AI-enhanced real-time cattle identification system through tracking across various environments.

Video-based monitoring is essential nowadays in cattle farm management systems for automated evaluation of cow health, encompassing body condition scores, lameness detection, calving events, and other factors. In order to efficiently monitor the well-being of each individual animal, it is vital to automatically identify them in real time. Although there are various techniques available for cattle identification, a significant number of them depend on radio frequency or visible ear tags, which are prone to being lost or damaged. This can result in financial difficulties for farmers. Therefore, this paper presents a novel method for tracking and identifying the cattle with an RGB image-based camera. As a first step, to detect the cattle in the video, we employ the YOLOv8 (You Only Look Once) model. The sample data contains the raw video that was recorded with the cameras that were installed at above from the designated lane used by cattle after the milk production process and above from the rotating milking parlor. As a second step, the detected cattle are continuously tracked and assigned unique local IDs. The tracked images of each individual cattle are then stored in individual folders according to their respective IDs, facilitating the identification process. The images of each folder will be the features which are extracted using a feature extractor called VGG (Visual Geometry Group). After feature extraction task, as a final step, the SVM (Support Vector Machine) identifier for cattle identification will be used to get the identified ID of the cattle. The final ID of a cattle is determined based on the maximum identified output ID from the tracked images of that particular animal. The outcomes of this paper will act as proof of the concept for the use of combining VGG features with SVM is an effective and promising approach for an automatic cattle identification system.

Hibernoma in the renal sinus: A case mimicking malignancy.

Introduction: Hibernomas are benign tumors of brown adipose tissue. Hibernoma in the renal sinus is extremely rare. Herein, we present the third known case of renal hibernoma. Case presentation: A 71-year-old man reported to our department with a left kidney tumor with an average growth rate of 5 mm/year and a progressive contrast effect on computed tomography. It was diagnosed as a hibernoma following a laparoscopic radical nephrectomy. Conclusion: We encountered a rare case of a hibernoma in the renal sinus. Development of new and accurate diagnostic methods for hibernoma, without resorting to nephrectomy, is essential.

Cribriform pattern 4/intraductal carcinoma of the prostate and persistent prostate-specific antigen after radical prostatectomy.

Objectives: The objective of this study is to identify the effect of cribriform pattern 4 carcinoma/intraductal carcinoma of the prostate (CC/IDCP) on persistent prostate-specific antigen (PSA) levels after robot-assisted radical prostatectomy (RARP) in patients with localized prostate cancer (PCa). Patients and Methods: This retrospective study included 730 consecutive patients with localized PCa who underwent RARP at Mie University (n = 392) and Aichi Medical University (n = 338) between 2015 and 2021. Patients with clinically metastatic PCa (cN1 and cM1) and those who received neoadjuvant and/or adjuvant therapy before biochemical recurrence were excluded. We evaluated the effects of CC/IDCP on persistent PSA levels after RARP. Persistent PSA was defined as PSA level ≥0.2 ng/mL at 1 month postoperatively and consecutively thereafter. Using factors from logistic regression analysis, models were developed to predict persistent PSA levels. Results: Approximately 6.3% (n = 46) of the patients had persistent PSA levels. Patients with biopsy CC/IDCP (bCC/IDCP) and pathological CC/IDCP (pCC/IDCP) based on RARP specimens were 11.6% (85/730) and 36.5% (267/730), respectively. Multivariate analysis of the prediction of persistent PSA levels using preoperative factors revealed that PSA density, percentage of positive cancer cores, biopsy grade group and bCC/IDCP were independent prognostic factors. Furthermore, multivariate analysis of the prediction of persistent PSA levels using postoperative factors, excluding pN1, revealed that pathological grade group, pCC/IDCP, seminal vesicle invasion and lymphovascular invasion were independent prognostic factors. In the receiver operating characteristic curve analysis for predicting persistent PSA after RARP, areas under the receiver operating characteristic curve for the model with preoperative factors, postoperative factors, including pN1, and postoperative factors, excluding pN1, were 0.827, 0.833 and 0.834, respectively. Conclusions: bCC/IDCP predicted persistent PSA after RARP in the overall population, while pCC/IDCP predicted persistent PSA only when the pN1 population was excluded. This may be useful for predicting susceptible patients with worse outcomes.

Development of a real-time cattle lameness detection system using a single side-view camera.

Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lameness detection in dairy farming. Our study employs image processing techniques and deep learning methods for cattle detection, tracking, and lameness classification. We utilize two powerful object detection algorithms: Mask-RCNN from Detectron2 and the popular YOLOv8. Their performance is compared to identify the most effective approach for this application. Bounding boxes are drawn around detected cattle to assign unique local IDs, enabling individual tracking and isolation throughout the video sequence. Additionally, mask regions generated by the chosen detection algorithm provide valuable data for feature extraction, which is crucial for subsequent lameness classification. The extracted cattle mask region values serve as the basis for feature extraction, capturing relevant information indicative of lameness. These features, combined with the local IDs assigned during tracking, are used to compute a lameness score for each cattle. We explore the efficacy of various established machine learning algorithms, such as Support Vector Machines (SVM), AdaBoost and so on, in analyzing the extracted lameness features. Evaluation of the proposed system was conducted across three key domains: detection, tracking, and lameness classification. Notably, the detection module employing Detectron2 achieved an impressive accuracy of 98.98%. Similarly, the tracking module attained a high accuracy of 99.50%. In lameness classification, AdaBoost emerged as the most effective algorithm, yielding the highest overall average accuracy (77.9%). Other established machine learning algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Random Forests, also demonstrated promising performance (DT: 75.32%, SVM: 75.20%, Random Forest: 74.9%). The presented approach demonstrates the successful implementation for cattle lameness detection. The proposed system has the potential to revolutionize dairy farm management by enabling early lameness detection and facilitating effective monitoring of cattle health. Our findings contribute valuable insights into the application of advanced computer vision methods for livestock health management.

Nine-year seroepidemiological study of severe fever with thrombocytopenia syndrome virus infection in feral horses in Cape Toi, Japan.

Severe fever with thrombocytopenia syndrome (SFTS) is a fatal zoonosis caused by ticks in East Asia. As SFTS virus (SFTSV) is maintained between wildlife and ticks, seroepidemiological studies in wildlife are important to understand the behavior of SFTSV in the environment. Miyazaki Prefecture, Japan, is an SFTS-endemic area, and approximately 100 feral horses, called Misaki horses (Equus caballus), inhabit Cape Toi in Miyazaki Prefecture. While these animals are managed in a wild-like manner, their ages are ascertainable due to individual identification. In the present study, we conducted a seroepidemiological survey of SFTSV in Misaki horses between 2015 and 2023. This study aimed to understand SFTSV infection in horses and its transmission to wildlife. A total of 707 samples from 180 feral horses were used to determine the seroprevalence of SFTSV using enzyme-linked immunosorbent assay (ELISA). Neutralization testing was performed on 118 samples. In addition, SFTS viral RNA was detected in ticks from Cape Toi and feral horses. The overall seroprevalence between 2015 and 2023 was 78.5% (555/707). The lowest seroprevalence was 55% (44/80) in 2016 and the highest was 92% (76/83) in 2018. Seroprevalence was significantly affected by age, with 11% (8/71) in those less than one year of age and 96.7% (435/450) in those four years of age and older (p < 0.0001). The concordance between ELISA and neutralization test results was 88.9% (105/118). SFTS viral RNA was not detected in ticks (n = 516) or feral horses. This study demonstrated that horses can be infected with SFTSV and that age is a significant factor in seroprevalence in wildlife. This study provides insights into SFTSV infection not only in horses but also in wildlife in SFTS-endemic areas.

Relationship between assistant's lens exposure and dose information during computed tomography examinations.

According to International Commission of Radiological Protection, the equivalent dose limit for the eye lens for occupational exposure is recommended to be 20 mSv yr-1, averaged over 5 years, with no single year above 50 mSv. Some studies reported the measurement of assistant's lens exposure in diagnostic computed tomography (CT) examinations, but further investigation is still required in the association between the lens dose for assistants and various dose parameters. Therefore, we measured the assistant's lens exposure using small optically stimulated luminescence dosimeters. The type of occupation, type of assistance, total scan time, total mAs, total scan length, and dose-length product (DLP) were recorded and analyzed in association with air kerma at the lens position. The assistance was classified into four types: 'assisted ventilation,' 'head holding,' 'body holding,' and 'raising patient's arm.' The air kerma of lens position was not significantly different for each assistance type (p< 0.05, Kruskal-Wallis test). Further, the lens doses for assistants correlated with DLP, but with various strengths of correlation with the assistance type and were influenced by the distance from the CT gantry. In conclusion, lens dose during assistance and DLP demonstrated the strongest correlation. 'Raising patient's arm' and 'head holding' exhibited stronger correlations, which required less table movement during the CT scan than 'assisted ventilation' and 'body holding'.

Revolutionizing Cow Welfare Monitoring: A Novel Top-View Perspective with Depth Camera-Based Lameness Classification.

This study innovates livestock health management, utilizing a top-view depth camera for accurate cow lameness detection, classification, and precise segmentation through integration with a 3D depth camera and deep learning, distinguishing it from 2D systems. It underscores the importance of early lameness detection in cattle and focuses on extracting depth data from the cow's body, with a specific emphasis on the back region's maximum value. Precise cow detection and tracking are achieved through the Detectron2 framework and Intersection Over Union (IOU) techniques. Across a three-day testing period, with observations conducted twice daily with varying cow populations (ranging from 56 to 64 cows per day), the study consistently achieves an impressive average detection accuracy of 99.94%. Tracking accuracy remains at 99.92% over the same observation period. Subsequently, the research extracts the cow's depth region using binary mask images derived from detection results and original depth images. Feature extraction generates a feature vector based on maximum height measurements from the cow's backbone area. This feature vector is utilized for classification, evaluating three classifiers: Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The study highlights the potential of top-view depth video cameras for accurate cow lameness detection and classification, with significant implications for livestock health management.

Customized Tracking Algorithm for Robust Cattle Detection and Tracking in Occlusion Environments.

Ensuring precise calving time prediction necessitates the adoption of an automatic and precisely accurate cattle tracking system. Nowadays, cattle tracking can be challenging due to the complexity of their environment and the potential for missed or false detections. Most existing deep-learning tracking algorithms face challenges when dealing with track-ID switch cases caused by cattle occlusion. To address these concerns, the proposed research endeavors to create an automatic cattle detection and tracking system by leveraging the remarkable capabilities of Detectron2 while embedding tailored modifications to make it even more effective and efficient for a variety of applications. Additionally, the study conducts a comprehensive comparison of eight distinct deep-learning tracking algorithms, with the objective of identifying the most optimal algorithm for achieving precise and efficient individual cattle tracking. This research focuses on tackling occlusion conditions and track-ID increment cases for miss detection. Through a comparison of various tracking algorithms, we discovered that Detectron2, coupled with our customized tracking algorithm (CTA), achieves 99% in detecting and tracking individual cows for handling occlusion challenges. Our algorithm stands out by successfully overcoming the challenges of miss detection and occlusion problems, making it highly reliable even during extended periods in a crowded calving pen.

Creation of synthetic contrast-enhanced computed tomography images using deep neural networks to screen for renal cell carcinoma.

In this study, we elucidate if synthetic contrast enhanced computed tomography images created from plain computed tomography images using deep neural networks could be used for screening, clinical diagnosis, and postoperative follow-up of small-diameter renal tumors. This retrospective, multicenter study included 155 patients (artificial intelligence training cohort [n = 99], validation cohort [n = 56]) who underwent surgery for small-diameter (≤40 mm) renal tumors, with the pathological diagnosis of renal cell carcinoma, during 2010-2020. We created a learned deep neural networks using pix2pix. We examined the quality of the synthetic enhanced computed tomography images created using this deep neural networks and compared them with real enhanced computed tomography images using the zero-mean normalized cross-correlation parameter. We assessed concordance rates between real and synthetic images and diagnoses according to 10 urologists by creating a receiver operating characteristic curve and calculating the area under the curve. The synthetic computed tomography images were highly concordant with the real computed tomography images, regardless of the existence or morphology of the renal tumor. Regarding the concordance rate, a greater area under the curve was obtained with synthetic computed tomography (area under the curve = 0.892) than with only computed tomography (area under the curve = 0.720; p < 0.001). In conclusions, this study is the first to use deep neural networks to create a high-quality synthetic computed tomography image that was highly concordant with a real computed tomography image. Our synthetic computed tomography images could be used for urological diagnoses and clinical screening.

Changes in population structure and genetic diversity of Misaki horses between 2015 and 2020.

For the preservation of Misaki horses, changes in the population structure and genetic diversity of the horses for 5 years were analyzed using population and genotype data from 2015-2020. The microsatellite genotyping was performed, and the average number of alleles (Na), expected heterozygosity (He), and observed value (Ho) were calculated. Moreover, the average generation length (GL) was estimated from the population management record. Then, no significant differences in Na, He, and Ho were found between 2015 and 2020, suggesting their genetic diversity had been maintained for 5 years. Moreover, the average GL was estimated as 4.6 years. Compared to other native horses, a short average GL suggesting a rapid generation renewing is a characteristic of the Misaki population.

Cow detection and tracking system utilizing multi-feature tracking algorithm.

In modern cattle farm management systems, video-based monitoring has become important in analyzing the high-level behavior of cattle for monitoring their health and predicting calving for providing timely assistance. Conventionally, sensors have been used for detecting and tracking their activities. As the body-attached sensors cause stress, video cameras can be used as an alternative. However, identifying and tracking individual cattle can be difficult, especially for black and brown varieties that are so similar in appearance. Therefore, we propose a new method of using video cameras for recognizing cattle and tracking their whereabouts. In our approach, we applied a combination of deep learning and image processing techniques to build a robust system. The proposed system processes images in separate stages, namely data pre-processing, cow detection, and cow tracking. Cow detection is performed using a popular instance segmentation network. In the cow tracking stage, for successively associating each cow with the corresponding one in the next frame, we employed the following three features: cow location, appearance features, as well as recent features of the cow region. In doing so, we simply exploited the distance between two gravity center locations of the cow regions. As color and texture suitably define the appearance of an object, we analyze the most appropriate color space to extract color moment features and use a Co-occurrence Matrix (CM) for textural representation. Deep features are extracted from recent cow images using a Convolutional Neural Network (CNN features) and are also jointly applied in the tracking process to boost system performance. We also proposed a robust Multiple Object Tracking (MOT) algorithm for cow tracking by employing multiple features from the cow region. The experimental results proved that our proposed system could handle the problems of MOT and produce reliable performance.

Detection and phylogenetic analysis of Dabieshan tick virus and Okutama tick virus in ticks collected from Cape Toi, Japan.

New technologies have led to the discovery of novel tick-borne and tick-associated viruses. Dabieshan tick virus (DaTV) and Okutama tick virus (OkTV), which belong to the family Phenuiviridae, were discovered in ticks in China and Japan, respectively, in the 2010s. Although it is unknown whether these viruses cause disease in animals or humans, all tick-associated viruses have the potential to become etiological agents of infectious diseases through gene reassortment. Therefore, it is important to elucidate the ecology of these viruses, regardless of their pathogenicity. In this study, ticks were collected year-round in Cape Toi, Miyazaki Prefecture, Japan, and an epidemiological survey of tick-associated phenuiviruses was performed. A total of 516 ticks collected from the vegetation by dragging flannel sheets were used for analysis. Pan-phenuivirus reverse transcription PCR was performed on the tick samples, and DaTV and OkTV were detected. We found that 37.0% (85/230) and 23% (16/71) of nymphal and adult Haemaphysalis longicornis were infected with DaTV, respectively, and 10% (6/62) and 13% (1/8) of nymphal and adult Haemaphysalis flava were infected with OkTV, respectively. Phylogenetic analysis indicated that the DaTV identified in this study formed a unique clade that was distinct from the strains identified in China. The survey revealed that DaTV is distributed not only in China, but also in Japan. We believe that this study contributes to our understanding of the prevalence of tick-associated viruses.

Application of liquid scintillation light guide (LSLG) to scattered X-ray measurement from dental panoramic radiography system.

A 1.2 m flexible liquid scintillation light guide (LSLG) detector connected to a portable electric device was developed and applied to monitor X-rays scattered from a panoramic dental X-ray imaging apparatus. The X-ray absorption of the LSLG tube was simulated for 20 and 40 keV X-rays. The LSLG detector was calibrated by comparing doses measured by a 3-inch NaI(Tl) detector using Am-241 and Cs-137 sources. A linear relationship was obtained between dose rates (µSv/h) and count rates (cps). The elapsed time profile of scattered radiation from a panoramic dental X-ray imaging apparatus was determined. Local absorbed doses were measured with small OSL dosimeters on the LSLG tube suspended over the shoulder and the eyeglass. Behind the ears and neck (in the center of the LSLG tube) showed high doses. The LSLG detector was useful for real-time monitoring of scattered X-rays.

Impact of the coronavirus disease 2019 pandemic on the number of undergoing radical nephroureterectomy and postoperative adjuvant systematic therapy for upper tract urothelial carcinomas in Japan: A multicenter retrospective study.

OBJECTIVES: The coronavirus disease 2019 pandemic has affected cancer management worldwide. For upper tract urothelial carcinomas, delays in treatments are not recommended even during the pandemic. We investigated the impact of the pandemic on patients with these carcinomas who underwent radical nephroureterectomy (RNU) and adjuvant systematic therapy before and after COVID-19 spread in Japan. METHODS: This multicenter retrospective study included 304 patients who underwent RNU for upper tract urothelial carcinomas between May 1, 2019, and December 31, 2021, in Aichi, Japan. The patients were categorized into three groups based on whether they underwent surgery in the prepandemic (before infection spread in Japan), early pandemic (between confirmation of the first case and vaccination initiation), and late pandemic (after the start of vaccination in Japan) phases. The patient characteristics, diagnostic methods, pathological findings, and postoperative therapy were compared among the three phases. RESULTS: Overall, 74, 152, and 78 patients underwent RNU in the prepandemic, early pandemic, and late pandemic phases, respectively. The number of patients who underwent preoperative ureteroscopy decreased significantly from the prepandemic phase to the late pandemic phase due to pandemic-related restrictions (p = 0.016). There was no difference in the time to the first visit or pathological findings. Among patients classified as high-risk according to existing clinical trials, the proportion receiving adjuvant systematic therapy after RNU decreased significantly from 52.3% to 19% (p = 0.003). CONCLUSIONS: There was no difference in the pathological findings. The number of patients receiving appropriate adjuvant systematic therapy decreased during the pandemic.

Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle.

Effective livestock management is critical for cattle farms in today's competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not feasible in today's farming systems. Fortunately, automatic tracking and identification systems have greatly improved in recent years. Moreover, correctly identifying individual cows is an integral part of predicting behavior during estrus. By doing so, we can monitor a cow's behavior, and pinpoint the right time for artificial insemination. However, most previous techniques have relied on direct observation, increasing the human workload. To overcome this problem, this paper proposes the use of state-of-the-art deep learning-based Multi-Object Tracking (MOT) algorithms for a complete system that can automatically and continuously detect and track cattle using an RGB camera. This study compares state-of-the-art MOTs, such as Deep-SORT, Strong-SORT, and customized light-weight tracking algorithms. To improve the tracking accuracy of these deep learning methods, this paper presents an enhanced re-identification approach for a black cattle dataset in Strong-SORT. For evaluating MOT by detection, the system used the YOLO v5 and v7, as a comparison with the instance segmentation model Detectron-2, to detect and classify the cattle. The high cattle-tracking accuracy with a Multi-Object Tracking Accuracy (MOTA) was 96.88%. Using these methods, the findings demonstrate a highly accurate and robust cattle tracking system, which can be applied to innovative monitoring systems for agricultural applications. The effectiveness and efficiency of the proposed system were demonstrated by analyzing a sample of video footage. The proposed method was developed to balance the trade-off between costs and management, thereby improving the productivity and profitability of dairy farms; however, this method can be adapted to other domestic species.

Inflammatory Changes and Composition of Collagen during Cervical Ripening in Cows.

Dystocia and stillbirths in cows pose a high risk of loss of both dams and fetuses, thereby resulting in high economic losses. One of the causes of these problems is birth canal abnormalities. Thus, to prevent these occurrences, it is necessary to understand the mechanisms underlying cervical ripening. Although physiological inflammatory responses and changes in collagen composition have been reported in humans and mice, related information is scarce for cows. We observed inflammatory changes and changes in the collagen composition in the cervix from late pregnancy to parturition to clarify some of the physiological changes associated with cervical ripening during normal calving in cows. Cervical mucus and tissue samples were collected from 41 Japanese Black cows at 200, 230, and 260 days of gestation and at 7-day intervals thereafter until parturition. The percentage of polymorphonuclear neutrophils (PMN%) in the mucus was calculated, and interleukin (IL)-8 concentration was determined by enzyme-linked immunosorbent assay. Blood samples were collected from the jugular vein, and leukocyte counts were determined. Picrosirius red-stained cervical tissue specimens were observed under a polarizing microscope, and the percentage of type I and type III collagen areas in the cervical tissue were calculated. The PMN% in cervical mucus was lowest at 200 days gestation (12−13 weeks before delivery), significantly increased 5 weeks before (21.7 ± 0.04), and was highest 1 week before calving (50.9 ± 0.04). IL-8 levels were increased at 295 days compared with those at 200 days of pregnancy (p < 0.05). No significant changes were observed in the white blood cell counts. The percentage of type I collagen in the cervical tissue reached a maximum (91.4 ± 0.02%) on day 200, significantly decreased after 274 days (3 weeks before calving), and continued to decrease thereafter until the week of parturition. There was no significant change in type III collagen levels. The results suggest that cervical ripening progresses when PMNs begin to infiltrate the cervix at around 260 days of gestation (5−4 weeks before parturition), IL-8, which increases at the end of pregnancy, mobilizes PMNs, and enhances inflammation, and that type I collagen changes are useful as an indicator of cervical ripening.

Impact of Histological Variants on Clinical Responses to Pembrolizumab in Patients With Metastatic Urothelial Cancer.

BACKGROUND: The efficacy of anti-programmed celldeath protein 1 treatment in patients with urothelial carcinoma (UC) with molecular subtypes of histological variants has not been investigated. This study aimed to examine the impact of histological variants classified according to molecular subtypes on clinical outcomes in patients with platinum-resistant metastatic UC treated with pembrolizumab. PATIENTS AND METHODS: Data of 168 patients with metastatic UC who received intravenous pembrolizumab after platinum-based chemotherapy between December 2017 and November 2020 were retrospectively reviewed. Relationships between histological variant type (basal or luminal molecular subtypes) and survival outcome and response to immunotherapy were examined. Clinicopathological factors were analyzed using the Cox proportional hazards model. RESULTS: UC with histological variants was identified in 19 (11.3%) cases (basal subtype in 12; luminal subtype in 7). The median age of the patients was 72.5 years (range=40-89 years). The performance status was 0-1 in 151 (89.9%) patients. Liver metastasis was detected in 44 (26.2%) patients. The median progression-free survival was 3.5 months (range=0.5-34.3 months). Treatment with immune checkpoint inhibitors resulted in an overall mean survival (from the start of treatment) of 8.1 months (range=1.2-34.3 months). Patients with basal-type UC had significantly shorter progression-free survival and cancer-specific survival than those with pure UC (p=0.010 and p=0.035, respectively). A complete response was observed in eight patients (seven with pure UC, one with basal type). CONCLUSION: The basal histological variant might be a potential prognostic indicator in patients with platinum-resistant metastatic UC treated with pembrolizumab.

Usefulness of a lead-acrylic shield for reducing lens dose of assistant in x-ray CT examination.

In computed tomography (CT) examinations, the usefulness of protective glasses for reducing lens exposure to assistants has been reported. The present study aimed to compare the dose reduction effect for assistants with lead-acrylic shields and protective glasses (0.07 mm Pb, 0.5 mm Pb) during CT examination. The air dose distribution in a CT examination room with and without a lead-acrylic shield was compared. It was found that the amount of scattered radiation was significantly reduced by installing a lead-acrylic shield at the CT gantry aperture. Moreover, the reduction rate of air kerma at the assistant's lens was higher using the lead acrylic shield than with the protective glasses-95.7% during head holding and 76.1% during assisted ventilation.

Interferon gamma, lipopolysaccharide, and modified-live viral vaccines stimulation alter the mRNA expression of tumor necrosis factor α, inducible nitric oxide synthase, and interferon ß in bovine alveolar macrophages.

To understand the pathogenesis of bovine respiratory disease (BRD), it is necessary to elucidate the mechanisms of alveolar macrophage regulation by cytokines and pathogen-associated molecular patterns (PAMPs). Moreover, "non-specific effects (NSEs)" an innate immune regulatory mechanism in response to vaccines containing PAMPs, has recently attracted attention. It may be applied to BRD control, but there is limited knowledge in bovine. To investigate this, we stimulated alveolar macrophages in vitro with lipopolysaccharide (LPS), polyinosinic-polycytidylic acid sodium salt (Poly I:C), interferon gamma (IFN-γ), and modified-live viral (MLV) vaccines, respectively, and analyzed changes in tumor necrosis factor alpha (TNF-α), inducible nitric oxide synthase (iNOS), and interferon beta (IFN-ß) mRNA expression levels. mRNA expression levels of TNF-α, iNOS, and IFN-ß were significantly increased in bovine alveolar macrophages stimulated by IFN-γ and MLV vaccine; LPS, IFN-γ, and MLV vaccine; and MLV vaccine only, respectively. Additionally, all MLV vaccine-stimulated mRNA expression increases were observed in a concentration-dependent manner. These results revealed in part, the mechanism of bovine alveolar macrophage regulation by cytokines and PAMPs. Understanding the regulatory mechanisms of alveolar macrophages will contribute to understanding the pathogenesis of BRD and preventive and therapeutic BRD management based on NSEs.

Antibiotic-resistant Escherichia coli isolated from dairy cows and their surrounding environment on a livestock farm practicing prudent antimicrobial use.

On a livestock farm where antimicrobial administration and its history had been managed for prudent use of antimicrobials, we surveyed antibiotic-resistant Escherichia coli strains isolated from cow feces and the surrounding environment (i.e., rat and crow feces, and water samples from a drainage pit and wastewater processing tank) every month for 1 year. Two strains (1.7%) in cow feces were resistant to tetracycline, whereas all other strains were susceptible to all other antimicrobials. Among 136 strains isolated from cows and wild animals, only one ampicillin-resistant strain was identified. The antibiotic resistance rate in the drainage from the barn was 8.3% (10/120), and all strains showed susceptibility for 8 months of the year. Tetracycline resistance was common in all resistant strains isolated from animal feces and water samples; all tetracycline-resistant strains carried tetA. These results strongly support the proper use and management of antibiotics on farms to minimize the outbreak and spread of antibiotic-resistant bacteria.