Extracellular vesicle-mediated delivery of anti-miR-106b inhibits morphine-induced primary ciliogenesis in the brain.

Repeated use of opioids such as morphine causes changes in the shape and signal transduction pathways of various brain cells, including astrocytes and neurons, resulting in alterations in brain functioning and ultimately leading to opioid use disorder. We previously demonstrated that extracellular vesicle (EV)-induced primary ciliogenesis contributes to the development of morphine tolerance. Herein, we aimed to investigate the underlying mechanisms and potential EV-mediated therapeutic approach to inhibit morphine-mediated primary ciliogenesis. We demonstrated that miRNA cargo in morphine-stimulated-astrocyte-derived EVs (morphine-ADEVs) mediated morphine-induced primary ciliogenesis in astrocytes. CEP97 is a target of miR-106b and is a negative regulator of primary ciliogenesis. Intranasal delivery of ADEVs loaded with anti-miR-106b decreased the expression of miR-106b in astrocytes, inhibited primary ciliogenesis, and prevented the development of tolerance in morphine-administered mice. Furthermore, we confirmed primary ciliogenesis in the astrocytes of opioid abusers. miR-106b-5p in morphine-ADEVs induces primary ciliogenesis via targeting CEP97. Intranasal delivery of ADEVs loaded with anti-miR-106b ameliorates morphine-mediated primary ciliogenesis and prevents morphine tolerance. Our findings bring new insights into the mechanisms underlying primary cilium-mediated morphine tolerance and pave the way for developing ADEV-mediated small RNA delivery strategies for preventing substance use disorders.

Observer-Based Adaptive Sliding Mode Compensation Position-Tracking Control for Drilling Tool Attitude Adjustment.

This study develops an adaptive sliding mode control approach for a drilling tool attitude adjustment system, aiming at solving the problems of model uncertainties and insufficient ability of disturbance suppression during the regulation behavior. To further improve the performance of the position-tracking loop in terms of response time, tracking accuracy, and robustness, a state observer based on an improved radial basis function is designed to approximate the model uncertainties, a valve dead-zone compensate controller is used to reduce control deviation, an adaptive sliding mode controller is designed to improve the position-tracking precision and attenuate sliding mode chattering. Finally, simulation and experimental results are carried out to verify the observability of the model uncertainties and position-tracking errors of the drilling tool attitude adjustment system, which can effectively improve the position-tracking performance and robustness of the drilling tool attitude adjustment system.

A Novel Detection Scheme in Image Domain for Multichannel Circular SAR Ground-Moving-Target Indication.

Circular synthetic aperture radar (CSAR), which can observe the region of interest for a long time and from multiple angles, offers the opportunity for moving-target detection (MTD). However, traditional MTD methods cannot effectively solve the problem of high probability of false alarm (PFA) caused by strong clutter. To mitigate this, a novel, three-step scheme combining clutter background extraction, multichannel clutter suppression, and the degree of linear consistency of radial velocity interferometric phase (DLRVP) test is proposed. In the first step, the spatial similarity of the scatterers and the correlation between sub-aperture images are fused to extract the strong clutter mask prior to clutter suppression. In the second step, using the data remaining after elimination of the background clutter in Step 1, an amplitude-based detector with higher processing gain is utilized to detect potential moving targets. In the third step, a novel test model based on DLRVP is proposed to further reduce the PFA caused by isolated strong scatterers. After the above processing, almost all false alarms are excluded. Measured data verified that the PFA of the proposed method is only 20% that of the comparison method, with improved detection of slow and weakly moving targets and with better robustness.

A Coherence Improvement Method Based on Sub-Aperture InSAR for Human Activity Detection.

Human activity detection plays an important role in social security monitoring. Since human activity is very weak, it is necessary to employ the repeat-pass Interferometric Synthetic Aperture Radar (InSAR) technique to detect the potential activity between two data acquisitions; a high level of coherence is required for detection. With the object of detecting human activity of interest, this paper presents a coherence improvement approach based on sub-aperture InSAR for human activity detection. Different sub-apertures contain different scattering information of the target, as they represent the backscatter of the target from a different range of angles. Integrating corresponding sub-aperture interferometric results can improve the coherence between two complex images compared to the entire synthetic aperture, as well as removing a little disturbance in some circumstances. To validate the method presented in this paper, the actual airborne Ka-band frequency modulated continuous wave (FMCW) InSAR data acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS) are utilized. The experimental results demonstrate that the proposed method can effectively improve the coherence between two complex SAR images and can validly detect human activity of interest.

A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter.

To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coal rock structure and high-power equipment of shearer in automatic coal mining working face. Roll and pitch angles are estimated using the MEMS gyroscope and accelerometer, and the accuracy is not reliable with time. In order to eliminate the measurement error of the sensors and obtain the high-accuracy attitude estimation of the system, an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data. Then the gradient descent algorithm is used to optimize the key parameter of unscented Kalman filter, namely process noise covariance, to improve the accuracy of attitude calculation. Finally, an experiment and industrial application show that the average measurement error of yaw angle is less than 2° and that of pitch angle and roll angle is less than 1°, which proves the efficiency and feasibility of the proposed system and method.

Analysis and compensation of telescopes' gaps effect on aperture synthesis in a multi-channel synthetic aperture ladar system.

The azimuth multi-channel synthetic aperture ladar (SAL), which arranges multiple telescopes along the flight direction of the platform, transmits signals through a single telescope and receives echoes by multiple telescopes simultaneously to obtain data. The aperture synthesis technology, which has the ability to achieve high resolution through multiple small telescopes, is applied to the multi-channel SAL system to realize the reconstruction of the complete azimuth Doppler spectrum in a short observation time. However, there are gaps inevitably between telescopes, which degrade the results of aperture synthesis. In this work, the effect of gaps on the instantaneous Doppler spectrum of each channel and the influence on the result of the azimuth impulse compression after aperture synthesis are analyzed. In addition, an estimation method of gaps based on the phase errors between channels is proposed to reduce the influence. The estimation accuracy of the proposed method is analyzed, and the effectiveness of the method is verified with simulations. The estimated gaps are used to compensate for the phase discontinuity of the azimuth signal after aperture synthesis caused by gaps. The method improves the result of aperture synthesis and reduces the side-lobe of the azimuth impulse compression after aperture synthesis.

Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence-Based Variable Translation Wavelet Neural Network.

As a sound signal has the advantages of non-contacted measurement, compact structure, and low power consumption, it has resulted in much attention in many fields. In this paper, the sound signal of the coal mining shearer is analyzed to realize the accurate online cutting pattern identification and guarantee the safety quality of the working face. The original acoustic signal is first collected through an industrial microphone and decomposed by adaptive ensemble empirical mode decomposition (EEMD). A 13-dimensional set composed by the normalized energy of each level is extracted as the feature vector in the next step. Then, a swarm intelligence optimization algorithm inspired by bat foraging behavior is applied to determine key parameters of the traditional variable translation wavelet neural network (VTWNN). Moreover, a disturbance coefficient is introduced into the basic bat algorithm (BA) to overcome the disadvantage of easily falling into local extremum and limited exploration ability. The VTWNN optimized by the modified BA (VTWNN-MBA) is used as the cutting pattern recognizer. Finally, a simulation example, with an accuracy of 95.25%, and a series of comparisons are conducted to prove the effectiveness and superiority of the proposed method.

HCRP1 is downregulated in non-small cell lung cancer and regulates proliferation, invasion, and drug resistance.

HCRP1 has been reported to have tumor suppressive function. However, its expression pattern and function in human non-small cell lung cancer (NSCLC) remain obscure. This study aims to explore clinical significance of HCRP1 in NSCLC. Immunohistochemical results showed high HCRP1 protein in normal bronchial epithelial tissue and downregulated HCRP1 expression in 47/98 lung cancer specimens. HCRP1 downregulation correlated with clinical stage (p = 0.0203), nodal status (p = 0.0168), and poor patient prognosis (log-rank, p = 0.0076). Univariate analysis showed that TNM stage (p < 0.0001) and HCRP1 (p = 0.0098) were significant prognostic factors; Cox regression model showed that TNM stage serves as an independent prognostic factor (p = 0.0011). We also found that HCRP1 was downregulated in lung cancer cells compared with normal HBE cells. HCRP1 plasmid transfection in H1299 cells inhibited proliferation, cell cycle progression, and invasion. HCRP1 depletion in A549 cells showed the opposite biological effects. In addition, we found that HCRP1 could inhibit MAPK and AKT signaling with downregulation of ERK and AKT phosphorylation, cyclin proteins, Bcl2 and MMP9, while HCRP1 depletion activated ERK and AKT signaling. The level of EGFR phosphorylation was also inhibited by HCRP1. In addition, we found that HCRP1 depletion confers multidrug resistance in H1299 cells. We employed paclitaxel and cisplatin in A549 cells with HCRP1 depletion. HCRP1 depletion decreased the effect of paclitaxel and cisplatin in A549 cells. Treatment with EGFR inhibitor AG1478 and AKT inhibitor LY249004 abolished the effect of HCRP1 depletion on drug resistance. In conclusion, the present study demonstrate that HCRP1 is downregulated in NSCLC and regulates proliferation, invasion, and drug resistance through modulation of EGFR signaling.

Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network.

In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy.

Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm.

Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.

A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network.

In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD) and Probabilistic Neural Network (PNN) is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF) components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method.

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory.

In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system.

Development of a GA-fuzzy-immune PID controller with incomplete derivation for robot dexterous hand.

In order to improve the performance of robot dexterous hand, a controller based on GA-fuzzy-immune PID was designed. The control system of a robot dexterous hand and mathematical model of an index finger were presented. Moreover, immune mechanism was applied to the controller design and an improved approach through integration of GA and fuzzy inference was proposed to realize parameters' optimization. Finally, a simulation example was provided and the designed controller was proved ideal.

Precise detection of coal and gangue based on natural γ-ray.

To address the technical limitations of automatic coal and gangue detection technology in fully mechanized top coal caving mining operations, the low radiation level radioactivity measurement method is utilized to assess the degree of coal-gangue mixture in top coal caving process. This approach is based on the distinguishing radiation characteristics of natural γ-rays between coal and gangue. This study analyzed the distribution characteristics of natural γ-rays in coal and rock layers of thick coal seams and the applicability of this method, introduced the basic principle of coal-gangue detection technology based on natural γ-ray, developed the test system about automatic coal-gangue detection, studied the radiation characteristics of coal and gangue, proposed determination model of the coal-gangue mixed degree, combined with the time sequence characteristics of the top coal's releasing flow and the energy spectrum characteristics of different layers of rock, realized the precise coal-gangue detection technology in complex structure thick coal seam with multiple gangue. Field tests were conducted in Lilou, Xiaoyu and Tashan Coal Mine. The test results were well corroborated with the research results and achieved the expected results, which laid the foundation for the field application of intelligent coal mining.

Effects of arbuscular mycorrhizal fungi on plant growth and herbivore infestation depend on availability of soil water and nutrients.

Introduction: Fitness of plants is affected by their symbiotic interactions with arbuscular mycorrhizal fungi (AMF), and such effects are highly dependent on the environmental context. Methods: In the current study, we inoculated the nursery shrub species Artemisia ordosica with AMF species Funneliformis mosseae under contrasting levels of soil water and nutrients (diammonium phosphate fertilization), to assess their effects on plant growth, physiology and natural infestation by herbivores. Results: Overall, plant biomass was synergistically enhanced by increasing soil water and soil nutrient levels. However, plant height was surprisingly repressed by AMF inoculation, but only under low water conditions. Similarly, plant biomass was also reduced by AMF but only under low water and nutrient conditions. Furthermore, AMF significantly reduced leaf phosphorus levels, that were strongly enhanced under high nutrient conditions, but had only minor effects on leaf chlorophyll and proline levels. Under low water and nutrient conditions, specific root length was enhanced, but average root diameter was decreased by AMF inoculation. The negative effects of AMF on plant growth at low water and nutrient levels may indicate that under these conditions AMF inoculation does not strongly contribute to nutrient and water acquisition. On the contrary, the AMF might have suppressed the direct pathway of water and nutrient absorption by the plant roots themselves despite low levels of mycorrhizal colonization. AMF inoculation reduced the abundance of the foliar herbivore Chrysolina aeruginosa on plants that had been grown on the low nutrient soil, but not on high nutrient soil. Fertilization enhanced the abundance of this herbivore but only in plants that had received the high water treatment. The lower abundance of the herbivore on AMF plants could be related to their decreased leaf P content. In conclusion, our results indicate that AMF negatively affect the growth of Artemisia ordosica but makes them less attractive to a dominant herbivore. Discussion: Our study highlights that plant responses to AMF depend not only on the environmental context, but that the direction of the responses can differ for different components of plant performance (growth vs. defense).

A novel positioning method of anti-punching drilling robot based on the fusion of multi-IMUs and visual image.

Anti-punching drilling robot (APDR) is the key equipment for underground rock burst relief in coal mine and the accurate position and attitude determination is the basis and premise for realizing unmanned pressure relief operation. In this paper, a novel positioning method of APDR based on spatial array inertial measurement units (IMUs) and visual image is proposed. Firstly, the spatial array layout of multiple IMUs is displayed creatively, and the data fusion model and the posture calculation process of spatial array IMUs are constructed. The superiority of proposed spatial array IMUs is verified through the motion trajectory simulation analysis and mobile carrier simulation experiments. Then, an image enhancement method (SSR-AF) is designed to overcome the problems of atomization, glare, uneven illumination and noise interference in coal mine images, so as to improve the accuracy of image feature extraction and matching. Accordingly, the positioning estimation model based on continuous frame images is established. Furthermore, the fusion positioning process of APDR is presented by using the loose coupling mode based on Kalman filtering algorithm. The position and attitude monitoring experimental platform of APDR is built in a simulated coal mine tunnel. The experimental results indicate that the calculation errors of displacement and attitude based on the proposed fusion positioning method is superior to the competing methods, which meet the actual positioning requirements, and verify the feasibility and practicability of the proposed positioning method for APDR.

Electrohydraulic proportional position and pressure loading control utilizing a state perception and processing method.

A new multifunctional proportional control triaxial stress loading apparatus is presented in this study, which can apply an output force on coal rock to simulate the conditions of triaxial stress loading, aiming at solving the problems of excessive consideration of static indices in the design and the incompleteness of the simulation and test verification system for the test parts at the performance analysis stage. This apparatus mainly combines the configuration of a triaxial stress loading system which can meet the stress loading requirements under different operating conditions, with the effective integration of multiple pressure loading operations based on the electrohydraulic proportional control method. In this context, a pressure and position combined control strategy based on the sliding mode is proposed to control the vertical and longitudinal loading hydraulic cylinders. Then, a co-simulation mode including the triaxial stress loading hydraulic system is established to verify the control strategy, system response characteristics and selection of the controller parameters. Furthermore, a multifunctional stress loading experimental platform is developed, and the stress loading characteristics with the proposed strategy are tested and analyzed. The results show that the triaxial stress loading hydraulic system can meet the response characteristics, the fluctuating deviation of the constant loading test can be restricted to 8.5%, the tracking error of the variable loading test is small, the minimum response time of instantaneous loading can reach 2.8 s, and the stress loading effect is noticeable. The experimental platform fully indicates that the stress loading system with the state perception and processing method as the core can meet a variety of verification indices of constant, variable and instantaneous loading tests. This research provides technical support for the smooth, synchronous control and intelligent operation of various types of hydraulic actuator machines.

Comparative analysis of the intestinal microbiota of black-necked cranes (Grus nigricollis) in different wintering areas.

Fecal microbiota is essential for host health because it increases digestive effectiveness. The crane species Grus nigricollis (G. nigricollis) is considered to be near threatened. The fecal microbial composition of crane is less understood, particularly in the Tibet, China. This study was performed to investigate the differences in fecal microbial composition and diversity of crane in different wintering areas using third-generation single-molecule real-time sequencing technology in the Tibet, China. According to the findings, 20 samples were used to generate 936 bacterial amplicon sequence variants (ASVs) and 1,800 fungal ASVs, only 4 bacterial ASVs and 20 fungal ASVs were shared in four distinct locations. Firmicutes were the dominant bacterial phylum in all samples, and Ascomycota and Basidiomycota were the dominant fungal phylum. At the genus level, Lactobacillus was the dominant genus in Linzhi City (LZ), Shannan City (SN), and Lasa City (LS), whereas Megamonas was the dominant genus in Rikaze City (RKZ). Naganishia and Mycosphaerella were the dominant fungal genera in SN and RKZ. Mycosphaerella and Tausonia were the dominant fungal genera in LZ. Naganishia and Fusarium were the dominant fungal genera in LS. And the fecal microbial composition varied between the four groups, as shown by the underweighted pair-group method with arithmetic means and principal coordinates analysis. This study offers a theoretical basis for understanding the fecal microbial composition of crane.

Transcriptomics-Based Study of Differentially Expressed Genes Related to Fat Deposition in Tibetan and Yorkshire Pigs.

Fat deposition traits are one of the key factors in pig production and breeding. The fat deposition capacity of pigs mainly affects the quality of pork and pig productivity. The aim of this study was to analyze the differential expression of mRNA levels in dorsal adipose tissue of Tibetan and York pigs at different growth stages using transcriptomic data to estimate key genes that regulate fat deposition in pigs. The results showed that a total of 32,747 positively expressed genes were present in the dorsal adipose tissue of the two breeds. Differentially expressed gene (DEG) screening of multiple combinations between the two breeds yielded 324 DEGS. Gene ontology (GO) biofunctional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that these DEGS were mainly involved in lipid metabolic pathways, steroid biosynthetic pathways and lipid biosynthetic processes, sterol biosynthetic processes, brown adipocyte differentiation, and other pathways related to lipid deposition and metabolism. The results showed that ACACA, SLC2A4 and THRSP genes positively regulated the lipid deposition ability and CHPT1 gene negatively regulated the lipid deposition ability in pigs. The results of this experiment suggest a theoretical basis for further studies on the regulatory mechanisms of fat deposition in pigs.

[Defensive characteristics of cones of Pinus armandii against seed-eating animals]. / 华山松球果对食种子动物的防御特征.

During the co-evolution with animals, plants evolved different defense strategies to resist predators and ensure their own survival and reproduction. We investigated the forging preference of nutcrackers (Nucifraga caryocatactes) for seeds in different parts of bending Pinus armandii cones in Southeast Tibet. We measured the morphological characteristics (length, width, thickness, and seed hull thickness) and the physical and chemical properties of concave and convex seeds of P. armandii (crude water, dry-matter at 70 ℃, crude fat, ash, protein and crude fiber). The results showed that there were significant differences in seed shell thickness, kernel percentage and empty shell percentage between the concave and convex seeds. The seed shell thickness of convex seeds (1.11±0.12 mm) was thicker than that of concave seeds (1.07±0.15 mm). The kernel weight percentage of convex seeds (24.0%) was smaller than that of concave seeds (25.4%). The empty shell percentage (11.2%), crude fat content (47.0%) of convex seeds were significantly lower than that of concave seeds (15.8% and 50.5%). The curving cones of P. armandii cause false hints to seed eaters, and protect high-quality seeds from being eaten as much as possible. Therefore, the curving cone is a defensive characteristics of P. armandii against seed predators.