Strong Support Research Articles

Peptides and primate personality: Central and peripheral oxytocin and vasopressin levels and social behavior in two baboon species (Papio hamadryas and Papio anubis)

The neurohormones oxytocin (OT) and arginine vasopressin (AVP) are involved in social behaviors and psychiatric conditions. However, more research on nonhuman primates with complex social behaviors is needed. We studied two closely-related primate species with divergent social and mating systems; hamadryas baboons (Papio hamadryas, n=38 individuals) and anubis baboons (Papio anubis, n=46). We measured OT in cerebrospinal fluid (CSF, n=75), plasma (n=81) and urine (n=77), and AVP in CSF (n=45), and we collected over 250 hours of focal behavioral observations. Using Bayesian multivariate models, we found no clear species difference in hormone levels; the strongest support was for hamadryas having higher CSF OT levels than anubis (posterior probability [PP] for females = 0.75, males = 0.84). Looking at nine specific behaviors, OT was associated with affiliative behaviors (approach, proximity, grooming, PP ∼ 0.85 – 1.00), albeit inconsistently across sources of measurement (CSF, plasma, and urine, which were uncorrelated with each other). Most behaviors had low repeatability (R ∼ 0 – 0.2), i.e. they did not exhibit stable between-individual differences (or “personality”), and different behaviors did not neatly coalesce into higher-order factors (or “behavioral syndromes”), which cautions against the use of aggregate behavioral measures and highlights the need to establish stable behavioral profiles when testing associations with baseline hormone levels. In sum, we found some associations between peptides and social behavior, but also many null results, OT levels from different sources were uncorrelated, and our behavioral measures did not indicate clear individual differences in sociability.

Merdeka Curriculum: Adaptation of Indonesian Education Policy in the Digital Era and Global Challenges

Objective: This paper examines the Merdeka Curriculum as a response to Indonesian education policy challenges in the digital era and globalization. The policy aims to enhance the flexibility and quality of education through a more adaptive approach to student needs and technological advancements. Theoretical Framework: The study is grounded in public policy adaptation theory and 21st-century education theory, which emphasizes critical thinking, creativity, collaboration, and digital literacy. This framework helps understand how the Merdeka Curriculum is implemented within the Indonesian education context. Method: A qualitative approach is used in this study, employing a case study method in several schools in Indonesia. Data were collected through in-depth interviews, classroom observations, and policy document analysis. Participants included teachers, students, and education policymakers. Results and Conclusions: The findings indicate that the Merdeka Curriculum provides greater flexibility for teachers in designing learning programs tailored to students' needs. However, implementation in the field faces various challenges, such as lack of technological infrastructure and teacher training. In conclusion, while the Merdeka Curriculum has the potential to improve educational quality, stronger support in technology and training is necessary for its success. Research Implications: This study has significant implications for the development of education policy in Indonesia, especially in the digital era. The findings encourage policymakers to consider greater investments in educational technology and teacher training to ensure the success of the Merdeka Curriculum. Originality/Value: This paper offers an original contribution by linking the Merdeka Curriculum policy to the challenges and opportunities of the digital era, providing practical insights into the implementation of education policy in Indonesia. It also adds to the literature on education policy adaptation in developing countries.

Hybridization in palms (Arecaceae).

Hybridization has significant evolutionary consequences across the Tree of Life. The process of hybridization has played a major role in plant evolution and has contributed to species richness and trait variation. Since morphological traits are partially a product of their environment, there may be a link between hybridization and ecology. Plant hybrid species richness is noted to be higher in harsh environments, and we explore this hypothesis with a keystone tropical plant lineage, palms (Arecaceae). Leveraging a recent literature review of naturally occurring palm hybrids, we developed a method to calculate hybrid frequency, and then tested if there is phylogenetic signal of hybrids using a phylogeny of all palms. Further, we used phylogenetic comparative methods to examine the interaction between hybrid frequency and presence in dry environments, on islands, and the species richness of genera. Phylogenetic generalized least squares models had stronger support than models of random association, indicating phylogenetic signal for the presence of hybrids in dry and island environments. However, all p-values were >.05 and therefore the correlation was poor between hybridization and the trait frequencies examined. Presence in particular environments are not strongly correlated to hybrid frequency, but phylogenetic signal suggests a role in its distribution in different habitats. Hybridization in palms is not evenly distributed across subfamilies, tribes, subtribes yet plays an important role in palm diversity, nonetheless. Increasing our understanding hybridization in this economically and culturally important plant family is essential, particularly since rates are projected to increase with climate change, reconfiguring the dynamics and distribution of biodiversity.

Factor structure of the Impact of Events Scale-Revised in Latin American young adults.

More than 550 million people speak Spanish and, yet, psychometric data on psychological instruments in Spanish lags. Given evidence of significant traumatic exposure and distress among Spanish speakers, the aim of the current study was to examine the factor structure of the Impact of Events Scale-Revised (IES-R), in a large sample of Spanish-speaking adults. Participants (n = 725) were university students living in Latin America (M = 21.02; SD = 3.12). Most were born in Mexico (77.6%) and the next largest subgroup was from Ecuador (18.9%). Respondents completed the 22-item IES-R. The IES-R mean score was 20.08 (SD = 21.34) and 26.6% of the sample met the cutoff score for clinically significant symptoms. Regarding factor structure, eight different factor structures that have demonstrated a good fit in the extant literature were examined. The one-factor model demonstrated an acceptable fit, χ²(209) = 839.13, p < .0001; root-mean-square error (RMSEA) = 0.06, 95% confidence interval (CI) [0.06, 0.07]; comparative fit index (CFI) = 0.91, Tucker-Lewis index (TLI) = 0.90. The two-factor model demonstrated good fit, χ²(208) = 746.70, p < .0001; RMSEA = 0.06, 95% CI [0.05, 0.06]; CFI = 0.92, TLI = 0.91, and nested model comparisons of the two-factor and one-factor models using the chi-square difference test supported the two-factor model. The most parsimonious of the multifactor models, a two-factor model with Avoidance symptoms as one factor and Intrusions and Hyperarousal combined into a second may be of greatest use for this particular version of the IES-R. The current research demonstrates strong psychometric support for Intrusion/Hyperarousal and Avoidance subscales when measuring traumatic stress in Spanish speakers and underscores the need for culturally and contextually sensitive assessment in this population, in which posttraumatic stress is prevalent. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Assessing prevalence, validity, and correlates of ICD-11 posttraumatic stress disorder and complex posttraumatic stress disorder in Ireland.

The most frequently used measure of the 11th edition of the International Classification of Diseases (ICD-11) complex posttraumatic stress disorder (CPTSD) is the International Trauma Questionnaire (ITQ). While there is strong support for the psychometric properties of the ITQ, few studies have assessed its reliability and validity in nationally representative samples. Additionally, several correlates of ICD-11 CPTSD have been identified; however, few studies have assessed multiple correlates simultaneously. To assess the factorial validity and internal reliability of the ITQ in a nationally representative sample of adults living in Ireland (N = 1,100); determine the prevalence rates of ICD-11 posttraumatic stress disorder (PTSD) and CPTSD; and identify correlates of CPTSD symptoms and how CPTSD symptoms relate to risk of suicide. Confirmatory factor analysis was performed to evaluate the factorial validity of the ITQ, and structural equation modeling (SEM) was used to determine the unique multivariate associations between 10 predictor variables (age, sex, urban dwelling, unemployment status, number of traumatic events, COVID-19 infection, knowing someone who died from COVID-19, loneliness, social support, and sleep problems) and symptoms of CPTSD, and the unique associations between CPTSD symptoms and suicide risk. The ITQ produces reliable and valid scores, 11.2% of people met requirements for ICD-11 PTSD (2.4%) or CPTSD (8.8%), exposure to a higher number of traumatic life events, higher levels of loneliness, and more sleep problems predicted CPTSD symptoms; and negative self-concept (NSC) symptoms were most strongly associated with suicidality. Where the risk of suicide is high, treating symptoms of NSC, loneliness, and sleep problems may be advisable. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

ATP-Independent Turnover of Dinitrogen Intermediates Captured on the Nitrogenase Cofactor.

Nitrogenase reduces N2 to NH3 at its active-site cofactor. Previous studies of an N2-bound Mo-nitrogenase from Azotobacter vinelandii suggest binding of three N2 species via asymmetric belt-sulfur displacements in the two cofactors of its catalytic component (designated Av1*), leading to the proposal of stepwise N2 reduction involving all cofactor belt-sulfur sites; yet, the evidence for the existence of multiple N2 species on Av1* remains elusive. Here we report a study of ATP-independent, EuII/SO3 2--driven turnover of Av1* using GC-MS and frequency-selective pulse NMR techniques. Our data demonstrate incorporation of D2-derived D by Av1* into the products of C2H2- and H+-reduction, and decreased formation of NH3 by Av1* concomitant with the release of N2 under H2; moreover, they reveal a strict dependence of these activities on SO3 2-. These observations point to the presence of distinct N2 species on Av1*, thereby providing strong support for our proposed mechanism of stepwise reduction of N2 via belt-sulfur mobilization.

Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants.

Distinguishing clinical subgroups for patients suffering with diseases characterized by a wide phenotypic spectrum is essential for developing precision therapies. Patients with gain-of-function (GOF) variants in the SCN8A gene exhibit substantial clinical heterogeneity, viewed historically as a linear spectrum ranging from mild to severe. To test for hidden clinical subgroups, we applied two machine-learning algorithms to analyze a dataset of patient features collected by the International SCN8A Patient Registry. We used two research methodologies: a supervised approach that incorporated feature severity cutoffs based on clinical conventions, and an unsupervised approach employing an entirely data-driven strategy. Both approaches found statistical support for three distinct subgroups and were validated by correlation analyses using external variables. However, distinguishing features of the three subgroups within each approach were not concordant, suggesting a more complex phenotypic landscape. The unsupervised approach yielded strong support for a model involving three partially ordered subgroups rather than a linear spectrum. Application of these machine-learning approaches may lead to improved prognosis and clinical management of individuals with SCN8A GOF variants and provide insights into the underlying mechanisms of the disease.

Serologic Survey of Selected Arthropod-Borne Pathogens in Free-Ranging Snowshoe Hares (Lepus americanus) Captured in Northern Michigan, USA.

Snowshoe hares (Lepus americanus) in the Upper Peninsula (UP) of Michigan, USA, occupy the southern periphery of the species' range and are vulnerable to climate change. In the eastern UP, hares are isolated by the Great Lakes, potentially exacerbating exposure to climate-change-induced habitat alterations. Climate change is also measurably affecting distribution and prevalence of vector-borne pathogens in North America, and increases in disease occurrence and prevalence can be one signal of climate-stressed wildlife populations. We conducted a serosurvey for vector-borne pathogens in snowshoe hares that were captured in the Hiawatha National Forest in the eastern UP of Michigan, USA, 2016-2017. The most commonly detected antibody response was to the mosquito-borne California serogroup snowshoe hare virus (SSHV). Overall, 24 (51%) hares screened positive for SSHV antibodies and of these, 23 (96%) were confirmed positive by plaque reduction neutralization test. We found a positive association between seroprevalence of SSHV and live weight of snowshoe hares. Additionally, we detected a significant effect of ecological land type group on seroprevalence of SSHV, with strong positive support for a group representing areas that tend to support high numbers of hares (i.e., acidic mineral containing soils with cedar, mixed swamp conifers, tamarack and balsam fir as common overstory vegetation). We also detected and confirmed antibodies for Jamestown Canyon virus and Silverwater virus in a single hare each. We did not detect antibodies to other zoonotic vector-borne pathogens, including Lacrosse encephalitis virus, West Nile virus, Borrelia burgdorferi, Powassan virus, and Francisella tularensis. These results provide a baseline for future serological studies of vector-transmitted diseases that may increase climate vulnerability of snowshoe hares in the UP of Michigan, as well as pose a climate-related zoonotic risk.

The polarization of the boundary layer around weakly magnetized neutron stars in X-ray binaries

Context. X-ray binaries hosting a compact object have been among the main targets of the Imaging X-ray Polarimetry Explorer (IXPE) since its launch, due to their high brightness in the 2–8 keV energy band. The spectropolarimetric analysis performed so far has proved to be of great importance in providing constraints on the accretion geometry of these systems. However, the data statistics is not enough to unambiguously disentangle the contribution of the single components to the net observed polarimetric signal. Aims. In this work, we aim to present a model for computing the polarization degree and polarization angle of the boundary layer around weakly magnetized neutron stars in low-mass X-ray binaries in the soft state. The main motivation is to provide strong theoretical support to data interpretation of observations performed by IXPE or future satellites for X-ray polarimetry. Methods. The results were obtained by modeling the boundary layer as an equatorial belt around the compact object and locally approximating it as a plane-parallel scattering atmosphere, for which the associated radiative transfer equation for polarized radiation in the Thomson limit was solved. The polarimetric quantities were then transformed from the comoving frame to the observer frame using the numerical methods formerly developed for X-ray pulsars. Results. For typical values of the optical depth and electron temperature of the boundary layer of these systems in a soft state, the polarization degree was less then 0.5%, while the polarization angle was rotated by ≲5° with respect to the neutron star spin axis due to special and general relativistic effects for fast rotation, the amount progressively decreasing for lower spin frequencies. The derived quantities can be used to remove degeneracy when multicomponent spectropolarimetry is performed.

LiFoL: An Efficient Framework for Financial Distress Prediction in High-Dimensional Unbalanced Scenario

Corporate financial distress will significantly damage the company’s and its stakeholders’ interests and even lead to a global financial crisis. Therefore, finding an efficient method for financial distress prediction (FDP) to avoid greater losses is essential. Although there is a lot of research and progress in this field, the existing methods rarely consider the problems of high dimensionality and class imbalance, which will largely limit the models to achieve satisfactory performance. To alleviate these problems, this article first proposes a novel Lightspace-SMOTE upsampling method, which can reduce the feature dimensionality and increase the signal-to-noise ratio (SNR) of the original data and then upsample it to increase the number of minor class samples. In addition, this article proposes an efficient ensemble framework (LiFoL) that combines Lightspace-SMOTE, focal loss (FL), and LightGBM, which can not only focus more on minor class and the hard-to-class samples but also obtain better performance. At the same time, the feature importance provided by the model can provide strong support for model interpretability. Experimental results show that the Lightspace-SMOTE upsampling method can help the model achieve higher scores in area under ROC curve (AUC) and recall, especially in the case of longer prediction periods. Compared with current methods, LiFoL can achieve more than 10% improvement in AUC and more than 20% in recall.

Public opinion on reforming U.S. primaries

AbstractObjectiveFew studies have measured public attitudes about reform proposals for changing direct primaries. Despite strong public support over the past century for holding primaries, does the public want to change the direct primary, given its very low voter turnout and its potential role in fostering political polarization?MethodUsing a unique nationally representative survey of 3000 U.S. adults conducted in March 2023 by YouGov, this study shows that a majority of Americans support reform of primary elections.ResultsThe reforms which receive the greatest support are those that seem more “democratic” such as establishing national congressional primary and holding open primaries. There is lower support for reforms that give political parties more control over selecting candidates, such as state party conventions. There are significant differences between Republicans, Democrats, and independents, while factors such as interest, education, gender, and race have a minimal impact. Individuals living in states with nonpartisan primaries are more favorable toward this specific reform, ranked‐choice voting, and open primaries, but primary type was not significant in regression models.ConclusionsThe public is opposed to reforms that give parties more say in choosing candidates, consistent with public sentiment a century earlier when the direct primary was adopted.

Multi-Think Transformer for Enhancing Emotional Health

The smart healthcare system not only focuses on physical health but also on emotional health. Music therapy, as a non-pharmacological treatment method, has been widely used in clinical treatment, but music selection and generation still require manual intervention. AI music generation technology can assist people in relieving stress and providing more personalized and efficient music therapy support. However, existing AI music generation highly relies on the note generated at the current time to produce the note at the next time. This will lead to disharmonious results. The first reason is the small errors being ignored at the current generated note. This error will accumulate and spread continuously, and finally make the music become random. To solve this problem, we propose a music selection module to filter the errors of generated note. The multi-think mechanism is proposed to filter the result multiple times, so that the generated note is as accurate as possible, eliminating the impact of the results on the next generation process. The second reason is that the results of multiple generation of each music clip are not the same or even do not follow the same music rules. Therefore, in the inference phase, a voting mechanism is proposed in this paper to select the note that follow the music rules that most experimental results follow as the final result. The subjective and objective evaluations demonstrate the superiority of our proposed model in generation of more smooth music that conforms to music rules. This model provides strong support for clinical music therapy, and provides new ideas for the research and practice of emotional health therapy based on the Internet of Things.

Internal revenue sharing methodology for virtual power plant clusters considering carbon incentive and penalty mechanisms

With the rapid development of renewable energy and the urgent need for global carbon emission reduction, virtual power plants have become a high-profile energy management model that can integrate multiple energy resources. How to effectively integrate renewable energy to reduce carbon emissions, how to optimize the use of different energy resources, and how to fairly distribute economic benefits within virtual power plant clusters while encouraging the reduction of carbon emissions are issues that need to be addressed in research. The study first established a virtual power plant model and conducted in-depth optimization for its economic and environmental indicators. Subsequently, the study constructed a game model within the virtual power plant cluster, aiming to solve the problem of income distribution in this diversified energy system. The research results found that commercial users have the highest carbon emissions, followed by industrial users, while residential users have the lowest carbon emissions. In terms of optimized user electricity consumption behavior, the peak-to-valley difference rate of industrial users has been reduced by 17%, and the daily load rate has increased by 6%; the peak-to-valley difference rate of commercial users has been reduced by 12%, and the daily load rate has increased by 6%; The peak-to-trough difference rate for residential users decreased by 8%, and the daily load rate increased by 4%. In addition, the research also proposes a method of internal revenue distribution of virtual power plant clusters based on a carbon reward and punishment mechanism, which provides a new way for the synergy effects and economic benefit distribution of virtual power plants. Research is of positive significance in solving pressing issues in the field of energy management and provides strong support for the development of future sustainable energy systems.

A reputation-based dynamic reorganization scheme for blockchain network sharding

While sharding technology helps solve performance bottlenecks in traditional blockchain networks, it also introduces new challenges. Random node allocation may lead to uneven distribution of malicious nodes, causing performance differences and security risks. Existing reputation-based sharding schemes often overlook node performance characteristics and fail to address security concerns related to leader election. In addition, full sharding reorganisation drastically reduces the performance of the entire blockchain, both in terms of overhead and time. In this paper, we propose a reputation-based sharding dynamic reorganisation network sharding scheme, which integrates node performance and behavioural characteristics into the reputation computation, and at the same time adds alternative leaders to maintain the stability of the sharding system. Based on this, through the partial reorganisation algorithm of the sharding, the nodes are reasonably allocated to balance the computing power of each shard, effectively stimulating the liveness of the sharding system and improving overall safety and performance. Simulation results show that this sharding scheme effectively reduces the consensus failure probability, solves the collusion attack problem caused by the aggregation of malicious nodes within shards, and reduces the latency of the blockchain system. This provides strong support for the reliability and performance of the blockchain sharding system.

Environmental harshness does not affect the propensity for social learning in great tits, Parus major

According to the harsh environment hypothesis, natural selection should favour cognitive mechanisms to overcome environmental challenges. Tests of this hypothesis to date have largely focused on asocial learning and memory, thus failing to account for the spread of information via social means. Tests in specialized food-hoarding birds have shown strong support for the effects of environmental harshness on both asocial and social learning. Whether the hypothesis applies to non-specialist foraging species remains largely unexplored. We evaluated the relative importance of social learning across a known harshness gradient by testing generalist great tits, Parus major, from high (harsh)- and low (mild)-elevation populations in two social learning tasks. We showed that individuals use social learning to find food in both colour-associative and spatial foraging tasks and that individuals differed consistently in their use of social learning. However, we did not detect a difference in the use or speed of implementing socially observed information across the elevational gradient. Our results do not support predictions of the harsh environment hypothesis suggesting that context-dependent costs and benefits as well as plasticity in the use of social information may play an important role in the use of social learning across environments. Finally, this study adds to the accumulating evidence that the harsh environment hypothesis appears to have more pronounced effects on specialists compared to generalist species.

Associations between patterns of social support and perinatal mental health among Chinese mother: the mediating role of social trust

This study aims to explore the correlation between different social support patterns and perinatal mental health, and the mediating role of social trust in this. A cross-sectional survey was conducted in Jiangsu, China, with a sample size of 1705 pregnant respondents. Latent class analysis (LCA) was utilized to identify various social support patterns, while a multiple regression model was employed to analyze the mediating effect of social trust on the relationship between social support patterns and perinatal mental health. The study found four distinct social support patterns among the respondents: primary relationship-centric support, overall weak support, primary-secondary relationship-balanced support, and overall strong support. In the relationship between social support patterns and perinatal mental health, social trust played both a partial and full mediating role. The findings indicate that a social support system that enhances maternal trust and promotes honest disclosure of symptoms can effectively promote perinatal mental health.

Mother's Curse effects on lifespan and aging.

The Mother's Curse hypothesis posits that mothers curse their sons with harmful mitochondria, because maternal mitochondrial inheritance makes selection blind to mitochondrial mutations that harm only males. As a result, mitochondrial function may be evolutionarily optimized for females. This is an attractive explanation for ubiquitous sex differences in lifespan and aging, given the prevalence of maternal mitochondrial inheritance and the established relationship between mitochondria and aging. This review outlines patterns expected under the hypothesis, and traits most likely to be affected, chiefly those that are sexually dimorphic and energy intensive. A survey of the literature shows that evidence for Mother's Curse is limited to a few taxonomic groups, with the strongest support coming from experimental crosses in Drosophila. Much of the evidence comes from studies of fertility, which is expected to be particularly vulnerable to male-harming mitochondrial mutations, but studies of lifespan and aging also show evidence of Mother's Curse effects. Despite some very compelling studies supporting the hypothesis, the evidence is quite patchy overall, with contradictory results even found for the same traits in the same taxa. Reasons for this scarcity of evidence are discussed, including nuclear compensation, factors opposing male-specific mutation load, effects of interspecific hybridization, context dependency and demographic effects. Mother's Curse effects may indeed contribute to sex differences, but the complexity of other contributing factors make Mother's Curse a poor general predictor of sex-specific lifespan and aging.

Same Game, Different Participation in Nigeria: A qualitative study of (2006) National Gender Policy

Equal participation in politics are a goal of democracy, independent of gender, race, or ethnicity. This essay contends that if these values are not given top priority and prominence in society, democracy will have failed. By examining the opinions of participants, this study assesses the implementation of the 2006 National Gender Policy in Nigeria. To learn how participants, assess the policy’s execution since it was created, the research employs a qualitative methodology. The results show that sexism, illiteracy, and a lack of strong government support are the main barriers to full implementation of the national gender policy. The policy implementation framework is hindered, as evidenced by the participants’ experiences, raising questions about its efficacy in promoting gender equality. In order to accomplish the goals of the policy, the government and other players will need to provide better support, raise awareness of the issue, and make firmer promises. The implications of these findings are examined within the social learning theory, which suggests that behavior is learned from the environment, and that people adapt by observing the consequences of their actions and the actions of others. The research reveals that social learning is critical in the implementation of gender policies, but the limited application of the policy undermines its ability to promote change. Therefore, this paper argues that there is an urgent need for comprehensive action and policy reform, aimed at addressing the limitations identified and ensuring gender equity in Nigeria.

Disaster Risk Assessment of Fluvial and Pluvial Flood Using the Google Earth Engine Platform: a Case Study for the Filyos River Basin

AbstractThe aim of this study is to conduct a risk analysis of fluvial and pluvial flood disasters, focusing on the vulnerability of those residing in the river basin in coastal regions. However, there are numerous factors and indicators that need to be considered for this type of analysis. Swift and precise acquisition and evaluation of such data is an arduous task, necessitating significant public investment. Remote sensing offers unique data and information flow solutions in areas where access to information is restricted. The Google Earth Engine (GEE), a remote sensing platform, offers strong support to users and researchers in this context. A data-based and informative case study has been conducted to evaluate the disaster risk analysis capacity of the platform. Data on three factors and 17 indicators for assessing disaster risk were determined using coding techniques and web geographic information system (web GIS) applications. The study focused on the Filyos River basin in Turkey. Various satellite images and datasets were utilized to identify indicators, while land use was determined using classification studies employing machine learning algorithms on the GEE platform. Using various applications, we obtained information on ecological vulnerability, fluvial and pluvial flooding analyses, and the value of indicators related to construction and population density. Within the scope of the analysis, it has been determined that the disaster risk index (DRI) value for the basin is 4. This DRI value indicates that an unacceptable risk level exists for the 807,889 individuals residing in the basin.

Psychological status of residents at the onset of nationwide COVID-19 lockdown in low- and medium-risk areas of China.

The coronavirus disease 2019 (COVID-19), a dual threat to public physical and mental health, prompted an investigation into the psychological well-being of residents in low- to medium-risk areas of China during the initial stages of the pandemic. We administered WeChat-based questionnaire surveys and employed chi-square tests and multiple logistic regression to analyze correlations between residents' age, gender, education, symptoms, COVID-19 close contact history, information sources, and anxiety, depression, and attitudes toward lockdown measures. We received 10,433 valid questionnaires, revealing 26% anxiety and 19.5% depression. Support for lockdown measures reached 98.2%. Factors such as female gender, self-diagnosed pneumonia symptoms, close contact history, and higher education levels increased anxiety risk. Having a doctorate posed a severe anxiety risk, at 4.5 times (P = .019, 95% CI 1.29-15.73). Older age acted as a protective factor, reducing severe anxiety risk to 0.98 and 0.22 times (P < .001, 95% CI 0.14-0.34). Females with a master degree or below and those receiving COVID-19 information from multiple channels faced higher depression risk. Pneumonia symptoms were a risk for all anxiety and depression degrees. Attitudes toward lockdown measures had no significant impact on psychological status, nor did any of the analyzed factors affect residents' overall attitude toward lockdown. Our findings underscore the need for increased psychological counseling, particularly for young females with lower educational backgrounds or self-suspected infection symptoms, to mitigate mild to moderate anxiety and depression in future epidemics or pandemics. The public, especially those of working age with doctorates or higher education, bears the highest risk of severe anxiety. Lockdown measures enjoy strong support in low- to medium-risk areas of China.