Genetic association analysis of human median voice pitch identifies a common locus for tonal and non-tonal languages

The genetic influence on human vocal pitch in tonal and non-tonal languages remains largely unknown. In tonal languages, such as Mandarin Chinese, pitch changes differentiate word meanings, whereas in non-tonal languages, such as Icelandic, pitch is used to convey intonation. We addressed this question by searching for genetic associations with interindividual variation in median pitch in a Chinese major depression case-control cohort and compared our results with a genome-wide association study from Iceland. The same genetic variant, rs11046212-T in an intron of the ABCC9 gene, was one of the most strongly associated loci with median pitch in both samples. Our meta-analysis revealed four genome-wide significant hits, including two novel associations. The discovery of genetic variants influencing vocal pitch across both tonal and non-tonal languages suggests the possibility of a common genetic contribution to the human vocal system shared in two distinct populations with languages that differ in tonality (Icelandic and Mandarin).

Antidepressant Exposure and DNA Methylation: Insights from a Methylome-Wide Association Study

Importance

Understanding antidepressant mechanisms could help design more effective and tolerated treatments.

Objective

Identify DNA methylation (DNAm) changes associated with antidepressant exposure.

Design

Case-control methylome-wide association studies (MWAS) of antidepressant exposure were performed from blood samples collected between 2006-2011 in Generation Scotland (GS). The summary statistics were tested for enrichment in specific tissues, gene ontologies and an independent MWAS in the Netherlands Study of Depression and Anxiety (NESDA). A methylation profile score (MPS) was derived and tested for its association with antidepressant exposure in eight independent cohorts, alongside prospective data from GS.

Setting

Cohorts; GS, NESDA, FTC, SHIP-Trend, FOR2107, LBC1936, MARS-UniDep, ALSPAC, E-Risk, and NTR.

Participants

Participants with DNAm data and self-report/prescription derived antidepressant exposure.

Main Outcomes and Measures

Whole-blood DNAm levels were assayed by the EPIC/450K Illumina array (9 studies, N exposed = 661, N unexposed = 9,575) alongside MBD-Seq in NESDA (N exposed = 398, N unexposed = 414). Antidepressant exposure was measured by self- report and/or antidepressant prescriptions.

Results

The self-report MWAS (N = 16,536, N exposed = 1,508, mean age = 48, 59% female) and the prescription-derived MWAS (N = 7,951, N exposed = 861, mean age = 47, 59% female), found hypermethylation at seven and four DNAm sites (p < 9.42x10 -8 ), respectively. The top locus was cg26277237 ( KANK1, p self-report = 9.3x10 -13 , p prescription = 6.1x10 -3 ). The self-report MWAS found a differentially methylated region, mapping to DGUOK-AS1 ( p adj = 5.0x10 -3 ) alongside significant enrichment for genes expressed in the amygdala, the "synaptic vesicle membrane" gene ontology and the top 1% of CpGs from the NESDA MWAS (OR = 1.39, p < 0.042). The MPS was associated with antidepressant exposure in meta-analysed data from external cohorts (N studies = 9, N = 10,236, N exposed = 661, f3 = 0.196, p < 1x10 -4 ).

Conclusions and Relevance

Antidepressant exposure is associated with changes in DNAm across different cohorts. Further investigation into these changes could inform on new targets for antidepressant treatments.

3 Key Points

Question: Is antidepressant exposure associated with differential whole blood DNA methylation?Findings: In this methylome-wide association study of 16,536 adults across Scotland, antidepressant exposure was significantly associated with hypermethylation at CpGs mapping to KANK1 and DGUOK-AS1. A methylation profile score trained on this sample was significantly associated with antidepressant exposure (pooled f3 [95%CI]=0.196 [0.105, 0.288], p < 1x10 -4 ) in a meta-analysis of external datasets. Meaning: Antidepressant exposure is associated with hypermethylation at KANK1 and DGUOK-AS1 , which have roles in mitochondrial metabolism and neurite outgrowth. If replicated in future studies, targeting these genes could inform the design of more effective and better tolerated treatments for depression.

Predictive modeling of neuroticism in depressed and non-depressed cohorts using voice features

BACKGROUND

Neuroticism's impact on psychopathological and physical health issues has significant public health implications. Multiple studies confirm its predictive effect on suicide risk among depressed patients. However, previous research lacks a standardized criterion for assessing neuroticism through speech, often relying on simple features (such as pitch, loudness and MFCCs). This study aims to improve upon this by extracting features using advanced pre-trained speaker embedding models (i-vector and x-vector extractors). Additionally, unlike prior studies utilizing general population data, we explore neuroticism prediction in depressed and non-depressed subgroups.

METHODS

We collected edited discourse data from clinical interviews of 3580 depressed individuals and 4016 healthy individuals from the CONVERGE study. Instead of solely extracting Low-Level Acoustic Descriptors, we incorporated i-vector and x-vector features. We compared the performance of three different features in predicting neuroticism and explored their combination to enhance model accuracy.

RESULTS

The SVR model, combining three speech features with downscaled features to 300, exhibited the highest performance in predicting neuroticism scores. It achieved a coefficient of determination (R-squared) of 0.3 or higher and a correlation of 0.56 between predicted and actual values. The predictive classification accuracy of speech features for neuroticism in specific populations (healthy and depressed) exceeded 60 %.

LIMITATIONS

This study included only women.

CONCLUSION

Combining diverse speech features enhances the predictive capacity of models using speech features to assess neuroticism, particularly in specific populations. This study lays the foundation for future exploration of speech features in neuroticism prediction.

Does wealth equate to happiness? an 11-year panel data analysis exploring socio-economic indicators and social media metrics

The Easterlin paradox questions the link between economic growth and national well-being, emphasizing the necessity to explore the impact of economic elasticity, income inequality, and their temporal and spatial heterogeneity on subjective happiness. Despite the importance of these factors, few studies have examined them together, thus ongoing debates about the impact of economics on well-being persist. To fill this gap, our analysis utilizes 11 years of panel data from 31 provinces in China, integrating macroeconomic indicators and social media content to reassess the Easterlin paradox. We use GDP per capita and the Gini coefficient as proxies for economic growth and income inequality, respectively, to study their effects on the subjective well-being expressed by citizens on social media in mainland China. Our approach combines machine learning and fixed effects models to evaluate these relationships. Key findings include: (1) In temporal relationships, a 46.70% increase in GDP per capita implies a 0.38 increase in subjective well-being, while a 0.09 increase in the Gini coefficient means a 1.47 decrease in subjective well-being. (2) In spatial relationships, for every 46.70% increase in GDP per capita, subjective well-being rises by 0.51; however, this relationship is buffered by unfair distribution, and GDP per capita no longer significantly affects subjective well-being when the Gini index exceeds 0.609. This study makes a synthetic contribution to the debate on the Easterlin paradox, indicating that economic growth can enhance well-being if income inequality is kept below a certain level. Although these results are theoretically enlightening for the relationship between economics and national well-being globally, this study's sample comes from mainland China. Due to differences in cultural, economic, and political factors, further research is suggested to explore these dynamics globally.

Identifying stress scores from gait biometrics captured using a camera: A cross-sectional study

BACKGROUND

Stress is a critical risk factor for various health issues, but an objective, non-intrusive and effective measurement approach for stress has not yet been established. Gait, the pattern of movements in human locomotion, has been proven to be a valid behavioral indicator for recognizing various mental states in a convenient manner.

RESEARCH QUESTION

This study aims to identify the severity of stress by assessing human gait recorded through an objective, non-intrusive measurement approach.

METHODS

One hundred and fifty-two participants with an average age of 23 years old (SD = 1.07) were recruited. The Chinese version of the Perceived Stress Scale with 10 items (PSS-10) was used to assess participants' stress levels. The participants were then required to walk naturally while being recorded with a regular camera. A total of 1320 time-domain and 1152 frequency-domain gait features were extracted from the videos. The top 40 contributing features, confirmed by dimensionality reduction, were input into models consisting of four machine-learning regression algorithms (i.e., Gaussian Process Regressor, Linear Regression, Random Forest Regressor, and Support Vector regression), to assess stress levels.

RESULTS

The models that combined time- and frequency-domain features performed best, with the lowest RMSE (4.972) and highest validation (r = 0.533). The Gaussian Process Regressor and Linear Regression outperformed the others. The greatest contribution to model performance was derived from gait features of the waist, hands, and legs.

SIGNIFICANCE

The severity of stress can be accurately detected by machine learning models using two-dimensional (2D) video-based gait data. The machine learning models used for assessing perceived stress were reliable. Waist, hand, and leg movements were found to be critical indicator in detecting stress.

Impact of COVID-19 on negative body image: Evidence based on social media data

Body experiences and conditions bear close relations to social development and human well-being. However, no consensus has been reached regarding the impact of coronavirus disease 2019 on negative body image. Investigating a reliable relationship between COVID-19 and negative body image, we developed a dictionary of negative body image to obtain panel data on body image for 31 Chinese provinces/municipalities/autonomous regions. We compared negative body image before and after the pandemic and explored the impact of pandemic severity. The prevalence of negative body image decreased following the outbreak and remained at a relatively low level for two years. After controlling regional and temporal effects, we verified epidemic severity as an important predictor of the decline in negative body image. The findings suggest that the public is likely to accept their physical appearances during lockdown due to changes in lifestyle and the fear of mortality. This research has important implications for gaining insights into the dynamic transformation of public negative body image under the influence of catastrophic public health events.

An Automated Brain Metastasis Detection and Segmentation System from MRI with a Large Multi-Institutional Dataset

PURPOSE/OBJECTIVE(S)

Developments of automated systems for brain metastasis (BM) detection and segmentation from MRI for assisting early detection and stereotactic radiosurgery (SRS) have been reported but most based upon relatively small datasets from single institutes. This work aims to develop and evaluate a system using a large multi-institutional dataset, and to improve both identification of small/subtle BMs and segmentation accuracy of large BMs.

MATERIALS/METHODS

A 3D U-Net system was trained and evaluated to detect and segment intraparenchymal BMs with a size > 2mm using 1856 MRI volumes from 1791 patients treated with SRS from seven institutions (1539 volumes for training, 183 for validation, and 134 for testing). All patients had 3D post-Gd T1w MRI scans pre-SRS. Gross tumor volumes (GTVs) of BMs for SRS were curated by each institute first. Then, additional efforts were spent to create GTVs for the untreated and/or uncontoured BMs, including central reviews by two radiologists, to improve accuracy of ground truth. The training dataset was augmented with synthetic BMs of 3773 MRIs using a 3D generative pipeline. Our system consists of two U-Nets with one using small 3D patches dedicated for detecting small BMs and another using large 3D patches for segmenting large BMs, and a random-forest based fusion module for combining the two network outputs. The first U-Net was trained with 3D patches containing at least one BM < 0.1 cm3. For detection performance, we measured BM-level sensitivity and case-level false-positive (FP) rate. For segmentation performance, we measured BM-level Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95). We also stratified performances based upon BM sizes.

RESULTS

For 739 BMs in the 134 testing cases, the overall lesion-level sensitivity was 0.870 with an average case-level FP of 1.34±1.92 (95% CI: 1.02-1.67). The sensitivity was >0.969 for the BMs >0.1 cm3, but dropped to 0.755 for the BMs < 0.1 cm3 (Table 1). The average DSC and HD95 for all detected BMs were 0.786 and 1.35mm. The worse performance for BMs > 20 cm3 was caused by a case with 83 cm3 GTV and artifacts in the MRI volume.

CONCLUSION

We achieved excellent detection sensitivity and segmentation accuracy for BMs > 0.1 cm3, and promising performance for small BMs (<0.1cm3) with a controlled FP rate using a large multi-institutional dataset. Clinical utility for assisting early detection and SRS planning will be investigated. Table 1: Per-lesion detection and segmentation performance stratified by individual BM size. N is the number of BMs in each category.

Measurements of neutrino oscillation parameters from the T2K experiment using 3.6×1021 protons on target

The T2K experiment presents new measurements of neutrino oscillation parameters using 19.7 ( 16.3 ) × 10 20 protons on target (POT) in (anti-)neutrino mode at the far detector (FD). Compared to the previous analysis, an additional 4.7 × 10 20 POT neutrino data was collected at the FD. Significant improvements were made to the analysis methodology, with the near-detector analysis introducing new selections and using more than double the data. Additionally, this is the first T2K oscillation analysis to use NA61/SHINE data on a replica of the T2K target to tune the neutrino flux model, and the neutrino interaction model was improved to include new nuclear effects and calculations. Frequentist and Bayesian analyses are presented, including results on sin 2 θ 13 and the impact of priors on the δ CP measurement. Both analyses prefer the normal mass ordering and upper octant of sin 2 θ 23 with a nearly maximally CP-violating phase. Assuming the normal ordering and using the constraint on sin 2 θ 13 from reactors, sin 2 θ 23 = 0 . 561 - 0.032 + 0.021 using Feldman-Cousins corrected intervals, and Δ m 32 2 = 2 . 494 - 0.058 + 0.041 × 10 - 3 eV 2 using constant Δ χ 2 intervals. The CP-violating phase is constrained to δ CP = - 1 . 97 - 0.70 + 0.97 using Feldman-Cousins corrected intervals, and δ CP = 0 , π is excluded at more than 90% confidence level. A Jarlskog invariant of zero is excluded at more than 2 σ credible level using a flat prior in δ CP , and just below 2 σ using a flat prior in sin δ CP . When the external constraint on sin 2 θ 13 is removed, sin 2 θ 13 = 28 . 0 - 6.5 + 2.8 × 10 - 3 , in agreement with measurements from reactor experiments. These results are consistent with previous T2K analyses.

First Dark Matter Search with Nuclear Recoils from the XENONnT Experiment

We report on the first search for nuclear recoils from dark matter in the form of weakly interacting massive particles (WIMPs) with the XENONnT experiment, which is based on a two-phase time projection chamber with a sensitive liquid xenon mass of 5.9 ton. During the (1.09±0.03)  ton yr exposure used for this search, the intrinsic ^{85}Kr and ^{222}Rn concentrations in the liquid target are reduced to unprecedentedly low levels, giving an electronic recoil background rate of (15.8±1.3)  events/ton yr keV in the region of interest. A blind analysis of nuclear recoil events with energies between 3.3 and 60.5 keV finds no significant excess. This leads to a minimum upper limit on the spin-independent WIMP-nucleon cross section of 2.58×10^{-47}  cm^{2} for a WIMP mass of 28  GeV/c^{2} at 90% confidence level. Limits for spin-dependent interactions are also provided. Both the limit and the sensitivity for the full range of WIMP masses analyzed here improve on previous results obtained with the XENON1T experiment for the same exposure.

Exploring the ability of vocal biomarkers in distinguishing depression from bipolar disorder, schizophrenia, and healthy controls

Background

Vocal features have been exploited to distinguish depression from healthy controls. While there have been some claims for success, the degree to which changes in vocal features are specific to depression has not been systematically studied. Hence, we examined the performances of vocal features in differentiating depression from bipolar disorder (BD), schizophrenia and healthy controls, as well as pairwise classifications for the three disorders.

Methods

We sampled 32 bipolar disorder patients, 106 depression patients, 114 healthy controls, and 20 schizophrenia patients. We extracted i-vectors from Mel-frequency cepstrum coefficients (MFCCs), and built logistic regression models with ridge regularization and 5-fold cross-validation on the training set, then applied models to the test set. There were seven classification tasks: any disorder versus healthy controls; depression versus healthy controls; BD versus healthy controls; schizophrenia versus healthy controls; depression versus BD; depression versus schizophrenia; BD versus schizophrenia.

Results

The area under curve (AUC) score for classifying depression and bipolar disorder was 0.5 (F-score = 0.44). For other comparisons, the AUC scores ranged from 0.75 to 0.92, and the F-scores ranged from 0.73 to 0.91. The model performance (AUC) of classifying depression and bipolar disorder was significantly worse than that of classifying bipolar disorder and schizophrenia (corrected p < 0.05). While there were no significant differences in the remaining pairwise comparisons of the 7 classification tasks.

Conclusion

Vocal features showed discriminatory potential in classifying depression and the healthy controls, as well as between depression and other mental disorders. Future research should systematically examine the mechanisms of voice features in distinguishing depression with other mental disorders and develop more sophisticated machine learning models so that voice can assist clinical diagnosis better.

Searching for Heavy Dark Matter near the Planck Mass with XENON1T

Multiple viable theoretical models predict heavy dark matter particles with a mass close to the Planck mass, a range relatively unexplored by current experimental measurements. We use 219.4 days of data collected with the XENON1T experiment to conduct a blind search for signals from multiply interacting massive particles (MIMPs). Their unique track signature allows a targeted analysis with only 0.05 expected background events from muons. Following unblinding, we observe no signal candidate events. This Letter places strong constraints on spin-independent interactions of dark matter particles with a mass between 1×10^{12} and 2×10^{17}  GeV/c^{2}. In addition, we present the first exclusion limits on spin-dependent MIMP-neutron and MIMP-proton cross sections for dark matter particles with masses close to the Planck scale.

[Feasibility and safety of transseptal transcatheter mitral valve replacement for severe mitral regurgitation]

A prospective, single-center, single-arm, and open-design study was performed to evaluate the feasibility and safety of transseptal transcatheter mitral valve replacement in the treatment of severe mitral regurgitation. Patients with symptomatic moderate-severe or severe mitral regurgitation at high-surgical risk and anatomically appropriate for the HighLife transseptal mitral valve replacement (TSMVR) system in West China Hospital, Sichuan University from December 2021 to August 2022 were enrolled. Four patients (1 male and 3 females) with severe mitral regurgitation were included, with a median age of 68.5 (64.0-77.0) years and a median Society of Thoracic Surgeons (STS) score of 8.1% (6.4%-8.9%). Technical success was achieved in all the patients. There was no residual mitral regurgitation, paravalvular leakage, or left ventricular outflow tract obstruction. Three major cardiovascular and cerebrovascular adverse events occurred within 30 days after the procedure, including ventricular tachycardia, iatrogenic atrial septal defect closure, and heart failure readmission. The current study preliminarily demonstrates that transcatheter mitral valve replacement using the HighLife system via the transseptal approach for severe mitral regurgitation is feasible and relatively safe.

Measuring the importance of influencing factor for COVID-19 vaccination intention in China

Background

Vaccination is considered an effective approach to deter the spread of coronavirus disease (COVID-19). However, vaccine hesitancy is a common issue that makes immunization programs more challenging. To promote vaccination in a targeted and efficient way, this study aims to develop and validate a measurement tool for evaluating the importance of influencing factors related to COVID-19 vaccination intention in China, and to examine the demographic differences.

Methods

In study 1, we developed a Factor Importance Evaluation Questionnaire (FIEQ) based on semi-structured interview results and used exploratory factor analysis (EFA) to explore its factor structure. In study 2, we verified the four-factor structure of FIEQ by confirmatory factor analysis (CFA). We then administered FIEQ to Chinese participants and conducted a student t-test and analysis of variance to examine the differences in the importance evaluation of factors based on gender and educational level.

Results

In study 1, we developed a four-factor construct and retained 20 items after EFA (N = 577), with acceptable reliability (alpha = 0.87) and validity. In study 2, we found that the model fit was good (χ2 = 748.03 (162), p < 0.001, GFI = 0.949, RMSEA = 0.049, SRMR = 0.048, AGFI = 0.934), and reliability was acceptable (alpha = 0.730) (N = 1,496). No gender difference was found in factor importance. However, individuals with different educational levels reported significantly different importance evaluations of three factors, including perceived benefits and social norms (F = 3.786, p = 0.005), perceived influences from reference groups (F = 17.449, p < 0.001), and perceived risks (F = 2.508, p = 0.04).

Conclusion

This study developed and validated FIEQ for measuring the importance of influencing factors related to the COVID-19 vaccination intention in Chinese participants. Moreover, our findings suggest that the educational level may play a role in how individuals evaluate the importance of factors. This study provides insights into the concerns that individuals have regarding vaccination and offers potentially effective and targeted strategies for promoting COVID-19 vaccination.

The impact of pandemic mental cognition on cultural values: an empirical study based on social media

BACKGROUND

COVID-19 has triggered a global public health crisis, and had an impact on economies, societies, and politics around the world. Based on the pathogen prevalence hypothesis suggested that residents of areas with higher infection rates are more likely to be collectivists as compared with those of areas with lower infection rates. Many researchers had studied the direct link between infectious diseases and individualism/collectivism (infectious diseases→ cultural values), but no one has focused on the specific psychological factors between them: (infectious diseases→ cognition of the pandemic→ cultural values). To test and develop the pathogen prevalence hypothesis, we introduced pandemic mental cognition and conducted an empirical study on social media (Chinese Sina Weibo), hoping to explore the psychological reasons behind in cultural value changes in the context of a pandemic.

METHODS

We downloaded all posts from active Sina Weibo users in Dalian during the pandemic period (January 2020 to May 2022) and used dictionary-based approaches to calculate frequency of words from two domains (pandemic mental cognition and collectivism/individualism), respectively. Then we used the multiple log-linear regression analysis method to establish the relationship between pandemic mental cognition and collectivism/individualism.

RESULTS

Among three dimensions of pandemic mental cognition, only the sense of uncertainty had a significant positive relationship with collectivism, and also had a marginal significant positive relationship with individualism. There was a significant positive correlation between the first-order lag term AR(1) and individualism, which means the individualism tendency was mainly affected by its previous level.

CONCLUSIONS

The study found that more collectivist regions are associated with a higher pathogen burden, and recognized the sense of uncertainty as its underlying cause. Results of this study validated and further developed the pathogen stress hypothesis in the context of the COVID-19 pandemic.

Empirical study of factors that influence the perceived usefulness of online mental health community members

Online mental health communities have become a major platform where individuals can talk about their mental health problems and obtain social support. This study aims to understand the antecedents of perceived usefulness among members in an online mental health community, while providing reference for the managers and users of online mental health communities. We obtained a total of 143,190 posts from ReachOut.com released by the CLPsych2017 shared task. Then, we used text mining to derive the independent and dependent variables. Next, a structural equation model observing the perceived usefulness of online mental health community members was constructed from the perspective of an information adoption model. The informativeness of help-seeking posts had a significant positive relationship with the information quality of reply posts; the information quality of reply posts was a significant positive predictor of perceived usefulness, with the information quality of reply posts partially mediating the relationship between the informativeness of help-seeking posts and perceived usefulness. The information provided by online mental health community members' help-seeking posts and the quality of replies were found to be the factors that influenced perceived usefulness. This study highlights the importance of the information quality of reply posts and provides useful insights for administrators who can help users to improve their response quality and obtain the support they need.

Using gait videos to automatically assess anxiety

Background

In recent years, the number of people with anxiety disorders has increased worldwide. Methods for identifying anxiety through objective clues are not yet mature, and the reliability and validity of existing modeling methods have not been tested. The objective of this paper is to propose an automatic anxiety assessment model with good reliability and validity.

Methods

This study collected 2D gait videos and Generalized Anxiety Disorder (GAD-7) scale data from 150 participants. We extracted static and dynamic time-domain features and frequency-domain features from the gait videos and used various machine learning approaches to build anxiety assessment models. We evaluated the reliability and validity of the models by comparing the influence of factors such as the frequency-domain feature construction method, training data size, time-frequency features, gender, and odd and even frame data on the model.

Results

The results show that the number of wavelet decomposition layers has a significant impact on the frequency-domain feature modeling, while the size of the gait training data has little impact on the modeling effect. In this study, the time-frequency features contributed to the modeling, with the dynamic features contributing more than the static features. Our model predicts anxiety significantly better in women than in men (rMale = 0.666, rFemale = 0.763, p &lt; 0.001). The best correlation coefficient between the model prediction scores and scale scores for all participants is 0.725 (p &lt; 0.001). The correlation coefficient between the model prediction scores for odd and even frame data is 0.801~0.883 (p &lt; 0.001).

Conclusion

This study shows that anxiety assessment based on 2D gait video modeling is reliable and effective. Moreover, we provide a basis for the development of a real-time, convenient and non-invasive automatic anxiety assessment method.

[Study on the relationship between the age at natural menopause and postmenopausal metabolic syndrome]

To explore the relationship between the early or delayed age at natural menopause and metabolic syndrome (MS) in women. A total of 4 734 natural menopausal women who completed the baseline survey from November 2017 to January 2020 in the Guangzhou Middle-aged and Elderly Chronic Disease Prospective Cohort Study were selected in this cross-sectional study. Data on general demographic characteristics, disease history and female physiological health indicators were collected. Logistic regression model and restricted cubic spline curve were used to analyze the relationship between the age at natural menopause and MS. The results showed that the mean age of the subjects was (60±6) years old. The median (Q₁,Q₃) age at natural menopause was 50 (49, 52) years old, and the prevalence of MS was 14.8%(699/4 734). After adjusting for confounders, the age at natural menopause was closely related to MS in an approximate"U"shape. Compared with the group of normal age at natural menopause, the early age at menopause (OR=1.52, 95%CI: 1.12-2.06) and delayed age at menopause (OR=1.77, 95%CI: 1.36-2.30) had a higher risk of MS. In the group with time since menopause ≤6 years and 7-9 years, the risk of MS in the group with delayed age at menopause was 2.40 times (95%CI: 1.54-3.75) and 2.19 times (95%CI: 1.11-4.31) higher than that in the group with normal menopausal age, respectively. In conclusion, the early and delayed age at natural menopause increased the risk of MS. The increased risk of MS in delayed age at natural menopause mainly occurred within 10 years since menopause.

Exploring the Influencing Factors of COVID-19 Vaccination Willingness among Young Adults in China

Vaccine uptake is considered as one of the most effective methods of defending against COVID-19 (coronavirus disease 2019). However, many young adults are hesitant regarding COVID-19 vaccines, and they actually play an important role in virus transmission. Based on a multi-theory model, this study aims to explore the influencing factors related to COVID-19 vaccine willingness among young adults in China. Using semi-structured interviews, this study explored the factors that would motivate young adults with vaccine hesitancy to get the COVID-19 vaccine. Thematic analysis was used to analyze the interview data with topic modeling as a complementarity method. After comparing the differences and similarities of results generated by thematic analysis and topic modeling, this study ultimately identified ten key factors related to COVID-19 vaccination intention, including the effectiveness and safety of vaccines, application range of vaccine, etc. This study combined thematic analysis with machine learning and provided a comprehensive and nuanced picture of facilitating factors for COVID-19 vaccine uptake among Chinese young adults. Results may be taken as potential themes for authorities and public health workers in vaccination campaigns.

Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study

BACKGROUND

Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and monitor online users' PWB in a nonintrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB.

OBJECTIVE

This study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way.

METHODS

We recruited 1427 participants. Their well-being was evaluated using 6 dimensions of PWB. Their posts on social media were collected, and 6 psychological lexicons were used to extract linguistic features. A multiobjective prediction model was then built with the extracted linguistic features as input and PWB as the output. Further, the validity of the prediction model was confirmed by evaluating the model's discriminant validity, convergent validity, and criterion validity. The reliability of the model was also confirmed by evaluating the split-half reliability.

RESULTS

The correlation coefficients between the predicted PWB scores of social media users and the actual scores obtained using the linguistic prediction model of this study were between 0.49 and 0.54 (P<.001), which means that the model had good criterion validity. In terms of the model's structural validity, it exhibited excellent convergent validity but less than satisfactory discriminant validity. The results also suggested that our model had good split-half reliability levels for every dimension (ranging from 0.65 to 0.85; P<.001).

CONCLUSIONS

By confirming the availability and stability of the linguistic prediction model, this study verified the predictability of social media corresponding to ground truth well-being data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in nonprofessional settings such as self-testing or a large-scale user study.

Nonlinear effects of pandemic uncertainty on depression, pandemic preventive behavior intentions, and positive life attitudes: Moderating effects of high and low uncertainty grouping

Backgrounds

COVID-19 is difficult to end in a short time and people are still facing huge uncertainties. Since people's lives are gradually returning to normal, the sense of control and intolerance of uncertainty, which were mainly focused by past studies, are not specific to COVID-19 and will be more influenced by some factors unrelated to the pandemic. Therefore, they may be difficult to accurately reflect the individuals' perceptions of uncertainty. Besides, past research just after the outbreak mainly investigated people in high levels of uncertainty, we don't know the impact of uncertainties on individuals' psychological states when people gradually recovered their sense of control. To solve these problems, we proposed the concept of "pandemic uncertainty" and investigated its impact on people's daily lives.

Methods

During October 20, 2021 to October 22, 2021, this study obtained data about uncertainty, depression, positive attitude, pandemic preventive behavior intentions, personality, and social support from 530 subjects using convenient sampling. The subjects were all college students from the Dalian University of Technology and Dalian Vocational and Technical College. According to the distribution of uncertainty, we divided the dataset into high and low groups. Subsequently, by using uncertainty as the independent variable, the grouping variable as the moderating variable, and other variables as the control variables, the moderating effects were analyzed for depression, positive attitude, and pandemic preventive behavior intentions, respectively.

Results

The results showed that the grouping variable significantly moderate the influence of uncertainty on positive attitude and pandemic preventive behavior intentions but had no significant effect on depression. Simple slope analysis revealed that high grouping uncertainty significantly and positively predicted positive attitude and pandemic preventive behavior intentions, while low grouping effects were not significant.

Conclusion

These results reveal a nonlinear effect of pandemic uncertainty on the pandemic preventive behavior intentions and positive life attitudes and enlighten us about the nonlinear relationship of psychological characteristics during a pandemic.

How social media expression can reveal personality

Background

Personality psychology studies personality and its variation among individuals and is an essential branch of psychology. In recent years, machine learning research related to personality assessment has started to focus on the online environment and showed outstanding performance in personality assessment. However, the aspects of the personality of these prediction models measure remain unclear because few studies focus on the interpretability of personality prediction models. The objective of this study is to develop and validate a machine learning model with domain knowledge introduced to enhance accuracy and improve interpretability.

Methods

Study participants were recruited via an online experiment platform. After excluding unqualified participants and downloading the Weibo posts of eligible participants, we used six psycholinguistic and mental health-related lexicons to extract textual features. Then the predictive personality model was developed using the multi-objective extra trees method based on 3,411 pairs of social media expression and personality trait scores. Subsequently, the prediction model's validity and reliability were evaluated, and each lexicon's feature importance was calculated. Finally, the interpretability of the machine learning model was discussed.

Results

The features from Culture Value Dictionary were found to be the most important predictors. The fivefold cross-validation results regarding the prediction model for personality traits ranged between 0.44 and 0.48 (p < 0.001). The correlation coefficients of five personality traits between the two "split-half" datasets data ranged from 0.84 to 0.88 (p < 0.001). Moreover, the model performed well in terms of contractual validity.

Conclusion

By introducing domain knowledge to the development of a machine learning model, this study not only ensures the reliability and validity of the prediction model but also improves the interpretability of the machine learning method. The study helps explain aspects of personality measured by such prediction models and finds a link between personality and mental health. Our research also has positive implications regarding the combination of machine learning approaches and domain knowledge in the field of psychiatry and its applications to mental health.

[Immunogenicity of group A+C meningococcal polysaccharide conjugate vaccine in infants: A phase Ⅲ clinical trial study]

Objective: To evaluate the immunogenicity of group A+C meningococcal polysaccharide conjugate vaccine in infants under 2 years old. Methods: From March 2017 to June 2018, 1 932 healthy infants in Biyang County, Henan Province, who were not vaccinated with meningococcal meningitis vaccine and whose axillary temperature was ≤37.0 ℃, were recruited as participants. The 3 months and 6-11 months old infants were allocated to the experiment group and the control group in a ratio of 1∶1. Infants aged 12-23 months were allocated to the 1-dose group, the 2-dose group and the control group in a ratio of 1∶1∶1, with 276 infants in each group. The infants in the experiment group were intramuscularly injected with freeze-dried group A+C meningococcal polysaccharide conjugate vaccine to be evaluated, and infants in the control group received intramuscular injection of commercially available freeze-dried group A+C meningococcal conjugate vaccine. The venous blood of infants was collected 30 days before the first dose and after the last dose of inoculation, and the antibody seroconversion of each group was determined and compared. Results: The completion rate of immunogenicity study was 95.2% (1 839/1 932). Before inoculation, there was no statistical difference in the geometric mean titer and positive rate of group A+C antibodies between the experiment group and the control group in 3 months and 6-11 months old infants (all P values >0.05). The geometric mean titers and positive rate of group A antibodies in the 1-dose group were higher than those in the control group (all P values <0.05), but there was no statistical difference between the 2-dose group and the control group (all P values >0.05) in infants aged 12-23 months. After inoculation, the differences (95%CI) in the positive conversion rate of group A+C antibodies between the experiment group and the control group were -0.12% (-6.01%-5.77%) and 0.82% (-4.23%-5.86%) in the 3 months old infants. At the age of 6-11 months, the differences were 6.75% (1.71%-11.79%) and -4.32% (-8.73%-0.08%), respectively. At the age of 12-23 months, the differences were 1.02% (-3.80%-5.83%) and -4.40% (-7.79%- -1.01%) in the 2-dose group and -7.22% (-12.90%- -1.54%) and -18.61% (-23.75%- -13.46%) in the 1-dose group, respectively. The geometric mean titers of group A+C antibodies in the 3 months old infants were 48.50 and 63.12, respectively, which had no significant difference from the control group (43.02 and 57.99, respectively) (both P values <0.05). The geometric mean titers of group A+C antibodies in the 6-11 months and 12-23 months old infants were 84.09 and 92.51 (2-dose group), which were higher than those in the corresponding control group (43.10 and 61.83, respectively) (all P values <0.001). Conclusion: Group A+C meningococcal conjugate vaccine has good immunogenicity in infants under 2 years old.

A Panel Data Analysis of Subjective Well-Being Based on Microblog User Information

Big data modelling using microblogs is applied to acquire nationwide representative panel data on subjective well-being. The analysis directly validates the influence of China's Livelihood Index on subjective well-being. Using panel data on subjective well-being collected for the period from 2010 to 2021 from users of the Weibo (Sina Corporation, Beijing, China) microblogging platform, this study finds Granger causality running from China's Livelihood Index to subjective well-being and that the two are positively correlated. We also find Granger causality running from a life stress indicator to a life satisfaction indicator. The education indicator model is found to be positively correlated with life satisfaction and positive emotions, whereas the life stress indicator and life satisfaction are negatively correlated. Medical and health indicators are positively related to life satisfaction, while a negative correlation is found between the traffic indicator model and life satisfaction. The relationship between economic development and subjective well-being also displays bidirectional Granger causality and a positive correlation. However, in China's provinces and prefecture-level cities with relatively strong economic growth, the correlation between the livelihood index and economic development appears to be weaker. We suggest boosting gross domestic product per capita and absolute per capita income to increase subjective well-being in less developed western China. Bridging the gross domestic product per capita gap nationwide may also positively influence subjective well-being. To achieve this, we suggest measures that include improving medical and health services, alleviating traffic congestion, increasing the teacher-student ratio and improving the education universalisation rate. These steps would improve the equitable and balanced development of China's Livelihood Index across the country's 31 provinces.

Exploring the impact of mass migration in Jin Dynasty by psycholinguistic analysis on Classical Chinese legacy text

The first mass migration in China took place at the end of the Western Jin, which resulted in the southward transfer of the Central Plains Culture and brought about huge social changes. Such social changes exerted significant impacts on the gentry of the Jin Dynasty. This paper used a huge volume of Classical Chinese legacy text of Jin gentry members. We used CC-LIWC to calculate frequencies of different word categories used in these text contents and conducted an analysis of variance to measure significant differences between the three groups. We found 16 categories of words with significant differences and calculated their effect sizes, such as tense markers (tensem), F = 3.588, P &lt; 0.05, η2 = 0.034; modal particles (modal_pa), F = 3.468, P &lt; 0.05, η2 = 0.053; words for affective processes (affect), F = 3.096, P &lt; 0.05, η2 = 0.028; words for cognitive processes (cogproc), F = 3.308, P &lt; 0.05, η2 = 0.031; words for perceptual processes (percept), F = 7.137, P &lt; 0.05, η2 = 0.06. Combining the psycholinguistics of the 16 categories of words and researches of historians on the Jin Dynasty, we then analyzed the direct and indirect, immediate and long-lasting psycholinguistic impacts of this mass migration on the gentry themselves and their descendants.

Tracking the 2022 Hunga Tonga-Hunga Ha'apai Aerosol Cloud in the Upper and Middle Stratosphere Using Space-Based Observations

On 15 January 2022, the submarine Hunga Tonga volcanic eruption lofted materials high into the upper stratosphere, reaching a record-breaking altitude of ∼58 km, unprecedented in the satellite observations era. Within two weeks, the bulk of the injected material circulated the globe between 20-30 km altitude, as observed by satellite instruments. We estimate that the stratospheric aerosol optical depth (sAOD) is the largest since the Pinatubo eruption and is at least twice as great as the sAOD after the 2015 Calbubo eruption despite the similar SO₂ injection from that eruption. We use space-based observations to monitor the Hunga-Tonga volcanic plume evolution and transport at different altitudes as it circulates the globe. While the main aerosol layer remains trapped in the tropical pipe, small parts have already made it to both the northern and southern hemisphere poles by April, which is almost certain to influence this year's ozone hole.

Search for New Physics in Electronic Recoil Data from XENONnT

We report on a blinded analysis of low-energy electronic recoil data from the first science run of the XENONnT dark matter experiment. Novel subsystems and the increased 5.9 ton liquid xenon target reduced the background in the (1, 30) keV search region to (15.8±1.3)  events/(ton×year×keV), the lowest ever achieved in a dark matter detector and ∼5 times lower than in XENON1T. With an exposure of 1.16 ton-years, we observe no excess above background and set stringent new limits on solar axions, an enhanced neutrino magnetic moment, and bosonic dark matter.

Photodynamic therapy in a pleural cavity using monte carlo simulations with 2D/3D Graphical Visualization

Cancer therapy using Photodynamic Therapy (PDT) has been investigated for some time [1,2] and now it is a growing area of interest in clinical trials [3]. Monte Carlo (MC) simulations were used for early laboratory studies [4,5] for analysis in PDT. Various improvements in the MC method have advanced the field in recent years.

Exploring the Suicide Mechanism Path of High-Suicide-Risk Adolescents-Based on Weibo Text Analysis

BACKGROUND

Adolescent suicide can have serious consequences for individuals, families and society, so we should pay attention to it. As social media becomes a platform for adolescents to share their daily lives and express their emotions, online identification and intervention of adolescent suicide problems become possible. In order to find the suicide mechanism path of high-suicide-risk adolescents, we explore the factors that influence is, especially the relations between psychological pain, hopelessness and suicide stages.

METHODS

We identified high-suicide-risk adolescents through machine learning model identification and manual identification, and used the Weibo text analysis method to explore the suicide mechanism path of high-suicide-risk adolescents.

RESULTS

Qualitative analysis showed that 36.2% of high-suicide-risk adolescents suffered from mental illness, and depression accounted for 76.3% of all mental illnesses. The mediating effect analysis showed that hopelessness played a complete mediating role between psychological pain and suicide stages. In addition, hopelessness was significantly negatively correlated with suicide stages.

CONCLUSION

mental illness (especially depression) in high-suicide-risk adolescents is closely related to suicide stages, the later the suicide stage, the higher the diagnosis rate of mental illness. The suicide mechanism path in high-suicide-risk adolescents is: psychological pain→ hopelessness → suicide stages, indicating that psychological pain mainly affects suicide risk through hopelessness. Adolescents who are later in the suicide stages have fewer expressions of hopelessness in the traditional sense.

Reliability and validity analysis of personality assessment model based on gait video

Personality affects an individual’s academic achievements, occupational tendencies, marriage quality and physical health, so more convenient and objective personality assessment methods are needed. Gait is a natural, stable, and easy-to-observe body movement that is closely related to personality. The purpose of this paper is to propose a personality assessment model based on gait video and evaluate the reliability and validity of the multidimensional model. This study recruited 152 participants and used cameras to record their gait videos. Each participant completed a 44-item Big Five Inventory (BFI-44) assessment. We constructed diverse static and dynamic time-frequency features based on gait skeleton coordinates, interframe differences, distances between joints, angles between joints, and wavelet decomposition coefficient arrays. We established multidimensional personality trait assessment models through machine learning algorithms and evaluated the criterion validity, split-half reliability, convergent validity, and discriminant validity of these models. The results showed that the reliability and validity of the Gaussian process regression (GPR) and linear regression (LR) models were best. The mean values of their criterion validity were 0.478 and 0.508, respectively, and the mean values of their split-half reliability were all greater than 0.8. In the formed multitrait-multimethod matrix, these methods also had higher convergent and discriminative validity. The proposed approach shows that gait video can be effectively used to evaluate personality traits, providing a new idea for the formation of convenient and non-invasive personality assessment methods.

The Impact of Mortality Salience, Negative Emotions and Cultural Values on Suicidal Ideation in COVID-19: A Conditional Process Model

As suicides incurred by the COVID-19 outbreak keep happening in many countries, researchers have raised concerns that the ongoing pandemic may lead to "a wave of suicides" in society. Suicidal ideation (SI) is a critical factor in conducting suicide intervention and also an important indicator for measuring people's mental health. Therefore, it is vital to identify the influencing factors of suicidal ideation and its psychological mechanism during the outbreak. Based on the terror management theory, in the present study we conducted a social media big data analysis to explore the joint effects of mortality salience (MS), negative emotions (NE), and cultural values on suicidal ideation in 337 regions on the Chinese mainland. The findings showed that (1) mortality salience was a positive predictor of suicidal ideation, with negative emotions acting as a mediator; (2) individualism was a positive moderator in the first half-path of the mediation model; (3) collectivism was a negative moderator in the first half-path of the mediation model. Our findings not only expand the application of the terror management theory in suicide intervention but provide some insights into post-pandemic mental healthcare. Timely efforts are needed to provide psychological interventions and counseling on outbreak-caused negative emotions in society. Compared with people living in collectivism-prevailing regions, those living in individualism-prevailing regions may be more vulnerable to mortality salience and negative emotions and need more social attention.

Material radiopurity control in the XENONnT experiment

The selection of low-radioactive construction materials is of the utmost importance for rare-event searches and thus critical to the XENONnT experiment. Results of an extensive radioassay program are reported, in which material samples have been screened with gamma-ray spectroscopy, mass spectrometry, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{222}$$\end{document}222Rn emanation measurements. Furthermore, the cleanliness procedures applied to remove or mitigate surface contamination of detector materials are described. Screening results, used as inputs for a XENONnT Monte Carlo simulation, predict a reduction of materials background (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}∼17%) with respect to its predecessor XENON1T. Through radon emanation measurements, the expected \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{222}$$\end{document}222Rn activity concentration in XENONnT is determined to be 4.2 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{+0.5}_{-0.7}$$\end{document}-0.7+0.5) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu $$\end{document}μBq/kg, a factor three lower with respect to XENON1T. This radon concentration will be further suppressed by means of the novel radon distillation system.

[Outcomes and prognosis of radical surgery in patients with stageⅠb2 and Ⅱa2 cervical squamous cell carcinoma]

Objective: To evaluate the survival, complications and prognostic factors in patients with stageⅠb2 and Ⅱa2 cervical squamous cell carcinoma treated by primarily radical surgery with or without postoperative adjuvant therapy. Methods: The clinical and pathological data of patients with stageⅠb2 and Ⅱa2 cervical squamous cell carcinoma treated in the Cancer Hospital of the University of Chinese Academy of Sciences from January 2015 to January 2018 were retrospectively analyzed. All patients underwent Querleu-Morrow classification (Q-M classification) C2 radical surgery, including extensive hysterectomy+pelvic lymphadenectomy with or without adjuvant therapy based on postoperative risk factors. Survival rate was calculated by Kaplan-Meier method and survival curve was drawn. Univariate analysis was performed by using the log-rank test to analyze the clinicopathological factors related to the prognosis of patients. Multivariate analysis was performed by using Cox regression method to analyze independent risk factors affecting survival prognosis. Results: (1) The median age of 643 patients with cervical squamous cell carcinoma was 50 years old (45-58 years old). Clinical stage: 260 cases (40.4%, 260/643) of stage Ⅰb2, 383 cases (59.6%, 383/643) of stage Ⅱa2. (2) Among 643 cases underwent Q-M classification C2 surgery, 574 cases (89.3%, 574/643) of them received adjuvant therapy and 184 cases (28.6%, 184/643) of them had grade 3-4 complications after treatment, including 134 cases (20.8%, 134/643) early complications and 66 cases (10.3%, 66/643) late complications. The incidence of grade 3-4 complications in 574 patients received postoperative adjuvant therapy was 30.1% (173/574), which was significantly different from that in 69 patients who received surgery alone (15.9%, 11/69; χ²=6.08, P=0.014). (3) All 643 cases were followed up, and the median follow-up time was 40 months (3-76 months). During the follow-up period, 117 cases (18.2%, 117/643) recurred, including 45 cases (7.0%, 45/643) of local recurrence, 54 cases (8.4%, 54/643) of distant metastasis, and 18 cases (2.8%, 18/643) of local recurrence and distant metastasis. The 5-year progression-free survival (PFS) and 5-year overall survival (OS) rates of patients with stage Ⅰb2 and Ⅱa2 cervical squamous cell carcinoma were 79.9% and 85.5%, respectively. Univariate analysis showed that pelvic lymph node metastasis, para-aortic lymph node metastasis, deep stromal infiltration, and lymph-vascular space invasion were significantly associated with 5-year PFS in patients with stage Ⅰb2 and Ⅱa2 cervical squamous cell carcinoma (all P<0.05). The maximum diameter of tumor, pelvic lymph node metastasis and para-aortic lymph node metastasis were significantly associated with the 5-year OS of cervical squamous cell carcinoma in stages Ⅰb2 and Ⅱa2 (all P<0.05). Multivariate analysis showed that pelvic lymph node metastasis and para-aortic lymph node metastasis were independent factors affecting 5-year PFS and 5-year OS in patients with stage Ⅰb2 and Ⅱa2 cervical squamous cell carcinoma (all P<0.01). Conclusion: Radical surgery is a feasible and effective primary treatment for stagesⅠb2 and Ⅱa2 cervical squamous cell carcinoma, with a high 5-year survival rate and an acceptable complication rate.

Predicting Risk Propensity Through Player Behavior in DOTA 2: A Cross-Sectional Study

As traditional methods such as questionnaires for measuring risk propensity are not applicable in some scenarios, a nonintrusive method that could automatically identify individuals' risk propensity could be valuable. This study utilized Defense of the Ancients 2 (DOTA 2) single match data and historical statistics to train predictive models to identify risk propensity by machine learning methods. Self-reported risk propensity scores from 218 DOTA 2 players were paired with their behavioral metrics. The best-performing model occurred with Gaussian process regression. The root mean square error of this model was 1.10, the correlation between predicted scores and self-reported questionnaire scores was 0.44, the R-squared was 0.17, and the test–retest reliability was 0.67. We discussed how selected behavioral features could contribute to predicting risk propensity and how the approach could be of potential value in the application of perceiving individuals' risk propensities. Moreover, the limitations of our study were discussed, and recommendations were made for future studies in this field.

Stigmatizing Attitudes Across Cybersuicides and Offline Suicides: Content Analysis of Sina Weibo

Background

The new reality of cybersuicide raises challenges to ideologies about the traditional form of suicide that does not involve the internet (offline suicide), which may lead to changes in audience’s attitudes. However, knowledge on whether stigmatizing attitudes differ between cybersuicides and offline suicides remains limited.

Objective

This study aims to consider livestreamed suicide as a typical representative of cybersuicide and use social media data (Sina Weibo) to investigate the differences in stigmatizing attitudes across cybersuicides and offline suicides in terms of attitude types and linguistic characteristics.

Methods

A total of 4393 cybersuicide-related and 2843 offline suicide-related Weibo posts were collected and analyzed. First, human coders were recruited and trained to perform a content analysis on the collected posts to determine whether each of them reflected stigma. Second, a text analysis tool was used to automatically extract a number of psycholinguistic features from each post. Subsequently, based on the selected features, a series of classification models were constructed for different purposes: differentiating the general stigma of cybersuicide from that of offline suicide and differentiating the negative stereotypes of cybersuicide from that of offline suicide.

Results

In terms of attitude types, cybersuicide was observed to carry more stigma than offline suicide (χ²₁=179.8; P<.001). Between cybersuicides and offline suicides, there were significant differences in the proportion of posts associated with five different negative stereotypes, including stupid and shallow (χ²₁=28.9; P<.001), false representation (χ²₁=144.4; P<.001), weak and pathetic (χ²₁=20.4; P<.001), glorified and normalized (χ²₁=177.6; P<.001), and immoral (χ²₁=11.8; P=.001). Similar results were also found for different genders and regions. In terms of linguistic characteristics, the F-measure values of the classification models ranged from 0.81 to 0.85.

Conclusions

The way people perceive cybersuicide differs from how they perceive offline suicide. The results of this study have implications for reducing the stigma against suicide.

[Primary ovarian squamous cell carcinoma: clinicopathological features and prognostic analysis of fifteen cases]

Objective: To assess the clinical features and treatment outcomes in patients with primary ovarian squamous cell carcinoma (POSCC). Methods: Fifteen patients with primary ovarian squamous cell carcinoma diagnosed from January 2009 to December 2018 in Cancer Hospital of the University of Chinese Academy of Sciences were collected. The expression of p16, hMLH1, hMSH2, hMSH6 and PMS2 in POSCC was detected by immunohistochemistry, and the status of high-risk human papillomavirus (HPV) by RNAscope test. Results: Squamous cell carcinoma with different degrees of differentiation was found in 15 cases, including three cases with high differentiation and 12 cases with medium to low differentiation. There were four cases with in situ squamous cell carcinoma, four cases with teratoma, one case with endometrial carcinoma/atypical hyperplasia, and one case with endometriosis. p16 was expressed in five cases (5/15), indicating coexisting high-risk HPV infection. There was no high-risk HPV infection in the remaining 10 cases, and p16 staining was negative. There was no deficient mismatch repair protein in all cases. The overall survival time (P=0.038) and progression free survival (P=0.045) of patients with high-risk HPV infection were longer than those without HPV infection. Conclusions: POSCC is more commonly noted in postmenopausal women and often occurs unilaterally. Elevated serological indexes CA125 and SCC are the most common finding. Morphologically, the tumors show variable degrees of differentiation, but the current data suggest that the degree of differentiation cannot be used as an independent prognostic index. High-risk HPV infection may be associated with the occurrence of POSCC, and that the prognosis of POSCC patients with HPV infection is better than that of patients without infection.

Automatic Decision-Making Style Recognition Method Using Kinect Technology

In recent years, somatosensory interaction technology, represented by Microsoft's Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.

Mechanism of cadmium tolerance in Salicornia europaea at optimum levels of NaCl

Many saline-alkali soils around the world are polluted by the heavy metal Cd, restricting the development of agriculture and ecology in those regions. The halophyte Salicornia europaea L. is capable of growing healthily in Cd-contaminated saline-alkali soil, suggesting that the species is tolerant to stress caused by both salt and heavy metals. In this study, the mechanism of Cd tolerance in this species was explored under 200 mM NaCl. Flame spectrophotometric assays for ions content and spectrophotometric for organic soluble substances, antioxidant enzyme activity, phytochelatins (PCs) content and phytochelatin synthase (PCS) activity, the photosynthetic parameters by portable photosynthesis measurement system, genes expression by qRT-PCR analysis were carried out. Cd treatment significantly decreased the dry weight, photosynthetic rate, K⁺ , Zn²⁺ , and Fe^2+/3+ content, while significantly increasing Na⁺ and Cd⁺ , soluble organic matter, and reactive oxygen species (ROS) levels. Compared with Cd treatment at 0 mM NaCl, Cd treatment at 200 mM NaCl significantly increased dry weight and photosynthetic rate while significantly decreasing ROS content through increased antioxidant enzyme activity. When exposed to Cd stress, treatment with 200 mM NaCl significantly increased PCs content and PCS activity and up-regulated the expression of the phytochelatin synthase genes CDA1 and PCS1 were, thereby increasing resistance to Cd. NaCl treatment increases the tolerance of S. europaea to the heavy metal Cd by growing rapidly, reducing the quantity of Cd²⁺ from entering the plant shoots, increasing the levels of PCs that chelate Cd²⁺ , thereby reducing its toxicity.

Evaluation of the Accuracy of Six Simple Screening Tools for Sarcopenia in Schizophrenic Patients

OBJECTIVES

Our objective was to evaluate if SARC-F, SARC-CalF, SARC-F-EBM, calf circumference (CC), mid-upper-arm circumference (MUAC) and Ishii test can be used to accurately screen for sarcopenia in schizophrenic patients.

METHOD

We enrolled schizophrenic patients aged 50 or older, who were regularly taking antipsychotic medications, at two mental health centres. Bioimpedance-based muscle-mass was analysed with an InBody 770 instrument, while muscle strength was measured with a digital grip-strength dynamometer. The physical performance of the patients was gauged from their gait speed over 6 m. Standard AWGS2019 diagnostic criteria were used, and the accuracies of the six screening methods were indicated by the sensitivity, negative predictive value (NPV), and area under receiver operating characteristic curve (AUC).

RESULTS

A total of 339 stable schizophrenic patients were enrolled. The overall prevalence of sarcopenia was 53.1%, and the prevalence was 55.6% and 47.66%, respectively, for males and females. The prevalence of sarcopenia obesity in the total population was 16.22%, and that of males and females was 18.97% and 10.28%, respectively. The SARC-F, SARC-CalF, SARC-F-EBM, CC, MUAC and Ishii test sensitivity/NPV in screening for sarcopenia were 41.86%/0.52, 79.07%/0.7, 28.68%/0.51, 78.3%/0.71, 76.74%/0.7, 89.92%/0.84, respectively, in males and 45.1%/0.59, 94.12%/0.91, 54.9%/0.7, 92.16%/60.91, 74.51%/0.77, 96.08%/0.94, respectively, in females. In males, the AUCs of the SARC-F, SARC-CalF, SARC-F-EBM, CC, MUAC and Ishii test were 0.601 (95%CI, 0.528-0.673), 0.754 (95%CI,0.69-0.817), 0.657 (95%CI,0.588-0.727), 0.8 (95%CI, 0.744-0.856), 0.781 (95%CI, 0.721-0.84) and 0.88 (95%CI, 0.837-0.922), respectively, and in females, they were 0.587(95%CI,0.479-0.696), 0.794 (95%CI,0.709-0.878), 0.799 (95%CI,0.71-0.888), 0.893 (95%CI, 0.833-0.953), 0.843 (95%CI, 0.772-0.915) and 0.855 (95%CI, 0.784-0.926), respectively.

CONCLUSION

The prevalence of sarcopenia in schizophrenic patients is high. Clinical doctors should screen for sarcopenia in schizophrenic patients and provide timely interventions to reduce the occurrence of adverse events. The above six tools can be used as screening tools, and the Ishii test is the most suitable for screening.

Association between the Severity of Sarcopenia and Pneumonia in Patients with Stable Schizophrenia: A Prospective Study

OBJECTIVES

This study investigates the associations between the severity of sarcopenia and pneumonia in patients with stable schizophrenia.

MATERIALS AND METHODS

This is a prospective study that includes patients from the rehabilitation wards of two mental health centres in western China, who were diagnosed with stable schizophrenia. Baseline data were collected from these patients between September 1 and September 30, 2020, while patients' data associated with pneumonia, diagnosed and treated by clinicians, were collected between October 2020 and October 2021. The severity of sarcopenia was diagnosed according to the diagnostic criteria defined by the Asian Working Group for Sarcopenia (AWGS) 2019. The association between the severity of sarcopenia and pneumonia in patients with schizophrenia was analyzed with the use of logistic regression analysis.

RESULTS

Three hundred and thirty-five patients with stable schizophrenia were enrolled in the prospective study, among whom 242 (72.24%) were≥60 years old, and 229 (68.36%) were males. Among the patients with stable schizophrenia, 130 (38.8%) were diagnosed with non-severe sarcopenia, whereas 47 (14.0%) had severe sarcopenia. Eighty-two (24.5%) of patients with schizophrenia fought pneumonia. Our study showed that the severe sarcopenia group had the highest incidence of pneumonia, followed by the non-severe sarcopenia group (severe sarcopenia vs. non-severe sarcopenia vs. normal, 38.3% vs. 28.46% vs. 17.09%, p=0.005). Compared with the normal group, the non-severe sarcopenia group (OR=1.93, 95%CI: 1.1-3.389) and the severe sarcopenia group (OR=3.011, 95%CI: 1.467-6.183) had a higher risk of pneumonia. We further adjusted the potential confounders such as sex, smoking history, chronic obstructive pulmonary disease (COPD), Patient Health Questionnaire (PHQ-9) score, and benzhexol and confirmed that only the severe sarcopenia group had an increased risk of pneumonia (OR=2.366, 95%CI: 1.078-5.191).

CONCLUSIONS

We have demonstrated that severe sarcopenia was associated with pneumonia in patients diagnosed with stable schizophrenia.

Ecological recognition of self-esteem leveraged by video-based gait

Self-esteem is a significant kind of psychological resource, and behavioral self-esteem assessments are rare currently. Using ordinary cameras to capture one's gait pattern to reveal people's self-esteem meets the requirement for real-time population-based assessment. A total of 152 healthy students who had no walking issues were recruited as participants. The self-esteem scores and gait data were obtained using a standard 2D camera and the Rosenberg Self-Esteem Scale (RSES). After data preprocessing, dynamic gait features were extracted for training machine learning models that predicted self-esteem scores based on the data. For self-esteem prediction, the best results were achieved by Gaussian processes and linear regression, with a correlation of 0.51 (p < 0.001), 0.52 (p < 0.001), 0.46 (p < 0.001) for all participants, males, and females, respectively. Moreover, the highest reliability was 0.92 which was achieved by RBF-support vector regression. Gait acquired by a 2D camera can predict one's self-esteem quite well. This innovative approach is a good supplement to the existing methods in ecological recognition of self-esteem leveraged by video-based gait.

Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders

Background: The application of polygenic risk scores (PRSs) in major depressive disorder (MDD) detection is constrained by its simplicity and uncertainty. One promising way to further extend its usability is fusion with other biomarkers. This study constructed an MDD biomarker by combining the PRS and voice features and evaluated their ability based on large clinical samples.

Methods: We collected genome-wide sequences and utterances edited from clinical interview speech records from 3,580 women with recurrent MDD and 4,016 healthy people. Then, we constructed PRS as a gene biomarker by p value-based clumping and thresholding and extracted voice features using the i-vector method. Using logistic regression, we compared the ability of gene or voice biomarkers with the ability of both in combination for MDD detection. We also tested more machine learning models to further improve the detection capability.

Results: With a p-value threshold of 0.005, the combined biomarker improved the area under the receiver operating characteristic curve (AUC) by 9.09% compared to that of genes only and 6.73% compared to that of voice only. Multilayer perceptron can further heighten the AUC by 3.6% compared to logistic regression, while support vector machine and random forests showed no better performance.

Conclusion: The addition of voice biomarkers to genes can effectively improve the ability to detect MDD. The combination of PRS and voice biomarkers in MDD detection is feasible. This study provides a foundation for exploring the clinical application of genetic and voice biomarkers in the diagnosis of MDD.

Motivational Tendency Differences Between the Pre-qin Confucianism and Legalism by Psycholinguistic Analysis

Among the hundred schools of thought that flourished during the pre-Qin era, Confucianism and Legalism are the most important ones as their thoughts cast a longstanding influence on the Chinese culture—cultural-psychological formation of the Chinese people. Most of the previous researches focused on analyzing the similarities and differences of the thoughts of Confucianism and Legalism, and few of them analyzed their motivational tendencies. This paper conducted a word frequency analysis of pre-Qin Confucian and Legalist classics with CC-LIWC, an independently developed program for classical text analysis, and made comparative research into the motivational tendencies of the two schools of thought in terms of psycholinguistic differentials. According to our research results, the use of words representing power (M = 0.1377, SD = 0.0104, p = 0.014) and reward (M = 0.0151, SD = 0.0042, p = 0.037) is more frequent in Legalist classics than in Confucian classics, whereas the use of words representing affiliation (p = 0.066), risk (p = 0.086), and achieve (p = 0.27) shows no significant difference between Confucian and Legalist classics. This paper believes that both Confucianism and Legalism are mainly motivated by power, which is the most distinct feature of their motivational tendencies, and that Legalism is more motivated by power and reward than Confucianism; both Confucianism and Legalism are outcomes of the monarchy society with the former showing the reserved side of monarchy and the latter showing the uninhibited side of monarchy; an effective political methodology is absent in Confucianism, while utilitarianism constitutes the cornerstone of the political philosophy of Legalism.

Stigma toward Wuhan people during the COVID-19 epidemic: an exploratory study based on social media

Background

Stigma associated with infectious diseases is common and causes various negative effects on stigmatized people. With Wuhan as the center of the COVID-19 outbreak in China, its people were likely to be the target of stigmatization. To evaluate the severity of stigmatization toward Wuhan people and provide necessary information for stigma mitigation, this study aimed to identify the stigmatizing attitudes toward Wuhan people and trace their changes as COVID-19 progresses in China by analyzing related posts on social media.

Methods

We collected 19,780 Weibo posts containing the keyword ‘Wuhan people’ and performed a content analysis to identify stigmatizing attitudes in the posts. Then, we divided our observation time into three periods and performed repeated-measures ANOVA to compare the differences in attitudes during the three periods.

Results

The results showed that stigma was mild, with 2.46% of related posts being stigmatizing. The percentages of stigmatizing posts differed significantly during the three periods. The percentages of ‘Infectious’ posts and ‘Stupid’ posts were significantly different for the three periods. The percentage of ‘Irresponsible’ posts was not significantly different for the three periods. After government interventions, stigma did not decrease significantly, and stigma with the ‘Infectious’ attitude even increased. It was not until the government interventions took effect that stigma significantly reduced.

Conclusions

This study found that stigma toward Wuhan people included diverse attitudes and changed at different periods. After government interventions but before they took effect, stigma with the ‘Infectious’ attitude increased. After government interventions took effect, general stigma and stigmas with ‘Infectious’ and ‘Stupid’ attitudes decreased. This study constituted an important endeavor to understand the stigma toward Wuhan people in China during the COVID-19 epidemic. Implications for stigma reduction and improvement of the public’s perception during different periods of epidemic control are discussed.

Psycho-Linguistic Changes Associated With Historical Celebrities in Henan Using Classical Chinese Big Data

Chinese civilization has a long history, and the Central Plains, with Henan as the core, is one of its birthplaces. Understanding the psycho-linguistic changes in Henan is of great significance for understanding the evolution and formation of national cultural psychology. The traditional method is mainly qualitative or speculative, which makes the consistency among the research results low. Moreover, because most of the research are conducted in a specific period or on a specific figure, they are relatively scattered and unsystematic. To systematically and quantitatively study the psychological changes in the Central Plains represented by Henan Province, this article aimed to examine the self-reported discourses of historical celebrities in Henan in official history and their psycho-linguistic changes based on the classical Chinese Linguistic Inquiry and Word Count (classical Chinese LIWC, CC-LIWC) psycholinguistic dictionary and the classical Chinese words segmentation system, using big data. The research found that the frequency of male words in Henan historical celebrities (F = 2.938, p < 0.05), of differ words (F = 4.767, p < 0.01), of motion words (F = 4.042, p < 0.01), and of time words (F = 5.412, p < 0.01) are significantly different among the five dynasties. The conclusion is that during the Spring and Autumn and the Warring States periods, 100 schools of thought contended, and the status of "scholars" in Henan rose. The frequency of male and differ words was at that point significantly higher than during the other dynasties. From "The Contention of a Hundred Schools of Thought" to "The Supremacy of Confucianism," the scholars in Henan were in decline, and differential cognitive tendencies had diminished since the Han dynasty. During the period of the Three Kingdoms and Jin and Southern and Northern Dynasties, political powers and territories changed, and the historical celebrities in Henan show a remarkable tendency that were related to time and space. The psycho-linguistic changes found in this study are highly consistent with the development of social history, which indicates that the guidance of the social, political, and cultural environment has an important influence on the psycho-linguistic changes of social class. This is the first time that the text analysis system has been applied in classical Chinese to carry out quantitative research on the psycho-linguistic changes of ancient Chinese people, which provides new ideas and new methods for humanistic research.