Abstract Microsporidia are prolific producers of effector molecules, encompassing both proteins and nonproteinaceous effectors, such as toxins, small RNAs, and small peptides. These secreted effectors play a pivotal role in the pathogenicity of microsporidia, enabling them to subvert the host's innate immunity and co‐opt metabolic pathways to fuel their own growth and proliferation. However, the genomes of microsporidia, despite falling within the size range of bacteria, exhibit significant reductions in both structural and physiological features, thereby affecting the repertoire of secretory effectors to varying extents. This review focuses on recent advances in understanding how microsporidia modulate host cells through the secretion of effectors, highlighting current challenges and proposed solutions in deciphering the complexities of microsporidial secretory effectors.
A variable structure sliding mode controller based on decoupling algorithm is proposed for two-dimensional under-actuated bridge crane to realize rapid positioning of trolley and anti swing of load. Firstly, the linear model of the bridge crane is decoupled, and the sliding mode controller is established according to the decoupled system model. The simulation results show that the control method not only solves the control input coupling problem of the bridge crane system, but also can quickly realize the trolley positioning and load anti swing, and has a good control effect.
To the Editor,We read Lee's paper [1] with great interest who reported rare epithelial cells in peripheral blood smear in a 56-year-old male.In this blood smear, a few clusters of medium-to-large cells containing elongated oval-grooved nuclei with pale blue frayed cytoplasm at both ends were found at the tail-end of the blood smear.The author reasoned that these cells were likely epithelial cells and reported as non-hematopoietic cells.Then the author discussed that the presence of these abnormal cells may be due to improper mixing before aspiration, or due to a blunted tip needle used or from repeated unsuccessful venipuncture attempts, and these abnormal cells can also be rarely seen from finger or heel prick due to transference of skin into the blood tube.However, confirmation of epithelial cells should be validated by imunhischemistry, and this study also did not mention the venipuncture status for this patient.As we know, alcohol disinfection is mandatory before the venipuncture.Thus, the transference of skin into the blood tube is rare.Thus, we propose other possible causes of these abnormal cells in Lee's paper[1], including vascular smooth muscle cells, subcutaneous fibroblasts, or even the synoviocytes around the elbow joint (venipuncture on the synovium of the elbow joint).However, whether these cells had the cluster feature is unclear, or needs other immunohistochemistry methods to corroborate.Additionally, these abnormal cells in Lee's blood smear[1] also look like vascular endothelial cells.Vascular endothelial cells have a highly irregular cell morphology, mostly in the shape of a long tail or spindle with intact cell membranes, irregular nucleus and often lack nucleoli.However, the vascular endothelial cell could be excluded in this study from our perspective.Because vascular endothelial cells are often arranged in a single-layer lining and sparse, regardless of whether they are brought out by vein or bone marrow puncture.Single or several endothelial cells have a certain trend of arrangement, which is inconsistent with the cell cluster feature in this study.Lastly, we appreciate Lee's paper [1] giving us a great opportunity to discuss these rare abnormal cells we could meet in the hematology examination although it is very rare.
In order to increase accuracy of temperature monitoring system in greenhouse, this paper proposed a system based on multi-wireless sensor data fusion algorithm.Firstly, the algorithm based on Dixon criterion eliminated a gross error data, which acquired by the wireless control terminal.Then calculated the arithmetic mean and sent it to the coordinator node, finally, the adaptive weighted fusion algorithm was used to conduct the greenhouse temperature and get the accurate greenhouse temperature.Through the experiments showed: compared with traditional method of average mean, this data fusion algorithm improved the sensor measurement accuracy, had smaller error and improved the system stability.
At present, the research of smart home security system has become one of the key research directions of smart home system. It is necessary to detect and judge the security of the home in real time through the system, so as to avoid the occurrence of unexpected disasters. This paper first studies and designs the overall scheme of security system in smart home. Then using the BP neural network information fusion to predict and analyze the safety system. Finally the predicted value compares with the expected value. The results show that the BP neural network information fusion can more accurately analyze the safety of smart home, which is conducive to further research and application of smart home security system.
We read the study by Azuma et al. [1] with great interest.The authors aimed to establish accurate diagnostic criteria and predictors of treatment response for postoperative acute exacerbation (AE) in patients with lung cancer and idiopathic interstitial pneumonia.Univariate and multivariate analyses identified that the FVC% pred, DLco% pred, pathologic staging, and classification of AE were factors associated with long-term survival.However, we raise statistical concerns about the predictor result in this study, which may change the predictor result for overall survival.As we know, the predictor logistic regression analysis should follow the basic statistical rule that ten outcomes can analyze, at most, one variable [2, 3].Conversely, there were eight variables in Table 3 of the univariate and multivariate analyses of overall survival for all suspected postoperative AE patients.Thus, at least 80 (8 × 10) suspected postoperative AE patients were needed for this predictor statistical analysis.In contrast, there were only 20 patients with postoperative AE among the total 93 patients in Azuma's study and this huge gap between 80 and 20 patients could not produce reliable statistical results.Thus, the FVC% pred and classification of AE may not be true predictors of long-term survival for these patients.To identify the predictor of survival of these patients, the authors should first compare the suspected postoperative AE group with the non-postoperative AE group, find the significant variables, and then use these significant variables to perform the predictor logistical analysis.Then, the predictor result for overall survival would be more accurate.Despite these concerns, we appreciate Azuma's paper [1] giving us the opportunity to discuss the predictors of survival for these patients, whom we often encounter in our clinics.
Lane line detection plays a guiding role in the safe driving of vehicles. A lane line detection algorithm is designed to improve the accuracy of the lane line detection system: First of all, precondition the road image. The preprocessing consists of the following algorithms: image grayscale processing, histogram equalization, median filtering, and Canny edge detection. Secondly, extract the region of interest (ROI) of the picture. Finally, realize the fitting of lane line through Hough transform. In this paper, use the Otsu algorithm to realize the threshold adaptation of the Canny algorithm. Then the lane line recognition is completed by Canny edge extraction and Hough transform.
Speech recognition technology has a very important application in the field of machine learning. Based on the use of BP neural network to recognize ten English words, this paper uses particle swarm algorithm to optimize the weights and thresholds of BP neural network to solve the problem of neural network weights easily falling into local extreme values. The simulation results show that the improved neural network has higher prediction accuracy and faster convergence speed, which improves the generalization ability of the neural network to a certain extent.
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