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This paper uses affective computing and mood measures to calculate the negative emotional status of college students during school closures. The psychological data of university students at the University of H were counted using the affective computing model and psychometric methods, the frequency of their negative emotion words was mined, and the changes of each psychometric index were analyzed in terms of the time dimension, stressor, anxiety status, and emotional state, respectively. The epidemic closure period had a great negative impact on students. From January 20 to December 20, 2021, college students’ positive mood characteristic value declined. The negative mood characteristic value increased, and the negative mood characteristic value was always higher than the positive mood characteristic value. Finally, the negative mood characteristic value of 0.27 was much higher than the positive mood characteristic value of 0.07, and there were various discomforts such as about 40% of people were nervous, depressed, lacked motivation, and had diminished interest; at the same time, feelings of difficulty in accomplishing goals, failure, fear, anxiety, and frustration emerged. The epidemic closure greatly impacted students’ psychology, and students’ psychological problems need to be taken seriously and receive timely guidance.

Haze aerosols have a profound impact on air quality and pose serious health risks to the public. Due to its geographical location, Beijing experienced haze events in the spring of 2024. Lidar is an active remote sensing technology with a high spatiotemporal resolution and the ability to classify aerosols, and it is essential for effective haze monitoring. This study utilizes fluorescence–Raman–Mie polarization lidar with an emission wavelength of 355 nm, employing the δ p - G f method based on the particle depolarization ratio at 355 nm ( δ p 355 ) and the fluorescence capacity ( G f ), and combines meteorological data and backward-trajectory analysis to observe and classify low-altitude haze aerosols in Beijing during the spring of 2024. Notably, a mining dust event with strong fluorescence backscatter was detected. The haze aerosols were categorized into three types: pollution aerosols, desert dust, and mining dust. Their optical properties were summarized and compared. Desert dust showed a particle depolarization ratio range of 0.23–0.39 and a fluorescence capacity range from 0.18 × 10−4 to 0.63 × 10−4. Pollution aerosols had a larger fluorescence capacity but a lower depolarization ratio compared to desert dust, with a fluorescence capacity ranging from 0.55 × 10−4 to 1.10 × 10−4 and a depolarization ratio ranging from 0.10 to 0.17. Mining dust shared similar depolarization characteristics with desert dust but had a larger fluorescence capacity, ranging from 0.71 × 10−4 to 1.23 × 10−4, with a depolarization ratio range of 0.30–0.39. This study validates the effectiveness of the δ p 355 - G f method in classifying low-altitude haze aerosols in Beijing. Additionally, it offers a new perspective for more detailed dust classification using lidar. Furthermore, utilizing the δ p 355 - G f classification method and the δ p 355 - G f distributions of three typical aerosol samples, we developed a set of equations for the analysis of mixed aerosols. This method facilitates the separation and fraction analysis of aerosol components under various mixing scenarios. It enables the characterization of variations in the three types of haze aerosols at different altitudes and times, offering valuable insights into the interactions between desert dust, mining dust, and pollution aerosols in Beijing. [ABSTRACT FROM AUTHOR]

Background: Esophageal cancer is a significant global health concern, ranking seventh in incidence and sixth in mortality. It encompasses two pathological types: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma, with ESCC being more prevalent globally and associated with higher mortality rates. The POU (Pit-Oct-Unc) domain family transcription factors, comprising 15 members, play important roles in embryonic development and organ formation. Aberrant expression of POUs has been observed in several human cancers, influencing cell proliferation, tumor invasion, and drug resistance. However, their specific role in ESCC remains unknown. Methods: We analyzed TCGA and GEO databases to assess POUs expression in ESCC tissues. Kaplan-Meier and ROC analyses were used to evaluate the prognostic value of POUs. Gene Set Enrichment Analysis and Protein-Protein interaction network were used to explore the potential pathway. Functional assays (Cell Counting Kit-8, EdU Staining assay, and cloning formation assay) and mechanism analyses (RNA-seq, flow cytometry, and Western blot) were conducted to determine the effects of POU4F1 knockdown on ESCC cell phenotypes and signaling pathways. Results: POU4F1 and POU6F2 were upregulated in various cancer tissues, including ESCC, compared to normal tissues. POU4F1 expression was significantly correlated with patient survival and superior to previous models (AUC = 0.776). Knockdown of POU4F1 inhibited ESCC cell proliferation and affected cell cycle, autophagy, and DNA damage pathways in ESCC cells. Conclusion: POU4F1 is a novel and promising prognostic and therapeutic target for ESCC patients, providing insights into potential treatment strategies. [ABSTRACT FROM AUTHOR]

Malachite is one of the most important copper-bearing oxide minerals; however, it shows poor floatability prior to sulfidization under the thiol collector system. This study investigated the reasons for the low recovery of malachite flotation without sulfidization. The results of adsorption capacity and contact angle test indicated that the malachite surface could adsorb a sufficient amount of the collector, obviously increasing the hydrophobicity of the malachite surface under static conditions. By measuring the amount of inorganic carbon in the flotation solution, it was found that the amount of inorganic carbon in the solution increased significantly when the thiol collectors were added into pulp, which could be attributed to the induced dissolution of the malachite surface by thiol collectors. Solubility tests further demonstrated that the copper ions released from the natural dissolution of malachite proved difficult in regard to reactions with thiol collector to form precipitates; however, the thiol collector induced the dissolution of malachite surface, and so the hydrophobic complexes' copper-collector could not firmly adsorb on the mineral surface. Fourier transform infrared (FTIR) analysis revealed that thiol collectors do not adsorb stably on malachite surfaces. This was considered to be a substantial reason for the poor performance of malachite flotation without sulfidization. [ABSTRACT FROM AUTHOR]

Forest microclimates can contrast substantially from the macroclimate outside forests. These microclimates regulate understory biodiversity and ecosystem functions. Studies have quantified the global patterns and driving factors of forest understory temperature offsets, but data from China were almost missing, making the global assessment incomplete. To fill this knowledge gap, we quantitatively synthesized 494 paired observations from China extracted from 91 publications to quantify mean (Tmean), maximum (Tmax) and minimum temperature offsets (Tmin). Results showed that (1) forest canopies significantly buffered understory Tmean and Tmax against macroclimatic temperature, with average offsets of 1.0 and 1.5°C, respectively, while understory Tmin offsets were not significantly different from zero; (2) forest type (broadleaved, mixed, vs. coniferous) and forest location (rural vs. urban) did not affect Tmean, Tmax or Tmin offsets, but climate zone and season showed significant impacts; and (3) macroclimatic temperature, wind speed, tree height and canopy density also impacted temperature offsets, although their effects varied among Tmean, Tmax and Tmin. Our results complement the global assessment of forest buffering capacity, and reiterate the necessity for incorporating microclimatic variability into future bioclimatic modelling of species demography and distributions. [ABSTRACT FROM AUTHOR]

Molybdenum diselenide (MoSe2) is a promising anode for alkali-ion storage due to its intrinsic advantages. However, MoSe2 still encounters the issues of structural instability and poor rate performance caused by drastic volume change and sluggish reaction kinetics. Reasonable design of electrode structure is crucial for achieving superior electrochemical performance. Herein, a novel hierarchical structure coupled with 1D/1D subunits is elaborately designed and constructed, in which the MoSe2/CoSe2 heterostructure is the "trunk" and the N-doped carbon nanotubes are the "branches" (MoSe2/CoSe2/NCNTs). Benefiting from the properties endowed by unique configurations, MoSe2/CoSe2/NCNTs electrodes manifest faster reaction kinetics and better structure durability. Evaluated as an anode for LIBs and SIBs, MoSe2/CoSe2/NCNTs deliver high reversible capacity, superior rate capability (452 at 10 A g−1 in LIBs and 296 at 10 A g−1 in SIBs), and prominent cycle life (553 after 2000 cycles at 5 A g−1 in LIBs and 310 after 2000 cycles at 5 A g−1 in SIBs). Such design conception can also provide guidance for the development of other high-performance electrodes. [ABSTRACT FROM AUTHOR]

Objective: This study explores the correlation between life satisfaction and ADHD symptoms. It also discusses whether resilience mediates the correlation between ADHD symptoms and life satisfaction. Method: We surveyed 297 dental students. A total of 291 completed a self-report scale consisting of the Adult ADHD Self-Report Scale, Wender Utah Rating Scale, Life Satisfaction Scale, and Conner–Davidson Resilience Scale. The study used hierarchical linear regression analysis, resampling, and asymptotic strategies for data processing. Results: The ADHD screening results of the self-report scale were positive for 6.87% of the students. This positive rate differed among participants of diverse ages and varying paternal education levels. ADHD symptoms were negatively correlated with life satisfaction and resilience. Life satisfaction was observably positively associated with resilience. Resilience serves as a mediating role between life satisfaction and the two symptoms of ADHD. Conclusion: Resilience intervention programs can enhance the life satisfaction of dental students, especially those with positive ADHD symptoms screening. [ABSTRACT FROM AUTHOR]

The terrible shuttling of lithium polysulfides (LiPSs) is a major obstacle for commercializing lithium–sulfur (Li–S) batteries as high‐performance energy storage systems. In this study, a carbon‐based interlayer with effective suppression capability on the shuttle effect is developed by simply coating a well‐dispersed mixture of soybean protein isolate/MXene onto the acidified carbon paper (ACP). The resultant composite interlayer (SM@ACP) is able to synergistically diminish the shuttle effect through chemical adsorption and physical blocking. Meanwhile, this interlayer displays excellent conductivity and facilitates the diffusion of Li ions due to the composite coating to promote both electron/ion conduction as well as the porous structure of ACP. Benefiting from the unique properties of the composite interlayer, the as‐assembled Li–S batteries with SM@ACP interlayers show a great improvement in the cycling stability and rate performance, delivering a very low‐capacity decay rate of 0.071% per cycle at 0.5 C even after 800 cycles. This work provides a feasible route to realize rational design and commercial mass production of desirable interlayers for promoting the commercialization of Li–S batteries. [ABSTRACT FROM AUTHOR]

Excessive fat accumulation and chronic inflammation are two typical characteristics of obesity. AMP-activated protein kinase (AMPK), a master regulator of energy metabolism, is involved in adipogenesis, lipogenesis, and inflammation modulation in adipose tissue (AT). Thus, effective lipid reduction and anti-inflammation through AMPK regulation play vital roles in treating obesity. Herein, an anti-obesity nanosandwich is fabricated through attaching polymetformin (PolyMet) onto photothermal agent black phosphorus nanosheets (BP). PolyMet activates AMPK to inhibit adipogenesis, promote browning, and mitigate AT inflammation by decreasing macrophage infiltration, repolarizing macrophage phenotype, and downregulating pro-inflammatory cytokines. Additionally, BP induces lipolysis and apoptosis of adipocytes and macrophages through a photothermal effect. By further functionalization using hyaluronic acid (HA) and MMP2 substrate-linking P3 peptide-modified HA (P3-HA), an enhanced anti-obesity effect is obtained by dual-targeting of P3 and HA, and HA-mediated CD44 poly-clustering after MMP2 cleavage. Upon laser irradiation, the designed nanosandwich (P3-HA/PM@BP) effectively inhibits obesity development in obese mice, increases M2/M1 ratio in AT, reduces the serum levels of cholesterol/triglyceride and improves insulin sensitivity, exhibiting promising research potential to facilitate the clinical development of modern anti-obesity therapies., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Objective: To identify cortical and subcortical volume, thickness and cortical area features and the networks they constituted related to anxiety in Parkinson's disease (PD) using structural magnetic resonance imaging (sMRI), and to integrate multimodal features based on machine learning to identify PD-related anxiety., Methods: A total of 219 patients with PD were retrospectively enrolled in the study. 291 sMRI features including cortical volume, subcortical volume, cortical thickness, and cortical area, as well as 17 clinical features, were extracted. Graph theory analysis was used to explore structural networks. A support vector machine (SVM) combination model, which used both sMRI and clinical features to identify participants with PD-related anxiety, was developed and evaluated. The performance of SVM models were evaluated. The mean impact value (MIV) of the feature importance evaluation algorithm was used to rank the relative importance of sMRI features and clinical features within the model., Results: 17 significant sMRI variables associated with PD-related anxiety was used to build a brain structural network. And seven sMRI and 5 clinical features with statistically significant differences were incorporated into the SVM model. The comprehensive model achieved higher performance than clinical features or sMRI features did alone, with an accuracy of 0.88, a precision of 0.86, a sensitivity of 0.81, an F1-Score of 0.83, a macro-average of 0.85, a weighted-average of 0.92, an AUC of 0.88, and a result of 10-fold cross-validation of 0.91 in test set. The sMRI feature right medialorbitofrontal thickness had the highest impact on the prediction model., Conclusion: We identified the brain structural features and networks related to anxiety in PD, and developed and internally validated a comprehensive model with multimodal features in identifying., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Jia, Yang, Li, Chen, Wu, Li, Wang, Wang, Liu and Wu.)

Object . The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination. Approach . First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance. Main results . Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods. Significance . In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations., (© 2024 Institute of Physics and Engineering in Medicine.)