Suicide behaviours in incarcerated males: Links to psychopathic traits,forms of aggression to others,personal characteristics,and current penitentiary variables

Self-injurious behaviours and suicide attempts among incarcerated people are higher when compared with the general population. The current research is designed to examine the prevalence of self-injurious behaviours and suicide attempts among incarcerated males in Spain, and the factors associated with these behaviours. 201 men imprisoned in Spain completed anonymous self-report measures of demographic variables (e.g., age, marital status), the Triarchic Psychopathy Model (boldness, meanness, and disinhibition) and impulsive/premeditated aggression and aggressive behaviour (including physical, verbal, hostility, and anger). Official records were also consulted for information related to criminality (i.e., prior imprisonment and type of offence). Overall, 37.5% of residents reported having injured themselves at a time of anger or despair and 24.9% reported attempting suicide at any point in their lives. Lower scores in meanness, higher scores on impulsive aggression, serious illnesses and previous imprisonment were associated with an increased risk of self-injurious and/or suicide attempting behaviours. Being married acted as a protective factor of attempting suicide. Trait variables related to psychopathy (lower meanness) and aggression (higher impulsive aggression) were associated with increased frequency of suicide attempts in prisoners even after controlling for personal and criminal variables. The prevention of high rates of suicide in prisons needs the treatment of those cognitive variables in addition to more contextual personal and criminal variables.     

Personality traits affect the influences of intensity perception and emotional responses on hedonic rating and preference rank toward basic taste solutions

This study aimed at determining, based on independent predictors of taste intensity and emotional response, whether individual personality traits could affect prediction models of overall liking and preference rank toward basic taste solutions. Sixty-seven participants rated taste intensities (TI) of four basic-taste solutions at both low and high concentrations, and of plain water. Emotional responses toward each sample were measured using a self-reported emotion questionnaire (SE), facial expressions (FE), and/or autonomic nervous system responses (ANS). Participants rated overall liking of the samples and ranked their preferences. Based on the results of a hierarchical cluster analysis of five personality traits measured using the Big Five Inventory, participants were classified into two clusters: cluster N (high neuroticism) and cluster E (high extraversion). Results showed that the SE measure for both clusters N and E was better than the TI, FE, and ANS measures in explaining variances of overall liking or preference rank. A measurement of effect size found that using FE and/or taste intensity measures, along with self-reported emotion measure, could enhance model predictability of overall liking or preference rank toward taste samples for cluster N, while the contribution to the prediction model for cluster E was minimal. ANS measures showed little contribution to the prediction model of overall liking for either cluster. In conclusion, this study shows that personality traits, in particular traits of extraversion and neuroticism, affect not only optimum measures of emotional responses, but also modulate predicting overall liking and preference rank toward basic taste solutions.     

Psychometric Qualities of the Dutch Version of the Substance Use Risk Profile Scale Adapted for Individuals with Mild Intellectual Disabilities and Borderline Intellectual Functioning

ABSTRACT

Introduction

Personality traits may predict the use of substances in individuals with mild intellectual disabilities (MID) or borderline intellectual functioning (BIF). The Dutch version of the Substance Use Risk Profile Scale (SURPS), adapted for this population, was tested on its psychometric properties.     

2型糖尿病患者饮食决策平衡与大五人格特征的相关性研究

目的探讨2型糖尿病患者饮食决策平衡现状及其与大五人格特征的相关性,以期为临床个体化健康饮食教育提供参考。方法便利抽样法选取重庆市3所三级甲等医院内分泌科住院的192例2型糖尿病患者为研究对象,采用饮食决策平衡量表和大五人格量表简化版(neuroticism extraversion openness five factor inventory,NEO-FFI)对其进行调查。结果2型糖尿病患者知觉利益得分为22.00(20.00,24.00)分,知觉障碍得分为20.50(16.00,23.00)分,饮食决策平衡得分为2.00(-3.00,8.00)分;饮食决策平衡与大五人格中的宜人性和严谨性呈正相关,与神经质呈负相关(均P<0.05)。结论2型糖尿病患者饮食决策平衡水平较低,与其人格特征密切相关。护理人员应有针对性地帮助患者采取有效的应对措施,以促进其饮食决策平衡水平的提高。     

Is personality associated with dementia risk? A meta-analytic investigation

This study provides a quantitative synthesis of the prospective associations between personality traits (neuroticism, extraversion, openness, agreeableness, conscientiousness) and the risk of incident Alzheimer’s disease and related dementias. We conducted five separate meta-analyses with 8–12 samples (N = 30,036 to 33,054) that were identified through a systematic literature search following the MOOSE guidelines. Higher neuroticism (HR = 1.24, 95% CI [1.17, 1.31]) and lower conscientiousness (HR = 0.77, 95% CI [0.73, 0.81]) were associated with increased dementia risk, even after accounting for covariates such as depressive symptoms. Lower extraversion (HR = 0.92, 95% CI [0.86, 0.97]), openness (HR = 0.91, 95% CI [0.86, 0.96]), and agreeableness (HR = 0.90, 95% CI [0.83, 0.98]) were also associated with increased risk, but these associations were less robust and not significant in fully adjusted models. No evidence of publication bias was found. The strength of associations was unrelated to publication year (i.e., no evidence of winner’s curse). Meta-regressions indicated consistent effects for neuroticism, openness, and conscientiousness across methods to assess dementia, dementia type, follow-up length, sample age, minority, country, and personality measures. The association of extraversion and agreeableness varied by country. Our findings indicate robust associations of neuroticism and conscientiousness with dementia risk.     

Genome-wide shifts in climate-related variation underpin responses to selective breeding in a widespread conifer

Locally adapted temperate tree populations exhibit genetic trade-offs among climate-related traits that can be exacerbated by selective breeding and are challenging to manage under climate change. To inform climatically adaptive forest management, we investigated the genetic architecture and impacts of selective breeding on four climate-related traits in 105 natural and 20 selectively bred lodgepole pine populations from western Canada. Growth, cold injury, growth initiation, and growth cessation phenotypes were tested for associations with 18,600 single-nucleotide polymorphisms (SNPs) in natural populations to identify “positive effect alleles” (PEAs). The effects of artificial selection for faster growth on the frequency of PEAs associated with each trait were quantified in breeding populations from different climates. Substantial shifts in PEA proportions and frequencies were observed across many loci after two generations of selective breeding for height, and responses of phenology-associated PEAs differed strongly among climatic regions. Extensive genetic overlap was evident among traits. Alleles most strongly associated with greater height were often associated with greater cold injury and delayed phenology, although it is unclear whether potential trade-offs arose directly from pleiotropy or indirectly via genetic linkage. Modest variation in multilocus PEA frequencies among populations was associated with large phenotypic differences and strong climatic gradients, providing support for assisted gene flow polices. Relationships among genotypes, phenotypes, and climate in natural populations were maintained or strengthened by selective breeding. However, future adaptive phenotypes and assisted gene flow may be compromised if selective breeding further increases the PEA frequencies of SNPs involved in adaptive trade-offs among climate-related traits.

Local adaptation of climate-related traits in widespread temperate conifers has been demonstrated for centuries using extensive long-term common garden experiments (1, 2). As early as the 17th century, foresters were instructed to recognize variation in desirable traits and select seed from trees with favorable phenotypes (3). Modern tree improvement programs systematically select upon genetic variation, primarily to achieve growth gains and meet economic objectives. Estimates of genetic variation and gains from selection made using quantitative genetic models assume many anonymous loci of small effect underlie both variation in continuously distributed phenotypes and their responses to selective breeding. However, the type, quantity, effect size, distribution, and dynamics of genes underlying locally adaptive phenotypic variation and responses to selective breeding in forest trees are still poorly understood (4).Directional selection over hundreds or thousands of generations has led to genomic features of domestication in agricultural crops including simplified genetic architectures underlying many traits, reduced genome-wide diversity, and numerous selective sweeps (5–7). Beyond high-gain, short-rotation clonal forestry [e.g., Eucalyptus spp. (8)], we know little about the effects of artificial selection on adaptive genetic variation in forest trees, yet many tree species undergo some degree of selective breeding. Two or three generations of conifer breeding is not expected to have the same magnitude of genetic effects seen in domesticated crops, but if artificial selection for increased productivity is detectable in conifer genomes, it may expose genetic relationships and potential sources of trade-offs between growth and climatically adaptive phenotypes.Climate-related adaptive traits are often intercorrelated due to pleiotropy, natural selection, or linkage disequilibrium (LD), so that strong directional selection on one trait can cause correlated responses in others. Pleiotropic allelic variants associated with phenotypes do not function in isolation. Antagonistic pleiotropic effects among traits can generate adaptive trade-offs among traits within populations, and limit gains from selection on a focal trait (9). Trait–trait correlations can also arise through strong selection acting in parallel on unlinked loci or from LD mediated by physical linkage of loci on chromosomes. Average genome-wide LD estimates in conifers appear to be low (10, 11) but may be greater (r² of 0.2–0.4) within genes under strong selection (12).Conifer studies have identified putatively adaptive phenotype-associated alleles on a locus-by-locus basis using quantitative trait loci (QTL) mapping or genotype–phenotype associations (GPAs) (also known as genome-wide association studies [GWAS]) (13). Genotype–environment association analyses in conifers have identified putatively adaptive environmentally associated loci (e.g., 14, 15), but loci are usually anonymous relative to adaptive phenotypes. All of these approaches are biased toward detecting loci with large phenotypic effects, but expectations that genome scans will discover individual adaptive loci with large effects or frequency differences among populations may be biologically and statistically unrealistic (16, 17). Conifer GPA studies typically detect relatively few statistically significant loci, and locus-by-locus analyses are insufficient to characterize adaptive genome-wide variation associated with adaptive traits and signatures of selective breeding. Multilocus tests for adaptive polygenic signatures of selection have been developed (e.g., refs. 18 and 19), but significant limitations remain (reviewed in refs. 20 and 21).Uncertainty about the effects of selective breeding on adaptive genetic variation is layered upon expectations that forest trees will become maladapted as climates shift (22). Efforts are being made to estimate maladaptation using genome-wide variation associated with adaptive traits and climate (23, 24), because conserving, managing, and efficiently redeploying genetic variation associated with adaptive phenotypes will be a necessary element of strategies to mitigate the effects of shifting climates on forest resources (25). Assisted gene flow strategies in temperate and subboreal forests generally aim to move trees to cooler climates in anticipation of future warming, but trees must then contend with the increased short- to medium-term risk of maladaptation to damaging frost. This means cold tolerance is, perhaps surprisingly, an important trait when planning for warming climates.Genetic approaches have the potential to efficiently and accurately characterize local adaptation to climate. Understanding whether this potential can be realized in a technically robust and operationally feasible way, and whether selection for faster growth compromises genetic variation associated with phenotypic adaptation to climate, has far-reaching implications for developing effective assisted gene flow strategies that mitigate negative climate change impacts on forest health and timber production (26, 27). In this context, our research objectives are to 1) identify the genetic architecture of climate-related adaptive traits in interior lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.); 2) identify genome-wide effects of artificial selection for increased productivity on climate-related traits; and 3) assess the implications of genetic responses to selection for assisted gene flow strategies.Our study combines climatic data, genotype data from ∼50,000 lodgepole pine single-nucleotide polymorphisms (SNPs), and seedling phenotypic data for height, cold injury, growth initiation, and growth cessation traits. These data were collected from a seedling common garden that sampled reforestation seed lots from 105 natural populations and 20 breeding populations from across the species’ range in Alberta (AB) and British Columbia (BC), Canada (Fig. 1 and SI Appendix, Table S1). For each of the four traits, we identify range-wide GPAs using 929 seedlings from all 105 natural populations. Then for the 1% most strongly phenotype-associated SNPs, we examine how artificial selection within breeding populations has changed allele frequencies at individual SNP loci, within individual seedlings, populations (breeding zones), and three climatic regions (Fig. 1). Using elements from the approach of Turchin et al. (18), we study changes in frequency of the alleles that have a positive effect on adaptive traits (positive effect alleles [PEAs]). At each SNP locus, a PEA is the allele associated with increasing numeric values of the respective phenotype, determined in this case through GPA analyses in the natural seedling populations. PEAs reported here are associated with greater seedling height, greater cold injury, delayed growth initiation, and delayed growth cessation. To parse physical genetic linkage from allelic associations due to other causes, we compare LD estimated from our natural seedlings with estimates of recombination among haploid megagametophytes from a single maternal parent, where physical linkage is the only cause of LD. Integrating genetic, climatic, and phenotypic data gives us a robust basis to detect the effects of artificial selection on climate-related genotypes that are relevant to breeding and assisted gene flow strategies.Open in a separate windowFig. 1.Geographic origins of the natural and selected seedling populations sampled from across the range of lodgepole pine in Alberta (AB) and British Columbia (BC). Natural populations are represented by filled circles; selected seedling breeding zones are represented by filled polygons. The three climatic regions we used were AB, BC-Central, and BC-South. AB breeding zones are formally identified as A, B1, B2, C, J, and K1. BC-Central breeding zone abbreviations are as follows: BV, Bulkley Valley; CP, Central Plateau; and PG, Prince George. BC-South breeding zone abbreviations are as follows: EK, East Kootenay; NE, Nelson; and TO, Thompson–Okanagan. Reprinted from ref. 35, with permission from Elsevier.     

Mean differences and individual changes in nursing students' attitudes toward statistics: The role of math background and personality traits

Nursing students have compulsory statistics courses in their degree program, but they usually have negative attitudes toward statistics that may hinder their learning. The present study aims: (i) to investigate nurse students’ attitudes toward statistics and the relationships with mathematical background and personality traits; (ii) to stress individual changes in attitude that occur during the course; and (iii) to explore if mathematical background and personality traits influence these changes. We adopted a one-group pre-post survey study. Fifty nursing students were enrolled in an introductory statistics course at the Sapienza University of Rome in Italy during the 2018–2019 academic year. Participants were surveyed at the beginning and end of the course administering a multidimensional measure of attitude toward statistics. Multiple regression analyses were run to establish the relative impact of mathematical background and Big Five personality factors on attitude components, as well as the changes in these attitude components. Results confirmed the predictive role of mathematic competence on some attitude dimensions and showed that also personality traits influenced attitudes toward statistics. However, the observed changes in attitudes during the course were minimally influenced by these factors. Findings suggest that course pedagogy can enhance students’ attitudes regardless their mathematical background and personality.     

A Family‐Based Robust Multivariate Association Test Using Maximum Statistic

For characterizing the genetic mechanisms of complex diseases familial data with multiple correlated quantitative traits are usually collected in genetic studies. To analyze such data, various multivariate tests have been proposed to investigate the association between the underlying disease genes and the multiple traits. Although these multivariate association tests may have better power performance than the univariate association tests, they suffer from loss of testing power when the genetic models of the putative genes are misspecified. To address the problem, in this paper we aim to develop a family‐based robust multivariate association test. We will first establish the optimal multivariate score tests for the recessive, additive, and dominant genetic models. Based on these optimal tests, a maximum‐type robust multivariate association test is then obtained. Simulations are conducted to compare the power of our method with that of other existing multivariate methods. The results show that the robust multivariate test does manifest the robustness in power over all plausible genetic models. A practical data set is applied to demonstrate the applicability of our approach. The results suggest that the robust multivariate test is more powerful than the robust univariate test when dealing with multiple quantitative traits.     

Personality traits and temporomandibular disorders in a group of children with bruxing behaviour

Summary  The aim of the present study was to compare the personality pattern, the anxiety level and the temporomandibular disorders (TMD) between bruxist and non-bruxist children with mixed dentition. Fifty-two subjects, with a mean age of 9·45 years (range 8–11) were evaluated and classified as bruxist ( n  = 26), according to the American Academy of Sleep Medicine (AASM) and the presence of dental wear clinically visible, under the same conditions of artificial light and position. The control children ( n  = 26) did not present dental wear and did not accomplished all the AASM criteria. The personality pattern and the anxiety of the bruxist children were studied by means of the Children's Personality Questionnaire (CPQ) and the Conners' Parents Rating Scales (CPRS), respectively, and compared with the personality traits and the anxiety level of a non-bruxist population. The TMD were also evaluated using the Research Diagnostic Criteria for temporomandibular disorders (RDC/TMD) criteria. The data were analysed with the student's t -test, Fisher's exact test and chi-squared tests. A multivariated analysis was performed using a logistic regression with the stepwise likelihood ratio method. Compared with the controls, the bruxist children had significantly higher tension personality and were more anxiety prone. The bruxist children presented more TMD-related signs and symptoms than children in the control group. A strong correlation was found among bruxism, TMD, the high anxiety level and the high tension personality trait.