Association between sleep duration and serum neurofilament light chain levels among adults in the United States.

Background: Neurofilaments are neuron specific skeleton proteins maintaining axon transduction speed, leaked into cerebrospinal fluid and serum after axonal injury or neuron death. Sleep duration change has long related to many health issues but lack laboratory examination. Methods: This study enrolled total 10,175 participants from 2013 to 2014 National Health and Nutrition Examination Survey and used a multi-variable linear model to analyze the relationship between sleep duration and serum neurofilament light chain (sNfL) level. Results: There was a fixed relationship between sleep duration and sNfL level (ß = 0.65, p = 0.0280). After adjusted for covariates, this relationship still (ß = 0.82, p = 0.0052). Segmented regression showed that the turning point of sleep duration was 7 h 1 h decrease in sleep duration was significantly associated with -1.26 higher sNfL level (95 % CI: 2.25, -0.28; p = 0.0115) when sleep duration <7 h; however, 1 h increase in sleep duration was significantly associated with 3.20 higher sNfL level (95 % CI: 2.13, 4.27; p < 0.0001) when sleep duration >7 h. Furthermore, the stratified analysis indicated that the associations between sleep duration and sNfL level were stronger among those normal body mass index and trouble sleeping (p-interaction <0.0001 and 0.0003). Conclusion: In summary, there was a J-shaped relationship between sleep duration and sNfL level in the United States of America representative group, these may suggest that extreme sleep duration can be deleterious judged by sNfL level. And still need large cohort study to determine the accurate relationship, and cluster analysis to infer the nervous disease connected with extreme sleep duration.

Role of histone lactylation interference RNA m6A modification and immune microenvironment homeostasis in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a severe disease resulting from progressive increases in pulmonary vascular resistance and pulmonary vascular remodeling, ultimately leading to right ventricular failure and even death. Hypoxia, inflammation, immune reactions, and epigenetic modifications all play significant contributory roles in the mechanism of PAH. Increasingly, epigenetic changes and their modifying factors involved in reprogramming through regulation of methylation or the immune microenvironment have been identified. Among them, histone lactylation is a new post-translational modification (PTM), which provides a novel visual angle on the functional mechanism of lactate and provides a promising diagnosis and treatment method for PAH. This review detailed introduces the function of lactate as an important molecule in PAH, and the effects of lactylation on N6-methyladenosine (m6A) and immune cells. It provides a new perspective to further explore the development of lactate regulation of pulmonary hypertension through histone lactylation modification.

Electrooxidative Fluorofunctionalization of Arylcyclopropanes.

The work herein demonstrates the viability of an electrochemical oxidative protocol for the expedient realization of 1,3-fluorofunctionalization of arylcyclopropanes under catalyst- and oxidant-free conditions. Given the relatively low nucleophilicity of fluoride ion, the counterintuitive outcome that the ring-opening is initiated by nucleophilic fluorination is rationalized by invoking tight ion pair between aryl radical cation and BF4- counterion. By integrating alcohols, acids, and N-heterocycles as the terminating nucleophiles, straightforward 1,3-fluorooxygenation and 1,3-fluoroamination are smoothly achieved.

Defluorinative Thiolation of gem-Difluoroalkenes by Convergent Paired Electrolysis.

Herein, we have successfully developed a convergent paired electrolysis strategy for the defluorinative thiolation process utilizing thiols and gem-difluoroalkenes as precursors. This protocol exhibits remarkable tolerance toward a wide range of functional groups, as exemplified by the successful late-stage defluorothiolation of complex molecules. Additionally, this strategy is amenable to gram-scale synthesis, making use of both anodic oxidation and cathodic reduction processes in an efficient manner. Several control studies were conducted and suggested a convergent paired electrolysis mechanism.

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US.

Satellite-based soil moisture products are suitable for large-scale regional monitoring due to the accessibility. Five soil moisture products including SMAP, ESA CCI, and AMSR2 (ascending, descending, and average) were selected in the continental United States (US) from 2016 to 2021. To evaluate the performance of the products and assess their applicability, ISMN (International Soil Moisture Network) data were used as the in situ measurement. PBIAS (Percentage of BIAS), R (Pearson correlation coefficient), RMSE (Root Mean Square Error), ubRMSE (unbiased RMSE), MAE (Mean Absolute Error), and MBE (Mean Bias Error) were selected for evaluation. The performance of five products over six observation networks and various land cover types was compared, and the differences were analyzed at monthly, seasonal, and annual scales. The results show that SMAP had the smallest deviation with the ISMN data because PBIAS was around -0.13, and MBE was around -0.02 m3/m3. ESA CCI performed the best in almost all aspects; its R reached around 0.7, and RMSE was only around 0.07 m3/m3 at the three time scales. The performance of the AMSR2 products varied greatly across the time scales, and increasing errors and deviations showed from 2016 to 2020. The PBO_H2O and USCRN networks could reflect soil moisture characteristics in the continental US, while iRON performed poorly. The evaluation of the networks was closely related to spatial distributions. All products performed better over grasslands and shrublands with R, which was greater than 0.52, and ubRMSE was around 0.1 m3/m3, while products performed worse over forests, where PBIAS was less than -0.62, and RMSE was greater than 0.2 m3/m3, except for ESA CCI. From the boxplot, SMAP was close to the ISMN data with differences less than 0.004 m3/m3 between the median and lower quartiles.

GIS-based precise predictive model of mountain beacon sites in Wenzhou, China.

In ancient China, where was frequently troubled by invaders, the government set up many beacon towers for alerting and transmitting military information along the border and the coast. Many beacon sites still exist in some areas, which are generally located in dangerous places with high mountains and rough terrain, bringing great difficulties to archaeological discovery. Therefore, it is particularly important to develop a predictive model applicable to the distribution of mountain beacon sites. Taking 68 beacon sites found in Wenzhou as research samples, this study used the superimposed method of logistic regression and viewshed analysis, forming a high-precision, scientific and operational predictive model for the distribution of beacon sites, which was verified by the cross-validation method. The results showed that the beacon site predictive model simulated in this study could reduce the probability scope of site location by 90% compared with the common logistic regression predictive model, which greatly improved the accuracy and ability of site prediction. At the same time, it could also be used to understand the relationship between the known sites and their surroundings to assist in decision-making about conservation and management.

A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study.

Background: Metastatic renal cell carcinoma (mRCC) is usually considered to have a poor prognosis, which has a high risk of early death (≤3 months). Our aim was to developed a predictive nomogram for early death of mRCC. Methods: The SEER database was accessed to obtain the related information of 6,005 mRCC patients between 2010 and 2015. They were randomly divided into primary cohort and validation cohort in radio of 7:3. The optimal cut-off point regarding age at diagnosis and tumor size were identified by the X-tile analysis. Univariate and multivariate logistic regression models were applied to determine significant independent risk factors contributed to early death. A practical nomogram was constructed and then verified by using calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA). Results: There were 6,005 patients with mRCC included in the predictive model, where 1,816 patients went through early death (death within ≤3 months of diagnosis), and among them 1,687 patients died of mRCC. Based on 11 significant risk factors, including age, grade, N-stage, histologic type, metastatic sites (bone, lung, liver and brain) and treatments (surgery, radiation, and chemotherapy), a practical nomogram was developed. The model's excellent effectiveness, discrimination and clinical practicality were proved by the AUC value, calibration plots and DCA, respectively. Conclusions: The nomogram may play a major part in distinguishing the early death of mRCC patients, which can assist clinicians in individualized medicine.

An anion exchange reaction: an effective approach to prepare alloyed Co-Fe bimetallic disulfide for improving the electrocatalytic activity.

A mild anion exchange approach is proposed for the synthesis of alloyed Co-Fe bimetallic disulfide. Abundant structural disorders and tunable compositions are effectively induced by the anion exchange process. The alloyed Co-Fe bimetallic disulfide exhibits a low overpotential of 205 mV to reach a current density of 100 mA cm-2 in an acidic electrolyte, which is significantly improved compared to the performance of individual disulfide. It is believed that this work paves a new strategy for the synthesis of bimetallic compounds and highlights the importance of tuning the catalyst composition for achieving high catalytic performance.

Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing.

Cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve the aforementioned problems. Resource scheduling is one of the key technologies in fog computing. We propose a resource scheduling method for fog computing in this paper. First, we standardize and normalize the resource attributes. Second, we combine the methods of fuzzy clustering with particle swarm optimization to divide the resources, and the scale of the resource search is reduced. Finally, we propose a new resource scheduling algorithm based on optimized fuzzy clustering. The experimental results show that our method can improve user satisfaction and the efficiency of resource scheduling.