Search Results (54)

Improper regulation of sugarcane feed volume can lead to harvester inefficiency or clogging. Accurate recognition of feed volume is therefore critical. However, visual recognition is challenging due to sugarcane stacking during feeding. To address this, we propose YOLO-ASM (YOLO Accurate Stereo Matching), a novel detection method. At the target detection level, we integrate a Convolutional Block Attention Module (CBAM) into the YOLOv5s backbone network. This significantly reduces missed detections and low-confidence predictions in dense stacking scenarios, improving detection speed by 28.04% and increasing mean average precision (mAP) by 5.31%. At the stereo matching level, we enhance the SGBM (Semi-Global Block Matching) algorithm through improved cost calculation and cost aggregation, resulting in Opti-SGBM (Optimized SGBM). This double-cost fusion approach strengthens texture feature extraction in stacked sugarcane, effectively reducing noise in the generated depth maps. The optimized algorithm yields depth maps with smaller errors relative to the original images, significantly improving depth accuracy. Experimental results demonstrate that the fused YOLO-ASM algorithm reduces sugarcane volume error rates across feed volumes of one to six by 3.45%, 3.23%, 6.48%, 5.86%, 9.32%, and 11.09%, respectively, compared to the original stereo matching algorithm. It also accelerates feed volume detection by approximately 100%, providing a high-precision solution for anti-clogging control in sugarcane harvester conveyor systems. Full article

Ovarian carcinoma is a gynecological cancer with poor long-term survival rates when detected at advanced disease stages. Early symptoms are non-specific, and currently, there are no adequate strategies to identify this disease at an early stage when much higher survival rates can be expected. Ovarian carcinoma is a heterogeneous disease, with various histotypes originating from different cells and tissues, and is characterized by distinct somatic mutations, progression profiles, and treatment responses. Our study presents a targeted metabolomics approach, characterizing seven different ovarian (cancer-) cell lines according to their extracellular, intracellular, and RNA-derived modified nucleoside profiles. Moreover, these data were correlated with transcriptomics data to elucidate the underlying mechanisms. Modified nucleosides are excreted in higher amounts in cancer cell lines due to their altered DNA/RNA metabolism. This study shows that seven different ovarian cancer cell lines, representing different molecular subtypes, can be discriminated according to their specific nucleoside pattern. We suggest modified nucleosides as strong biomarker candidates for ovarian cancer with the potential for subtype-specific discrimination. Extracellular modified nucleosides have the highest potential in the distinguishing of cell lines between control cell lines and themselves, and represent the closest to a desirable, non-invasive biomarker, since they accumulate in blood and urine. Full article

As a critical task in underground coal mining, personnel identification and positioning in fully mechanized mining faces are essential for safety. Yet, complex environmental factors—such as narrow tunnels, heavy dust, and uneven lighting—pose significant challenges to accurate detection. In this paper, we propose a personnel detection network, MSS-YOLO, for fully mechanized mining faces based on YOLOv8. By designing a Multi-Scale Edge Enhancement (MSEE) module and fusing it with the C2f module, the performance of the network for personnel feature extraction under high-dust or long-distance conditions is effectively enhanced. Meanwhile, by designing a Spatial Pyramid Shared Conv (SPSC) module, the redundancy of the model is reduced, which effectively compensates for the problem of the max pooling being prone to losing the characteristics of the personnel at long distances. Finally, the lightweight Shared Convolutional Detection Head (SCDH) ensures real-time detection under limited computational resources. The experimental results show that compared to Faster-RCNN, SSD, YOLOv5s6, YOLOv7-tiny, YOLOv8n, and YOLOv11n, MSS-YOLO achieves AP50 improvements of 4.464%, 10.484%, 3.751%, 4.433%, 3.655%, and 2.188%, respectively, while reducing the inference time by 50.4 ms, 11.9 ms, 3.7 ms, 2.0 ms, 1.2 ms, and 2.3 ms. In addition, MSS-YOLO is combined with the SGBM binocular stereo vision matching algorithm to provide a personnel 3D spatial position solution by using disparity results. The personnel location results show that in the measurement range of 10 m, the position errors in the x-, y-, and z-directions are within 0.170 m, 0.160 m, and 0.200 m, respectively, which proves that MSS-YOLO is able to accurately detect underground personnel in real time and can meet the underground personnel detection and localization requirements. The current limitations lie in the reliance on a calibrated binocular camera and the performance degradation beyond 15 m. Future work will focus on multi-sensor fusion and adaptive distance scaling to enhance practical deployment. Full article

Fabry disease, the second most common lysosomal storage disorder, is caused by a deficiency of α-galactosidase A (α-Gal A), which leads to an accumulation of glycosphingolipids (GSL), mainly globotriaosylceramide (also known as Gb3). This aberrant GSL metabolism subsequently causes cellular dysfunction; however, the underlying cellular and molecular mechanisms are still unknown. There is growing evidence that damage to organelles, including lysosomes, mitochondria, and plasma membranes, is associated with substrate accumulation. Current methods for the detection of Gb3 are based on anti-Gb3 antibodies, the specificity and sensitivity of which are problematic for glycan detection. This study presents a robust method using lectins, specifically the B-subunit of Shiga toxin (StxB) from Shigella dysenteriae and LecA from Pseudomonas aeruginosa, as alternatives for Gb3 detection in Fabry fibroblasts by flow cytometry and confocal microscopy. StxB and LecA showed superior sensitivity, specificity, and consistency in different cell types compared to all anti-Gb3 antibodies used in this study. In addition, sphingolipid metabolism was analyzed in primary Fabry fibroblasts and α-Gal A knockout podocytes using targeted tandem liquid chromatography-mass spectrometry. Our findings establish lectins as a robust tool for improved diagnostics and research of Fabry disease and provide evidence of SL changes in cultured human cells, filling a knowledge gap. Full article

Crack detection and quantification play crucial roles in assessing the condition of concrete structures. Herein, a novel real-time crack detection and quantification method that leverages binocular vision and a lightweight deep learning model is proposed. In this methodology, the proposed method based on the following four modules is adopted: a lightweight classification algorithm, a high-precision segmentation algorithm, a semi-global block matching algorithm (SGBM), and a crack quantification technique. Based on the crack segmentation results, a framework is developed for quantitative analysis of the major geometric parameters, including crack length, crack width, and crack angle of orientation at the pixel level. Results indicate that, by incorporating channel attention and spatial attention mechanisms in the MBConv module, the detection accuracy of the improved EfficientNetV2 increased by 1.6% compared with the original EfficientNetV2. Results indicate that using the proposed quantification method can achieve low quantification errors of 2%, 4.5%, and 4% for the crack length, width, and angle of orientation, respectively. The proposed method can contribute to crack detection and quantification in practical use by being deployed on smart devices. Full article

The advancement of unloading technology in combine harvesting is crucial for the intelligent development of agricultural machinery. Accurately measuring material pile height in transport vehicles is essential, as uneven accumulation can lead to spillage and voids, reducing loading efficiency. Relying solely on manual observation for measuring stack height can decrease harvesting efficiency and pose safety risks due to driver distraction. This research applies binocular vision to agricultural harvesting, proposing a novel method that uses a stereo matching algorithm to measure material pile height during harvesting. By comparing distance measurements taken in both empty and loaded states, the method determines stack height. A linear regression model processes the stack height data, enhancing measurement accuracy. A binocular vision system was established, applying Zhang’s calibration method on the MATLAB (R2019a) platform to correct camera parameters, achieving a calibration error of 0.15 pixels. The study implemented block matching (BM) and semi-global block matching (SGBM) algorithms using the OpenCV (4.8.1) library on the PyCharm (2020.3.5) platform for stereo matching, generating disparity, and pseudo-color maps. Three-dimensional coordinates of key points on the piled material were calculated to measure distances from the vehicle container bottom and material surface to the binocular camera, allowing for the calculation of material pile height. Furthermore, a linear regression model was applied to correct the data, enhancing the accuracy of the measured pile height. The results indicate that by employing binocular stereo vision and stereo matching algorithms, followed by linear regression, this method can accurately calculate material pile height. The average relative error for the BM algorithm was 3.70%, and for the SGBM algorithm, it was 3.35%, both within the acceptable precision range. While the SGBM algorithm was, on average, 46 ms slower than the BM algorithm, both maintained errors under 7% and computation times under 100 ms, meeting the real-time measurement requirements for combine harvesting. In practical operations, this method can effectively measure material pile height in transport vehicles. The choice of matching algorithm should consider container size, material properties, and the balance between measurement time, accuracy, and disparity map completeness. This approach aids in manual adjustment of machinery posture and provides data support for future autonomous master-slave collaborative operations in combine harvesting. Full article

Mitochondria provide cells with energy and regulate the cellular metabolism. Almost all mitochondrial proteins are nuclear-encoded, translated on ribosomes in the cytoplasm, and subsequently transferred to the different subcellular compartments of mitochondria. Here, we developed OptoMitoImport, an optogenetic tool to control the import of proteins into the mitochondrial matrix via the presequence pathway on demand. OptoMitoImport is based on a two-step process: first, light-induced cleavage by a TEV protease cuts off a plasma membrane-anchored fusion construct in close proximity to a mitochondrial targeting sequence; second, the mitochondrial targeting sequence preceding the protein of interest recruits to the outer mitochondrial membrane and imports the protein fused to it into mitochondria. Upon reaching the mitochondrial matrix, the matrix processing peptidase cuts off the mitochondrial targeting sequence and releases the protein of interest. OptoMitoImport is available as a two-plasmid system as well as a P2A peptide or IRES sequence-based bicistronic system. Fluorescence studies demonstrate the release of the plasma membrane-anchored protein of interest through light-induced TEV protease cleavage and its localization to mitochondria. Cell fractionation experiments confirm the presence of the peptidase-cleaved protein of interest in the mitochondrial fraction. The processed product is protected from proteinase K treatment. Depletion of the membrane potential across the inner mitochondria membrane prevents the mitochondrial protein import, indicating an import of the protein of interest by the presequence pathway. These data demonstrate the functionality of OptoMitoImport as a generic system with which to control the post-translational mitochondrial import of proteins via the presequence pathway. Full article

This study develops a YOLO (You Only Look Once)-based 3D perception algorithm for UVMS (Underwater Vehicle-Manipulator Systems) for precise object detection and localization, crucial for enhanced grasping tasks. The object detection algorithm, YOLOv5s-CS, integrates an enhanced YOLOv5s model with C3SE attention and SPPFCSPC feature fusion, optimized for precise detection and two-dimensional localization in underwater environments with sparse features. Distance measurement is further improved by refining the SGBM (Semi-Global Block Matching) algorithm with Census transform and subpixel interpolation. Ablation studies highlight the YOLOv5s-CS model’s enhanced performance, with a 3.5% increase in mAP and a 6.4% rise in F1 score over the base YOLOv5s, and a 2.1% mAP improvement with 15% faster execution than YOLOv8s. Implemented on a UVMS, the algorithm successfully conducted pool grasping experiments, proving its applicability for autonomous underwater robotics. Full article

In Holoscopic imaging, a single aperture is used to acquire full-colour spatial images like a fly’s eye by gently altering angles between nearby lenses with a micro-lens array. Due to its simple data collection and visualisation methods, which provide robust and scalable spatial information, and its motion parallax, binocular disparity, and convergence, this technique may be able to overcome traditional 2D imaging issues like depth, scalability, and multi-perspective problems. A novel disparity-map-generating method uses angular information from a single Holoscopic image’s micro-images, or Elemental Images (EIs), to create a scene’s disparity map. Not much research has used EIs instead of Viewpoint Images (VPIs) for disparity estimation. This study investigates whether angular perspective data may replace spatial orthographic data. Using noise reduction and contrast enhancement, EIs with a low resolution and lack of texture are pre-processed to calculate the disparity. The Semi-Global Block Matching (SGBM) technique is used to calculate the disparity between EI pixels. A multi-resolution approach overcomes EIs’ resolution constraints, and a content-aware analysis dynamically modifies the SGBM window size settings to generate disparities across different texture and complexity levels. A background mask and nearby EIs with accurate backgrounds detect and rectify EIs with erroneous backgrounds. Our method generates disparity maps that outperform two state-of-the-art deep learning algorithms and VPIs in real images. Full article

In the realm of ocean engineering and maintenance of subsea structures, accurate underwater distance quantification plays a crucial role. However, the precision of such measurements is often compromised in underwater environments due to backward scattering and feature degradation, adversely affecting the accuracy of visual techniques. Addressing this challenge, our study introduces a groundbreaking method for underwater object measurement, innovatively combining image sonar with stereo vision. This approach aims to supplement the gaps in underwater visual feature detection with sonar data while leveraging the distance information from sonar for enhanced visual matching. Our methodology seamlessly integrates sonar data into the Semi-Global Block Matching (SGBM) algorithm used in stereo vision. This integration involves introducing a novel sonar-based cost term and refining the cost aggregation process, thereby both elevating the precision in depth estimations and enriching the texture details within the depth maps. This represents a substantial enhancement over existing methodologies, particularly in the texture augmentation of depth maps tailored for subaquatic environments. Through extensive comparative analyses, our approach demonstrates a substantial reduction in measurement errors by 1.6%, showing significant promise in challenging underwater scenarios. The adaptability and accuracy of our algorithm in generating detailed depth maps make it particularly relevant for underwater infrastructure maintenance, exploration, and inspection. Full article

Despite substantial heterogeneity of studies, there is evidence that antibiotics commonly used in primary care influence the composition of the gastrointestinal microbiota in terms of changing their composition and/or diversity. Benzyl isothiocyanate (BITC) from the food and medicinal plant nasturtium (Tropaeolum majus) is known for its antimicrobial activity and is used for the treatment of infections of the draining urinary tract and upper respiratory tract. Against this background, we raised the question of whether a 14 d nasturtium intervention (3 g daily, N = 30 healthy females) could also impact the normal gut microbiota composition. Spot urinary BITC excretion highly correlated with a weak but significant antibacterial effect against Escherichia coli. A significant increase in human beta defensin 1 as a parameter for host defense was seen in urine and exhaled breath condensate (EBC) upon verum intervention. Pre-to-post analysis revealed that mean gut microbiome composition did not significantly differ between groups, nor did the circulating serum metabolome. On an individual level, some large changes were observed between sampling points, however. Explorative Spearman rank correlation analysis in subgroups revealed associations between gut microbiota and the circulating metabolome, as well as between changes in blood markers and bacterial gut species. Full article

The universally conserved protein YidC aids in the insertion and folding of transmembrane polypeptides. Supposedly, a charged arginine faces its hydrophobic lipid core, facilitating polypeptide sliding along YidC’s surface. How the membrane barrier to other molecules may be maintained is unclear. Here, we show that the purified and reconstituted E. coli YidC forms an ion-conducting transmembrane pore upon ribosome or ribosome-nascent chain complex (RNC) binding. In contrast to monomeric YidC structures, an AlphaFold parallel YidC dimer model harbors a pore. Experimental evidence for a dimeric assembly comes from our BN-PAGE analysis of native vesicles, fluorescence correlation spectroscopy studies, single-molecule fluorescence photobleaching observations, and crosslinking experiments. In the dimeric model, the conserved arginine and other residues interacting with nascent chains point into the putative pore. This result suggests the possibility of a YidC-assisted insertion mode alternative to the insertase mechanism. Full article

Understanding the complex mechanisms of mycobacterial pathophysiology and adaptive responses presents challenges that can hinder drug development. However, employing physiologically relevant conditions, such as those found in human macrophages or simulating physiological growth conditions, holds promise for more effective drug screening. A valuable tool in this pursuit is proteomics, which allows for a comprehensive analysis of adaptive responses. In our study, we focused on Mycobacterium smegmatis, a model organism closely related to the pathogenic Mycobacterium tuberculosis, to investigate the impact of various carbon sources on mycobacterial growth. To facilitate this research, we developed a cost-effective, straightforward, and high-quality pipeline for proteome analysis and compared six different carbon source conditions. Additionally, we have created an online tool to present and analyze our data, making it easily accessible to the community. This user-friendly platform allows researchers and interested parties to explore and interpret the results effectively. Our findings shed light on mycobacterial adaptive physiology and present potential targets for drug development, contributing to the fight against tuberculosis. Full article

Rice lodging is a crucial problem in rice production. Lodging during growing and harvesting periods can decrease rice yields. Practical lodging judgment for rice can provide effective reference information for yield prediction and harvesting. This article proposes a binocular camera-based lodging judgment method for rice in real-time. As a first step, the binocular camera and Inertial Measurement Unit (IMU) were calibrated. Secondly, Census and Grayscale Level cost features are constructed for stereo matching of left and right images. The Cross-Matching Cost Aggregation method is improved to compute the aggregation space in the LAB color space. Then, the Winner-Takes-All algorithm is applied to determine the optimal disparity for each pixel. A disparity map is constructed, and Multi-Step Disparity Refinement is applied to the disparity map to generate the final one. Finally, coordinate transformation obtains 3D world coordinates corresponding to pixels. IMU calculates the real-time pose of the binocular camera. A pose transformation is applied to the 3D world coordinates of the rice to obtain its 3D world coordinates in the horizontal state of the camera (pitch and roll angles are equal to 0). Based on the distance between the rice and the camera level, thresholding was used to determine whether the region to be detected belonged to lodging rice. The disparity map effect of the proposed matching algorithm was tested on the Middlebury Benchmark v3 dataset. The results show that the proposed algorithm is superior to the widely used Semi-Global Block Matching (SGBM) stereo-matching algorithm. Field images of rice were analyzed for lodging judgments. After the threshold judgment, the lodging region results were accurate and could be used to judge rice lodging. By combining the algorithms with binocular cameras, the research results can provide practical technical support for yield estimation and intelligent control of rice harvesters. Full article