Effects of Crate Size on Mortality and Postural Behavior in Post-farrowing Sows

Preweaning mortality (PWM) is a economic and animal well-being issue for swine producers in the US. On average, producers face about a 16% pre-weaning mortality. A significant portion of this, around 6%, is because sows accidentally overlay their piglets in farrowing crates. The posture behavior of sow plays a vital role in overlaying piglets and this behavior has a significant effect on farrowing crate dimensions. This study aims to explore the interplay between farrowing crate size, mortality rates, and postural behavior in post-farrowing sows. This study employed three farrowing stalls: Traditional (T), Expanded creep area (C), and Expanded sow area (S). Expanded creep area extended the piglet area (2.44 m × 1.83 m) while retaining the sow area identical to Traditional, while Expanded sow area included an extended piglet area (2.44 m × 1.83 m) with a sow area of 2.13 m × 0.71 m. Postural data were collected from 18 post-farrowing sows over three consecutive days, each comprising a 24-hour period. A computer vision system employing top-down Kinect V2 depth sensors captured images at a rate of 10 frames per minute throughout the duration of the study. Depth images were utilized exclusively due to their ability to convey relative height differences of the animals, while RGB images were disregarded due to variations in light intensity. A pre trained YOLO v8_cls model that trained with six different postural categories (standing (STAND), kneeling (KNEEL), sitting (SIT), sternal lying (LOB), lying on the right side (LOR), and lying on the left side (LOL)) was utilized for inferencing all the depth images to posture database. The mortality class high and low was determined from the piglet survival rate. The results showed that the SIT posture has a significant effect on the mortality rate (p-value = 0.004) and the sows from expanded sow area crates (S type) shown more time spent in SIT (average 38 min/day) in this posture compared to traditional one (average 29.89 min/day). This study provides valuable insights for farmers to make decisions about crate size

Classification of Sow Postures Using Convolutional Neural Network and Depth Images

The United States swine industry reports an average preweaning mortality of approximately 16% where approximately 6% of them are attributed to piglets overlayed by sows. Detecting postural transitions and estimating sows’ time budgets for different postures are valuable information for breeders and engineering design of farrowing facilities to eventually reduce piglet death. Computer vision tools can help monitor changes in animal posture accurately and efficiently. To create a more robust system and eliminate varying lighting issues within a day including daytime/ nighttime differences, there is an advantage to using depth cameras over digital cameras. In this study, a computer vision system was used for continuous depth image acquisition in several farrowing crates. The images were captured by top down view Kinect v2 depth sensors in the crates at 10 frames per minute for 24 hours. The captured depth images were converted into Jet colormap images. A total of 14,277 images from six different sows from 18 different days were randomly selected and labeled into six posture categories (standing, kneeling, sitting, sternal lying, lying on the right and lying on the left). The Convolutional Neural Network (CNN) architectures, that is, Resnet-50, Inception v3 with ‘imagenet’ pre-trained weight, were used for model training and posture images were tested. The dataset was randomly split training (75%) and validation (roughly 25%) sets. For testing, another dataset with 2,885 images obtained from six different sows (from 12 different days) was labelled. Among the models tested in the test dataset, the Inception v3 model outperformed all the models, resulting in 95% accuracy in predicting sow postures. We found an F1 score between 0.90 and 1.00 for all postures except the kneeling posture (F1 = 0.81) since this is a transition posture. This preliminary result indicates the potential use of transfer learning models for this specific task. This result also indicates that depth images are suitable for identifying the postures of sows. The outcome of this study will lead to the identification and generation of posture data in a commercial farm scale to study the behavioral differences of sows within different characteristics of farm facilities, health status, mortality rates, and overall production parameters

Classification of Sow Postures Using Convolutional Neural Network and Depth Images

The United States swine industry reports an average preweaning mortality of approximately 16% where approximately 6% of them are attributed to piglets overlayed by sows. Detecting postural transitions and estimating sows’ time budgets for different postures are valuable information for breeders and engineering design of farrowing facilities to eventually reduce piglet death. Computer vision tools can help monitor changes in animal posture accurately and efficiently. To create a more robust system and eliminate varying lighting issues within a day including daytime/ nighttime differences, there is an advantage to using depth cameras over digital cameras. In this study, a computer vision system was used for continuous depth image acquisition in several farrowing crates. The images were captured by top down view Kinect v2 depth sensors in the crates at 10 frames per minute for 24 h. The captured depth images were converted into Jet colormap images. A total of 14,277 images from six different sows from 18 different days were randomly selected and labeled into six posture categories (standing, kneeling, sitting, sternal lying, lying on the right and lying on the left). The Convolutional Neural Network (CNN) architectures, that is, Resnet-50, Inception v3 with ‘imagenet’ pre-trained weight, were used for model training and posture images were tested. The dataset was randomly split training (75%) and validation (roughly 25%) sets. For testing, another dataset with 2,885 images obtained from six different sows (from 12 different days) was labelled. Among the models tested in the test dataset, the Inception v3 model outperformed all the models, resulting in 95% accuracy in predicting sow postures. We found an F1 score between 0.90 and 1.00 for all postures except the kneeling posture (F1 = 0.81) since this is a transition posture. This preliminary result indicates the potential use of transfer learning models for this specific task. This result also indicates that depth images are suitable for identifying the postures of sows. The outcome of this study will lead to the identification and generation of posture data in a commercial farm scale to study the behavioral differences of sows within different characteristics of farm facilities, health status, mortality rates, and overall production parameters

Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U.S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16.1% where 5.55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM (P \u3c 0.05). Landrace-sired litters had a PWM of 16.26% (±0.13), whereas Yorkshire-sired litters had 15.91% (±0.13). PWM increased with higher parity orders (P \u3c 0.05) due to larger litter sizes. The RF model provided the best fit for PWM prediction with a root mean squared errors of 2.28 and a correlation coefficient (r) of 0.89 between observed and predicted values. Features’ importance from the RF model indicated that, PWM increased with the increase of litter size (mean decrease accuracy (MDA) = 93.17), decrease in mean birth weight (MDA = 22.72), increase in health diagnosis (MDA = 15.34), longer gestation length (MDA = 11.77), and at older parity (MDA = 10.86). However, in this study, the location of the farrowing crate, seasonal differences, and litter line turned out to be the least important predictors for PWM. For overlays, parity order was the highest importance predictor (MDA = 7.68) followed by litter size and mean birth weight. Considering the challenges to reducing the PWM in the larger litters produced in modern swine industry and the limited studies exploring multiple major contributing factors, this study provides valuable insights for breeding and production management, as well as further investigations on postural transitions and behavior analysis of sows during the lactation period

Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U.S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16.1% where 5.55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM (P \u3c 0.05). Landrace-sired litters had a PWM of 16.26% (±0.13), whereas Yorkshire-sired litters had 15.91% (±0.13). PWM increased with higher parity orders (P \u3c 0.05) due to larger litter sizes. The RF model provided the best fit for PWM prediction with a root mean squared errors of 2.28 and a correlation coefficient (r) of 0.89 between observed and predicted values. Features’ importance from the RF model indicated that, PWM increased with the increase of litter size (mean decrease accuracy (MDA) = 93.17), decrease in mean birth weight (MDA = 22.72), increase in health diagnosis (MDA = 15.34), longer gestation length (MDA = 11.77), and at older parity (MDA = 10.86). However, in this study, the location of the farrowing crate, seasonal differences, and litter line turned out to be the least important predictors for PWM. For overlays, parity order was the highest importance predictor (MDA = 7.68) followed by litter size and mean birth weight. Considering the challenges to reducing the PWM in the larger litters produced in modern swine industry and the limited studies exploring multiple major contributing factors, this study provides valuable insights for breeding and production management, as well as further investigations on postural transitions and behavior analysis of sows during the lactation period

Statistical and Machine Learning Approaches to Describe Factors affecting Preweaning Mortality of Piglets

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the United States Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16.1% where 5.55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM (P \u3c 0.05). Landrace-sired litters had a PWM of 16.26% (± 0.13), whereas Yorkshire-sired litters had 15.91% (± 0.13). PWM increased with higher parity orders (P \u3c 0.05) due to larger litter sizes. The RF model provided the best fit for PWM prediction with a root mean squared errors of 2.28 and a correlation coefficient (r) of 0.89 between observed and predicted values. Features’ importance from the RF model indicated that, PWM increased with the increase of litter size (mean decrease accuracy (MDA) = 93.17), decrease in mean birth weight (MDA = 22.72), increase in health diagnosis (MDA = 15.34), longer gestation length (MDA = 11.77), and at older parity (MDA = 10.86). However, in this study, the location of the farrowing crate, seasonal differences, and litter line turned out to be the least important predictors for PWM. For overlays, parity order was the highest importance predictor (MDA = 7.68) followed by litter size and mean birth weight. Considering the challenges to reducing the PWM in the larger litters produced in modern swine industry and the limited studies exploring multiple major contributing factors, this study provides valuable insights for breeding and production management, as well as further investigations on postural transitions and behavior analysis of sows during the lactation period

Towards accurate and precise T1 and extracellular volume mapping in the myocardium: a guide to current pitfalls and their solutions

Mapping of the longitudinal relaxation time (T1) and extracellular volume (ECV) offers a means of identifying pathological changes in myocardial tissue, including diffuse changes that may be invisible to existing T1-weighted methods. This technique has recently shown strong clinical utility for pathologies such as Anderson- Fabry disease and amyloidosis and has generated clinical interest as a possible means of detecting small changes in diffuse fibrosis; however, scatter in T1 and ECV estimates offers challenges for detecting these changes, and bias limits comparisons between sites and vendors. There are several technical and physiological pitfalls that influence the accuracy (bias) and precision (repeatability) of T1 and ECV mapping methods. The goal of this review is to describe the most significant of these, and detail current solutions, in order to aid scientists and clinicians to maximise the utility of T1 mapping in their clinical or research setting. A detailed summary of technical and physiological factors, issues relating to contrast agents, and specific disease-related issues is provided, along with some considerations on the future directions of the field. Towards accurate and precise T1 and extracellular volume mapping in the myocardium: a guide to current pitfalls and their solutions. Available from: https://www.researchgate.net/publication/317548806_Towards_accurate_and_precise_T1_and_extracellular_volume_mapping_in_the_myocardium_a_guide_to_current_pitfalls_and_their_solutions [accessed Jun 13, 2017]

Psychosocial risk factors for obesity among women in a family planning clinic

BACKGROUND: The epidemiology of obesity in primary care populations has not been thoroughly explored. This study contributes to filling this gap by investigating the relationship between obesity and different sources of personal stress, mental health, exercise, and demographic characteristics. METHODS: A cross-sectional survey using a convenience sample. Five hundred women who attended family planning clinics were surveyed and 274 provided completed answers to all of the questions analyzed in this study. Exercise, self-rated mental health, stress, social support, and demographic variables were included in the survey. Multiple logistic regression analysis was performed. RESULTS: After adjusting for mental health, exercise, and demographic characteristics of subjects, analysis of the data indicated that that being having a large family and receiving no support from parents were related to obesity in this relatively young low-income primary care sample, but self-reported stress and most types of social support were not significant. CONCLUSION: Obesity control programs in primary care centers directed at low-income women should target women who have large families and who are not receiving support from their parents

Signaling from β1- and β2-adrenergic receptors is defined by differential interactions with PDE4

β1- and β2-adrenergic receptors (βARs) are highly homologous, yet they play clearly distinct roles in cardiac physiology and pathology. Myocyte contraction, for instance, is readily stimulated by β1AR but not β2AR signaling, and chronic stimulation of the two receptors has opposing effects on myocyte apoptosis and cell survival. Differences in the assembly of macromolecular signaling complexes may explain the distinct biological outcomes. Here, we demonstrate that β1AR forms a signaling complex with a cAMP-specific phosphodiesterase (PDE) in a manner inherently different from a β2AR/β-arrestin/PDE complex reported previously. The β1AR binds a PDE variant, PDE4D8, in a direct manner, and occupancy of the receptor by an agonist causes dissociation of this complex. Conversely, agonist binding to the β2AR is a prerequisite for the recruitment of a complex consisting of β-arrestin and the PDE4D variant, PDE4D5, to the receptor. We propose that the distinct modes of interaction with PDEs result in divergent cAMP signals in the vicinity of the two receptors, thus, providing an additional layer of complexity to enforce the specificity of β1- and β2-adrenoceptor signaling