Cephaliverins A-D, four new lignans from Cephalotaxus oliveri Mast.

Four new lignans named cephaliverins A-D (1-4), along with seven known analogues (5-11), were isolated from Cephalotaxus oliveri Mast. Their structures were elucidated on the basis of HR-ESI-MS and NMR analyses, and their absolute configurations were determined by ECD comparison. Cephaliverin A (1), herpetotriol (5) and hedyotol A (6) exhibited moderate antitumor activity against HepG2 and A549 cell lines.

Analysis of new-onset seizures following use of COVID-19 vaccinations in children based on VAERS.

BACKGROUND: Recently, there have been some reports of seizures related with COVID-19 vaccinations. However, no studies have systematically investigated the relationship between seizures and various COVID-19 vaccines. RESEARCH DESIGN AND METHODS: This research aimed to analyze the characteristics and risk signals of new-onset seizures in children caused by various COVID-19 vaccines based on the data of the Vaccine Adverse Event Reporting System (VAERS). To identify potential risk signals, a disproportionality analysis was conducted. The reporting odds ratio (ROR) and the Proportional Reporting Ratio (PRR) were used to detect signals. RESULTS: A total of 695 children with new-onset seizures events associated with COVID-19 vaccinations were retrieved from the VAERS database. Compared with influenza vaccinations, the percentage and rate of COVID-19 vaccinations related seizures was all reduced. The median onset time of seizures was 1 day after COVID-19 vaccines. No signal was detected for an association between the COVID-19 vaccines and new-onset seizures, neither when compared with influenza vaccines nor with non-COVID-19 vaccines. CONCLUSION: No statistically significant risk signal of COVID-19 vaccine-related seizures was found in this study. However, it is still necessary to monitor the possibility of new-onset seizures when children are immunized with COVID-19 vaccines.

Health shocks, basic medical insurance and common prosperity: Based on the analysis of rural middle-aged and elderly groups.

Health is a major part of human welfare. The index system of common prosperity was constructed for middle-aged and elderly people in rural areas. Besides, the impart of health shocks and rural basic medical insurance on common prosperity was explored. The data for this study came from China Health and Retirement Longitudinal Survey (CHARLS) in 2013, 2015, and 2018. The finding shows that health shocks hindered the improvement of the common prosperity of the middle-aged and elderly in rural areas, among which daily activities produced the greatest negative effect. The heterogeneity analysis shows that health shocks have a stronger negative effect on the common prosperity of low-income groups than that of high-income ones. The shock of daily activity ability has the greatest influence on the middle-aged and elderly between 45 and 55 years old. However, acute health shocks have a strong negative effect on those aged above 56. The mechanism analysis shows that rural basic medical insurance can alleviate the health shocks to middle-aged and elderly people, but the effect is limited. In general, low-income groups benefit more. Therefore, China should speed up the promotion of the Healthy China Strategy and the reform of the rural basic medical insurance system, and prompt changes from an inclusive to a targeted policy to provide more precise safeguards for vulnerable groups.

DeepMatch: Toward Lightweight in Point Cloud Registration.

From source to target, point cloud registration solves for a rigid body transformation that aligns the two point clouds. IterativeClosest Point (ICP) and other traditional algorithms require a long registration time and are prone to fall into local optima. Learning-based algorithms such as Deep ClosestPoint (DCP) perform better than those traditional algorithms and escape from local optimality. However, they are still not perfectly robust and rely on the complex model design due to the extracted local features are susceptible to noise. In this study, we propose a lightweight point cloud registration algorithm, DeepMatch. DeepMatch extracts a point feature for each point, which is a spatial structure composed of each point itself, the center point of the point cloud, and the farthest point of each point. Because of the superiority of this per-point feature, the computing resources and time required by DeepMatch to complete the training are less than one-tenth of other learning-based algorithms with similar performance. In addition, experiments show that our algorithm achieves state-of-the-art (SOTA) performance on both clean, with Gaussian noise and unseen category datasets. Among them, on the unseen categories, compared to the previous best learning-based point cloud registration algorithms, the registration error of DeepMatch is reduced by two orders of magnitude, achieving the same performance as on the categories seen in training, which proves DeepMatch is generalizable in point cloud registration tasks. Finally, only our DeepMatch completes 100% recall on all three test sets.

[Determination of nutrient elements in transgenic insect-resistant cotton tissues by three different spectroscopical methods].

In order to find out the effects of exogenous genes, such as Bt and Bt coupled with CpTI, on nutrition metabolism in transgenic plants, totally eleven types of nutrient elements in transgenic Bt (Z30) and Bt-CpTI (CCRI41 and SGK321) cotton were determined using methods of flame atomic absorption spectroscopy, flame atomic emission spectroscopy and spectrophotometry at flowering stage and boll-opening stage. The results showed that the chemical composition of plant nutrition in transgenic insect-resistant cotton differed in comparison with non-transgenic cotton counterparts related to varieties, tissues and stages. The content of total N in transgenic cotton changed most significantly. Especially, it increased by 21% for transgenic Bt cotton Z30 compared to non-transgenic cotton Z16. These changes in total N content were probably caused by both transgenes expression in transgenic cotton and other processes not studied in this experiment. The content of Mg, Na and Cu in transgenic cotton varied significantly only in some certain varieties or tissues. It was unobvious how the incorporation of transgenes impacted on the content of organic C, total P, total S, K, Ca, Fe and Zn in transgenic cotton. The authors speculated that there were no significant changes in utilization and accumulation of these nutrient elements between transgenic insect-resistant cotton and their non-transgenic cotton counterparts (Z16, CCRI23 and SY321, respectively).