Single-cell allele-specific expression (ASE) provides valuable insights into gene regulatory mechanisms. However, its utility is limited by the lack of dedicated computational tools. We present DAESC + , a dual-module end-to-end software package for the processing and analysis of single-cell ASE. The preprocessing module, DAESC-P, is a user-friendly bioinformatics pipeline to obtain ASE counts from multiplexed scRNA-seq data. The analysis module, DAESC-GPU, is a scalable tool for differential ASE analysis powered by graphics processing units (GPUs). We demonstrated that DAESC-P is more accurate than the existing SALSA pipeline. DAESC-GPU is dozens of times faster than our previous method (DAESC) and scalable to over a million cells. Applying DAESC + to a subset of the OneK1K cohort, we identified 15 genes exhibiting differential regulatory patterns between naïve and central memory CD4 + T cells, and 2 genes between naïve and memory B cells.

In mandibular angle split osteotomy (MASO), prominent mandibular angles need to be cut off with saws such as reciprocating saws. Compared to traditional-freehand methods, robot-assisted methods provide potentials for better cutting performance. In the robot-assisted mandibular angle split osteotomy (RAMASO), a cutting method based on shared control is proposed along with an optimization-planned osteotomy curve. Experimental verification using planes and skull phantoms were conducted and discussed for evaluation of accuracy and safety. The results in 7 cutting experiments for the following error were mainly within 0.76mm and -1.00mm (Q3±1.5*IQR), peaking at 1.80 mm. The maximum of time-consuming was 304.0s, with the average human robot interactive force reaching around 3.3 N. Experiments indicate the proposed method achieves better performance in accuracy and efficiency compared with the free hand. Note to Practitioners—This paper is inspired by the curve-cutting osteotomy task under the combination of pre-defined virtual fixtures and the kinematic constraint of the reciprocating saw. The motions of current surgical osteotomy robots are mostly generated by either of virtual fixtures and kinematic constraints, which is representing less autonomy on surgery. The introduction of autonomy in surgical robotics can greatly increase the surgeon’s performance in efficiency, accuracy, and safety. The technique of shared control is capable of achieving the semi-autonomy task to significantly improve the accuracy with feedback mechanisms in control science. Thus, the advanced control strategies are required into the process of surgical osteotomy operation. In this article, we proposed a novel methodology to control the hands-on robot executing a curve path. The developed control scheme has the following functionalities: 1) it enables the lateral control for the cutting task for the hands-on robot system. 2) it maintains the pre-defined path-generated virtual fixtures and finds the optimal-parameters of the virtual fixtures. For the convenience of presentation, the mandibular angle split osteotomy is chosen as the background, but in fact this method can be extended to more surgical and even industrial applications with a similar scenario.

Quantifying the surface vegetation growth behavior is crucial to the understanding of the complex response of alpine ecosystems to climate variability. This study investigated the drought-induced phenological shifts of Qinghai Lake Basin (QLB). Phenological metrics including green-up and dormancy were retrieved from satellite vegetation index records from 1982 to 2022. Phenological metrics were characterized using simple linear regression method. The results showed that the long-term trends of QLB phenology were various. Meadow greenup and steppe dormancy were significantly advanced and the earlier trend of meadow dormancy and steppe greenup are insignificant. Drought conditions of the QLB were obtained using multiscale standardized precipitation evapotranspiration index (SPEI). The 6-month SPEI of April explains the most of the interannual shifts in greenup of meadow and steppe ranging from 11.56% to 19.36%. The 12-month SPEI of August and 6-month SPEI of April explains the most of variations in dormancy across QLB.

Remote sensing inversion of global PM2.5is an important research topic. In the present study, the Aerosol Optical Depth (AOD) dataset was established by four geostationary satellites to estimate global PM2.5concentrations using improved Geographic Time-Weighted Regression model (IGTWR) models. Then a global hourly PM2.5concentration dataset was obtained in May 2020. The estimated result for PM2.5is verified at ground stations with R of 0.71 and RMSE (Root Mean Square Error) of 26.6 μg/m3. The results indicate that PM2.5has obvious spatial and temporal distribution in the world.

Vegetation net primary productivity (NPP) is an important factor in determining ecosystem quality and carbon sink. It reflects the productive capacity and ecological process of vegetation community, and is of great significance to adjust the global carbon balance and enhance the ecological service function. Using soil respiration model and improved CASA model, combined with MODIS and meteorological data, the net ecosystem productivity (NEP) of vegetation in Qinghai since 2000 was estimated, the spatial and temporal distribution of vegetation NEP and carbon sink, the influences of precipitation and air temperature on vegetation NEP were analyzed in this paper. Meanwhile, global warming has become a hot topic since the 20th century. In this study, the timing of snow-melt is taken as an important factor to study the impact on carbon source/sink of ecosystem in Qinghai province.

Mining cities are an essential part of China's urban agglomerations, and as mining cities continue to develop, ecological and environmental pollution has become a primary problem. In the present study, the Aerosol Optical Depth (AOD) retrieval of major mining urban agglomerations in China from the Gaofen-1 satellite data. Then a new hybrid model based on CTM (chemical transport model) Transport Model 5 (TM5) and GTWR (Geographic Time-Weighted Regression model) is proposed for PM2.5 and PM10mass concentration estimation. According to the different transformation stages and urban structure of mining cities, the temporal and spatial analysis of particulate matter characteristics is carried out in mining urban agglomerations. The estimated result for PM2.5 is verified at ground stations with R2 of 0.956 and RMSE (Root Mean Square Error) of 10.377 μg/m3, Moreover, the estimated result for PM10is verified at ground stations with R2 of 0.926 and RMSE of 16.669 μg/m3, The results indicate that PM2.5 and PM10have distinct spatial and temporal distribution patterns as Chinese mining cities are undergoing different types of transformation processes.

Multi-Stud Tensioning Machine (MSTM) is a specialized equipment used to open/seal the cover of the Reactor Pressure Vessel (RPV) during nuclear power plant maintenance. The tensioning residual values of the 58 studs are monitored for procedure evaluation. It is time-consuming for human operators to place the measurement meters into working positions. In order to reduce labor intensity and eliminate radiation exposure time, we develop a robot-assisted automatic measurement system to achieve meter placement and real-time data monitoring. The Field Programmable Gate Array (FPGA)-based distributed control scheme realizes high-speed data acquisition and coordinated control of the 58 node robots. The control software performs data analysis and sends emergency stop signals to the MSTM control PLC. The proposed system is validated in China Nuclear Power Technology Research Institute. Total operation time decreases from over 580 s to less than 120 s.