MCP: Multi-Chicken Pose Estimation Based on Transfer Learning.

Poultry managers can better understand the state of poultry through poultry behavior analysis. As one of the key steps in behavior analysis, the accurate estimation of poultry posture is the focus of this research. This study mainly analyzes a top-down pose estimation method of multiple chickens. Therefore, we propose the "multi-chicken pose" (MCP), a pose estimation system for multiple chickens through deep learning. Firstly, we find the position of each chicken from the image via the chicken detector; then, an estimate of the pose of each chicken is made using a pose estimation network, which is based on transfer learning. On this basis, the pixel error (PE), root mean square error (RMSE), and image quantity distribution of key points are analyzed according to the improved chicken keypoint similarity (CKS). The experimental results show that the algorithm scores in different evaluation metrics are a mean average precision (mAP) of 0.652, a mean average recall (mAR) of 0.742, a percentage of correct keypoints (PCKs) of 0.789, and an RMSE of 17.30 pixels. To the best of our knowledge, this is the first time that transfer learning has been used for the pose estimation of multiple chickens as objects. The method can provide a new path for future poultry behavior analysis.

Ceftazidime-Avibactam Combination Therapy versus Monotherapy for the Treatment Carbapenem-Resistant Gram-Negative Bacterial Infections: A Retrospective Observational Study.

Purpose: Since the introduction of ceftazidime-avibactam (CZA) in the Chinese market, accumulating clinical evidence has substantiated its efficacy in the treatment of infections caused by carbapenem-resistant gram-negative bacteria (CR-GNB). Nevertheless, an ongoing debate persists concerning the choice between monotherapy and combination therapy when devising clinical anti-infection protocols. Patients and Methods: This retrospective, single-center observational study enrolled patients with CR-GNB infections who received CZA treatment between December 2019 and August 2023. The primary outcome assessed was 30-day mortality, and the secondary outcome measured was 14-day bacterial clearance. A multivariate Cox regression model was used to identify variables that were independently associated with 30-day mortality rate. Results: Eighty-three patients were enrolled in the study; of which, 45 received CZA monotherapy, whereas 38 received combination therapy. The overall 30-day mortality rate was 31.3%, and no significant difference was observed in the 30-day mortality rates between the CZA combination therapy and monotherapy groups (31.6% vs 31.1%, p=0.963). After adjustment by propensity score matching, the 30-day mortality rate was not significantly different between the two groups (28.6% vs 31.4%, p=0.794). Multivariate COX analysis revealed that age and SOFA score were independent predictors of 30-day mortality. Conclusion: Combination therapy with CZA and other antimicrobials was not found to have an advantage over monotherapy in reducing the 30-day mortality rate.

Hotspots and Trends in Research on Treating Pain with Electroacupuncture: A Bibliometric and Visualization Analysis from 1994 to 2022.

Purpose: Electroacupuncture is widely used to pain management. A bibliometric analysis was conducted to identify the hotspots and trends in research on electroacupuncture for pain. Methods: We retrieved studies published from 1994-2022 on the topic of pain relief by electroacupuncture from the Web of Science Core Collection database. We comprehensively analysed the data with VOSviewer, CiteSpace, and bibliometrix. Seven aspects of the data were analysed separately: annual publication outputs, countries, institutions, authors, journals, keywords and references. Results: A total of 2030 papers were analysed, and the number of worldwide publications continuously increased over the period of interest. The most productive country and institution in this field were China and KyungHee University. Evidence-Based Complementary and Alternative Medicine was the most productive journal, and Pain was the most co-cited journal. Han Jisheng, Fang Jianqiao, and Lao Lixing were the most representative authors. Based on keywords and references, three active areas of research on EA for pain were mechanisms, randomized controlled trials, and perioperative applications. Three emerging trends were functional magnetic resonance imaging (fMRI), systematic reviews, and knee osteoarthritis. Conclusion: This study comprehensively analysed the research published over the past 28 years on electroacupuncture for pain treatment, using bibliometrics and science mapping analysis. This work presents the current status and landscape of the field and may serve as a valuable resource for researchers. Chronic pain, fMRI-based mechanistic research, and the perioperative application of electroacupuncture are among the likely foci of future research in this area.

Super-resolution fusion optimization for poultry detection: a multi-object chicken detection method.

Accurate poultry detection is crucial for studying poultry behavior using computer vision and video surveillance. However, in free-range farming environments, detecting chickens can often be challenging due to their small size and mutual occlusion. The current detection algorithms exhibit a low level of accuracy, with a high probability of false and missed detections. To address this, we proposed a multi-object chicken detection method named Super-resolution Chicken Detection, which utilizes super-resolution fusion optimization. The algorithm employs the residual-residual dense block to extract image features and used a generative adversarial network to compensate for the loss of details during deep convolution, producing high-resolution images for detection. The proposed algorithm was validated with the B1 data set and the MC1 multi-object data set, demonstrating that the reconstructed images possessed richer pixel features compared to original images, specifically it improved detection accuracy and reduced the number of missed detections. The structural similarity of the reconstructed images was 99.9%, and the peak signal-to-noise ratio was above 30. The algorithm improved the Average Precision50:95 of all You Only Look Once Version X (YOLOX) models, with the largest improvement for the B1 data set with YOLOX-Large (+6.3%) and for the MC1 data set with YOLOX-Small (+4.1%). This was the first time a super-resolution reconstruction technique was applied to multi-object poultry detection. Our method will provide a fresh approach for future poultry researchers to improve the accuracy of object detection using computer vision and video surveillance.

In free-range farming environments, accurately detecting individual chickens has been a persistent challenge for researchers. Due to mutual occlusion and limitations of camera capturing distance, existing detection algorithms have had low detection accuracy, leading to a high probability of false and missed detections. To address this problem, a multi-object chicken detection method named Super-resolution Chicken Detection (SRCD) was developed. The proposed algorithm utilized super-resolution reconstruction and You Only Look Once Version X object detection networks to achieve accurate chicken detection. Through extensive experimentation with two data sets, SRCD demonstrated its superiority in detecting chickens, reducing missed detections likely related to occlusion and the chickens' distance to the camera. Additionally, the SRCD algorithm enriched the pixel features of the chickens with minimal changes to the original images, resulting in a reconstructed image with high similarity to the original image. As a result, this method provided a practical solution for more accurate detection in small free-range farming environments, improving poultry production efficiency.

Study on Poultry Pose Estimation Based on Multi-Parts Detection.

Poultry pose estimation is a prerequisite for evaluating abnormal behavior and disease prediction in poultry. Accurate pose-estimation enables poultry producers to better manage their poultry. Because chickens are group-fed, how to achieve automatic poultry pose recognition has become a problematic point for accurate monitoring in large-scale farms. To this end, based on computer vision technology, this paper uses a deep neural network (DNN) technique to estimate the posture of a single broiler chicken. This method compared the pose detection results with the Single Shot MultiBox Detector (SSD) algorithm, You Only Look Once (YOLOV3) algorithm, RetinaNet algorithm, and Faster_R-CNN algorithm. Preliminary tests show that the method proposed in this paper achieves a 0.0128 standard deviation of precision and 0.9218 ± 0.0048 of confidence (95%) and a 0.0266 standard deviation of recall and 0.8996 ± 0.0099 of confidence (95%). By successfully estimating the pose of broiler chickens, it is possible to facilitate the detection of abnormal behavior of poultry. Furthermore, the method can be further improved to increase the overall success rate of verification.

Transcriptome analysis of genes associated with autolysis of Coprinus comatus.

Coprinus comatus, widely known as "Jituigu", is an important commodity and food in China. The yield of C. comatus, however, is substantially reduced by the autolysis of the fruiting bodies after harvest. To gain insight into the molecular mechanism underlying this autolysis, we divided the growth of C. comatus fruiting bodies into four stages: infant stage (I), mature stage (M), discolored stage (D), and autolysis stage (A). We then subjected these stages to de novo transcriptomic analysis using high-throughput Illumina sequencing. A total of 12,946 unigenes were annotated and analyzed with the Gene Ontology (GO), Clusters of Orthologous Groups of proteins (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG). We analyzed the differentially expressed genes (DEGs) between stages I and M, M and D, and D and A. Because the changes from M to D are thought to be related to autolysis, we focused on the DEGs between these two stages. We found that the pathways related to metabolic activity began to vary in the transition from M to D, including pathways named as autophagy-yeast, peroxisome, and starch and sucrose metabolism. This study also speculates the possible process of the autolysis of Coprinus comatus. In addition, 20 genes of interest were analyzed by quantitative real-time PCR to verify their expression profiles at the four developmental stages. This study, which is the first to describe the transcriptome of C. comatus, provides a foundation for future studies concerning the molecular basis of the autolysis of its fruiting bodies.

Ligustilide counteracts carcinogenesis and hepatocellular carcinoma cell-evoked macrophage M2 polarization by regulating yes-associated protein-mediated interleukin-6 secretion.

Cross-communication between cancer cells and macrophages within the tumor microenvironment fulfills the critical roles in the progression of cancers, including hepatocellular carcinoma (HCC). Ligustilide exerts anti-inflammation, anti-injury, and anti-tumor pleiotropic pharmacological functions. Nevertheless, its roles in HCC cells and tumor microenvironment remain elusive. In the current study, ligustilide dramatically restrained HCC cell viability and migration but had little cytotoxicity to normal hepatocytes. Importantly, ligustilide antagonized HCC cell co-culture-induced macrophage recruitment and M2 polarization by enhancing the percentage of CD14+CD206+ cells and macrophage M2 markers (CD163, Arg1, CD206, CCL22, IL-10, and TGF-ß). Mechanistically, ligustilide repressed yes-associated protein (YAP) activation by reducing nuclear translocation, protein expression, transcriptional regulatory activity of YAP, and increasing p-YAP levels. Noticeably, blocking the YAP offset the suppressive effects of ligustilide on macrophage recruitment and M2 polarization evoked by HCC cells. Moreover, the release of interleukin-6 (IL-6) was mitigated by ligustilide in a YAP-dependent manner in HCC cells, concomitant with inhibition of IL-6R/STAT3 signaling activation. Of interest, interdicting the IL-6 aggravated ligustilide-mediated suppression in HCC-induced macrophage recruitment and M2 polarization; whereas exogenous IL-6 treatment reversed the above effects. Additionally, blockage of IL-6R signaling also overturned IL-6-induced macrophage recruitment and M2 phenotype. Consequently, these findings support a notion that ligustilide not only restrains HCC cell malignancy but also antagonizes HCC cell-evoked macrophage recruitment and M2 polarization by inhibiting YAP/IL-6 release-induced activation of the IL-6 receptor/signal transducer and activator of transcription 3 (IL-6R/STAT3) signaling. Thus, ligustilide may be a promising therapeutic agent to fight HCC by regulating cancer cells and cross-talk between tumor cells and macrophages in tumor microenvironment.