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The accuracy of straightness monitoring of scraper conveyors seriously restricts the development of unmanned mining technology in underground coal mines. Fiber Bragg grating (FBG) sensors have the characteristics of passive intrinsic safety and have broad application prospects in coal mine underground monitoring technology. In this paper, the motion characteristics between two adjacent Middle Chutes of the scraper were fully analyzed, the mathematical expressions were illustrated, furthermore, a 3D vector sensor is designed and manufactured using FBG sensors. The calibration and testing of the appropriate number of sensors was realized, and the test results showed that the FBG 3D vector sensor we designed can be used to measure the straightness of the scraper conveyor. The research introduces three main contributions: (1) a novel mathematical model characterizing the motion between adjacent scrapers; (2) the design and fabrication of a 3D vector sensor based on FBG technology; and (3) experimental validation through calibration and testing. Results demonstrate that the developed sensor system effectively measures Middle Chutes straightness with accuracy meeting practical application requirements.

Stropharia rugosoannulata is a well-renowned edible mushroom due to its nutritional and nutraceutical properties. This article focuses on the study of stipe cracking in S. rugosoannulata , a common issue in outdoor cultivation of this mushroom in South China. The findings reveal that the stipe cracks of S. rugosoannulata are primarily horizontal (transverse). Typically, cracks appear between the annulus and the middle part of the stipe prior to the opening of the pileus. Following the opening of the pileus, a fresh crack appears on the upper part of the stipe above the annulus. During the growth of S. rugosoannulata , two distinct elongation sections are observed in the stipe, separated by the annulus. The location of cracks coincides with these elongation sections, and the sequence of crack occurrences matches with the sequence of these elongation sections. The frequency of stipe cracking varies according to developmental stages and humidity conditions. The conclusion of this study is that S. rugosoannulata stipes crack during elongation and within elongation sections when humidity is low (≤ 60%), with the S3 developmental stage having the highest risk of cracking.

Magnetotelluric (MT) forward modeling is a key technique in magnetotelluric sounding, and deep learning has been widely applied to MT forward modeling. In three-dimensional (3-D) problems, although existing methods can predict forward modeling results with high accuracy, they often use multiple networks to simulate multiple forward modeling parameters, resulting in low efficiency. We apply multi-task learning (MTL) to 3-D MT forward modeling to achieve simultaneous inference of apparent resistivity and impedance phase, effectively improving overall efficiency. Furthermore, through comparative analysis of feature map differences in various decoder layers of the network, we identify the optimal branching point for multi-task learning decoders. This enhances the feature extraction capabilities of the network and improves the prediction accuracy of forward modeling parameters. Additionally, we introduce an uncertainty-based loss function to dynamically balance the learning weights between tasks, addressing the shortcomings of traditional loss functions. Experiments demonstrate that compared with single-task networks and existing multi-task networks, the proposed network (MT-FeatureNet) achieves the best results in terms of Structural Similarity Index Measure (SSIM), Mean Relative Error (MRE), and Mean Absolute Error (MAE). The proposed multi-task learning model not only improves the efficiency and accuracy of 3-D MT forward modeling but also provides a novel approach to the design of multi-task learning network structures.

Acute lymphoblastic leukemia (ALL) is the most common hematologic malignancy in children, posing challenges for early diagnosis, relapse prediction, and individualized treatment due to its high cellular and immune heterogeneity. This study addresses these challenges by integrating deep learning-based imaging analysis with single-cell transcriptomics and T-cell receptor sequencing (TCR-seq). A ResNet50-based deep learning model was developed to classify leukemia single-cell images, achieving an accuracy of ~ 85% and an AUC of 0.86. Gradient-weighted Class Activation Mapping (Grad-CAM) indicated that nuclear morphology was the key feature for cell identification. Concurrently, Louvain clustering of single-cell RNA sequencing data revealed 18 distinct cellular subpopulations, with Cluster 4 exhibiting strong immune activity and pronounced TCRβ rearrangement. Functional analyses highlighted the roles of UBE2C and HMGB2 in leukemogenesis. Integration with TCR-seq further uncovered immune microenvironment alterations and potential relapse biomarkers. These results demonstrate that combining deep learning with single-cell multi-omics is a powerful strategy for elucidating disease mechanisms, improving early diagnosis, and informing personalized therapies for pediatric ALL.

Subclonal copy number alterations are a prevalent feature in tumors with high chromosomal instability and result in heterogeneous cancer cell populations with distinct phenotypes. However, the extent to which subclonal copy number alterations contribute to clone-specific phenotypes remains poorly understood. We develop TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population. TreeAlign accurately encodes dosage effects from subclonal copy number alterations, the impact of allelic imbalance on allele-specific transcription, and obviates the need to define genotypic clones from a phylogeny a priori, leading to highly granular definitions of clones with distinct expression programs. These improvements enable clone-clone gene expression comparisons with higher resolution and identification of expression programs that are genomically independent. Our approach sets the stage for dissecting the relative contribution of fixed genomic alterations and dynamic epigenetic processes on gene expression programs in cancer. Quantifying the impact of copy-number alterations (CNAs) on gene expression at the subclone level in cancer remains a challenge. Here, the authors develop TreeAlign, a method that integrates sample-matched single-cell DNA and RNA sequencing data to infer the impact of CNAs on subclonal gene expression.

Giant Stropharia (S. rugoso-annulata) is an edible mushroom recommended for consumption by the Food and Agriculture Organization of the United Nations. It possesses significant culinary and medicinal functionalities. The characteristics of this mushroom include high protein content, abundant bioactive compounds, delicious and sweet taste, and pleasant aroma. In recent years, the S. rugoso-annulata industry has seen strong growth, especially in China. This article presents the first comprehensive and systematic review of the nutritional, bioactive, and flavor components of S. rugoso-annulata, as well as their influencing factors. This article provides scientific evidence for the production of high-quality S. rugoso-annulata mushrooms, the extraction of bioactive components, post-harvest storage, and culinary processing, aiming to promote the consumption of S. rugoso-annulata and the health of consumers.

The increasing prevalence of multidrug-resistant tuberculosis (MDR-TB) highlights the urgent need for an efficient approach to identify Mycobacterium tuberculosis complex (MTBC) strains resistant to rifampicin (RIF) and isoniazid (INH). In response, we developed a CRISPR-Cas14a MTB RIF/INH platform that can detect the most common mutations associated with RIF and INH resistance. To evaluate the sensitivity and specificity of our CRISPR-Cas14a MTB RIF/INH platform, we carried out a comprehensive assessment using clinical isolates of M. tuberculosis and sputum samples from TB patients, making direct comparisons with phenotypic drug susceptibility testing (pDST). A total of 60 clinical isolates from TB patients were utilized, consisting of 18 RIF mono-resistant, 15 INH mono-resistant, 24 MDR isolates, and 3 fully susceptible isolates. Among the 42 RIF-resistant isolates, our platform accurately identified 39, achieving a sensitivity of 93.3% (95% CI, 80.0-98.5) and a specificity of 100% (95% CI, 81.6–100). Similarly, out of the 39 INH-resistant isolates, the platform successfully identified 38, demonstrating a sensitivity of 97.5% (95% CI, 86.5–99.9) and a specificity of 100% (95% CI, 83.8–100) when compared with pDST. Moreover, in the analysis of 55 sputum samples, our platform accurately identified RIF resistance in 10 out of 12 samples (85.7%) and INH resistance in all 11 samples (100%). Notably, excluding the nucleic acid extraction step, the entire testing procedure can be completed in approximately 1.5 h. These results suggest that the CRISPR-Cas14a MTB RIF/INH platform is a reliable and promising novel tool for detecting RIF and INH resistance in isolates or directly from sputum samples.

It is well known that regular physical exercise has associated benefits; yet, participation remains suboptimal. Mobile health (mHealth) has become an indispensable medium to deliver behavior change interventions, and there is a growing interest in the gamification apps in mHealth to promote physical activity (PA) participation. Gamification could use game design elements (such as points, leaderboards, and progress bars), and it has the potential to increase motivation for PA and engagement. However, mHealth-based gamification interventions are still emerging, and little is known about the application status and efficacy of such interventions. This systematic review aims to investigate gamification apps in mHealth for improving PA levels and simultaneously summarize the impact of gamification interventions on PA participation. We searched PubMed, Scopus, Web of Science, Embase, CINAHL (EBSCO host), and IEEE Xplore from inception to December 20, 2020. Original empirical research exploring the effects of gamification interventions on PA participation was included. The papers described at least one outcome regarding exercise or PA participation, which could be subjective self-report or objective indicator measurement. Of note, we excluded studies about serious games or full-fledged games. Of 2944 studies identified from the database search, 50 (1.69%) were included, and the information was synthesized. The review revealed that gamification of PA had been applied to various population groups and broadly distributed among young people but less distributed among older adults and patients with a disease. Most of the studies (30/50, 60%) combined gamification with wearable devices to improve PA behavior change, and 50% (25/50) of the studies used theories or principles for designing gamified PA interventions. The most frequently used game elements were goal-setting, followed by progress bars, rewards, points, and feedback. This review demonstrated that gamification interventions could increase PA participation; however, the results were mixed, and modest changes were attained, which could be attributed to the heterogeneity across studies. Overall, this study provides an overview of the existing empirical research in PA gamification interventions and provides evidence for the efficacy of gamification in enhancing PA participation. High-quality empirical studies are needed in the future to assess the efficacy of a combination of gamification and wearable activity devices to promote PA, and further exploration is needed to investigate the optimal implementation of these features of game elements and theories to enhance PA participation.

Metasurfaces have shown their great capability to manipulate electromagnetic waves. As a new concept, reconfigurable metasurfaces attract researchers’ attention. There are many kinds of reconfigurable components, devices and materials that can be loaded on metasurfaces. When cooperating with reconfigurable structures, dynamic control of the responses of metasurfaces are realized under external excitations, offering new opportunities to manipulate electromagnetic waves dynamically. This review introduces some common methods to design reconfigurable metasurfaces classified by the techniques they use, such as special materials, semiconductor components and mechanical devices. Specifically, this review provides a comparison among all the methods mentioned and discusses their pros and cons. Finally, based on the unsolved problems in the designs and applications, the challenges and possible developments in the future are discussed.

Fin-angle feedback control is usually used in conventional fin stabilizers, and its actual anti-rolling effect is difficult to reach theoretical design requirements. Primarily, lift of control torque is a theoretical value calculated by static hydrodynamic characteristics of fin. However, hydrodynamic characteristics of fin are dynamic while fin is moving in waves. As a result, there is a large deviation between actual value and theoretical value of lift. Firstly, the reasons of deviation are analyzed theoretically, which could avoid a variety of interference factors and complex theoretical derivations. Secondly, a new device is designed for direct measurement of actual lift, which is composed of fin-shaft combined mechanism and sensors. This new device can make fin-shaft not only be the basic function of rotating fin, but also detect actual lift. Through analysis using stiffness matrix of Euler-Bernoulli beam, displacement of shaft-core end is measured instead of lift which is difficult to measure. Then quantitative relationship between lift and displacement is defined. Three main factors are analyzed with quantitative relationship. What is more, two installation modes of sensors and a removable shaft-end cover are proposed according to hydrodynamic characteristics of fin. Thus the new device contributes to maintenance and measurement. Lastly, the effectiveness and accuracy of device are verified by contrasting calculation and simulation on the basis of actual design parameters. And the new measuring lift method can be proved to be effective through experiments. The new device is achieved from conventional fin stabilizers. Accordingly, the reliability of original equipment is inherited. The alteration of fin stabilizers is minor, which is suitable for engineering application. In addition, the flexural properties of fin-shaft are digitized with analysis of stiffness matrix. This method provides theoretical support for engineering application by carrying out finite element analysis with computers.