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Three-dimensional weaving structures have high strength and resistance to interlayer shear due to their integrated manufacturing features. The traditional weaving equipment is generally huge and is not effective for weaving small-sized products. Therefore, this paper proposes a new composite forming technology and weaving mechanism based on grid-enhanced structures to braid small-sized preforms, combining the continuous fiber printing technology, the rope drive technology and the 3D weaving technology. This paper adopted the screw theory for forward and inverse kinematic analysis of the weaving mechanism and applied software Adams2020 for trajectory simulation analysis. The theoretical calculation results were basically consistent with the simulation results, which verified the rationality and feasibility of the designed weaving mechanism in small, enhanced grids. [ABSTRACT FROM AUTHOR]

Currently, artificial meniscus prostheses are mostly homogenous, low strength, and difficult to mimic the distribution of internal fibers in the native meniscus. To promote the overall mechanical performance of meniscus prostheses, this paper designed a new artificial braided meniscus model and conducted finite element analysis. Firstly, we designed the spatial fiber interweaving structure of meniscus model to mimic the internal fiber distribution of the native meniscus. Secondly, we provided the detailed braiding steps and forming process principles based on the weaving structure. Thirdly, we adopted the models of the fiber-embedded matrix and multi-scale methods separately for finite element analysis to achieve the reliable elastic properties. Meanwhile, we compared the results for two models, which are basically consistent, and verified the accuracy of analysis. Finally, we conducted the comparative simulation analysis of the meniscus model and the pure matrix meniscus model based on the solved elastic constants through Abaqus, which indicated a 60% increase in strength. [ABSTRACT FROM AUTHOR]

Based on the clinical states of the patient, dynamic treatment regime technology can provide various therapeutic methods, which is helpful for medical treatment policymaking. Reinforcement learning is an important approach for developing this technology. In order to implement the reinforcement learning algorithm efficiently, the computation of health data is usually outsourced to the untrustworthy cloud server. However, it may leak, falsify, or delete private health data. Encryption is a common method for solving this problem. But the cloud server is difficult to calculate encrypted health data. In this paper, based on Cheon et al.'s approximate homomorphic encryption scheme, we first propose secure computation protocols for implementing comparison, maximum, exponentiation, and division. Next, we design a homomorphic reciprocal of square root protocol firstly, which only needs one approximate computation. Based on the proposed secure computation protocols, we design a secure asynchronous advantage actor-critic reinforcement learning algorithm for the first time. Then, it is used to implement a secure treatment decision-making algorithm. Simulation results show that our secure computation protocols and algorithms are feasible. [ABSTRACT FROM AUTHOR]

Laser speckle contrast imaging (LSCI) has been widely used for mapping blood flow. LSCI is well known to be highly sensitive to movement artifacts. Previous studies attempted to register the global misalignment by using raw speckle images and rigid registration algorithm. However, it is difficult to correct the local non-rigid motion only by using granular raw speckle images, especially in the tissue dominated by small blood vessels. Here, we propose a method of imaging the tissue structure and its laser speckle simultaneously by a colour camera combined with a non-rigid registration algorithm to improve the motion correction of speckle imaging. The results of animal experiments show that this method significantly reduces the influence of non-rigid, spatially non-uniform motion on the estimation of blood flow, and improves the visualization of blood vessels in moving tissue, which can help to promote the practical application of LSCI technology. [ABSTRACT FROM AUTHOR]

Infectious diseases pose a fatal risk to neonates. Timely and accurate pathogen detection is crucial for proper clinical diagnosis and therapeutic strategies. Limited sample volumes from neonatal patients seriously hindered the accurate detection of pathogens. Here, we unravel that metagenomic next-generation sequencing (mNGS) of cell-free DNA (cfDNA) and RNA can achieve unbiased detection of trace pathogens from different kinds of body fluid samples and blood samples. We enrolled 168 neonatal patients with suspected infections from whom blood samples ( n  = 153), cerebrospinal fluid samples ( n  = 127), and respiratory tract samples (RTSs) (including bronchoalveolar lavage fluids, sputa, and respiratory secretions) ( n  = 51) were collected and analyzed using mNGS. High rates of positivity (70.2%; 118/168) of mNGS were observed, and the coincidence rate against the final clinical diagnosis in positive mNGS cases reached 68.6% (81/118). The most common causative pathogens were Klebsiella pneumoniae ( n  = 12), Escherichia coli ( n  = 12), and Streptococcus pneumoniae ( n  = 8). mNGS using cfDNA and RNA can identify microbes that cannot be detected by conventional methods in different body fluid and blood samples, and more than 50% of these microbes were identified as causative pathogens. Further local polynomial regression fitting analysis revealed that the best timing for mNGS detection ranged from 1 to 3 days after the start of continuous antimicrobial therapy. Diagnosed and guided by mNGS results, the therapeutic regimens for 86 out of 117 neonatal patients were changed, most of whom (80/86) completely recovered and were discharged, while 44 out of 86 patients completely or partially stopped unnecessary medication. Our findings highlight the importance of mNGS in detecting causative DNA and RNA pathogens in infected neonatal patients. IMPORTANCE To the best of our knowledge, this is the first report on evaluating the performance of mNGS using cfDNA and RNA from body fluid and blood samples for diagnosing neonatal infections. mNGS of RNA and cfDNA can achieve the unbiased detection and identification of trace pathogens from different kinds of neonatal body fluid and blood samples with a high total coincidence rate (226/331; 68.3%) against final clinical diagnoses by sample. The best timing for mNGS detection in neonatal infections ranged from 1 to 3 days, rather than 0 days, after the start of continuous antimicrobial therapy. Our findings highlight the importance of mNGS in detecting causative DNA and RNA pathogens, and the extensive application of mNGS for the diagnosis of neonatal infections can be expected.