Correlation of personality with individual reproductive success in shrub-nesting birds depends on their life history style

Two questions in the research of animal personality—whether there is a correlation between a personality trait and individual reproductive success,and what is the genetic basis underlying a personality trait—remain unresolved.We addressed these two questions in three shrub-nesting birds,the Azure-winged Magpie(Cyanopica cyanus,AM),White-collared Blackbird(Turdus albocinctus,WB),and Brown-cheeked Laughingthrush(Trochalopteron henrici,BL).The personality type of an individual was first identified according to its response to a territorial intruder.Then,we compared the fleeing distance,breeding parameters,and differential expressed genes(DEGs) in the brain transcriptome between bold and shy breeders.In the three species,bold breeders exhibited more aggressiveness towards an intruder of their territory than did shy breeders.The reproductive success of bold breeders was significantly higher than that of shy breeders in AM but not in WB and BL.The three species shared one DEG,crabp1,which was up-regulated in bold relative to in shy individuals.By regulating the expression of corticotropin-releasing hormone,higher crabp1 gene expression can decrease cellular response to retinoic acid.Therefore,bold individuals are insensitive to external stresses and able to exhibit more aggressiveness to intruders than their shier counterparts.Aggressiveness is beneficial to bold individuals in AM but not in WB and BL because the former could evoke neighbors to make the same response of defending against intruders but the latter could not.Although a personality trait may have the same genetic basis across species,its correlation with reproductive success depends largely on the life history style of a species.

The Impact of Big Five Personality Traits on Older Europeans’ Physical Health

Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness. .

The Relationship between Mental Disorders and Personality of Outpatients in a Psychiatric Clinic in Nanjing, China

Psychosis has increasingly become a social problem,emphasizing the need to understand the relationship between mental disorders and personality.This study aimed to investigate the relationship between mental disorders and personality among psychiatric outpatients based on real-world data.Symptom Checklist 90(SCL-90)and Eysenck Personality Questionnaire(EPQ)were used to evaluate the personality and psychopathological symptoms of patients(n=8409)in the Psychiatric Outpatient Department at Nanjing Drum Tower Hospital.t-test was used to compare scores between patients and national norms.Pearson’s correlation coefficient and path analysis were used to explore the relationship between mental health status and personality.The correlation coefficient between the neuroticism(N)score and each factor score of the SCL-90 test,as well as the correlation between psychoticism(P)and hostility and paranoia,exceeded 0.4.Path analysis revealed that the standardized path coefficients of N score and SCL-90 were all higher than 0.4.In addition,the standardized path coefficient of hostility and paranoia on P score were 0.313 and 0.280,respectively.Interpersonal sensitivity,depression and obsessive-compulsive symptoms were affected by extraversion(E)score,with standardized path coefficients of-0.149,-0.138,and-0.105,respectively.The path analysis also showed the direct and indirect effects of age,gender,education,and marital status on SCL-90.Patients characterized as melancholic had higher scores in all factors of SCL-90.In conclusion,mental health was related to personality traits of neuroticism,psychoticism and introversion.

Personality and Nomophobia: A Moderated Mediation Model of Self-Esteem and Non-Self-Determined Solitude

Background:With the development of information technology,mobile phone has brought much convenience to people’s lives but also caused many negative consequences due to excessive use,such as mobile phone addiction and nomophobia.Previous studies have explored the relationship between the Big Five Personality and proble-matic mobile phone use(PMPU).However,they focus on mobile phone addiction.Although there is a correlation between nomophobia and mobile phone addiction,the psychological structure is different.Therefore,it is neces-sary to explore the relationship between personality and nomophobia and the underlying mechanism.This study aims to examine the relationship between Big Five Personality and nomophobia,then construct a moderated mediation model to explore the mediation effect of solitude between Big Five Personality and nomophobia,as well as the moderation effect of self-esteem.Method:Data from 678 college students(351 females,51.77%)were collected.Participants completed the Big Five Personality Inventory,Solitude Behavior Scale,Nomophobia Scale and Self-esteem Scale.Analyses were conducted via mediation and moderated mediation.Results:Structural equation models revealed that solitude mediated the relationship between neuroticism and nomophobia.The results showed that neuroticism positively predicted solitude,which in turn positively predicted nomophobia.Four types of solitude partially mediated the relationship between neuroticism and nomophobia.We also found that self-esteem moderated the association between neuroticism and non-self-determined solitude.It is note-worthy that high self-esteem cannot protect people from negative factors.However,because of its characteristics,it is easy to receive more social information,and people high in neuroticism are sensitive to negative social infor-mation.It may cause maladaptive behavior.Conclusion:Findings demonstrated a process through which neu-roticism relates to nomophobia and a context under which these relationships may have occurred.

Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

Knowing each other is obligatory in a multi-agent collaborative environment.Collaborators may develop the desired know-how of each other in various aspects such as habits,job roles,status,and behaviors.Among different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral pattern.It has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become biased.They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves.Such writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,etc.Stream of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the narrator.More-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding techniques.This article proposes an empirical approach to assessing personality by applying convolutional networks to text documents.Bidirectional Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.

An analysis of association of personality traits with job burnout among nurses working in hospital

Objective:The objective of the present study is to explore the effects of personality traits on job burnout among hospital nurses.Materials and Methods:This cross-sectional research was done during 2019-2020 at Kashan Shahid Beheshti Hospital.The data analysis procedures included descriptive statistics and the partial least squares-based structural equation modeling.The participants were 150 nursing professionals.A questionnaire indicating information on demographics,burnout(measured using the Maslach Burnout Inventory with three dimensions of depersonalization,emotional exhaustion,and personal accomplishment),and personality profile(measured employing the neuroticism extraversion openness five-factor inventory including extroversion,conscientiousness,agreeableness,neuroticism,and openness to experience dimensions)was used to gather the required data.Results:The results of the study showed that the validity and reliability of the measurement model were desirable(factor load higher than 0.5,the Cronbach’s alpha value and the composite reliability are>0.7).Structural model showed statistically drastic,negative relationship between the nurses’burnout levels and neuroticism(β=0.722)and openness to experience(β=0.437).However,the relationship was significantly positive between the nurses’burnout levels and conscientiousness(β=0.672),agreement(β=0.594),and extraversion(β=0.559)(P<0.03).Conclusions:The present study helped the recognition of burnout among nurses working in hospitals and approved the effects of personality features on the burnout experience.

Constitutional Nature of Personality Rights and Its Manifestation in Civil Law

The formation of personality comes from people’s choices and pursuit of self-realization,which is influenced by objective factors but not determined by them,so personality does not belong to the domain of objectivity.The concept of general personality rights in the German Constitution was initially premised on the objective determinability in the field of personality,but in constitutional jurisprudence,it gradually shifted to something with individual autonomy as the core and personal self-realization as the goal,and the scope of relevant rights expanded accordingly,so that they could not be clearly distinguished from general freedom of action and thus became the general principle of constitutional rights.The protection of constitutional personality rights in the United States and Japan can also confirm this process,providing evidence for the constitutional nature of personality rights.Deeper research shows that constitutional personality rights actually manifest the highest value of modern constitutions—human dignity.In contrast,the theoretical justification of personality rights in civil law just lies in the objectivity and defensive nature of personality elements.

On the Construction and Application Scenarios of——Analysis of Environmental Personality Interests Based on Human Rights

Environmental personality interests based on human rights reflect the multiple values of ecological order,ecological justice,and ecological freedom,and are closely linked to the protection of the right to life and the right to health.They are also related to human dignity and the personal freedom of civil subjects and conform to formal and essential standards of personality rights,which should be included in the scope of personality rights for protection.The construction and application of environmental personality rights faces bottlenecks such as the partiality of subjects,limitation of objects,and hysteresis of responsibilities in the protection of environmental personality rights.Environmental personality rights are supposed to reflect the needs of the development of modern human rights.We should expand the scope of its connotative power and function based on the Green Principle of the Civil Code,and follow a networked,typified,and systematic path of protection,so as to manifest the people-centered philosophy of the Civil Code and the Environmental Protection Law.

Correlation analysis of mental health conditions and personality of patients with alcohol addiction

BACKGROUND Alcohol addiction,or alcohol dependence,refers to a psychological state of strong craving for alcohol caused by drinking when both the drinking times and alcohol consumption reach a certain level.Alcohol addiction can cause irreversible damage,leading to mental illness or mental disorders,negative changes in their original personality,and a tendency to safety incidents such as committing suicide or violent attacks on others.Significant attention needs to be given to the mental health of alcohol addicts,which could reflect their abnormal personality traits.However,only a few papers on this issue have been reported in China.AIM To investigate the correlation between mental health and personality in patients with alcohol addiction.METHODS In this single-center observational study,we selected 80 patients with alcohol addiction as the research subjects,according to the criteria of the K10 scale to evaluate the mental health of patients with alcohol addiction,and divided these patients into four groups based on the evaluation results:Good,average,relatively poor and bad.And then analyzed the correlation between mental health conditions and personality characteristics from these four groups of patients.RESULTS The average score of the K10 scale(Kessler 10 Simple Psychological Status Assessment Scale)in 80 patients with alcohol addiction was 25.45 points,the median score was 25 points,the highest score was 50 points,and the lowest score was 11 points.Pearson's analysis showed that the K10 score was positively correlated with the scores of these two subscales,such as the P-subscale and the N-subscale(P<0.05).In contrast,the K10 score had no significant correlation with the scores from the E-subscale and the L-subscale(P>0.05).CONCLUSION The mental health conditions of patients with alcohol addiction are positively correlated with their personality characteristics.

The moderating role of NTSR1 polymorphisms on personality and coping styles among breast cancer patients

Background:Some personality traits could be a predictor for coping styles.The objective of this investigation was to examine the correlation between personality traits and coping mechanisms in the context of gene regulation among breast cancer patients.Material and methods:This cross-sectional study was performed in 2021.A total of 200 breast cancer patients were included in this study.Data were collected using the Trait Coping Style Questionnaire(TCSQ)and the Eysenck Personality Questionnaire-Revised Short Scale for Chinese(EPQ-RSC).And individuals’blood was tested for genotype.Results:The findings indicated that there existed an inverse relationship between extraversion and negative coping style while also demonstrating a significant association between extraversion and positive coping style.Neuroticism was positively correlated with negative coping style but negatively with positive coping style.The SNP of NTSR1 only moderated the relationship between neuroticism and negative coping styles in breast cancer patients.Conclusion:The present investigation delves into the correlation between personality traits and coping mechanisms at the molecular level in breast cancer patients.

The Enlightenment of Cai Yuanpei’s Aesthetic Education to Personality Education

With the development of human society and the progress of science and technology,people’s cultural cognition has constantly strengthened.With people’s research and thinking,all kinds of cultural education came into being,aesthetic education,moral education,and sports,all play a significant role in people’s physical and mental development,among which the aesthetic education thought advocated by Cai Yuanpei has a great value as guidelines for the significance of personality education.It is of great importance to people’s physical and mental development.

Socio-Spatial Dynamics of Land in Southwest Niger: The Case of the Commune of Gothèye

In south-west Niger, ecosystems are losing several hectares of their surface area every year due to internally displaced persons and refugees. The commune of Gothèye is not immune to this situation. The aim of this research is to assess the impact of displaced persons and refugees on socio-spatio-temporal dynamics of ecosystems using Landsat images. To achieve this, Landsat TM, Landsat ETM+ and OLI 8 satellite images from September and March were used (2010 to 2024). Operations on Envi 5.3, field validation output and finally mapping on ArcGIS were the steps involved. Discrimination is significant, with kappa coefficients of 0.97, 0.96, 0.86 and 0.85. The results obtained indicate a degradation of natural ecosystems, reflected in a change in landscape structure, with a marked reduction in the quantity and quality of ecosystem goods. Analysis of the evolution of land use showed that 31% of the land remained in its initial state (unchanged), 69% underwent modifications, and 11% was converted to cropland. Over these fourteen years, the study area has undergone changes in land use patterns, which have resulted in a modification of landscape structure, with a marked decline in the quantity and quality of ecosystem services.

Immune-related gene characteristics:A new chapter in precision treatment of gastric cancer

Gastric cancer ranks as the sixth most prevalent cancer worldwide.In recent research within the realm of gastric cancer treatment,the identification and application of immune-related genetic features have emerged as groundbreaking advancements.The study by Ma et al,which developed a prognostic model based on 10 genes,categorizes patients into high and low-risk groups to predict their responsiveness to immune checkpoint inhibitor therapy.This research underscores the potential of immune-related genes as biomarkers for personalized treatment,offering insights into tumor mutation burden and immune phenotype scores.We advocate for further validation,understanding of biological mechanisms,and integration of diverse datasets to enhance the model's predictive accuracy and clinical application,marking a significant step towards personalized and precise treatment for gastric cancer.

COVID-19 and the Vascular Elderly Subject: Illustration of Therapeutic Management with Corticosteroids in an Elderly Diabetic Patient with COVID-19

Introduction: The severity of Sars-Cov-2 infection is associated with the development of acute respiratory distress syndrome (ARDS). The progression to ARDS appears to be driven by a major inflammatory mechanism potentially sensitive to corticosteroids. Observation: This article describes the case of an elderly patient was admitted to emergency departments for intense asthenia, accompanied by motor diarrhoea, dyspnoea with desaturation in ambient air, in a context of strong suspicion of infection linked to COVID-19. The article also reviews the existing literature on the diagnosis and treatment of this severe form of the disease. Conclusion: Corticosteroids, and in particular dexamethasone, have been shown to be effective in the management of patients with COVID-19, an oxygen-releasing disease.

Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

The Development of Human Rights Protection in Cross-border Personality Rights and Tort Conflict Laws

Personality rights are basic rights related to human dignity in the concept of human rights protection,and respect for personality rights is the basic prerequisite for safeguarding other human rights.Human rights protection exerts a profound impact on the value of legislation and enforcement practices of private international laws in the field of cross-border personality rights infringement in various countries,which is mainly manifested in the integration of substantive justice and the humanistic care concept of protecting the rights of vulnerable groups into formal justice,and expands the development space of human rights protection ideas in cross-border personality rights and tort conflict laws.the diversity of human rights protection values determines that cross-border personality rights infringement may lead to conflicts between personality rights and other basic rights,such as freedom of speech.to reconcile such conflicts,a workable method is to prioritize personality rights protection in accordance with the hierarchy of rights theory in the value judgment of public order reservation or to clarify the limits of the right holder’s tolerance obligation through the limited application of the principle of proportionality.China’s current cross-border personality rights and tort conflict law can optimize the protection of the rights of vulnerable groups by diversifying the options of available legal methods,and establish a balancing mechanism between personality rights and freedom of speech,so as to improve China’s legal protection system for human rights in the field of foreign-related civil and commercial affairs.

Personality Trait Detection via Transfer Learning

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.

Personalized treatment of perihilar cholangiocarcinoma based on tumor genetic and molecular characteristics

This editorial discusses the article written by Tchilikidi et al that was published in the latest edition of the World Journal of Gastrointestinal Surgery.Genetic and molecular profiling of perihilar cholangiocarcinoma(pCCA)has identified a number of key abnormalities that drive tumor growth and spread,including pyruvate kinase M2,proline rich 11,and transcription factor 7,etc.pCCA has specific genetic and molecular features that can be used to develop personalized treatment plans.Personalized treatment approaches offer new opportunities for effectively targeting the underlying drivers of tumor growth and progression.The findings based on tumor genetic and molecular characteristics highlight the importance of developing personalized treatment strategies.

Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.