Penetration detection with intention recognition for cooperatively controlled robotic needle insertion

In lumbar puncture surgeries, force and position information throughout the insertion procedure is vital for needle tip localization, because it reflects different tissue properties. Especially in pediatric cases, the changes are always insignificant for surgeons to sense the crucial feeling of loss of resistance. In this study, a robot system is developed to tackle the major clinical difficulties. Four different control algorithms with intention recognition ability are applied on a novel lumbar puncture robot system for better human–robot cooperation. Specific penetration detection based on force and position derivatives captures the feeling of loss of resistance, which is deemed crucial for needle tip location. Kinematic and actuation modeling provides a clear description of the hardware setup. The control algorithm experiment compares the human–robot cooperation performance of proposed algorithms. The experiment also dictates the clear role of designed penetration detection criteria in capturing the penetration, improving the success rate, and ensuring operational safety.

Research on Athlete Behavior Recognition Technology in Sports Teaching Video Based on Deep Neural Network

In recent years, due to the simple design idea and good recognition effect, deep learning method has attracted more and more researchers’ attention in computer vision tasks. Aiming at the problem of athlete behavior recognition in mass sports teaching video, this paper takes depth video as the research object and cuts the frame sequence as the input of depth neural network model, inspired by the successful application of depth neural network based on two-dimensional convolution in image detection and recognition. A depth neural network based on three-dimensional convolution is constructed to automatically learn the temporal and spatial characteristics of athletes’ behavior. The training results on UTKinect-Action3D and MSR-Action3D public datasets show that the algorithm can correctly detect athletes’ behaviors and actions and show stronger recognition ability to the algorithm compared with the images without clipping frames, which effectively improves the recognition effect of physical education teaching videos.

New Polymerizable Tetraaza Macrocycle Containing Two Acridine Units for Selective Fluorescence Sensing of Metal Ions

A new fluorescent bis(acridino)-macrocycle containing two allyl groups was synthesized and photophysically studied. Studies were carried out on metal ion recognition and selectivity-influencing effects including the determination of the relevant thermodynamic constants as logK and pKa. The proposed sensor molecule is recommended for the development of Zn2+-selective optochemical analyzers based on covalently immobilized ionophores as it has a unique pH-independent metal ion recognition ability, which is not influenced by anions and other potentially occurring metal ions in biological samples.

Image recognition based on Boltzmann machine algorithm

Due to the influence of recognition parameters, image recognition has low recognition accuracy, long recognition time and large storage cost. Therefore, an automatic image recognition method based on Boltzmann machine is proposed. Based on threshold method and fuzzy set method, image malformation correction is performed. The mean filter and median filter are combined to eliminate the influence of image filtering, and the pre-processing of image is completed by using the fuzzy enhancement of image. Based on the restricted Boltzmann method, the network model is dynamically evolved, and the identification parameters of each shape and contour are obtained. Different shapes and contours are classified and recognized. Simulation results show that image recognition method based on human-computer interaction has high recognition ability, shortens the time cost and greatly reduces the space needed for node storage.

A short prokaryotic argonaute cooperates with membrane effector to confer antiviral defense

SummaryArgonaute (Ago) proteins are widespread nucleic acid-guided enzymes that recognize targets through complementary base pairing. While in eukaryotes Agos are involved in RNA silencing, the functions of prokaryotic Agos (pAgos) remain largely unknown. In particular, a clade of truncated and catalytically inactive pAgos (short pAgos) lacks characterization. Here, we reveal that a short pAgo protein in Sulfolobus islandicus, together with its two genetically associated proteins, Aga1 and Aga2, provide robust antiviral protection via abortive infection. Aga2 is a membrane-associated toxic effector that binds anionic phospholipids via a basic pocket, which is essential for its cell killing ability. Ago and Aga1 form a stable complex that exhibits RNA-directed nucleic acid recognition ability and directly interacts with Aga2, pointing to an immune sensing mechanism. Together, our results highlight the cooperation between pAgos and their widespread associated proteins, suggesting an uncharted diversity of pAgo-derived immune systems that await to be discovered.

Sevoflurane Promotes Neurodegeneration Through Inflammasome Formation in APP/PS1 Mice

Sevoflurane (SEVO) is a highly fluorinated methyl isopropyl ether used as an inhalational anesthetic for general anesthesia. Previous studies have shown that SEVO may induce impaired memory and recognition ability and may be associated with neurodegenerative disease, e.g., Alzheimer’s disease (AD). However, the underlying mechanism remains unknown. Here, we used a mouse AD model, APP/PS1, to study the effects of SEVO on neurodegeneration occurring in AD. We found that SEVO exposure significantly impaired the spatial reference memory, sensorimotor, and cognitive function of the mice. Mechanistically, we found that SEVO induced formation of NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasome and its downstream caspase 1-mediated production of IL-1β and IL-18, which subsequently deactivated brain-derived neurotrophic factor (BDNF) to promote neurodegeneration. Together, these data suggest that NLRP3 inflammasome is essential for SEVO-induced AD.