Stress and deformation analysis of the steel pipe arch integral lifting construction process of a continuous beam arch bridge.

The Changjinghuang Railway Xinjiang Xizhi Bridge is a (90 + 180 + 90) m continuous beam arch bridge, and the arch rib steel pipe installation adopts "short bracket assembly and overall lifting method". In order to ensure the accuracy of closure, the stress and deformation of the arch rib and bracket must be strictly controlled. Midas Civil is used to establish the finite element model to simulate the overall lifting construction process of the arch rib. Based on the model, the stress and deformation of the arch rib and the supports are analyzed, and the determination method of the horizontal cable force under temperature variations is proposed. The results show that the stress and deformation of the arch rib and bracket meet the requirements. Considering the variation of temperature, the tension force of the horizontal cables is taken as 200 t. The construction plan proposed under the guidance of numerical calculation results has been proven by practical engineering to meet the requirement of closure accuracy, which can be used as a reference for similar projects.

Stability mechanism and countermeasure of the solid coal rib in deep gob-side entry retaining: Insights from theoretical analysis numerical simulation.

The stability and integrity of the solid coal rib in deep gob-side entry retaining (GER) can be compromised due to the cyclic loading and unloading caused by mining-induced stress. This can lead to failure of the deep GER during depressurized mining operations. In this study, we focus on a specific case at the 94103 working face in Qishan Coal Mine of Xuzhou Mining Bureau. We establish an engineering model that describes the interaction between the solid coal rib and the main roof in GER, aiming to elucidate the characteristics of main roof failure and instability throughout the entire GER process. this model particularly emphasizes the mechanical properties of the solid coal rib as a contributing factor. Additionally, developed a limit stress state model for evaluating bolt-supported plastic solid coal ribs, which helps determine appropriate support resistance levels to prevent two common forms of failure in these ribs. Furthermore, created a numerical calculation model to investigate different bolt conditions' impact on solid coal rib failure mechanisms. Finally, based on field monitoring data validation, we propose control measures for reinforcing solid coal ribs along with suggestions for roof support design and filling body construction schemes under similar geological conditions. These research findings offer valuable guidance for developing effective reinforcement strategies for filling bodies.

A network-based study reveals multimorbidity patterns in people with type 2 diabetes.

Patients with type 2 diabetes mellitus (T2DM) are at a heightened risk of living with multiple comorbidities. However, the comprehension of the multimorbidity characteristics of T2DM is still scarce. This study aims to illuminate T2DM's prevalent comorbidities and their interrelationships using network analysis. Using electronic medical records (EMRs) from 496,408 Chinese patients with T2DM, we constructed male and female global multimorbidity networks and age- and sex-specific networks. Employing diverse network metrics, we assessed the structural properties of these networks. Furthermore, we identified hub, root, and burst diseases within these networks while scrutinizing their temporal trends. Our findings uncover interconnected T2DM comorbidities manifesting as emergence in clusters or age-specific outbreaks and core diseases in each sex that necessitate timely detection and intervention. This data-driven methodology offers a comprehensive comprehension of T2DM's multimorbidity, providing hypotheses for clinical considerations in the prevention and therapeutic strategies.

Case report of non-tracheal intubation-an alternative for postpneumonectomy patients undergoing contralateral pulmonary resection.

BACKGROUND: Surgery on the contralateral or other lungs after pneumonectomy on one side is highly challenging and complex. It is critical to creating conditions for fluent surgical maneuvers while ensuring adequate ventilation for a patient during such an operation in the same chest cavity that appears incompatible. CASE PRESENTATION: We have reported herein the case of a patient who, following a left pneumonectomy, underwent a right upper pulmonary nodule wedge resection via video-assisted thoracoscopic surgery without requiring endotracheal intubation. We managed ventilation with a laryngeal mask airway under general anesthesia combined with a thoracic epidural block. The diseased lobe collapsed well for the surgical procedure during VATS without hypoxia, after which the resection was safely performed. CONCLUSIONS: Non-tracheal intubation anesthesia can be a potentially attractive alternative for patients undergoing contralateral pulmonary resection after pneumonectomy.

Trends and Gaps in Statin Use for Cardiovascular Disease Prevention in Type 2 Diabetes: A Real-World Study in Shanghai, China.

BACKGROUND: Cardiovascular disease (CVD) is the major cause of death among persons with diabetes. As the preventative use of statin has been proved to reduce CVD risks, understanding the current status and the trend in statin use is crucial to improve clinical treatment strategies. OBJECTIVE: Our study aimed to answer the question of what were the status and trend of statin use in Shanghai, China. METHODS: Our study estimated statin use and trends from 2015 to 2021 among 702 727 patients with type 2 diabetes mellitus (T2DM) based on electronic health records from the Shanghai Hospital Link Database. Patients were grouped according to the presence of CVDs, tested separately for statin primary and secondary prevention use, and stratified by age and sex. RESULTS: In the study population, 221 127 patients (31.5%) received statin therapy, and among patients with CVD, 157 622 patients (51.62%) received statin therapy for secondary prevention, but only 15% of patients received statins for primary prevention. The trend in the use of statins was still on the rise from 28.3% in 2015. Statin use increased with age (18-39 years, 14.0%; 40-59 years, 26.8%; 60-74 years, 33.35%; and 75 and over, 36.1%), and women (29.7%, n = 93 977) were less likely to receive statin therapy compared to men (32.9%, n = 127 150). CONCLUSION: Despite the rise in statin use in T2DM in recent decades, a large proportion of subjects with T2DM did not receive statin therapy.

Insulin use and gout risk among patients with type 2 diabetes mellitus: a real-world cohort study in Shanghai, China.

OBJECTIVES: The effect of insulin use on gout risk remains unknown. This study aimed to investigate the association between insulin use and gout risk among patients with type 2 diabetes mellitus (T2DM). METHODS: Based on the Shanghai Link Healthcare Database, patients with newly diagnosed T2DM, with or without insulin exposure, were identified from January 1, 2014 to December 31, 2020, and followed until December 31, 2021. Apart from the original cohort, we also established a 1:2 propensity score-matched cohort. A time-dependent Cox proportional hazards model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for gout incidence associated with insulin exposure. RESULTS: A total of 414,258 patients with T2DM, including 142,505 insulin users and 271,753 insulin non-users, were enrolled in this study. After a median follow-up of 4.08 years (interquartile range, 2.46-5.90 years), the incidence of gout was significantly higher in insulin users than in insulin non-users (319.35 versus 302.20 cases per 100,000 person-years; HR 1.09, 95% CI 1.03-1.16). The results were robust in propensity score-matched cohort, sensitivity analyses, and stratified analysis of aspirin. In other stratified analyses, the association between insulin use and increased gout risk was found only in patients who were female, or aged 40-69 years, or without hypertension, dyslipidemia, ischemic heart disease, chronic lung disease, kidney disease, or not using diuretic. CONCLUSIONS: Insulin use is associated with a significantly increased risk of gout among patients with T2DM. Key Points • The first real-world study to investigate the effect of insulin use on gout risk. • Insulin use is associated with a significantly increased risk of gout among patients with type 2 diabetes mellitus.

Spatial and temporal effect and driving factors of ecosystem service trade-off in the Qingjiang River Basin, China.

Identifying the spatiotemporal differentiation characteristics of trade-offs/synergies relationships of ecosystem service in watersheds and their influencing factors is essential for ecosystem management and regulation. It is of great significance for the efficient allocation of environmental resources and the rational formulation of ecological and environmental policies. We used correlation analysis and root mean square deviation to analyze the trade-offs/synergies relationships among grain provision, net primary productivity (NPP), soil conservation, and water yield service in the Qingjiang River Basin from 2000 to 2020. Then, we analyzed the critical factors affecting the trade-offs of ecosystem services by using the geographical detector. The results showed that grain provision service in the Qingjiang River Basin presented a decreasing trend from 2000 to 2020, and that NPP, soil conservation, as well as water yield service showed an increasing trend. There was a decreasing trend in the degree of trade-offs between grain provision and soil conservation services, NPP and water yield service, and an increasing trend in the intensity of trade-offs between other services. Grain provision and NPP, soil conservation and water yield showed trade-off in the northeast and synergy in the southwest. There was a synergistic relationship between NPP with soil conservation and water yield in the central part and a trade-off relationship in the surrounding area. Soil conservation and water yield showed a high degree of synergy. Land use and normalized difference of vegetation index were the dominant factors in the intensity of trade-offs between grain provision and other ecosystem services. Precipitation, temperature, and elevation were the dominant factors in the intensity of trade-offs between water yield service and other ecosystem services. The intensity of ecosystem service trade-offs was not only affected by a single factor. In contrast, the interaction between the two services or the common factors behind the two services was the determining factor. Our results could provide a reference for developing ecological restoration planning strategies in the national land space.

Developing a prediction model for all-cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China.

BACKGROUND: All-cause mortality risk prediction models for patients with type 2 diabetes mellitus (T2DM) in mainland China have not been established. This study aimed to fill this gap. METHODS: Based on the Shanghai Link Healthcare Database, patients diagnosed with T2DM and aged 40-99 years were identified between January 1, 2013 and December 31, 2016 and followed until December 31, 2021. All the patients were randomly allocated into training and validation sets at a 2:1 ratio. Cox proportional hazards models were used to develop the all-cause mortality risk prediction model. The model performance was evaluated by discrimination (Harrell C-index) and calibration (calibration plots). RESULTS: A total of 399 784 patients with T2DM were eventually enrolled, with 68 318 deaths over a median follow-up of 6.93 years. The final prediction model included age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, glycosylated hemoglobin, and high-density lipoprotein cholesterol. The model showed good discrimination and calibration in the validation sets: the mean C-index value was 0.8113 (range 0.8110-0.8115) and the predicted risks closely matched the observed risks in the calibration plots. CONCLUSIONS: This study constructed the first 5-year all-cause mortality risk prediction model for patients with T2DM in south China, with good predictive performance.

Acute and lag effects of ambient fine particulate matter on the incidence of dyslipidemia in Chengdu, China: A time-series study.

High levels of ambient fine particulate matter (PM2.5) might increase the risk of death due to cardiovascular diseases (CVDs). As a critical risk factor for CVDs, dyslipidemia can cause CVDs or exacerbate pre-existing ones. This study aimed to investigate whether a short-time exposure to PM2.5 leads to dyslipidemia (HyperTC, HyperLDL-C, HyperTG and HypoHDL-C) in adults. The serum lipid data were provided by the Sichuan Provincial People's Hospital Medical Examination Center. We included 309,654 subjects aged 18-79 between May 10, 2015, and May 10, 2017. An advanced distributed lag nonlinear model (DLNM) was applied to investigate the acute and lag effects of ambient PM2.5 on the risk of dyslipidemia. This study was also stratified by sex, age, BMI and season to examine potential effect modification. We observed that the associations between an interquartile increase in PM2.5 (43 µg/m3) and dyslipidemia were [relative risk (RR); 95% confidence interval (CI)]: 1.042 (1.013, 1.071) for HyperLDL-C and 1.027 (1.006, 1.049) for HyperTC at lag0 day. The lag effects were found at lag6 day for HyperLDL-C, in lag4-6 days for HyperTC and lag4-7 days for HyperTG. Short-term exposure to ambient PM2.5 was related to dyslipidemia and the effect modification was observed in the subgroup analysis. The female and normal-weight populations were more susceptible to the risks of PM2.5 on HyperLDL-C and HyperTC.

The Impact of Public Health Events on COVID-19 Vaccine Hesitancy on Chinese Social Media: National Infoveillance Study.

BACKGROUND: The ongoing COVID-19 pandemic has brought unprecedented challenges to every country worldwide. A call for global vaccination for COVID-19 plays a pivotal role in the fight against this virus. With the development of COVID-19 vaccines, public willingness to get vaccinated has become an important public health concern, considering the vaccine hesitancy observed worldwide. Social media is powerful in monitoring public attitudes and assess the dissemination, which would provide valuable information for policy makers. OBJECTIVE: This study aimed to investigate the responses of vaccine positivity on social media when major public events (major outbreaks) or major adverse events related to vaccination (COVID-19 or other similar vaccines) were reported. METHODS: A total of 340,783 vaccine-related posts were captured with the poster's information on Weibo, the largest social platform in China. After data cleaning, 156,223 posts were included in the subsequent analysis. Using pandas and SnowNLP Python libraries, posts were classified into 2 categories, positive and negative. After model training and sentiment analysis, the proportion of positive posts was computed to measure the public positivity toward the COVID-19 vaccine. RESULTS: The positivity toward COVID-19 vaccines in China tends to fluctuate over time in the range of 45.7% to 77.0% and is intuitively correlated with public health events. In terms of gender, males were more positive (70.0% of the time) than females. In terms of region, when regional epidemics arose, not only the region with the epidemic and surrounding regions but also the whole country showed more positive attitudes to varying degrees. When the epidemic subsided temporarily, positivity decreased with varying degrees in each region. CONCLUSIONS: In China, public positivity toward COVID-19 vaccines fluctuates over time and a regional epidemic or news on social media may cause significant variations in willingness to accept a vaccine. Furthermore, public attitudes toward COVID-19 vaccination vary from gender and region. It is crucial for policy makers to adjust their policies through the use of positive incentives with prompt responses to pandemic-related news to promote vaccination acceptance.

RNAm5CPred: Prediction of RNA 5-Methylcytosine Sites Based on Three Different Kinds of Nucleotide Composition.

5-methylcytosine (m5C) is one of the most common and abundant post-transcriptional modifications (PTCMs) in RNA. Recent studies showed that m5C plays important roles in many biological functions such as RNA metabolism and cell fate decision. Because most experimental methods that determine m5C sites across the transcriptome are time-consuming and expensive, it is urgent to develop accurate computational methods to identify m5C sites effectively. A benchmark dataset is important for developing and evaluating computational methods. In this work, we constructed four different datasets according to the data redundancy and imbalance. Based on these datasets, we generated three different kinds of features, i.e., KNFs (K-nucleotide frequencies), KSNPFs (K-spaced nucleotide pair frequencies), and pseDNC (pseudo-dinucleotide composition), and then used a support vector machine (SVM) to build our models. Based on the imbalanced and nonredundant dataset, Met935, we extensively studied the three kinds of features and determined an optimal combination of the features. Based on the feature combination, we built models on the three different datasets and compared them with state-of-the-art models. According to the predictive results of the stringent jackknife test, the models based on the three features, 4NF, 1SNPF, and pseDNC, are superior or comparable to other methods. To determine the best model between the models based on the imbalanced dataset Met935 and the balanced dataset Met240, we further evaluated the two models on an independent test set Test1157. Our results demonstrate that the model based on the balanced dataset Met240 achieved the highest recall (68.79%) and the highest Matthews correlation coefficient (MCC) (0.154). In addition, the model is also superior to other state-of-the-art methods according to the integrated parameter MCC on the independent test set. Thus, we selected the model based on Met240 as our final model, which was named RNAm5CPred. In addition, a web server for RNAm5CPred (http://zhulab.ahu.edu.cn/RNAm5CPred/) has been provided to facilitate experimental research.

Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring.

A bathroom has higher probability of accidents than other rooms due to a slippery floor and temperature change. Because of high privacy and humidity, we face difficulties in monitoring inside a bathroom using traditional healthcare methods based on cameras and wearable sensors. In this paper, we present a danger-pose detection system using commodity Wi-Fi devices, which can be applied to bathroom monitoring, preserving privacy. A machine learning-based detection method usually requires data collected in target situations, which is difficult in detection-of-danger situations. We therefore employ a machine learning-based anomaly-detection method that requires a small amount of data in anomaly conditions, minimizing the required training data collected in dangerous conditions. We first derive the amplitude and phase shift from Wi-Fi channel state information (CSI) to extract low-frequency components that are related to human activities. We then separately extract static and dynamic features from the CSI changes in time. Finally, the static and dynamic features are fed into a one-class support vector machine (SVM), which is used as an anomaly-detection method, to classify whether a user is not in bathtub, bathing safely, or in dangerous conditions. We conducted experimental evaluations and demonstrated that our danger-pose detection system achieved a high detection performance in a non-line-of-sight (NLOS) scenario.

PseUI: Pseudouridine sites identification based on RNA sequence information.

BACKGROUND: Pseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement. RESULTS: In this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI , and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations. CONCLUSION: In this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites.

Highly selective binding of nitric oxide by Co(III) and Fe(III) complexes.

In order to construct compounds with highly selective binding activity for NO, two Co(III) and two Fe(III) complexes with square-planar N(2)O(2)-type donor sets, N-[2-(2-hydroxybenzylamino)ethyl]-2-hydroxybenzamide (H(3)L1) and 1,2-bis(2-hydroxybenzoylamino)ethane (H(4)L2), [Co(III)(L1)] (1), Na[Co(III)(L2)] (2), [Fe(III)(L1)] (3), and (PPh(4))[Fe(III)(L2)] (4), were designed and synthesized. These compounds were characterized by electronic absorption, FT-IR, (1)H-NMR spectroscopies, ESI-mass spectrometry, and elemental analyses. The redox potentials of the Co(III) and Fe(III) complexes with L1, 1 and 3, have quasi-reversible waves at -0.51 and -0.49 V, respectively, and those with L2, 2 and 4, afforded reversible and irreversible waves at -0.96 and -1.04 V, respectively. Interestingly, all complexes quickly react with NO under an Ar atmosphere to form nitrosyl complexes, as monitored by UV-vis spectroscopy. The formation of nitrosyl complexes was confirmed by the appearance of the N-O stretching vibration at about 1650 cm(-1); 1649 for 1, 1651 for 2, 1648 for 3, and 1650 cm(-1) for 4. The reactivity of each of these complexes with other small molecules such as NO(2)(-), NO(3)(-), CO, and O(2) was also studied. None of the complexes react with CO and O(2). Co(III) complexes 1 and 2 react with NO2(-), while Fe(III) complexes, 3 and 4, do not react with small amounts of NO(2)(-). Complex 3 reacts with NO(2)(-) at concentrations above 100 equiv. of NO(2)(-). We succeeded in preparing complexes with highly selective reactivity for NO.