Fitness promotion in college: the relationships among students' perceived physical literacy, knowledge, and physical fitness.

Purpose: The purpose of this study was to examine the relationships among perceived physical literacy (PPL), knowledge of physical activity and fitness (PAF knowledge), and physical fitness. Methods: Undergraduates (N = 968, female = 414; Mage = 18.64) from a public university in central China completed a simplified Chinese version of the PPL instrument, an online test for PAF knowledge, and seven health-related physical fitness tests. The PPL includes three dimensions: (a) confidence and physical competence, (b) motivation, and (c) interaction with the environment. The fitness tests measured lung capacity, body mass index (BMI), and performances in 800 (female)/1000 (male) meters run, 50 meters dash (50 M), sit-up (female) / pull-up (male), standing long jump (SLJ), and sit-and-reach. Results: PPL and its dimensions significantly predicted six fitness test performances both in male (ß: -0.42 - 0.37; p < 0.01-0.05; R2: 0.01-0.13) and female (ß: -0.59 - 0.49; p < 0.01-0.05; R2: 0.03-0.13) students. PAF knowledge (ß: -0.17 - 0.18; p < 0.01-0.05; R2: 0.01-0.05) significantly predicted BMI (males) and performances in 50 M (females) and SLJ (females) tests. Conclusion: To support college students' fitness development and maintenance, tailored physical activity and fitness promotion programs are needed to strengthen students' PPL and PAF knowledge.

Review and perspectives on TS-1 catalyzed propylene epoxidation.

Titanium silicate zeolite (TS-1) is widely used in the research on selective oxidations of organic substrates by H2O2. Compared with the chlorohydrin process and the hydroperoxidation process, the TS-1 catalyzed hydroperoxide epoxidation of propylene oxide (HPPO) has advantages in terms of by-products and environmental friendliness. This article reviews the latest progress in propylene epoxidation catalyzed by TS-1, including the HPPO process and gas phase epoxidation. The preparation and modification of TS-1 for green and sustainable production are summarized, including the use of low-cost feedstocks, the development of synthetic routes, strategies to enhance mass transfer in TS-1 crystal and the enhancement of catalytic performance after modification. In particular, this article summarizes the catalytic mechanisms and advanced characterization techniques for propylene epoxidation in recent years. Finally, the present situation, development prospect and challenge of propylene epoxidation catalyzed by TS-1 were prospected.

Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy.

OBJECTIVE: This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. METHODS: This prospective cohort study included 9,895 pregnant women who received prenatal care at a maternal health facility in China from January 2021 to December 2022. Data on demographics, medical history, lifestyle factors, and mental health were collected. A multivariable logistic regression analysis was performed to develop the prediction model with spontaneous abortion as the outcome. The model was internally validated using bootstrapping techniques, and its discrimination and calibration were assessed. RESULTS: The spontaneous abortion rate was 5.95% (589/9,895) 1. The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score 1. The model showed good discrimination with a C-statistic of 0.88 (95% CI 0.87‒0.90) 1, and its calibration was adequate based on the Hosmer-Lemeshow test (p = 0.27). CONCLUSIONS: The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. Further external validation is recommended before clinical application.

Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy

Abstract Objective: This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. Methods: This prospective cohort study included 9,895 pregnant women who received prenatal care at a maternal health facility in China from January 2021 to December 2022. Data on demographics, medical history, lifestyle factors, and mental health were collected. A multivariable logistic regression analysis was performed to develop the prediction model with spontaneous abortion as the outcome. The model was internally validated using bootstrapping techniques, and its discrimination and calibration were assessed. Results: The spontaneous abortion rate was 5.95% (589/9,895) 1. The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score 1. The model showed good discrimination with a C-statistic of 0.88 (95% CI 0.87‒0.90) 1, and its calibration was adequate based on the Hosmer-Lemeshow test (p = 0.27). Conclusions: The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. Further external validation is recommended before clinical application.

Analysis of the status and factors influencing physical activity in patients undergoing ovarian cancer chemotherapy.

Background: Ovarian cancer is a common gynecological malignancy, leading to approximately 200,000 deaths globally in 2020. Research has shown that regular physical activity can reduce the toxic side effects of cancer treatment, reduce morbidity and mortality, extend survival time, and improve quality of life. We aimed to evaluate physical activity regimens in patients undergoing chemotherapy for ovarian cancer and analyze the factors influencing physical activity levels. Methods: To facilitate the selection of patients with ovarian cancer hospitalized for chemotherapy in the Third Affiliated Hospital of Zhengzhou University and the First Affiliated Hospital of Zhengzhou University from August 2022 to February 2023, questionnaire surveys were conducted using the General Information Questionnaire, International Physical Activity Questionnaire, Hospital Anxiety and Depression Scale, and Revised Piper Fatigue Scale. Results: Data were collected from 167 patients with ovarian cancer. Overall, 96 (57.5%) patients had low physical activity levels, 53 (31.7%) had moderate physical activity levels, and 18 (10.8%) had high physical activity levels. Logistic regression analysis revealed that sleep status, social support, anxiety, depression, and cancer-related fatigue were the main factors influencing physical activity in patients undergoing chemotherapy for ovarian cancer. Conclusions: Physical activity levels of patients undergoing ovarian cancer chemotherapy were generally low. Therefore, healthcare professionals should pay greater attention to the physical activity in these patients. Targeted and individualized health guidance is recommended, and activity interventions should be implemented according to the influencing factors to promote disease understanding and increase physical activity levels.

Analyzing the epidemic situation and influencing factors of childhood pneumonia during the COVID-19 epidemic period in Laiwu, China.

This study aimed to analyze the trends of childhood pneumonia before and after the corona virus disease 2019 (COVID-19) pandemic in Laiwu, China, and explore the associated risk factors to provide a reference for the prevention, control, and treatment of childhood pneumonia. In this cross-sectional study, data were collected from 11,716 children aged 3 to 12 years who underwent chest radiography/computed tomography from January 2018 to December 2021 in Laiwu, China. The generalized estimating equation model was used to analyze the time trend of the pneumonia detection rate. Multivariate logistic regression analysis was used to analyze the risk factors for pneumonia in children. The overall pneumonia detection rate was 40.3% (4721/11,716). The pneumonia detection rate was 41.5% and 39.5% in 2018 and 2019, respectively, before the COVID-19 pandemic, which decreased to 38.1% in 2020 during the pandemic and increased to 40.7% in 2021 after the pandemic. Multivariate logistic regression analysis showed that preterm birth (odds ratio [OR] = 1.68), frequent exposure to secondhand smoke (OR = 1.79), hospitalization ≥ 1 time in half a year (OR = 2.13), and history of allergic rhinitis (OR = 2.14) were risk factors for pneumonia in children. Wearing masks when outdoors (OR = 0.89) and engaging in regular physical activity (OR = 0.65) were protective factors. The pneumonia detection rate in children showed a decreasing trend during the COVID-19 pandemic in 2020 but an increasing trend after the pandemic in 2021. Premature birth, exposure to secondhand smoke, frequent hospitalization, and allergic rhinitis were risk factors for pneumonia in children. Wearing masks when outdoors and exercising may reduce the risk of pneumonia in children.

Methylene-Bridged Dimerization by Cu-Catalyzed Deconstructive C-C Cleavage of Oxacycloalkane.

Herein, we demonstrate the successful utilization of copper catalysis and oxygen oxidation for consecutive C(sp3)-C(sp3) bond cleavage in alkyl cyclic ethers. A key step involves a copper-oxygen autoxidation process, generating in situ alkoxy radicals and triggering sequential C-C bond cleavage. This ß-oxidative cleavage strategy enables the use of cyclic ethers as valuable C1 building blocks for the synthesis of bridged methylene dimers. This reaction holds promise for inspiring alternative methods targeting inert C(sp3)-C(sp3) bond cleavage.

Synthesis of 2-Aminofurans and 2-Aminothiophenes through Elemental Sulfur-Promoted Switchable Redox Condensation Reaction of Enaminones with Methylene Nitriles.

Herein, we report an elemental sulfur-promoted switchable redox condensation reaction that can selectively prepare 2-aminofurans and 2-aminothiophenes from the corresponding enaminones and methylene nitriles, respectively. Mechanistic studies demonstrated that the enaminones, as dual nucleophiles, reacted with nitrile acetate to produce 2-aminofurans via 3,5-annulation under promotion by elemental sulfur. These reactions used readily available starting materials, transition metal-free, eco-friendly procedures, gram-scale syntheses, and wide functional group tolerance. The methodology may be useful for the construction of 2-aminofuran and 2-aminothiophene derivatives with potential biological activity.

LayoutGAN: Synthesizing Graphic Layouts With Vector-Wireframe Adversarial Networks.

Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric relations of different types of 2D elements. The generator of LayoutGAN takes as input a set of randomly-placed 2D graphic elements, represented by vectors and uses self-attention modules to refine their labels and geometric parameters jointly to produce a realistic layout. Accurate alignment is critical for good layouts. We, thus, propose a novel differentiable wireframe rendering layer that maps the generated layout to a wireframe image, upon which a CNN-based discriminator is used to optimize the layouts in image space. We validate the effectiveness of LayoutGAN in various experiments including MNIST digit generation, document layout generation, clipart abstract scene generation, tangram graphic design, mobile app layout design, and webpage layout optimization from hand-drawn sketches.

Attribute-Conditioned Layout GAN for Automatic Graphic Design.

Modeling layout is an important first step for graphic design. Recently, methods for generating graphic layouts have progressed, particularly with Generative Adversarial Networks (GANs). However, the problem of specifying the locations and sizes of design elements usually involves constraints with respect to element attributes, such as area, aspect ratio and reading-order. Automating attribute conditional graphic layouts remains a complex and unsolved problem. In this article, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions. Due to the complexity of graphic designs, we further propose an element dropout method to make the discriminator look at partial lists of elements and learn their local patterns. In addition, we introduce various loss designs following different design principles for layout optimization. We demonstrate that the proposed method can synthesize graphic layouts conditioned on different element attributes. It can also adjust well-designed layouts to new sizes while retaining elements' original reading-orders. The effectiveness of our method is validated through a user study.

Serum miR-221-3p as a new potential biomarker for depressed mood in perioperative patients.

MicroRNAs (miRNAs) modulate various genes associated with brain disorders and circulating miRNAs may therefore serve as biomarkers for these neurological diseases. We previously found that the miRNA miR-221-3p was highly expressed in cerebrospinal fluid and the serum of major depressive disorder (MDD) patients. Here, we examined whether miR-221-3p could be used as a biomarker for depressed mood in perioperative patients. We first examined the relative expression of serum miR-221-3p by real-time quantitative PCR in perioperative patients with different degrees of depressive mood assessed by the Patient Health Questionnaire-9 (PHQ-9) diagnostic form. We found that miR-221-3p expression in the mild depressive mood group (PHQ-9 scores 5-9) was 2.21 fold that of the normal group (PHQ-9 scores 0-4) and the moderate＆severe depressive mood group (PHQ-9 scores ≥ 10) showed miR-221-3p expression levels 3.66 fold that of the normal group. Then the absolute quantification of serum miR-221-3p was obtained using an miRNA standard curve. We found that the amount of serum miR-221-3p was positively correlated with depressed mood; when serum miR-221-3p > 1.7 × 107 copies/µg RNA, all indicated PHQ-9 scores were higher than 6. Subsequently, we found that miR-221-3p could indirectly increase the expression of IFN-α (Interferon alpha) in astrocytes by targeting IRF2 (Interferon Regulatory Factor 2) and that miR-221-3p participated in the anti-neuroinflammatory signaling cascades induced by ketamine and paroxetine via the IRF2/IFN-α pathway. Our results indicate that elevated serum miR-221-3p can be used as a biomarker for depressed mood in perioperative patients and that IFN-α-induced NF-κB activation in astrocytes mediated by miR-221-3p targeting of IRF2 may be one of the potential mechanisms.

Top-Down Visual Saliency via Joint CRF and Dictionary Learning.

Top-down visual saliency is an important module of visual attention. In this work, we propose a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a visual dictionary. The proposed model incorporates a layered structure from top to bottom: CRF, sparse coding and image patches. With sparse coding as an intermediate layer, CRF is learned in a feature-adaptive manner; meanwhile with CRF as the output layer, the dictionary is learned under structured supervision. For efficient and effective joint learning, we develop a max-margin approach via a stochastic gradient descent algorithm. Experimental results on the Graz-02 and PASCAL VOC datasets show that our model performs favorably against state-of-the-art top-down saliency methods for target object localization. In addition, the dictionary update significantly improves the performance of our model. We demonstrate the merits of the proposed top-down saliency model by applying it to prioritizing object proposals for detection and predicting human fixations.

Transferring visual prior for online object tracking.

Visual prior from generic real-world images can be learned and transferred for representing objects in a scene. Motivated by this, we propose an algorithm that transfers visual prior learned offline for online object tracking. From a collection of real-world images, we learn an overcomplete dictionary to represent visual prior. The prior knowledge of objects is generic, and the training image set does not necessarily contain any observation of the target object. During the tracking process, the learned visual prior is transferred to construct an object representation by sparse coding and multiscale max pooling. With this representation, a linear classifier is learned online to distinguish the target from the background and to account for the target and background appearance variations over time. Tracking is then carried out within a Bayesian inference framework, in which the learned classifier is used to construct the observation model and a particle filter is used to estimate the tracking result sequentially. Experiments on a variety of challenging sequences with comparisons to several state-of-the-art methods demonstrate that more robust object tracking can be achieved by transferring visual prior.