Strong Predictive Effect Research Articles

Study on the changing patterns of production performance of laying hens and their relationships with environmental factors in a large-scale henhouse

The production performance of laying hens is influenced by various environmental factors within the henhouse. The intricate interactions among these factors make the impact process highly complicated. The exact relationships between production performance and environmental variables are still not well understood. In this study, we measured the production performance of laying hens and various environmental variables across different parts of the henhouse, evaluated the weight of each environmental variable, and constructed a laying rate prediction model. Results displayed that body weight, laying rate, egg weight and eggshell thickness of hens decrease gradually from WCA to FA (P < 0.05). Serum levels of FSH and LH, as well as antibody level of H5 Re-13, gradually decrease from WCA to FA (P < 0.05). Moreover, the values for temperature (T), temperature-humidity index (THI), air velocity (AV), carbon dioxide (CO2), and particulate matter (PM2.5) gradually increase from WCA to FA (P < 0.05). Conversely, the relative humidity (RH) value gradually decreases from FA to WCA (P < 0.05). Additionally, the weights of the environmental variables, determined using a combination of the grey relational analysis (GRA) and analytic hierarchy process (AHP), were as follows in descending order: RH, THI, T, light intensity (LI), AV, PM2.5, NH3, and CO2. When the number of decision trees in the laying rate prediction model was set to 2,500, the results displayed a high level of agreement between the model's predictions and the observed outcomes. The model's performance evaluation yielded an R2 value of 0.89995 for the test set, suggesting strong predictive effects. In conclusion, the current study revealed significant differences in both the production performance of laying hens and the environmental variables across different parts of the henhouse. Furthermore, the study demonstrated that different environmental factors have distinct impacts on laying rate, with humidity and temperature identified as the primary factors. Finally, a multi-variable prediction model was constructed, exhibiting high accuracy in predicting laying rate.

The relationship between neuroticism and social aggression: a moderated mediation model

Neuroticism appears to be a factor that triggers social aggression, but the relationship between neuroticism and social aggression and its underlying mechanisms is unclear. Questionnaire data from 942 college students ranging in age from 17 to 24 (Mage = 20.33, SD = 1.03) were analysed to assess whether depression symptoms mediated the relationship between neuroticism and social aggression, and to test a moderating effect of perceived social support. Results showed that neuroticism positively predicted social aggression and this association was mediated by depression symptoms. Moderation was found for the association between neuroticism and depression symptoms, as well as between neuroticism and social aggression, and that neuroticism had a stronger predictive effect on depression symptoms and social aggression under low compared to high perceived social support. These findings may inform prevention and intervention efforts to reduce social aggression.

Muscle structure predictors of vertical jump performance in elite male volleyball players: a cross-sectional study based on ultrasonography.

The objective of this investigation is to examine the contribution of key muscle groups in the lower limbs to vertical jumping performance in elite male volleyball players. Specifically, the study focuses on the rectus femoris (RF), vastus lateralis (VL), and lateral gastrocnemius (LG), as well as exploring differences between attack jump and other vertical jump types. To achieve this, we employed B-mode ultrasound to evaluate the anatomical cross-sectional area (ACSA), muscle thickness (MT), pennation angle (PA), and fascicle length (FL) of the RF, VL, and LG in the participants. Fifteen elite male volleyball players were recruited as participants for this study. Jump heights were measured for four types of vertical jumps: attack jump (AJ), countermovement jump (CMJ), squat jump (SJ), and drop jump (DJ). We conducted regression analyses to assess whether the previously mentioned muscle structures could predict jump performance. Our findings reveal that the muscle structure of the RF does not exhibit any significant correlation with the height of any jump. However, VL-ACSA displays a significant and the most potent predictive effect on jump height for all four jump types (AJ: R 2 = 0.32, p = 0.001; CMJ: R 2 = 0.37, p = 0.005; SJ: R 2 = 0.52, p = 0.001; DJ: R 2 = 0.25, p = 0.021). Conversely, LG-FL only demonstrates a significant and stronger predictive effect on AJ jump height (R 2 = 0.18, p = 0.009). Combining VL-ACSA, LG-FL, and training age through multiple linear regression analysis resulted in a highly significant model for predicting AJ jump height (F = 13.86, R 2 = 0.73). Moreover, the model incorporating VL-ACSA and training age is also important for predicting CMJ, SJ, and DJ jump heights (F = 8.41, R 2 = 0.51; F = 13.14, R 2 = 0.63; F = 5.95, R 2 = 0.41; respectively). The muscle structure indicators in the lower limbs significantly predict jump performance among elite male volleyball players. However, different jump types are influenced by distinct indicators, particularly in the case of AJ, which is associated with LG-FL. This suggests that enhancing LG-FL may positively impact AJ ability, thereby emphasizing the importance of specificity in training. To optimize specialized jump performance in volleyball players, practitioners are advised to assess VL-ACSA and LG-FL and incorporate step-up and eccentric strength training targeting the calf muscles to yield considerable benefits.

Clinical relevance of Staphylococcus saccharolyticus detection in human samples: a retrospective cohort study.

To characterize the clinical relevance of S. saccharolyticus and to identify criteria to distinguish between infection and contamination. We retrospectively investigated clinical features of patients with S. saccharolyticus detection between June 2009 and July 2021. Based on six criteria, infection was considered likely for patients with a score from 3 to 6 points, infection was considered unlikely for patients with a score from 0 to 2 points. We performed group comparison and logistic regression to identify factors than are associated with likely infection. In addition, whole genome sequencing (WGS) of 22 isolates was performed. Of 93 patients in total, 44 were assigned to the group "infection likely" and 49 to the group "infection unlikely". Multiple regression analysis revealed "maximum body temperature during hospital stay" to have the strongest predictive effect on likely infection (adjusted odds ratio 4.40, 95% confidence interval 2.07-9.23). WGS revealed two different clades. Compared to isolates from clade A, isolates from clade B were more frequently associated with implanted medical devices (3/10 vs. 9/12, p = 0.046) and a shorter time to positivity (TTP) (4.5 vs. 3, p = 0.016). Both clades did neither differ significantly in terms of causing a likely infection (clade A 7/10 vs. clade B 5/12, p = 0.23) nor in median length of hospital stay (28 vs. 15.5 days, p = 0.083) and length of stay at the ICU (21 vs. 3.5 days, p = 0.14). These findings indicate that S. saccharolyticus can cause clinically relevant infections. Differentiation between infection and contamination remains challenging.

Vitamin D and Cognitive Performance in Older Adults: A Cross-Sectional and Mendelian Randomization Study.

Cognitive decline is a prevalent health problem in older adults, and effective treatments remain to be produced. Serum vitamin D, a commonly used biochemical marker, is widely recognized as an indicator of various diseases. Existing research has not fully elucidated the relationship between vitamin D and cognitive function. The aim of this study is to investigate the real relationship between vitamin D and cognitive function and to identify indicators that have a strong predictive effect on cognitive decline. At first, we used the dataset of the genome-wide association studies studying vitamin D and cognitive performance to conduct Mendelian randomization analysis. Subsequently, we employed linear regression and smooth curve fitting methods to assess the relationship using the National Health and Nutrition Examination Survey data. Finally, we investigated other predictive features of cognitive performance utilizing a machine learning model. We found that a 1-unit increase in vitamin D is associated with a 6.51% reduction (P < .001) in the risk of cognitive decline. The correlation between vitamin D and cognitive performance is nonlinear, with the inflection point at 79.9 nmol/L (left: β = 0.043, P < .001; right: β = -0.007, P = .420). In machine learning, the top 5 predictors are vitamin D, weight, height, age, and body mass index. There is a causal relationship between vitamin D and cognitive performance. 79.9 nmol/L could be the optimal dose for vitamin D supplementation in the elderly. Further consideration of other factors in vitamin D interventions is necessary.

Is the Self-efficacy of Single Kid in Remote Education Higher?

This study uses the PISA database to explore the impact and mechanism of the family background of only children on self-efficacy in distance education. The analysis results show that (1) family background prominent and positively predicts self-efficacy; (2) Self educational expectations partially mediate the connection between family background and self-efficacy; (3) The direct effect of family background on self-efficacy and the mediating effect of self-education expectations are both moderated by the presence or absence of only children: family background in non only child families has a stronger predictive effect on self-education expectations than in only child families; The impact of family background on self-efficacy and self-education expectations on self-efficacy is stronger in only child families than in non only child families. This study is beneficial in revealing the pathways and influence of the family background of only child and non only child families on self-efficacy under distance education, which has certain implications for demographic research related to social fertility.

Integrative Analyses of Bulk and Single-Cell RNA Seq Identified the Shared Genes in Acute Respiratory Distress Syndrome and Rheumatoid Arthritis.

Acute respiratory distress syndrome (ARDS), a progressive status of acute lung injury (ALI), is primarily caused by an immune-mediated inflammatory disorder, which can be an acute pulmonary complication of rheumatoid arthritis (RA). As a chronic inflammatory disease regulated by the immune system, RA is closely associated with the occurrence and progression of respiratory diseases. However, it remains elusive whether there are shared genes between the molecular mechanisms underlying RA and ARDS. The objective of this study is to identify potential shared genes for further clinical drug discovery through integrated analysis of bulk RNA sequencing datasets obtained from the Gene Expression Omnibus database, employing differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). The hub genes were identified through the intersection of common DEGs and WGCNA-derived genes. The Random Forest (RF) and least absolute shrinkage and selection operator (LASSO) algorithms were subsequently employed to identify key shared target genes associated with two diseases. Additionally, RA immune infiltration analysis and COVID-19 single-cell transcriptome analysis revealed the correlation between these key genes and immune cells. A total of 59 shared genes were identified from the intersection of DEGs and gene clusters obtained through WGCNA, which analyzed the integrated gene matrix of ALI/ARDS and RA. The RF and LASSO algorithms were employed to screen for target genes specific to ALI/ARDS and RA, respectively. The final set of overlapping genes (FCMR, ADAM28, HK3, GRB10, UBE2J1, HPSE, DDX24, BATF, and CST7) all exhibited a strong predictive effect with an area under the curve (AUC) value greater than 0.8. Then, the immune infiltration analysis revealed a strong correlation between UBE2J1 and plasma cells in RA. Furthermore, scRNA-seq analysis demonstrated differential expression of these nine target genes primarily in T cells and NK cells, with CST7 showing a significant positive correlation specifically with NK cells. Beyond that, transcriptome sequencing was conducted on lung tissue collected from ALI mice, confirming the substantial differential expression of FCMR, HK3, UBE2J1, and BATF. This study provides unprecedented evidence linking the pathophysiological mechanisms of ALI/ARDS and RA to immune regulation, which offers novel understanding for future clinical treatment and experimental research.

Driving forces of digital transformation in chinese enterprises based on machine learning

With advanced science and digital technology, digital transformation has become an important way to promote the sustainable development of enterprises. However, the existing research only focuses on the linear relationship between a single characteristic and digital transformation. In this study, we select the data of Chinese A-share listed companies from 2010 to 2020, innovatively use the machine learning method and explore the differences in the predictive effects of multi-dimensional features on the digital transformation of enterprises based on the Technology-Organization-Environment (TOE) theory, thus identifying the main drivers affecting digital transformation and the fitting models with stronger predictive effect. The study found that: first, by comparing machine learning and traditional linear regression models, it is found that the prediction ability of ensemble earning method is generally higher than that of tradition measurement method. For the sample data selected in this research, XGBoost and LightGBM have strong explanatory ability and high prediction accuracy. Second, compared with the technical driving force and environmental driving force, the organizational driving force has a greater impact. Third, among these characteristics, equity concentration and executives’ knowledge level in organizational dimension have the greatest impact on digital transformation. Therefore, enterprise managers should always pay attention to the decision-making role of equity concentration and executives’ knowledge level. This study further enriches the literature on digital transformation in enterprises, expands the application of machine learning in economics, and provides a theoretical basis for enterprises to enhance digital transformation.

Subjective Well-Being and Self-Assessed Health of Adolescents: A Longitudinal Cohort Study.

The aim of this study was to investigate the stability and predictors of subjective well-being and self-perceived health in adolescents over a two-year period, focusing on the importance of mental health in overall well-being. Participants in this longitudinal cohort study were surveyed at the ages of 15 (n = 441) and 17 (n = 354) through questionnaires. The data were analyzed using both descriptive and inferential statistical methods. Hierarchical regression was employed to investigate significant predictors of subjective well-being. The subjective well-being and self-perceived health dimensions showed a consistent level of stability throughout the two-year period of secondary education. Additionally, there was a significant correlation between well-being at the beginning and end of this education period. Furthermore, self-perceived health dimensions, particularly general health, vitality, and mental health, were positively associated with well-being at the end of secondary education, highlighting their role in overall subjective well-being. The regression analysis revealed that self-perceived health factors, notably "General health" and "Mental health", significantly predicted overall subjective well-being, enhancing the model's explanatory power beyond gender and economic status. Nevertheless, baseline subjective well-being has the strongest predictive effect on final well-being. This study highlights the importance of psychological and health factors, particularly mental health, that affect the overall well-being of adolescents and emphasizes the need to focus on and improve these factors in order to improve subjective well-being.

Global terrestrial invasions: Where naturalised birds, mammals, and plants might spread next and what affects this process.

More species live outside their native range than at any point in human history. Yet, there is little understanding of the geographic regions that will be threatened if these species continue to spread, nor of whether they will spread. We predict the world's terrestrial regions to which 833 naturalised plants, birds, and mammals are most imminently likely to spread, and investigate what factors have hastened or slowed their spread to date. There is huge potential for further spread of naturalised birds in North America, mammals in Eastern Europe, and plants in North America, Eastern Europe, and Australia. Introduction history, dispersal, and the spatial distribution of suitable areas are more important predictors of species spread than traits corresponding to habitat usage or biotic interactions. Natural dispersal has driven spread in birds more than in plants. Whether these taxa continue to spread more widely depends partially on connectivity of suitable environments. Plants show the clearest invasion lag, and the putative importance of human transportation indicates opportunities to slow their spread. Despite strong predictive effects, questions remain, particularly why so many birds in North America do not occupy climatically suitable areas close to their existing ranges.

Investigating the Relationships between Obsessive Compulsive Symptoms (OCS) and Depression Symptoms and Intolerance of Uncertainty in Turkish Adolescents during Covid-19

Abstract Introduction:This study aims at examining the direct and indirect mediating role of the intolerance of uncertainty (IU) variable in the relationship between COVID-19-induced OCS and depression in a Turkish adolescent sample. Methods: The sample consists of 427 people (248 females, 179 males) between the ages of 14-18, living in Turkey and selected by convenient sampling method. The data were collected through the COVID-19 Obsessive Compulsive Disorder (OCD) Scale, Depression, Stress and Anxiety Scale (DASS-21) and Intolerance of Uncertainty Index-A for Children (IUI-A-C). Results: The findings show that COVID-19-induced OCS have strong predictive effects on depression symptoms. The findings also revealed that IU directly and indirectly mediates the relationship between COVID-19 OCD and depression symptoms, as well as its negative predictive effect for depression symptoms. Discussion: It can be said that OCS caused by COVID-19 trigger depressive symptoms in adolescents. In addition, it can be thought that IU, with its mediator effect, may play a triggering role in the emergence of COVID-induced OCS. Another research finding is that IU may be an important transdiagnostic construct for depressive symptoms. Limitations: The current study has also some limitations. First, the study was carried out as a cross-sectional study. The fact that the sample group is non-clinical and a clinical group is not included can be considered as second delimitation. Third, the current study just used scales to evaluate the students’ self-report. At this point, a different perspective can be developed by taking the opinions of the parents. Conclusions: The results show that COVID-19-induced OCS increase depressive symptoms in Turkish adolescents and IU has a mediating effect in this relationship. In addition, the results provide important data for the treatment of mental symptoms related to the pandemic.

The Moderating Role of the DYX1C1 Gene in the Effect of Home Supervision on Chinese Children's Reading Achievements: Evidence from the Diathesis-Stress Model.

The current study aimed to explore whether susceptible children (with differences in DYX1C1 (dyslexia susceptibility 1 candidate gene 1) gene) are more likely to be influenced by either supportive or adverse home supervision in their reading achievements. Home supervision, reading achievements, and genotype data were collected from a total of 745 fourth and fifth grade children and their parents in Chongqing, China. The results showed that there was a significant interaction between the rs11629841 polymorphism of the DYX1C1 gene and home supervision on children's reading achievements. A further analysis based on the re-parameterized regression model showed that the interaction best fit a weak diathesis-stress model, which indicated that the home supervision had a stronger predictive effect on children's reading achievements among children with the susceptible genotype than children with a non-susceptible genotype in a more adverse environment rather than in a supportive environment. These results suggested that children carrying different genotypes may need targeted interventions and that their parents should emphasize home supervision to develop their children's reading skills.

Burnout and Disengagement at Work Among Health Professionals-Interrelations and Associations With Stress Indicators and Job Resources.

The aim of the study is to explore common risk and protective factors of burnout and disengagement at work among healthcare workers. Cross-sectional survey data of 1232 health professionals and employees of five public hospitals and rehabilitation clinics collected in 2015/16 in German-speaking Switzerland were used and analyzed. Different stress measures and job resources were studied as predictors of burnout and work engagement. Burnout was found to be largely explained by work stress (β = 0.22) and particularly by general stress (β = 0.54) whereas work engagement was only marginally determined by these stress indicators. Job autonomy or supervisor support on the other hand had no protective effect on burnout at all but a fairly strong predictive effect on work engagement (β = 0.27/0.23). Burnout turned out to be mainly stress-induced whereas work engagement emerged largely as a result of job autonomy and supervisor support.

Comparison of four early warning scores in predicting the prognosis of critically ill patients in secondary hospitals

To explore the predictive value of acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), quick sequential organ failure assessment (qSOFA) and modified early warning score (MEWS) in evaluating the prognosis of patients in intensive care unit (ICU) of secondary hospitals, and to provide guidance for clinical application. The clinical data of adult critical patients admitted to the ICU of Wanzhou District First People's Hospital from October 2022 to April 2023 were retrospectively analyzed. According to the clinical outcome of ICU, the patients were divided into improvement group and death group. The general information, blood routine, heart, liver and kidney function indicators, coagulation indicators, blood gas analysis, APACHE II score, SOFA score, qSOFA score, MEWS score at the time of admission to the ICU, the number of cases of invasive mechanical ventilation (IMV) and continuous blood purification (CBP) were compared between the two groups. Univariate analysis was performed, and multivariate Logistic regression analysis was used to analyze the related factors of death. Receiver operator characteristic curve (ROC curve) was used to analyze the predictive value of the four scores in ICU patients. A total of 126 patients were included, of which 45 patients died in the ICU and 81 patients improved and transferred out. Univariate analysis of death-related critically ill patients showed that procalcitonin (PCT), serum creatinine (SCr), blood urea nitrogen (BUN), albumin (ALB), prothrombin time (PT), activated partial prothrombin time (APTT), D-dimer, pH value, HCO3-, blood lactic acid (Lac), number of patients treated with IMV and CBP, APACHE II score, SOFA score, qSOFA score and MEWS score were significantly different between the two groups (all P < 0.05). Multivariate Logistic regression analysis showed that the APACHE II score [odds ratio (OR) = 1.115, 95% confidence interval (95%CI) was 1.025-1.213, P = 0.011], SOFA score (OR = 1.204, 95%CI was 1.037-1.398, P = 0.015), MEWS score (OR = 1.464, 95%CI was 1.102-1.946, P = 0.009), and APTT (OR = 1.081, 95%CI was 1.015-1.152, P = 0.016) were independent risk factors affecting the mortality of critically ill patients in the ICU. ROC curve analysis showed that APACHE II, SOFA, qSOFA, and MEWS scores could predict the prognosis of critically ill ICU patients, among which SOFA score had the strongest predictive effect, and the area under the curve (AUC) was 0.808. There was a statistically significant difference in the time required for the four scores (F = 117.333, P < 0.001), among which the MEWS scoring required the shortest time [(1.03±0.39) minutes], and the APACHE II scoring required the longest time [(2.81±1.04) minutes]. APACHE II, SOFA, qSOFA, and MEWS scores can be used to assess the severity of critically ill patients and predict in-hospital mortality. The SOFA score is superior to other scores in predicting severity. The MEWS is preferred because its assessment time is shortest. Early warning score can help secondary hospitals to detect potentially critical patients early and provide help for clinical rapid urgent emergency decision-making.

External and Internal Predictors of Student Satisfaction with Online Learning Achievement

Building and testing a framework of interactive and indirect predictors of student satisfaction would help us understand how to improve student online learning experience. The current study proposed that external predictors such as poor technological, environmental, and pedagogical factors would be internalized as negative psychological traits and indirectly predict student satisfaction in online learning. Results of multivariate regressions with 5824 Chinese undergraduate students demonstrated that instructors’ online teaching experience and communication with students had a stronger predictive effect on student satisfaction than wireless network quality and learning environment. Providing after-class reviewing materials to students or having longer self-learning time would not buffer students from negative external factors. Structural equation modeling analysis results showed that inferior technological, environmental, and pedagogical factors would be internalized into negative attitudes and emotions toward online learning and indirectly predict student satisfaction. Our study has implications for better understanding the extensive influence of online learning barriers caused by external conditions and building preventive mechanisms through the improvement of instructors’ teaching experience and communication with students.

Correlation Between the Variability of Different Obesity Indices and Diabetic Kidney Disease: A Retrospective Cohort Study Based on Populations in Taiwan.

To investigate the association of five obesity indices and the variability of these indices with diabetic kidney disease (DKD) in patients with type 2 diabetes and compare the predictive validity of these markers for the risk of DKD in this large longitudinal cohort study. A total of 2659 patients with type 2 diabetes who did not have DKD were enrolled between 2006 and 2019 at Lee's United Clinic in Taiwan. Data were collected for each subject, including demographic data, personal medical history, clinical parameters and calculated Body mass index (BMI), visceral adiposity index (VAI), lipid accumulation product (LAP), body roundness index (BRI) and variability of five obesity indices. Cox regression analysis was performed to determine the relationship between different obesity indicators and DKD risk. Cox's proportional hazards model was evaluated the predictive effect of obesity indices on DKD. The risk of developing DKD increased with an increase in the BRI, LAP, VAI, WC and BMI (all P trend<0.05), and the variability of VAI was significantly associated with DKD [HR=1.132, 95% CI (1.001, 1.281)] after adjusting for corresponding variables. BRI had the strongest predictive effect on DKD. BRI had the best predictive performance, with AUC of 0.807, 0.663 and 0.673 at 1, 3 and 5 years, respectively. Cox regression analysis of risk factors for DKD in patients stratified by BRI quartiles showed that patients in the Q4 group had the highest risk of developing DKD [HR=1.356, 95% CI (1.131, 1.626)]. BMI, WC, VAI, LAP, BRI and VAI variability were associated with a significant increase in the risk of DKD events, and BRI was superior and alternative obesity index for predicting DKD.

Embracing life's challenges: Developing a tool for assessing resilient mindset in second wave positive Psychology

The purpose of this study is to develop and validate the Resilient Mindset Scale (RMS), a brief tool designed to measure resilient mindset among Turkish individuals. Additionally, the study aims to explore the relationship between resilient mindset and mental well-being among adolescents and young adults, providing further evidence in this domain. The exploratory factor analysis, conducted with a sample of 327 participants, revealed that the RMS has a unidimensional structure consisting of six items that effectively measure core indicators of a resilient mindset. Subsequent confirmatory factor analysis, conducted with a sample of 338 participants, confirmed the one-factor structure, demonstrating a good-data model fit with strong factor loadings and internal reliability estimates. Further analyses demonstrated moderate to strong correlations between resilient mindset and mental well-being indicators. Moreover, the latent variables path model revealed that the measurement model had a moderate to strong predictive effect on positive academic functioning, psychological well-being, and psychological distress. These findings establish the psychometric reliability and validity of the RMS as a measurement tool for assessing a resilient mindset among adolescents and young adults. Mental health providers can integrate the concept of a resilient mindset into therapeutic approaches and interventions to foster resilience and enhance mental well-being.

Predicting late-stage age-related macular degeneration by integrating marginally weak SNPs in GWA studies.

Introduction: Age-related macular degeneration (AMD) is a progressive neurodegenerative disease and the leading cause of blindness in developed countries. Current genome-wide association studies (GWAS) for late-stage age-related macular degeneration are mainly single-marker-based approaches, which investigate one Single-Nucleotide Polymorphism (SNP) at a time and postpone the integration of inter-marker Linkage-disequilibrium (LD) information in the downstream fine mappings. Recent studies showed that directly incorporating inter-marker connection/correlation into variants detection can help discover novel marginally weak single-nucleotide polymorphisms, which are often missed in conventional genome-wide association studies, and can also help improve disease prediction accuracy. Methods: Single-marker analysis is performed first to detect marginally strong single-nucleotide polymorphisms. Then the whole-genome linkage-disequilibrium spectrum is explored and used to search for high-linkage-disequilibrium connected single-nucleotide polymorphism clusters for each strong single-nucleotide polymorphism detected. Marginally weak single-nucleotide polymorphisms are selected via a joint linear discriminant model with the detected single-nucleotide polymorphism clusters. Prediction is made based on the selected strong and weak single-nucleotide polymorphisms. Results: Several previously identified late-stage age-related macular degeneration susceptibility genes, for example, BTBD16, C3, CFH, CFHR3, HTARA1, are confirmed. Novel genes DENND1B, PLK5, ARHGAP45, and BAG6 are discovered as marginally weak signals. Overall prediction accuracy of 76.8% and 73.2% was achieved with and without the inclusion of the identified marginally weak signals, respectively. Conclusion: Marginally weak single-nucleotide polymorphisms, detected from integrating inter-marker linkage-disequilibrium information, may have strong predictive effects on age-related macular degeneration. Detecting and integrating such marginally weak signals can help with a better understanding of the underlying disease-development mechanisms for age-related macular degeneration and more accurate prognostics.

Investigation of the Sleep Quality Among Hospitalized Cardiovascular Patients Using Regression Models and Qualitative Comparative Analysis.

This study aimed to explore the relationships between modifiable and non-modifiable factors that influence sleep quality in hospitalized patients with cardiovascular disease in China. This study design was cross-sectional and descriptive. This study involved a convenience sample of 242 patients hospitalized for cardiovascular issues. Hierarchical regression model and qualitative comparative analysis model were performed. Regression revealed that age and depression both had statistically significant effects on sleep quality. The combination of conditions with strongest predictive effect on sleep quality was male of younger age, high level of depression, high level of anxiety, and low level of social support. Other combinations with predictive power on sleep quality are as follows: (1) younger male patients (aged 47-63 years) with high level of anxiety, high level of social support, and low level of depression; (2) older male patients (aged 63-79 years) with high level of depression, high level of social support, and low levels of anxiety; and (3) older male patients with high level of anxiety and low level of social support. Nurses may promote primary and secondary prevention of cardiovascular health prior to hospitalization, through routine screening for depressive and anxious symptoms and assessment of the patient's social support network. It is unknown whether these findings are unique to hospitalized patients. Therefore, future work should assess the fit of the model to other more diverse groups of hospitalized patients.

How Do Young Children Manage Their Reputation among Peers?

Young children’s reputation management is closely related to their social development. The purpose of our study is to examine the interaction between theory of mind and partner choice on children’s reputation management. Participants consisted of 270 children who were 3 to 5 years old. First, we measured participants’ theory of mind capabilities using the unexpected location task and unexpected content task and then randomly divided the participants into the control group, non-partner–choice group, and partner-choice group. We measured reputation management by comparing children’s willingness to share and sharing behavior between these groups. The findings are as follows: (1) Children from ages 3 to 5 demonstrated reputation management, and their reputation management followed a significant developmental trend. The reputation management of 4- to 5-year-old children was significantly better than that of 3-year-old children. (2) Scores on the theory of mind tasks positively predicted children’s reputation management. (3) Partner choice affected children’s reputation management. In the partner-choice group, children’s reputation management was more apparent. (4) Partner choice did moderate the relationship between theory of mind and children’s reputation management. In the partner-choice group, theory of mind had a stronger predictive effect on children’s reputation management.