Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer

BackgroundProgrammed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim of this study was to investigate the association between various programmed cell death patterns and the prognosis of breast cancer (BRCA) patients.MethodsThe levels of 19 different programmed cell deaths in breast cancer were assessed by ssGSEA analysis, and these PCD scores were summed to obtain the PCDS for each sample. The relationship of PCDS with immune as well as metabolism-related pathways was explored. PCD-associated subtypes were obtained by unsupervised consensus clustering analysis, and differentially expressed genes between subtypes were analyzed. The prognostic signature (PCDRS) were constructed by the best combination of 101 machine learning algorithm combinations, and the C-index of PCDRS was compared with 30 published signatures. In addition, we analyzed PCDRS in relation to immune as well as therapeutic responses. The distribution of genes in different cells was explored by single-cell analysis and spatial transcriptome analysis. Potential drugs targeting key genes were analyzed by Cmap. Finally, the expression levels of key genes in clinical tissues were verified by RT-PCR.ResultsPCDS showed higher levels in cancer compared to normal. Different PCDS groups showed significant differences in immune and metabolism-related pathways. PCDRS, consisting of seven key genes, showed robust predictive ability over other signatures in different datasets. The high PCDRS group had a poorer prognosis and was strongly associated with a cancer-promoting tumor microenvironment. The low PCDRS group exhibited higher levels of anti-cancer immunity and responded better to immune checkpoint inhibitors as well as chemotherapy-related drugs. Clofibrate and imatinib could serve as potential small-molecule complexes targeting SLC7A5 and BCL2A1, respectively. The mRNA expression levels of seven genes were upregulated in clinical cancer tissues.ConclusionPCDRS can be used as a biomarker to assess the prognosis and treatment response of BRCA patients, which offers novel insights for prognostic monitoring and treatment personalization of BRCA patients

Effects of diverse resistance training modalities on performance measures in athletes: a network meta-analysis

Background: Jumping ability is one of the necessary qualities for athletes. Previous studies have shown that plyometric training and complex training including plyometrics can improve athletes’ jumping ability. With the emergence of various types of complex training, there is uncertainty about which training method has the best effect. This study conducted a meta-analysis of randomized controlled trials of plyometric-related training on athletes’ jumping ability, to provide some reference for coaches to design training plans.Methods: We systematically searched 3 databases (PubMed, Web of Science, and Scopus) up to July 2023 to identify randomized controlled trials investigating plyometrics related training in athletes. The two researchers conducted literature screening, extraction and quality assessment independently. We performed a network meta-analysis using Stata 16.Results: We analyzed 83 studies and found that complex training, which includes high-intensity intervals and plyometric exercises, was the most effective method for improving squat jumps (SURCA = 96%). In the case of countermovement jumps a combination of electrostimulation and plyometric training yielded the best results (SURCA = 97.6%). Weightlifting training proved to be the most effective for the standing long jump (SURCA = 81.4%), while strength training was found to be the most effective for the five bounces test (SURCA = 87.3%).Conclusion: Our current study shows that complex training performs more efficient overall in plyometric-related training. However, there are different individual differences in the effects of different training on different indicators (e.g., CMJ, SJ, SLJ, 5BT) of athletes. Therefore, in order to ensure that the most appropriate training is selected, it is crucial to accurately assess the physical condition of each athlete before implementation.Clinical Trial Registration:https://www.crd.york.ac.uk/PROSPERO/, Registration and protocol CRD42023456402

A Secure Random Key Distribution Scheme Against Node Replication Attacks in Industrial Wireless Sensor Systems

With the widely deployment of wireless sensor networks in smart industrial systems, lots of unauthorized attacking from the adversary is greatly threating the security and privacy of the entire industrial systems, of which node replication attacks can hardly be defended since it is conducted in the physical layer. To solve this problem, we propose a secure random key distribution scheme, called SRKD, which provides a new method for the defense against the attack. Specifically, we combine a localized algorithm with a voting mechanism to support the detection and revocation of malicious nodes. We further change the meaning of the parameter s to help prevent the replication attack. Furthermore, the experimental results show that the detection ratio of replicate nodes exceeds 90% when number of network nodes reaches 200, which demonstrates the security and effectiveness of our scheme. Compared with existing state-of-the-art schemes, SRKD also has good storage and communication efficiency.acceptedVersion© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Research on rotary geosteering drilling technology and equipment in underground coal mine

In view of the limitations of directional drilling technology and equipment for underground gas drainage in coal mining areas, such as low degree of automation, low drilling ratio of target formation, slow drilling rate of rock formation, and small diameter, the research of rotary geosteering drilling technology and equipment underground coal mine has been carried out, with the core technical problems has overcame such as wireless remote control operation of directional drilling rigs, mechanically automatic drill pipe addition, real-time parameter monitoring and fault diagnosis, coal and rock identification while drilling, and trajectory rotation control, ZDY25000LDK electro-hydraulic controlled directional drilling rig, BLY800/12 mud pump truck with high-pressure and large-volume, YSG(A) Mine geosteering MWD system by dynamic azimuth gamma, ϕ133 mm hydraulic push type rotary steering drilling system and supporting drilling tools have developed, with the maximum torque was 25 000 N·m, the automatically add drill pipe time was less than 55s of the directional drilling rig, the rated flow was 800 L/min of the mud pump truck, the natural gamma measurement error was less than ±5% and the stratum exploration distance was ≥0.5 m of the geosteering MWD system, the applicable speed range was 80−200 r/min and the deflection force was 1.4 t of the rotary steerable drilling system; geosteering drilling technology and rotary steerable drilling technology for near horizontal hole has developed with the technical characteristics and process flow are introduced in detail. The field test was carried out in Tangjiahui Coal Mine in Inner Mongolia. 4 directional drillings were completed, with the maximum drilling depth was 820 m, the total footage was 2 419 m, the directional drilling diameter was 172 mm, the drilling efficiency was increased 25% in coal seam and 30% in rock, which realized the directional drilling from “geometric steering” to “rotary geosteering” underground coal mine, the intelligent level and drilling trajectory quality of underground directional drilling in coal mine has been improved, and provided technical equipment support for efficient underground gas drainage and intelligent mine construction

Accurate Recognition of Jujube Tree Trunks Based on Contrast Limited Adaptive Histogram Equalization Image Enhancement and Improved YOLOv8

The accurate recognition of tree trunks is a prerequisite for precision orchard yield estimation. Facing the practical problems of complex orchard environment and large data flow, the existing object detection schemes suffer from key issues such as poor data quality, low timeliness and accuracy, and weak generalization ability. In this paper, an improved YOLOv8 is designed on the basis of data flow screening and enhancement for lightweight jujube tree trunk accurate detection. Firstly, the key frame extraction algorithm was proposed and utilized to efficiently screen the effective data. Secondly, the CLAHE image data enhancement method was proposed and used to enhance the data quality. Finally, the backbone of the YOLOv8 model was replaced with a GhostNetv2 structure for lightweight transformation, also introducing the improved CA_H attention mechanism. Extensive comparison and ablation results show that the average precision of the quality-enhanced dataset over that of the original dataset increases from 81.2% to 90.1%, and the YOLOv8s-GhostNetv2-CA_H model proposed in this paper reduces the model size by 19.5% compared to that of the YOLOv8s base model, with precision increasing by 2.4% to 92.3%, recall increasing by 1.4%, [email protected] increasing by 1.8%, and FPS being 17.1% faster

Multi-View Jujube Tree Trunks Stereo Reconstruction Based on UAV Remote Sensing Imaging Acquisition System

High-quality agricultural multi-view stereo reconstruction technology is the key to precision and informatization in agriculture. Multi-view stereo reconstruction methods are an important part of 3D vision technology. In the multi-view stereo 3D reconstruction method based on deep learning, the effect of feature extraction directly affects the accuracy of reconstruction. Aiming at the actual problems in orchard fruit tree reconstruction, this paper designs an improved multi-view stereo structure based on the combination of remote sensing and artificial intelligence to realize the accurate reconstruction of jujube tree trunks. Firstly, an automatic key frame extraction method is proposed for the DSST target tracking algorithm to quickly recognize and extract high-quality data. Secondly, a composite U-Net feature extraction network is designed to enhance the reconstruction accuracy, while the DRE-Net feature extraction enhancement network improved by the parallel self-attention mechanism enhances the reconstruction completeness. Comparison tests show different levels of improvement on the Technical University of Denmark (DTU) dataset compared to other deep learning-based methods. Ablation test on the self-constructed dataset, the MVSNet + Co U-Net + DRE-Net_SA method proposed in this paper improves 20.4% in Accuracy, 12.8% in Completion, and 16.8% in Overall compared to the base model, which verifies the real effectiveness of the scheme

Soil Particle Modeling and Parameter Calibration for Use with Discrete Element Method

HighlightsSoil particle shapes were statistically analyzed, and four representative particles were obtained.A particle model was established using three-dimensional non-contact surface topography.This study used a response surface design method to calibrate significant soil parameters.The simulation parameters were verified by rotary tiller experiment.Abstract. The discrete element method (DEM) has broad prospects for application in soil-tool simulations. To ensure the reliability of simulations, appropriate simulation parameters and particle modeling are essential. Therefore, in this article, a method combining simulation and actual tests is proposed to calibrate the critical soil parameters. First, the effect of soil particle shape on particle contact was considered. Soil particle shapes were statistically analyzed using an improved GrabCut algorithm and k-means algorithm. Four representative soil particles were obtained. Second, a soil particle model was established by microscope and three-dimensional non-contact surface topography. Finally, taking the angle of repose as the response value, the three parameters with significant effects on the angle of repose, i.e., soil shear modulus, Hertz-Mindlin with Johnson-Kendall-Roberts contact model (JKR), and soil-soil restitution coefficient, were obtained via a Plackett-Burman experiment. The optimal value intervals of the significant parameters were determined by the steepest climbing test. A polynomial regression model between the angle of repose and the three significant parameters was established with a Box-Behnken experiment using three factors and three levels. The interactions between the three significant parameters were not significant, as revealed by response surface analysis. The optimal values of the significant parameters were obtained by taking the actual angle of repose as the target and resulted in a soil shear module of 9.8 MPa, JKR of 0.063, and soil-soil restitution coefficient of 0.478. To verify the reliability of the calibrated parameters, the soil angles of repose from the simulation and from actual tests were compared and analyzed. For a simulated angle of repose of 38.5°, the actual angle of repose was 38.6°, and the relative error was 0.26%. DEM was also used to simulate a rotary tiller with the calibrated parameters. The maximum error of the simulated soil throwing angle was less than 10% when compared with the actual throwing angle. The experimental results showed that the calibrated parameters were accurate and can provide a reference for the selection of soil discrete element parameters. Keywords: Angle of repose, Numerical simulation, Parameter calibration, Shape survey, Soil.</jats:p

Experimental Study on Shear Mechanical Characteristics of Jujube Branches in Winter Pruning Period

The objective of this study was to design and optimize the pruning and crushing machinery of jujube branches, and to provide the corresponding data reference, so as to improve the cutting and crushing efficiency of jujube branches. Take fresh jujube branches (31.7±5 % w.b) as the research object. The morphology and microstructure of jujube branches were detected by scanning electron microscope. Four kinds of cutting blades were selected and manufactured, and the cutting characteristics of four kinds of blades against branches were evaluated. The shear fracture process of jujube branches was analyzed according to the microstructure of branches. Covariance analysis was used to separate the influence of water content on shear strength, and the significance level of the influencing factors is evaluated. The analysis of the test results showed that: the significant effects on shearing strength of the branches are shearing angle, cutter shape and shear speed from large to small. The force-displacement curves of cutting tool branches with different shapes of edge lines are different, and the peak of the shear force generally appears in the II&amp;IV stages (xylem) shearing process. Under the same shear mode and the same shear velocity, the shear strength of the cambered cutter is the smallest.</jats:p