Plasma lipidomic profiling of thiopurine-induced leukopenia after NUDT15 genotype-guided dosing in Chinese IBD patients

IntroductionThiopurines, azathiopurine (AZA) and mercaptopurine (6-MP) have been regularly used in the treatment of inflammatory bowel disease (IBD). Despite optimized dosage adjustment based on the NUDT15 genotypes, some patients still discontinue or change treatment regimens due to thiopurine-induced leukopenia.MethodsWe proposed a prospective observational study of lipidomics to reveal the lipids perturbations associated with thiopurine-induced leukopenia. One hundred and twenty-seven IBD participants treated with thiopurine were enrolled, twenty-seven of which have developed thiopurine-induced leucopenia. Plasma lipid profiles were measured using Ultra-High-Performance Liquid Chromatography-Tandem Q-Exactive. Lipidomic alterations were validated with an independent validation cohort (leukopenia n = 26, non-leukopenia n = 74).ResultsUsing univariate and multivariate analysis, there were 16 lipid species from four lipid classes, triglyceride (n = 11), sphingomyelin (n = 1), phosphatidylcholine (n = 1) and lactosylceramide (n = 3) identified. Based on machine learning feature reduction and variable screening strategies, the random forest algorithm established by six lipids showed an excellent performance to distinguish the leukopenia group from the normal group, with a model accuracy of 95.28% (discovery cohort), 79.00% (validation cohort) and an area under the receiver operating characteristic (ROC) curve (ROC-AUC) of 0.9989 (discovery cohort), 0.8098 (validation cohort).DiscussionOur novel findings suggested that lipidomic provided unique insights into formulating individualized medication strategies for thiopurines in IBD patients

Electroneurography abnormality in Parkinson’s disease: a potential biomarker to help diagnosis

Parkinson’s disease (PD) is a common neurodegenerative disease, pathologically characterized by the progressive degeneration of dopaminergic neurons in the substantia nigra. Although various biomarkers and imaging criteria for PD have been established, objective and reliable evaluation methods are still lacking. Electroneurography, as an objective measurement of evoked compound muscle action potentials, is used to assess the integrity of the peripheral nerve and is important in the diagnosis and differential diagnosis of PD with neuromuscular injury. Moreover, it provides references for the evaluation and quantification of the motor function in PD. Here, we summarize recent advances in clinical research of electroneurography in PD, including the peripheral nerve conduction velocity, needle electromyography, surface electromyography, and motion unit number estimation. The potential values of electroneurography in PD diagnosis are also involved

Autonomous security analysis and penetration testing model based on attack graph and deep Q-learning network

With the continuous development and widespread application of network technology, network security issues have become increasingly prominent.Penetration testing has emerged as an important method for assessing and enhancing network security.However, traditional manual penetration testing methods suffer from inefficiency,human error, and tester skills, leading to high uncertainty and poor evaluation results.To address these challenges, an autonomous security analysis and penetration testing framework called ASAPT was proposed, based on attack graphs and deep Q-learning networks (DQN).The ASAPT framework was consisted of two main components:training data construction and model training.In the training data construction phase, attack graphs were utilized to model the threats in the target network by representing vulnerabilities and possible attacker attack paths as nodes and edges.By integrating the common vulnerability scoring system (CVSS) vulnerability database, a “state-action”transition matrix was constructed, which depicted the attacker’s behavior and transition probabilities in different states.This matrix comprehensively captured the attacker’s capabilities and network security status.To reduce computational complexity, a depth-first search (DFS) algorithm was innovatively applied to simplify the transition matrix, identifying and preserving all attack paths that lead to the final goal for subsequent model training.In the model training phase, a deep reinforcement learning algorithm based on DQN was employed to determine the optimal attack path during penetration testing.The algorithm interacted continuously with the environment, updating the Q-value function to progressively optimize the selection of attack paths.Simulation results demonstrate that ASAPT achieves an accuracy of 84% in identifying the optimal path and exhibits fast convergence speed.Compared to traditional Q-learning, ASAPT demonstrates superior adaptability in dealing with large-scale network environments, which could provide guidance for practical penetration testing

Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision

The pushing robot working in the complex farming environment encounters several problems. For example, the precision of its navigation path extraction is low, and its working quality is greatly affected by the weather. In view of this, a method of farm operation path extraction based on machine vision is proposed in this study in order to solve the problems above and realize the autonomous and intelligent operation of the robot. First of all, the RGB images of the working area in front of the robot are obtained by using an RGB camera installed on the machine. Then, the collected images are preprocessed by means of sky removal, denoising and grayscale transformation. After that, the image is segmented to obtain the front fence, feed belt and ground data. Finally, the navigation path is obtained by extracting the features of the feed belt. The test results show that the absolute deviation of the pushing robot at different initial lateral distances is less than ±15 cm, and the deviation between the actual navigation route and the target route is within the expected range. The absolute value of the maximum lateral deviation in five test areas is 8.9 cm, and the absolute value of the average maximum lateral deviation is 7.6 cm. These experimental results show that the pushing robot can work stably without disturbing the feeding of cows. Particle swarm optimization is used to optimize the parameters of the PID and find the optimal parameters. This makes the system balanced and more responsive. Through this test, it is found that the initial direction of the robot will have a certain impact on the path production and tracking efficiency, and this effect is more significant when the robot changes the working area or turns. In which case, the trajectory of the robot should be in such a way that it immediately faces the next row at a small angular deviation, thus ensuring smoother motion. The method proposed in this study can provide support for the automatic navigation of pushing robots in dairy farms

Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision

The pushing robot working in the complex farming environment encounters several problems. For example, the precision of its navigation path extraction is low, and its working quality is greatly affected by the weather. In view of this, a method of farm operation path extraction based on machine vision is proposed in this study in order to solve the problems above and realize the autonomous and intelligent operation of the robot. First of all, the RGB images of the working area in front of the robot are obtained by using an RGB camera installed on the machine. Then, the collected images are preprocessed by means of sky removal, denoising and grayscale transformation. After that, the image is segmented to obtain the front fence, feed belt and ground data. Finally, the navigation path is obtained by extracting the features of the feed belt. The test results show that the absolute deviation of the pushing robot at different initial lateral distances is less than ±15 cm, and the deviation between the actual navigation route and the target route is within the expected range. The absolute value of the maximum lateral deviation in five test areas is 8.9 cm, and the absolute value of the average maximum lateral deviation is 7.6 cm. These experimental results show that the pushing robot can work stably without disturbing the feeding of cows. Particle swarm optimization is used to optimize the parameters of the PID and find the optimal parameters. This makes the system balanced and more responsive. Through this test, it is found that the initial direction of the robot will have a certain impact on the path production and tracking efficiency, and this effect is more significant when the robot changes the working area or turns. In which case, the trajectory of the robot should be in such a way that it immediately faces the next row at a small angular deviation, thus ensuring smoother motion. The method proposed in this study can provide support for the automatic navigation of pushing robots in dairy farms

Bacteria and poisonous plants were the primary causative hazards of foodborne disease outbreak: a seven-year survey from Guangxi, South China

Abstract Background Foodborne diseases are a worldwide public health problem. However, data regarding epidemiological characteristics are still lacking in China. We aimed to analyze the characteristics of foodborne diseases outbreak from 2010 to 2016 in Guangxi, South China. Methods A foodborne disease outbreak is the occurrence of two or more cases of a similar foodborne disease resulting from the ingestion of a common food. All data are obtained from reports in the Public Health Emergency Report and Management Information System of the China Information System for Disease Control and Prevention, and also from special investigation reports from Guangxi province. Results A total of 138 foodborne diseases outbreak occurred in Guangxi in the past 7 years, leading to 3348 cases and 46 deaths. Foodborne disease outbreaks mainly occurred in the second and fourth quarters, and schools and private homes were the most common sites. Ingesting toxic food by mistake, improper cooking and cross contamination were the main routes of poisoning which caused 2169 (64.78%) cases and 37 (80.43%) deaths. Bacteria (62 outbreaks, 44.93%) and poisonous plants (46 outbreaks, 33.33%) were the main etiologies of foodborne diseases in our study. In particular, poisonous plants were the main cause of deaths involved in the foodborne disease outbreaks (26 outbreaks, 56.52%). Conclusions Bacteria and poisonous plants were the primary causative hazard of foodborne diseases. Some specific measures are needed for ongoing prevention and control against the occurrence of foodborne diseases