PLoS ONE最新文献

Background: Individuals reporting self-injury are at greater risk of several adverse outcomes, including suicide. There is reason to be concerned how these individuals cope when stressful life events increase. This study aimed to investigate the trajectories of anxiety and depressive symptoms and the predictive value of self-injury history in individuals with psychiatric symptoms during the unique and stressful conditions of the COVID-19 pandemic.

Methods: In a longitudinal population cohort study (N = 1810) ranging from 2020 to 2022, anxiety (measured by Generalized Anxiety Disorder, GAD-7) and depressive symptoms (measured by Patient Health Questionnaire, PHQ-9) were self-reported monthly during 12 months. Latent growth curve models with and without self-reported self-injury history as predictors were conducted.

Results: Overall, anxiety and depressive symptoms decreased from baseline, but remained at moderate severity at follow-up. Individuals reporting suicidal or nonsuicidal self-injury reported significantly higher symptom severity at baseline. In addition, individuals reporting suicidal self-injury demonstrated a slower rate of decline in the symptom load over the course of 12 months.

Conclusions: Over the course of 12 months, anxiety and depressive symptoms decreased in individuals with psychiatric symptoms, but still indicate a psychiatric burden. Individuals with a history of self-injury could be more vulnerable in face of stressful conditions such as those experienced during the COVID-19 pandemic.

[This corrects the article DOI: 10.1371/journal.pone.0271715.].

Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (DL)-based methods are popular for EEG decoding. Whereas the utilization efficiency of EEG features in frequency and temporal domain is not sufficient, which results in poor MI classification performance. To address this issue, an EEG-based MI classification model based on a frequency enhancement module, a deformable convolutional network, and a crop module (FDCN-C) is proposed. Firstly, the frequency enhancement module is innovatively designed to address the issue of extracting frequency information. It utilizes convolution kernels at continuous time scales to extract features across different frequency bands. These features are screened by calculating attention and integrated into the original EEG data. Secondly, for temporal feature extraction, a deformable convolution network is employed to enhance feature extraction capabilities, utilizing offset parameters to modulate the convolution kernel size. In spatial domain, a one-dimensional convolution layer is designed to integrate all channel information. Finally, a dilated convolution is used to form a crop classification module, wherein the diverse receptive fields of the EEG data are computed multiple times. Two public datasets are employed to verify the proposed FDCN-C model, the classification accuracy obtained from the proposed model is greater than that of state-of-the-art methods. The model's accuracy has improved by 14.01% compared to the baseline model, and the ablation study has confirmed the effectiveness of each module in the model.

Foreign Direct Investment (FDI) and environmental degradation are some of the most controversial debates, especially in the context of global warming and climate change. This study aims to shed light on the impact of FDI on environmental degradation in the countries with the highest carbon dioxide (CO2) emissions, taking into account 513 country-years between the years 1996 and 2022. CO2 and the ecological footprint were used as indicators of environmental degradation. The possible non-linear linkage between FDI and environmental degradation has also been analyzed. Gross domestic product (GDP) growth and inflation rate were used as control variables. The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. This study also highlights the importance of international agreements and frameworks, such as the Sustainable Development Goals and the Paris Agreement, in guiding nations towards a more sustainable future. These empirical results are vital for regulators, emphasizing the need for a holistic and multidimensional approach to both economic prosperity and environmental protection.

The virtual crop stands as a vital content in crop model research field, and has become an indispensable tool for exploring crop phenotypes. The focal objective of this undertaking is to realize three-dimensional (3D) dynamic visualization simulations of rice individual and rice populations, as well as to predict rice phenotype using virtual rice. Leveraging our laboratory's existing research findings, we have realized 3D dynamic visualizations of rice individual and populations across various growth degree days (GDD) by integrating the synchronization relationship between the above-ground parts and the root system in rice plant. The resulting visualization effects are realistic with better predictive capability for rice morphological changes. We conducted a field experiment in Anhui Province in 2019, and obtained leaf area index data for two distinct rice cultivars at the tiller stage, jointing stage, and flowering stage. A method of segmenting leaf based on the virtual rice model is employed to predict the leaf area index. A comparative analysis between the measured and simulated leaf area index yielded relative errors spanning from 7.58% to 12.69%. Additionally, the root mean square error, the mean absolute error, and the coefficient of determination were calculated as 0.56, 0.55, and 0.86, respectively. All the evaluation criteria indicate a commendable level of accuracy. These advancements provide both technical and modeling support for the development of virtual crops and the prediction of crop phenotypes.

Background: Iron deficiency anaemia in pregnancy is a significant contributor to maternal and perinatal morbidity and mortality globally. Despite international and national guidelines for its screening and treatment, knowledge and prescription practices of healthcare providers vary.

Aim: To determine maternal healthcare workers' screening and treatment practices for iron deficiency in anaemic pregnancy women in two states in Nigeria.

Methodology: This cross-sectional study sampled maternal healthcare workers from 84 randomly selected public health facilities in Lagos and Kano States. Data on methods of diagnosis and prescription practices for iron deficiency anaemia were collected using a self-administered questionnaire. Means and percentages were reported using probability weights, and a comparison of practices of anaemia treatment between doctors and nurses/midwives was done using Chi-square test or Fishers exact.

Results: Of the 467 maternal healthcare workers surveyed (232 from Lagos, 235 from Kano), 40.0% were doctors, 54.0% nurses or midwives and 6.0% community health extension workers. In the sample, 27.6% always and 58.7% sometimes screened anaemic pregnant women for iron deficiency; among these, 84.7% screened using complete blood count. Oral iron for treatment of iron deficiency anaemia was prescribed by 96.9%. Intravenous iron for treatment was prescribed by 30.2%, but by only by 18.6% as first-line drug (as iron dextran by 69.3% and as iron sucrose by 31.5% of intravenous iron prescribers). Commonest reasons for low usage of intravenous iron were cost and need for venepuncture. Fifty-three percent of maternal healthcare workers' prescribed iron supplements for anaemia during concomitant infection, with the prescription practice similar among doctors versus nurse/midwives (p = 0.074).

Conclusion: We found suboptimal levels of screening for iron deficiency among anaemic pregnant women. Iron deficiency anaemia in pregnancy is almost exclusively treated with oral iron by maternal healthcare workers in the two Nigerian states, similarly between doctors and nurses/midwives. Further research into potential reasons for low screening for iron deficiency and low use of intravenous iron are needed.

A cross-sectional survey was conducted at Polytechnic High School (PHS) to assess the spread of COVID-19 infection among students and staff. A random cluster sampling was conducted between May 19 and August 18, 2022, after the fourth wave of COVID-19 in Senegal. IgM and IgG SARS-CoV-2 antibodies were screened using WANTAI SARS-CoV-2 ELISA assays. Seroprevalence and descriptive statistics were calculated, and associations between seropositivity and different factors were determined using logistic regression. A total of 637 participants were recruited and the median age was 21 years [18-63]. 62.0% of the participants were female, and 36.89% were male, with a male-to-female ratio = 0.59. The overall IgG and IgM seroprevalence were 92% and 6.91% respectively. Among those who tested positive for IgM, 6.75% were also positive for IgG, and 0.15% were negative for IgG. Interestingly, 6.90% of participants tested negative for both IgM and IgG. We found a higher IgM seroprevalence in men than women (9.4% vs. 5.6%) and a lower IgM seroprevalence in (18-25) age group compared to (55-65) years. We revealed a significant difference according to IgG seroprevalence among participants who declared fatigue symptoms [92.06% (95% CI: 89.96-94.16)] compared to those who did not [80.39% (95% CI: 77.31-83.47)], p = 0.0027. IgM seropositivity was found to be associated with Body Mass Index (BMI) categories (O.R. 0.238, p = 0.043), ethnic group (O.R. 0.723, p = 0.046), and marital status (O.R. 2.399, p = 0.021). Additionally, IgG seropositivity was linked to vaccination status (O.R. 4.741, p < 0.001). Our study found that most students and staff at PHS were exposed to SARS-CoV-2, confirming the virus's circulation at the time of the survey. We also identified differences in individual susceptibility that need further clarification. Our results highlight the importance of seroepidemiological surveys to assess the true impact of the COVID-19 pandemic in a community and to monitor variations in antibody response.

Chinese vocational colleges, under global and educational pressures, encounter challenges in IT integration for English teaching, which risks dampening student motivation due to heightened cognitive load. This study addresses the need to elucidate the mediating role of cognitive load in the relationship between self-efficacy and learning motivation among these students. By integrating Social Cognitive Theory(SCT) and Cognitive Load Theory(CLT), the research employs a structural equation model to analyze survey data, revealing that self-efficacy positively impacts deep and achievement motivation and negatively influences cognitive load, which in turn affects learning motivation. These insights underscore the importance of fostering self-efficacy and managing cognitive load to enhance vocational students' English learning motivation, offering valuable guidance for educational practices and curriculum development in the face of global challenges.

Soil erosion is one of the main issues that endangers global ecosystems. This study explored the spatiotemporal distribution of soil erosion and its drivers in the karst plateau mountainous region. A detailed examination of topography, soil, vegetation, land use, and precipitation data from 2000 to 2020 was conducted in Bijie City using the revised universal soil loss equation model. We also explored the driving forces using a geographical detector. The findings show that between 2000 and 2020, soil erosion first decreased, followed by an increase. The southwest, south, and northern regions contained the highest intensity of soil erosion. Land use, slope, and precipitation are the primary factors influencing soil erosion, with slopes having the greatest impact. By improving our understanding of the dynamics of soil erosion and the primary variables that influence it in karst plateau mountainous environments, our findings can assist in the development of strategies and technical support for sustainable soil and water conservation.

Background: Perinatal mortality is a major global public health concern, especially in sub-Saharan Africa (SSA). Despite perinatal mortality being a major public health concern in SSA, there are very limited studies on the incidence and factors associated with perinatal mortality. Therefore, we aimed to investigate the factors associated with perinatal mortality in SSA.

Methods: A secondary data analysis was conducted based on the Demographic and Health Survey (DHS) data of 27 SSA countries. About 314,099 births in the preceding five years of the surveys were considered for the analysis. A multilevel binary logistic regression model was fitted to identify factors associated with perinatal mortality. Deviance (-2Log-Likelihood Ratio (LLR)) was used for model comparison. The Adjusted Odds Ratio (AOR) with the 5% Confidence Interval (CI) of the best-fitted model was used to verify the significant association between factors and perinatal mortality.

Results: The perinatal mortality rate in sub-Saharan Africa (SSA) was 37.31 per 1,000 births (95% CI: 36.65, 37.98). In the final best-fit model, factors significantly associated with higher perinatal mortality included media exposure (AOR: 1.12, 95% CI: 1.08, 1.17), maternal age ≥ 35 years (AOR: 1.13, 95% CI: 1.06, 1.21), health facility delivery (AOR: 1.10, 95% CI: 1.06, 1.15), having 2-4 births (AOR: 1.35, 95% CI: 1.25, 1.47), five or more births (AOR: 1.69, 95% CI: 1.53, 1.86), residence in West Africa (AOR: 1.30, 95% CI: 1.24, 1.36) or Central Africa (AOR: 1.05, 95% CI: 1.00, 1.11), rural residency (AOR: 1.08, 95% CI: 1.02, 1.13), and difficulty accessing a health facility (AOR: 1.06, 95% CI: 1.02, 1.10). In contrast, factors significantly associated with lower perinatal mortality were a preceding birth interval of 2-4 years (AOR: 0.70, 95% CI: 0.67, 0.74) or five or more years (AOR: 0.91, 95% CI: 0.84, 0.97), Antenatal Care (ANC) visit (AOR: 0.66, 95% CI: 0.63, 0.69), higher education levels (AOR: 0.82, 95% CI: 0.73, 0.93), middle household wealth (AOR: 0.93, 95% CI: 0.88, 0.98), and richer household wealth (AOR: 0.93, 95% CI: 0.87, 0.99).

Conclusion: Perinatal mortality was a major public health problem in SSA. Maternal socio-demographic, obstetrical, and healthcare-related factors are significantly associated with perinatal mortality. The findings of this study highlighted the need for holistic healthcare interventions targeting enhancing maternal healthcare services to reduce the incidence of perinatal mortality.