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Abstract Single-cell technology depicts integrated tumor profiles including both tumor cells and tumor microenvironments, which theoretically enables more robust diagnosis than traditional diagnostic standards based on only pathology. However, the inherent challenges of single-cell RNA sequencing (scRNA-seq) data, such as high dimensionality, low signal-to-noise ratio (SNR), sparse and non-Euclidean nature, pose significant obstacles for traditional diagnostic approaches. The diagnostic value of single-cell technology has been largely unexplored despite the potential advantages. Here, we present a graph neural network-based framework tailored for molecular diagnosis of primary liver tumors using scRNA-seq data. Our approach capitalizes on the biological plausibility inherent in the intercellular communication networks within tumor samples. By integrating pathway activation features within cell clusters and modeling unidirectional inter-cellular communication, we achieve robust discrimination between malignant tumors (including hepatocellular carcinoma, HCC, and intrahepatic cholangiocarcinoma, iCCA) and benign tumors (focal nodular hyperplasia, FNH) by scRNA data of all tissue cells and immunocytes only. The efficacy to distinguish iCCA from HCC was further validated on public datasets. Through extending the application of high-throughput scRNA-seq data into diagnosis approaches focusing on integrated tumor microenvironment profiles rather than a few tumor markers, this framework also sheds light on minimal-invasive diagnostic methods based on migrating/circulating immunocytes.

Pet food formulated with raw meat can pose health risks to pets and humans. High-pressure processing (HPP) was evaluated to achieve a 5-log reduction of Salmonella, E. coli STEC, and L. monocytogenes in commercial raw pet foods and maintain a 5-log reduction throughout post-HPP storage. Three formulation types that varied in the amounts of striated meat, organ meat, bone, seeds, and other ingredients (fruits, vegetables, and minor ingredients) designated as A-, S-, and R-formulations were used.Eight raw diet pet foods, consisting of three beef formulations (A-, S- and R-Beef), three chicken formulations (A-, S-, and R-Chicken), and two lamb formulations (A- and S-Lamb), were inoculated with 7 log CFU/g cocktails of Salmonella, E. coli STEC or L. monocytogenes, HPP at 586 MPa for 1–4 min, and stored refrigerated (4°C) or frozen (−10 to −18°C) for 21 days with microbiological analyses at various time intervals.A- formulations (20–46% meat, 42–68% organs, 0.9–1.3% seeds, and 10.7–11.1% fruits, vegetables, and minor ingredients) inoculated with Salmonella and treated at 586 MPa for at least 2 min achieved a 5-log reduction 1 day post-HPP and maintained that inactivation level during frozen storage. A- and S-formulations inoculated with E. coli STEC and treated at 586 MPa for at least 2 min achieved a 5-log reduction from day 6 of frozen storage.L. monocytogenes was more HPP resistant than Salmonella and E. coli STEC. S-formulations containing chicken or beef and stored frozen post-HPP had lower inactivation of L. monocytogenes compared to A-formulations containing chicken or beef. S-Lamb had higher frozen storage inactivation (5.95 ± 0.20 log CFU/g) compared to chicken (2.52 ± 0.38 log CFU/g) or beef (2.36 ± 0.48 log CFU/g). HPP coupled with frozen storage time was effective in achieving and maintaining a 5-log reduction of Salmonella and E. coli STEC while L. monocytogenes was more resistant and requires further optimization to achieve a 5-log reduction.

Abstract Background An increasing number of studies have shown the merits of endoscopic retrograde appendicitis therapy (ERAT) in diagnosing and treating acute uncomplicated appendicitis. However, no related prospective controlled studies have been reported yet. Our aim is to assess the feasibility and safety of ERAT in the treatment of acute uncomplicated appendicitis. Methods In this open-label, randomized trial, participants were randomly allocated to the ERAT group, laparoscopic appendectomy (LA) group and open appendectomy (OA) group. The primary outcome was the clinical success rate of the treatment. Intention-to-treat analysis was used in the study. Results The study comprised of 99 patients, with 33 participants in each group. The clinical success rate was 87.88% (29/33), 96.97% (32/33) and 100% (33/33) in the ERAT, LA and OA group, respectively. In the ERAT group, 4 patients failed ERAT due to difficult cannulation. In LA group, 1 patient failed because of abdominal adhesion. There were no significant differences among the three treatment groups regarding the clinical success rate (P = 0.123). The median duration of follow-up was 22 months. There were no significant differences (P = 0.693) among the three groups in terms of adverse events and the final crossover rate of ERAT to surgery was 21.21% (7/33). Conclusion ERAT can serve as an alternative and efficient method to treat acute uncomplicated appendicitis. Trial registration The study is registered with the WHO Primary Registry-Chinese Clinical Trial Registry (ChiCTR1900025812).

In planetary science, it is an important basic work to recognize and classify the features of topography and geomorphology from the massive data of planetary remote sensing. Therefore, this article proposes a lightweight model based on VGG-16, which can selectively extract some features of remote sensing images, remove redundant information, and recognize and classify remote sensing images. This model not only ensures the accuracy, but also reduces the parameters of the model. According to our experimental results, our model has a great improvement in remote sensing image classification, from the original accuracy of 85%–98% now. At the same time, the model has a great improvement in convergence speed and classification performance. By inputting the remote sensing image data of ultra-low pixels (64 * 64) into our model, we prove that our model still has a high accuracy rate of 95% for the remote sensing image with ultra-low pixels and less feature points. Therefore, the model has a good application prospect in remote sensing image fine classification, very low pixel, and less image classification.

A growing number of evidence have demonstrated the involvement of enhancer RNAs (eRNAs) in tumor progression. However, the possible functions of eRNAs in hepatocellular carcinoma (HCC) remain largely unclear. Our present research aimed to screen critical eRNAs and to further delve into the clinical significance of eRNAs in HCC patients. In this study, we identified 124 prognosis-related eRNAs by analyzing The Cancer Genome Atlas (TCGA) datasets. Among them, SPRY4 antisense RNA 1 (SPRY4-AS1) may be a key eRNA involved in HCC progression. SPRY4 was a regulatory target of SPRY4-AS1. High SPRY4-AS1 expression was associated with poor prognosis of HCC patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) assays revealed that the mainly enriched biological process included Human papillomavirus infection, Hippo signaling pathway, and Proteoglycans in cancer. Besides, RT-PCR and immunohistochemical staining confirmed SPRY4-AS1 as an overexpressed eRNA in HCC specimens. The pan-cancer assays revealed that SPRY4-AS1 was associated with glioblastoma multiforme (GBM), adrenocortical carcinoma (ACC), brain lower grade glioma (LGG) and mesothelioma(MESO). Positive associations were observed between SPRY4-AS1 and SPRY4 (its target gene) in 16 tumor types. Collectively, our findings reveal a novel eRNA SPRY4-AS1 for HCC progression and suggest that SPRY4-AS1 may be a potential biomarker and therapeutic target for HCC.

Driver fatigue is the culprit of most traffic accidents. Visual technology can intuitively judge whether the driver is in the state of fatigue. A driver fatigue detection system based on the residual channel attention network (RCAN) and head pose estimation is proposed. In the proposed system, Retinaface is employed for face location and outputs five face landmarks. Then the RCAN is proposed to classify the state of eyes and the mouth. The RCAN includes a channel attention module, which can adaptively extract key feature vectors from the feature map, which significantly improves the classification accuracy of the RCAN. In the self-built dataset, the classification accuracy of the eye state of the RCAN reaches 98.962% and that of the mouth state reaches 98.561%, exceeding other classical convolutional neural networks. The percentage of eyelid closure over the pupil over time (PERCLOS) and the mouth opening degree (POM) are used for fatigue detection based on the state of eyes and the mouth. In addition, this article proposes to use a Perspective-n-Point (PnP) method to estimate the head pose as an essential supplement for driving fatigue detection and proposes over-angle to evaluate whether the head pose is excessively deflected. On the whole, the proposed driver fatigue system integrates 3D head pose estimation and fatigue detection based on deep learning. This system is evaluated by the four datasets and shows success of the proposed method with their high performance.

Human norovirus (NoV) is a major cause of fresh produce associated outbreaks. In this study a non-thermal process, high pressure processing (HPP), was evaluated for efficacy against a human NoV GII.4 strain, and Tulane virus (TV). Virus survival following HPP treatment was determined using direct quantitative reverse transcriptase PCR (RT-qPCR), the porcine gastric mucin magnetic bead (PGM-MB) binding assay followed by RT-qPCR, and plaque assay. HPP (400 MPa) inactivation of human NoV and TV was enhanced in simple mediums at a low initial temperature (4°C) and neutral pH (pH 7). HPP treatment of human NoV and TV in strawberry puree at 400 MPa did not significantly affect the level of RNA detected using the PGM-MB binding assay; increasing the pressure level to 600 MPa lead to inactivation of both human NoV and TV. Overall, HPP inactivation of human NoV GII.4 and TV is enhanced at lower initial temperature and neutral pH, and viruses can be inactivated in the food matrix at commercially acceptable pressure levels.

Simple Summary: Cattle behavior recognition is an important field in animal husbandry. It can be used to understand the health status, emotions and needs of cattle. In this paper, an accurate and lightweight behavioral multi-detection model is proposed, which is adapted to real weather conditions. An innovation in the head, neck, detection head and loss function of the model is proposed, which improves the accuracy of behavior detection in cattle, and greatly reduces the number of parameters and calculations. It not only has high accuracy in recognition tasks, but is also very friendly to edge devices. This gives breeders insight into cattle behavior, helping them to better manage their herds, improve breeding efficiency and ensure the health and welfare of their cattle. In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the behavior of cattle. We not only generate a variety of weather conditions, but also introduce multi-target detection technology to achieve comprehensive monitoring of cattle and their status. We introduce Inner-MPDIoU Loss and we have innovatively designed the Multi-Convolutional Focused Pyramid module to explore and learn in depth the detailed features of cattle in different states. Meanwhile, the Lightweight Multi-Scale Feature Fusion Detection Head module is proposed to take advantage of deep convolution, achieving a lightweight network architecture and effectively reducing redundant information. Experimental results prove that our method achieves an average accuracy of 90.2% with a reduction of 3.9 G floating-point numbers, an increase of 7.4%, significantly better than 12 kinds of SOTA object detection models. By deploying our approach on monitoring computers on farms, we expect to advance the development of automated cattle monitoring systems to improve animal welfare and farm management. [ABSTRACT FROM AUTHOR]

Simple Summary: Through gesture recognition and detection of sika deer, farmers can observe the gestures of sika deer without physical contact, providing data and technical support for the intelligent and welfare-oriented breeding of sika deer. This study is based on the YOLOv8 network model. By optimizing the convolution module, incorporating the attention mechanism, and enhancing the detection head module, a new method for detecting sika deer poses was developed. The method was assessed using four behavioral datasets, which included standing, lying, eating, and attacking. The pose-recognition accuracy of sika deer significantly improved to an average of 91.6%, laying a foundation for the health assessment and information management of sika deer. As the sika deer breeding industry flourishes on a large scale, accurately assessing the health of these animals is of paramount importance. Implementing posture recognition through target detection serves as a vital method for monitoring the well-being of sika deer. This approach allows for a more nuanced understanding of their physical condition, ensuring the industry can maintain high standards of animal welfare and productivity. In order to achieve remote monitoring of sika deer without interfering with the natural behavior of the animals, and to enhance animal welfare, this paper proposes a sika deer individual posture recognition detection algorithm GFI-YOLOv8 based on YOLOv8. Firstly, this paper proposes to add the iAFF iterative attention feature fusion module to the C2f of the backbone network module, replace the original SPPF module with AIFI module, and use the attention mechanism to adjust the feature channel adaptively. This aims to enhance granularity, improve the model's recognition, and enhance understanding of sika deer behavior in complex scenes. Secondly, a novel convolutional neural network module is introduced to improve the efficiency and accuracy of feature extraction, while preserving the model's depth and diversity. In addition, a new attention mechanism module is proposed to expand the receptive field and simplify the model. Furthermore, a new pyramid network and an optimized detection head module are presented to improve the recognition and interpretation of sika deer postures in intricate environments. The experimental results demonstrate that the model achieves 91.6% accuracy in recognizing the posture of sika deer, with a 6% improvement in accuracy and a 4.6% increase in mAP50 compared to YOLOv8n. Compared to other models in the YOLO series, such as YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv9, and YOLOv10, this model exhibits higher accuracy, and improved mAP50 and mAP50-95 values. The overall performance is commendable, meeting the requirements for accurate and rapid identification of the posture of sika deer. This model proves beneficial for the precise and real-time monitoring of sika deer posture in complex breeding environments and under all-weather conditions. [ABSTRACT FROM AUTHOR]

Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response in real scenes. Therefore, this paper improves the YOLOv8n model for efficient multi-target pest detection. Two feature enhancement modules, EMSFEM and AFEM_SIE, are proposed in this paper. The EMSFEM module enriches the model's receptive field through the combination of multi-scale asymmetric convolution kernel and different expansion rates and can better extract the width, height, texture, and edge information of the target. The AFEM_SIE module captures the similarities and differences between upper and lower features through spatial information exchange and enhances feature representation through inter-feature information exchange. In addition, an improved feature fusion operation, Concat_Weighting, is proposed on the basis of Concat. The module uses the learned weights to carry out channel weighting and feature graph weighting for input features, which realizes more flexible and effective feature fusion. The results of experiments conducted on the publicly available large-scale crop pest and disease dataset IP102 show that the performance of the AEC-YOLOv8n model is significantly improved compared with the original YOLOv8n model, with mAP50 increased by 8.9%, accuracy increased by 6.8%, and recall rate increased by 6.3%. The AEC-YOLOv8n model proposed in this study can effectively identify and deal with a variety of crop pests and has achieved the best detection accuracy on the IP102 dataset, which has high application value. [ABSTRACT FROM AUTHOR]

Sustained casing pressure (SCP) is a crucial issue in the oil and gas production lifecycle. Epoxy resins, exhibiting exceptional compressive strength, ductility, and shear bonding strength, have the potential to form reliable barriers. The injectivity and sealing capacity of the epoxy resin is crucial parameters for the success of shallow remediation operations. This study aimed to develop and assess a novel solid-free resin sealant as an alternative to Portland cement for mitigating fluid leakage. The investigation evaluated the viscosity, compressive strength, and brittleness index of the epoxy resin sealant, as well as its tangential and normal shear strengths in conjunction with casing steel. The flow characteristics and sealing abilities of conventional cement and epoxy resin were comparatively analyzed in cracks. The results showed that the application of a viscosity reducer facilitated control over the curing time of the epoxy resin, ranging from 1.5 to 6 h, and reduced the initial viscosity from 865.53 to 118.71 mPa,s. The mechanical properties of the epoxy resin initially increased with a rise in curing agent content before experiencing a minor decrease. The epoxy resin containing 30% curing agent exhibited optimal mechanical properties. After a 14-day curing period, the epoxy resin's compressive strength reached 81.37 MPa, 2.12 times higher than that of cement, whereas the elastic modulus of cement was 2.99 times greater than that of the epoxy resin. The brittleness index of epoxy resin is only 3.42, demonstrating high flexibility and toughness. The tangential and normal shear strengths of the epoxy resin exceeded those of cement by 3.17 and 2.82 times, respectively. In a 0.5 mm-wide crack, the injection pressure of the epoxy resin remained below 0.075 MPa, indicating superior injection and flow capabilities. Conversely, the injection pressure of cement surged dramatically to 2.61 MPa within 5 min. The breakthrough pressure of 0.5 PV epoxy resin reached 7.53 MPa, decreasing the crack's permeability to 0.02 D, a mere 9.49% of the permeability observed following cement plugging. Upon sealing a 2 mm-wide crack using epoxy resin, the maximum breakthrough pressure attained 5.47 MPa, 3.48 times of cement. These results suggest that epoxy resin sealant can be employed safely and effectively to seal cracks in the cement. [ABSTRACT FROM AUTHOR]

A large spherulite structure deteriorates the mechanical properties of crystalline polymers, and therefore various methods have been explored to increase primary nucleation density. Recently, chain-end modification has been proposed as an effective approach for regulating polymer crystal nucleation. However, the relevant nucleation mechanism still requires investigation. Therefore, in this work, 2-ureido-4[1H]-pyrimidinone (UPy) units, which can form stacks via quadruple hydrogen bonds with each other, are introduced as end groups for the preparation of interacting telechelic poly(butylene succinate) (PBS-UPy) oligomers with different molecular weights (Mns). The crystallization, especially the nucleation behavior of PBS-UPy, is studied in detail by comparing with the corresponding pre-polymer, the hydroxyl-terminal PBS (PBS-OH). The thermal properties of PBS-UPy exhibit similar Mn-dependent tendency to those of PBS-OH, but with weaker total crystallization rate. The spherulite growth rate is significantly reduced, whereas the primary nucleation density is highly promoted, after introducing UPy groups. Further investigation reveals that the mechanism of UPy stacks’ influence on nucleation ability changes from inhibition to promotion with respect to Mn. Even under an inhibition of nucleation ability, the final nucleation density is obviously increased because of a significant decline of the growth rate. In addition, the change in the impact of UPy stacks on nucleation ability is speculated to originate from the memory expression feasibility of ordered conformation in the melt during crystallization.