Characteristic Changes Defined via Comparison of the Big-Five Personalities in Japanese University Freshmen from Years 2000 to 2016

Introduction: With the arrival of the 21st century, drastic worldwide changes to natural, economic and social events have occurred. These include the Great East Japan Earthquake, and the global financial crisis. Furthermore, Japan experienced the introduction of a specific education system. In this study, we traced and compared the psychological characters of university freshmen for 16 years (years 2000-2016) using Big-Five personalities inventory (Japanese version). Methods: A total of 1189 physically and mentally healthy university freshmen participated in this study. They were divided into four groups according to the year they entered university. They completed a self-filled questionnaire, Big-Five personalities inventory, which consists of Extraversion, Agreeableness, Neuroticism, Conscientiousness and Openness. Result and Discussion: Compared with the year 2008, Extraversion nominally increased in 2012, however, it statistically significantly decreased in 2016. Agreeableness notably decreased from 2000 to 2008, then showed an increase in 2012. Compared to 2000, Neuroticism was statistically significantly lower in 2008 and 2016. Such statistically significant differences were not observed in the other two personalities. The personalities of university freshmen have changed in the last 16 years, which may be been caused by drastic changes. However, further research (e.g., larger-scale studies or studies including other university students) is required to support the current findings.

Different handsets,different personalities

iPhone owners are vainer(虚荣)and spend more on clothes and grooming(仪容)than those who have BlackBerrys and Android phones,new research claims.BlackBerry owners earn the most and are more likely to have long-term relationships,while people with Android handsets are most creative and the best cooks.The study,carried out by TalkTalk Mobile,surveyed2,000 owners of the three major smartphone brands to determine whether the choice of handset was an

Analysis of the Positive Personalities of Scarlett in Gone with the Wind

Gone with the Wind,the masterpiece of the Margaret,M who has spent about ten years on writing this novel and it takes over half a year of her to verify the specific time and place of the involved historical event.Once the masterpiece is published on June 30,1936,it soon broke several records in sales of American publishing;later,it won the prize of Pulitzer and Association of American publishers.While the leading lady of this novel,Scarlett,become an object of admiration among thousands of people.What attract the readerséyes deeply is her unique origin,outstanding features and noble actions.In the meantime,the charming personality of Margaret's Scarlett has conquered us.She is strong to the difficulties;she is persistent to her love;and she is witty to her family.She is the strongest among the people who has endured the hardship as her no matter she has suffered from the war,the poor and leaving her family.What makes her be responsible to the Taylor Manor is her braveness.At the end of the story,she still tells us:tomorrow is another day! We admire her positive attitude to life,and this is why the female character can be everlasting.

Human-Related Emotional Stimuli Can Cause a Hippocampal and Thalamic Over-Response in People with Unstable Personalities

Hippocampus is crucial for the formation of emotional memory. We found the relationship between hippocampal responses to emotional stimuli and the mental stabilities of people in our preliminary study. In this study, we have also evaluated how the emotional stimuli would affect amygdala and thalamus in the brain, and how the personality stabilities could relate to the responses in the brain using functional magnetic resonance imaging (fMRI). We evaluated the subjects’ personality features with the Yatabe-Guilford Personality Test (Y-G test) and psychosomatic symptoms with the Cornell Medical Index (CMI). The subjects were categorized into the mentally stable group and the mentally unstable group according to the total scores of the Y-G test and the CMI. The brain functional responses under emotional stimuli were measured using fMRI. The region of interest (ROI) analysis was performed to abstract significant changes in order to compare responses among the different emotional stimuli. We conducted the regression analysis to abstract the relationship between the mean % signal change from fMRI and the personality stability. The fMRI results showed that the hippocampus, thalamus, and right amygdala activities under the human relationship stimuli increased with ascending value of mental instability. Our findings suggest that the memory process in the hippocampus and the threat alarm system in the thalamus under the human-related stimuli crucially influence the emotional reaction of mentally unstable people. These processes in the brain would affect the event that stresses on human relationships that often cause people to suffer from mental disorders.

Differences of Confucianism and Individualism on the Development of Chinese and American Personalities

Different personalities are shaped by different cultures.Chinese Confucianism and American Individualism took totally different developing routes.Understanding and contemplating their differences in personality can greatly enhance inter-cultural communication between these two countries.Based on the present researches and studies,this paper discusses the differences and current situation of Chinese and American personalities.Relevant suggestions are initiated aiming at learning from each other’s strong points and bridging the gap as to enhance the development of healthy personality and harmonious world.

Differences in the Personalities of Two Miser-type Characters-Shylock and Jia Ren

For centuries, various miserly figures have been portrayed both in Chinese and alien literature. Shylock and Jia Ren are such characters in two famous plays. This thesis explores and analyses the differences in their personalities in order to bring a deepened understanding of miser-type characters as the outcome of their societies.

An Analysis of the Heroine's Personalities in A Tale of Two Cities

Lucie was full of love in that period in which The French Revolution was happening. In that cold-blooded era which was lack of positive energy, Lucie used her own warm heart to influence others, to let other people strongly feel that there were love, kindness and humanity still existing in their society. Lucie was such a kind and loving person. Her relationship with three important characters and many things happening among them were vividly presented in A Tale of Two Cities. To a large extent, as the main female character, Lucie was different from Madame Defarge. Madame Defarge was full of hatred and revenge. She was just aware of the harm that others had had taken to her relatives and kept it in mind firmly, rather than melt it into haze. By contrast, Lucie would transform her hatred into love.

An Analysis of Personalities in The Rocking-Horse Winner from the Perspective of Cooperative Principle

Language serves for the expression of mind.By analyzing the conversations in the short story The Rocking-Horse Winner from the perspective of Grice’s cooperative principle,the paper draws a conclusion about the distinct personalities of the protago nists in the novel.According to the findings,the conversational implicature of the protagonists actually reflects their different char acters which are or aren’t the results of the industrial background,hypocritical or pure,greedy or diligent.In this way,the study hopes to provide readers an access to a deep understanding of the story and drive them to care more about the ever important yet complex parents-child,especially mother-child relation.

Personalities and health in older cat and dog owners: A HUNT-study

The aim of this population study was to identify personality traits among older (>65 years) male and female owners of cats and dogs and to compare their general health status in relation to their personality. Further, the aim was to examine whether current cat and dog ownership could be predicted by the owners’ personality and health. Data were collected from the North-Tr&#248ndelag Health Study (HUNT) in Norway. Included were a total of 1897 cat or dog owners between the ages of 65 years and 101 years. The results showed that there were a higher proportion of introverted male cat owners than extraverted ones. Moreover, a majority of women with cats reported that their health was not good. Furthermore, female cat owners who displayed higher scores on neurotic traits experienced significantly poorer health compared to those female cat owners that experienced good health. The same was true for female cat owners who considered themselves to be introverted. Neither personality nor health could predict pet-ownership, but it was more likely for older individuals (80-101 years) to own a cat than a dog. This study has shown that human personality is associated with cat and dog ownership, but there are other factors connected with pet ownership as well.

多主体协同开发社区教育课程的PERSONAL模式探索——基于“我爱我家”的个案研究

通过对“我爱我家”这一社区自发课程的跟踪调查,发现社区自发课程开发实质上是一个组织学习过程,经过个体直觉、知识解释、知识整合等环节实现组织的制度化学习,在推动学习型无边界组织生成的同时,生成社区教育课程。在这一过程中,政策(policies)支持、专家(expert)指导、区域需求和区域特征(regional)、社区系统(system)、组织化(organization)等多要素叠加发挥作用,通过不断拓展新鲜(new)领域的积极行动(action)整合所有资源,并推动跨越边界的学习(learning)贯穿始终。因此,多主体协同的社区自发课程可以不完全地概括为PERSONAL模式。要以PERSONAL模式发展社区自发课程,需要以学习者为中心,注重发展人际互动,营造有利于跨组织交互学习的教育生态系统。

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.

Self-supervised recalibration network for person re-identification

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

Personal Thermal Management by Radiative Cooling and Heating

Maintaining thermal comfort within the human body is crucial for optimal health and overall well-being.By merely broadening the setpoint of indoor temperatures,we could significantly slash energy usage in building heating,ventilation,and air-conditioning systems.In recent years,there has been a surge in advancements in personal thermal management(PTM),aiming to regulate heat and moisture transfer within our immediate surroundings,clothing,and skin.The advent of PTM is driven by the rapid development in nano/micro-materials and energy science and engineering.An emerging research area in PTM is personal radiative thermal management(PRTM),which demonstrates immense potential with its high radiative heat transfer efficiency and ease of regulation.However,it is less taken into account in traditional textiles,and there currently lies a gap in our knowledge and understanding of PRTM.In this review,we aim to present a thorough analysis of advanced textile materials and technologies for PRTM.Specifically,we will introduce and discuss the underlying radiation heat transfer mechanisms,fabrication methods of textiles,and various indoor/outdoor applications in light of their different regulation functionalities,including radiative cooling,radiative heating,and dual-mode thermoregulation.Furthermore,we will shine a light on the current hurdles,propose potential strategies,and delve into future technology trends for PRTM with an emphasis on functionalities and applications.

Moisture‑Electric–Moisture‑Sensitive Heterostructure Triggered Proton Hopping for Quality‑Enhancing Moist‑Electric Generator

Moisture-enabled electricity(ME)is a method of converting the potential energy of water in the external environment into electrical energy through the interaction of functional materials with water molecules and can be directly applied to energy harvesting and signal expression.However,ME can be unreliable in numerous applications due to its sluggish response to moisture,thus sacrificing the value of fast energy harvesting and highly accurate information representation.Here,by constructing a moisture-electric-moisture-sensitive(ME-MS)heterostructure,we develop an efficient ME generator with ultra-fast electric response to moisture achieved by triggering Grotthuss protons hopping in the sensitized ZnO,which modulates the heterostructure built-in interfacial potential,enables quick response(0.435 s),an unprecedented ultra-fast response rate of 972.4 mV s^(−1),and a durable electrical signal output for 8 h without any attenuation.Our research provides an efficient way to generate electricity and important insight for a deeper understanding of the mechanisms of moisture-generated carrier migration in ME generator,which has a more comprehensive working scene and can serve as a typical model for human health monitoring and smart medical electronics design.

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

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.

From prediction to prevention:Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

In this editorial,we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma.Hepatocellular carcinoma(HCC),which is characterized by high incidence and mortality rates,remains a major global health challenge primarily due to the critical issue of postoperative recurrence.Early recurrence,defined as recurrence that occurs within 2 years posttreatment,is linked to the hidden spread of the primary tumor and significantly impacts patient survival.Traditional predictive factors,including both patient-and treatment-related factors,have limited predictive ability with respect to HCC recurrence.The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research.The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence.Challenges persist,including sample size constraints,issues with handling data,and the need for further validation and interpretability.This study emphasizes the need for collaborative efforts,multicenter studies and comparative analyses to validate and refine the model.Overcoming these challenges and exploring innovative approaches,such as multi-omics integration,will enhance personalized oncology care.This study marks a significant stride toward precise,efficient,and personalized oncology practices,thus offering hope for improved patient outcomes in the field of HCC treatment.

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.

An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,BiLSTM,and hybrid models exhibited superior performance in individual classification when compared to prevailing state-of-the-art techniques.This validates our method’s efficacy and underscores its potential to outperform existing technologies,marking a significant stride forward in the realm of individual identification through handwriting analysis.

Spastin and alsin protein interactome analyses begin to reveal key canonical pathways and suggest novel druggable targets

Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein–protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as “causative” for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration–approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.