling in multiparameter models and exact or near-exact versus approximate likelihood methods are discussed and curvature measures are connected with the failure of the Wald approximations regularly used in statistical software. The discussion in the main paper has been kept at an introductory level and it can be covered on a first reading; additional details given in the Appendices can be worked through upon further study. The associated online Supplementary Information also provides the data and R computer code which can be easily adapted to aid researchers to fit nonlinear models to their data.

Correlation between autistic traits and brain functional connectivity in preschoolers with autism spectrum disorder: a resting state MEG study.

BACKGROUND: Neurophysiological studies recognized that Autism Spectrum Disorder (ASD) is associated with altered patterns of over- and under-connectivity. However, little is known about network organization in children with ASD in the early phases of development and its correlation with the severity of core autistic features. METHODS: The present study aimed at investigating the association between brain connectivity derived from MEG signals and severity of ASD traits measured with different diagnostic clinical scales, in a sample of 16 children with ASD aged 2 to 6 years. RESULTS: A significant correlation emerged between connectivity strength in cortical brain areas implicated in several resting state networks (Default mode, Central executive, Salience, Visual and Sensorimotor) and the severity of communication anomalies, social interaction problems, social affect problems, and repetitive behaviors. Seed analysis revealed that this pattern of correlation was mainly caused by global rather than local effects. CONCLUSIONS: The present evidence suggests that altered connectivity strength in several resting state networks is related to clinical features and may contribute to neurofunctional correlates of ASD. Future studies implementing the same method on a wider and stratified sample may further support functional connectivity as a possible biomarker of the condition.

Evaluating inter-rater reliability in the context of "Sysmex UN2000 detection of protein/creatinine ratio and of renal tubular epithelial cells can be used for screening lupus nephritis": a statistical examination.

BACKGROUND: The evaluation of inter-rater reliability (IRR) is integral to research designs involving the assessment of observational ratings by two raters. However, existing literature is often heterogeneous in reporting statistical procedures and the evaluation of IRR, although such information can impact subsequent hypothesis testing analyses. METHODS: This paper evaluates a recent publication by Chen et al., featured in BMC Nephrology, aiming to introduce an alternative statistical approach to assessing IRR and discuss its statistical properties. The study underscores the crucial need for selecting appropriate Kappa statistics, emphasizing the accurate computation, interpretation, and reporting of commonly used IRR statistics between two raters. RESULTS: The Cohen's Kappa statistic is typically used for two raters dealing with two categories or for unordered categorical variables having three or more categories. On the other hand, when assessing the concordance between two raters for ordered categorical variables with three or more categories, the commonly employed measure is the weighted Kappa. CONCLUSION: Chen and colleagues might have underestimated the agreement between AU5800 and UN2000. Although the statistical approach adopted in Chen et al.'s research did not alter their findings, it is important to underscore the importance of researchers being discerning in their choice of statistical techniques to address their specific research inquiries.

Spatiotemporal epidemiology of substance-related accidental acute toxicity deaths in Canada from 2016 to 2017.

OBJECTIVES: In Canada, substance-related accidental acute toxicity deaths (AATDs) continue to rise at the national and sub-national levels. However, it is unknown if, where, when, and to what degree AATDs cluster in space, time, and space-time across the country. The objectives of this study were to 1) assess for clusters of AATDs that occurred in Canada during 2016 and 2017 at the national and provincial/territorial (P/T) levels, and 2) examine the substance types detected in AATD cases within each cluster. METHODS: Two years of person-level data on AATDs were abstracted from coroner and medical examiner files using a standardized data collection tool, including the decedent's postal code and municipality information on the places of residence, acute toxicity (AT) event, and death, and the substances detected in the death. Data were combined with Canadian census information to create choropleth maps depicting AATD rates by census division. Spatial scan statistics were used to build Poisson models to identify clusters of high rates (p < 0.05) of AATDs at the national and P/T levels in space, time, and space-time over the study period. AATD cases within clusters were further examined for substance types most present in each cluster. RESULTS: Eight clusters in five regions of Canada at the national level and 24 clusters in 15 regions at the P/T level were identified, highlighting where AATDs occurred at far higher rates than the rest of the country. The risk ratios of identified clusters ranged from 1.28 to 9.62. Substances detected in clusters varied by region and time, however, opioids, stimulants, and alcohol were typically the most commonly detected substances within clusters. CONCLUSION: Our findings are the first in Canada to reveal the geographic disparities in AATDs at national and P/T levels using spatial scan statistics. Rates associated with substance types within each cluster highlight which substance types were most detected in the identified regions. Findings may be used to guide intervention/program planning and provide a picture of the 2016 and 2017 context that can be used for comparisons of the geographic distribution of AATDs and substances with different time periods.

Disrupted Functional Brain Network Architecture in Sufferers with Boxing-Related Repeated Mild Traumatic Brain Injury: A Resting-State EEG Study.

BACKGROUND: Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG). METHODS: Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach. RESULTS: Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers. CONCLUSIONS: This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.

Generalized Confidence Intervals for Ratios of Standard Deviations Based on Log-Normal Distribution when Times Follow Weibull Distributions.

Modern anesthetic drugs ensure the efficacy of general anesthesia. Goals include reducing variability in surgical, tracheal extubation, post-anesthesia care unit, or intraoperative response recovery times. Generalized confidence intervals based on the log-normal distribution compare variability between groups, specifically ratios of standard deviations. The alternative statistical approaches, performing robust variance comparison tests, give P-values, not point estimates nor confidence intervals for the ratios of the standard deviations. We performed Monte-Carlo simulations to learn what happens to confidence intervals for ratios of standard deviations of anesthesia-associated times when analyses are based on the log-normal, but the true distributions are Weibull. We used simulation conditions comparable to meta-analyses of most randomized trials in anesthesia, n ≈ 25 and coefficients of variation ≈ 0.30 . The estimates of the ratios of standard deviations were positively biased, but slightly, the ratios being 0.11% to 0.33% greater than nominal. In contrast, the 95% confidence intervals were very wide (i.e., > 95% of P ≥ 0.05). Although substantive inferentially, the differences in the confidence limits were small from a clinical or managerial perspective, with a maximum absolute difference in ratios of 0.016. Thus, P < 0.05 is reliable, but investigators should plan for Type II errors at greater than nominal rates.

Pooling test statistics across multiply imputed datasets for nonnormal items.

In structural equation modeling, when multiple imputation is used for handling missing data, model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under the normality assumption, the two most popular pooling approaches were proposed by Li et al. (Statistica Sinica, 1(1), 65-92, 1991) and Meng and Rubin (Biometrika, 79(1), 103-111, 1992). When the assumption of normality is violated, it is not clear how well these pooling approaches work with the test statistics generated from various robust estimators and multiple imputation methods. Jorgensen and colleagues (2021) implemented these pooling approaches in their R package semTools; however, no systematical evaluation has been conducted. In this simulation study, we examine the performance of these approaches in working with different imputation methods and robust estimators under nonnormality. We found that the naïve pooling approach based on Meng and Rubin (Biometrika, 79(1), 103-111, 1992; D3SN) worked the best when combining with the normal-theory-based imputation and either MLM or MLMV estimator.

Quantification of Major Inorganic Contaminants in a Mediterranean Coastal Lagoon with a Large Dystrophic Crisis.

In August 2021, the Mar Menor, a saltwater lagoon located in the Region of Murcia (Spain), suffered a tragic environmental episode of dystrophic crisis and anoxia. The appearance of numerous dead fish in different areas of the lagoon over the course of days put all the authorities and the population of the area on alert. This paper shows a case study of what happened in the lagoon in terms of the presence of the most common inorganic pollutants. Measurements of the concentration of nitrogen species, phosphates and main heavy metals were carried out at different sampling sites in the Mar Menor from May 2021 to November 2022. Chemical analyses were carried out for each of the species under study. These analyses provide valuable information about the dystrophic crisis caused by a classic eutrophication process that began with the excessive nutrient input into the Mar Menor. Ion chromatography and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) were used as instrumentation for the quantification of these samples. The species whose values were greatly increased after the tragic episode described above were nitrates. The concentration varied significantly at the different sampling sites throughout the study. On the last sampling date, decreased concentrations of all the species were measured at each of the sampling sites, coinciding with the apparent good state of the lagoon.

A gene based combination test using GWAS summary data.

BACKGROUND: Gene-based association tests provide a useful alternative and complement to the usual single marker association tests, especially in genome-wide association studies (GWAS). The way of weighting for variants in a gene plays an important role in boosting the power of a gene-based association test. Appropriate weights can boost statistical power, especially when detecting genetic variants with weak effects on a trait. One major limitation of existing gene-based association tests lies in using weights that are predetermined biologically or empirically. This limitation often attenuates the power of a test. On another hand, effect sizes or directions of causal genetic variants in real data are usually unknown, driving a need for a flexible yet robust methodology of gene based association tests. Furthermore, access to individual-level data is often limited, while thousands of GWAS summary data are publicly and freely available. RESULTS: To resolve these limitations, we propose a combination test named as OWC which is based on summary statistics from GWAS data. Several traditional methods including burden test, weighted sum of squared score test [SSU], weighted sum statistic [WSS], SNP-set Kernel Association Test [SKAT], and the score test are special cases of OWC. To evaluate the performance of OWC, we perform extensive simulation studies. Results of simulation studies demonstrate that OWC outperforms several existing popular methods. We further show that OWC outperforms comparison methods in real-world data analyses using schizophrenia GWAS summary data and a fasting glucose GWAS meta-analysis data. The proposed method is implemented in an R package available at https://github.com/Xuexia-Wang/OWC-R-package CONCLUSIONS: We propose a novel gene-based association test that incorporates four different weighting schemes (two constant weights and two weights proportional to normal statistic Z) and includes several popular methods as its special cases. Results of the simulation studies and real data analyses illustrate that the proposed test, OWC, outperforms comparable methods in most scenarios. These results demonstrate that OWC is a useful tool that adapts to the underlying biological model for a disease by weighting appropriately genetic variants and combination of well-known gene-based tests.

Disrupted topologic efficiency of brain functional connectome in de novo Parkinson's disease with depression.

Growing evidence supports that depression in Parkinson's disease (PD) depends on disruptions in specific neural networks rather than regional dysfunction. According to the resting-state functional magnetic resonance imaging data, the study attempted to decipher the alterations in the topological properties of brain networks in de novo depression in PD (DPD). The study also explored the neural network basis for depressive symptoms in PD. We recruited 20 DPD, 37 non-depressed PD and 41 healthy controls (HC). The Graph theory and network-based statistical methods helped analyse the topological properties of brain functional networks and anomalous subnetworks across these groups. The relationship between altered properties and depression severity was also investigated. DPD revealed significantly reduced nodal efficiency in the left superior temporal gyrus. Additionally, DPD decreased five hubs, primarily located in the temporal-occipital cortex, and increased seven hubs, mainly distributed in the limbic cortico-basal ganglia circuit. The betweenness centrality of the left Medio Ventral Occipital Cortex was positively associated with depressive scores in DPD. In contrast to HC, DPD had a multi-connected subnetwork with significantly lower connectivity, primarily distributed in the visual, somatomotor, dorsal attention and default networks. Regional topological disruptions in the temporal-occipital region are critical in the DPD neurological mechanism. It might suggest a potential network biomarker among newly diagnosed DPD patients.

Stability of specific personality network features corresponding to openness trait across different adult age periods: A machine learning analysis.

The functional connectivity patterns of the brain during resting state are closely related to an individual's cognition, emotion, behavior, and social interactions, making it an important research method to measure personality traits in an unbiased way, replacing traditional paper-and-pencil tests. However, due to the dynamic nature of the brain, whether the changes in functional connectivity caused by age can stably map onto personality traits has not been previously investigated. This study focuses on whether network features that are significantly related to personality traits can effectively distinguish subjects with different personality traits, and whether these network features vary across different periods of adulthood. The study included 343 healthy adult participants, divided into early adulthood and middle adulthood groups according to the age threshold of 35. Resting-state functional magnetic resonance imaging (fMRI) and the Big Five personality questionnaire were collected. we investigated the relationship between personality traits and intrinsic whole-brain functional connectome. We then used support vector machine (SVM) to evaluate the performance of personality network features in distinguishing subjects with high and low scores in the early-adulthood sample, and cross-validated in the mid-adulthood sample. Additionally, edge-based analysis (NBS) was used to explore the stability of personality networks across the two age samples. Our results show that the network features corresponding to openness personality trait are stable and can effectively differentiate subjects with different scores in both age samples. Furthermore, this study found that these network features vary to some extent across different periods of adulthood. These findings provide new evidence and insights into the application of resting-state functional connectivity patterns in measuring personality traits and help us better understand the dynamic characteristics of the human brain.

Kappa statistic considerations in evaluating inter-rater reliability between two raters: which, when and context matters.

BACKGROUND: In research designs that rely on observational ratings provided by two raters, assessing inter-rater reliability (IRR) is a frequently required task. However, some studies fall short in properly utilizing statistical procedures, omitting essential information necessary for interpreting their findings, or inadequately addressing the impact of IRR on subsequent analyses' statistical power for hypothesis testing. METHODS: This article delves into the recent publication by Liu et al. in BMC Cancer, analyzing the controversy surrounding the Kappa statistic and methodological issues concerning the assessment of IRR. The primary focus is on the appropriate selection of Kappa statistics, as well as the computation, interpretation, and reporting of two frequently used IRR statistics when there are two raters involved. RESULTS: The Cohen's Kappa statistic is typically utilized to assess the level of agreement between two raters when there are two categories or for unordered categorical variables with three or more categories. On the other hand, when it comes to evaluating the degree of agreement between two raters for ordered categorical variables comprising three or more categories, the weighted Kappa is a widely used measure. CONCLUSION: Despite not substantially affecting the findings of Liu et al.?s study, the statistical dispute underscores the significance of employing suitable statistical methods. Rigorous and accurate statistical results are crucial for producing trustworthy research.