Regulation of Wnt/β-Catenin Signaling in Cardiac Valve Development and Disease

Non-syndromic Mitral Valve Prolapse (MVP) is a common disease with associated morbidities and mortality. Affecting 2-3% of the global population, MVP has become a significant health burden in developed countries. We recently identified mutations in the cilia gene, DZIP1 in multiple families with MVP. To initially identify the function of DZIP1 in valve biology, we performed proteomics-based approaches with the goal of identifying unique binding partners for DZIP1. These studies revealed a direct interaction between DZIP1 and the β-catenin antagonist, CBY1. We hypothesized that DZIP1 suppresses the Wnt/ β-catenin pathway during mitral valve development through CBY1. Immunofluorescence staining revealed overlap between DZIP1 and CBY1 protein at the basal body of the primary cilium. Increase of activated β-catenin was observed in the Dzip1S14R/+ valves. Co-immunoprecipitation confirmed an interaction between DZIP1, CBY1 and β-catenin. Ensuing immunofluorescence staining suggested overlap between β-catenin and the basal body. DZIP1 truncation mutants identified a minimal CBY1 interaction motif within the C-terminus of DZIP1. A membrane permeant mimetic peptide against this motif was synthesized and confirmed as being able to interact with CBY1 and β-catenin. Treatment of chicken valve interstitial cells with the mimetic peptide resulted in significant decrease in activated nuclear β-catenin. To test whether this pathway was relevant in the context of the DZIP1 mutation, we assayed nuclear vs. cytoplasmic β-catenin expression in Dzip1S14R/+ MEFs. Western blot analysis showed a significant increase in nuclear β-catenin from the mutant cells. An additional family was identified with a rare DZIP1 variant within the DZIP1-CBY1 interaction motif. The mutation resulted in reduced DZIP1 and CBY1 protein stability and a peptide synthesized with the mutation resulted in an enhanced interaction between DZIP1 and β-catenin and an inhibitory effect on β-catenin signaling. Through analysis of nuclear β-catenin expression profiles during cardiac valve development, we conclude that Wnt/β-catenin signaling is temporally and spatially regulated. It is down regulated after E13.5 and undetectable in the adult. However, β-catenin signaling is significantly upregulated in human myxomatous valves and thus may be a major contributor to disease phenotype. In conclusion, DZIP1 suppresses Wnt activity to direct mitral valve development through interacting with and stabilizing CBY1. This study characterizes the β-catenin expression profile during murine cardiac valve development and reveals a molecular mechanism, by which mutations in DZIP1 alter valve development leading to increased β-catenin signaling. Altered β-catenin signaling may be an early initiating signal in the pathogenesis of MVP

A Machine Vision-Based Method for Monitoring Scene-Interactive Behaviors of Dairy Calf

Requirements for animal and dairy products are increasing gradually in emerging economic bodies. However, it is critical and challenging to maintain the health and welfare of the increasing population of dairy cattle, especially the dairy calf (up to 20% mortality in China). Animal behaviors reflect considerable information and are used to estimate animal health and welfare. In recent years, machine vision-based methods have been applied to monitor animal behaviors worldwide. Collected image or video information containing animal behaviors can be analyzed with computer languages to estimate animal welfare or health indicators. In this proposed study, a new deep learning method (i.e., an integration of background-subtraction and inter-frame difference) was developed for automatically recognizing dairy calf scene-interactive behaviors (e.g., entering or leaving the resting area, and stationary and turning behaviors in the inlet and outlet area of the resting area) based on computer vision-based technology. Results show that the recognition success rates for the calf’s science-interactive behaviors of pen entering, pen leaving, staying (standing or laying static behavior), and turning were 94.38%, 92.86%, 96.85%, and 93.51%, respectively. The recognition success rates for feeding and drinking were 79.69% and 81.73%, respectively. This newly developed method provides a basis for inventing evaluation tools to monitor calves’ health and welfare on dairy farms

An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS

An improved method for retrieving Above-ground Biomass (AGB) and Canopy Height (CH) based on an observable from Cyclone Global Navigation Satellite System (CYGNSS), soil moisture from Soil Moisture Active Passive (SMAP) and location is proposed. The observable derived from CYGNSS is more sensitive to vegetation. The CYGNSS observable, soil moisture and the location are used as the input features of an Artificial Neural Network (ANN) to retrieve AGB and CH. The sensitivity analysis of the CYGNSS observable to target parameters shows that the proposed observable is more sensitive to AGB/CH than the conventional observable. The AGB/CH retrievals of the improved method show that it has better performance than that of the traditional method, especially in the areas with AGB in the range of 0 to100 Mg/ha and CH in the range of 0 to10 m. For AGB retrievals, the root mean square error (RMSE) and correlation coefficient are 64.84 Mg/ha and 0.80 in the range of 0 to 550 Mg/ha. Compared with the traditional method, the RMSE is decreased by 11.63%, while the correlation coefficient is increased by 5.26%. For CH retrievals, the RMSE and correlation coefficient are 5.97 m and 0.83 in the range of 0 to 45 m. The RMSE is decreased by 12.59%, while the correlation coefficient is increased by 5.06%. The analysis of the improved method in different areas shows that the performance of the improved method over the area with high vegetation is better than the area with low vegetation. The results obtained here further strengthens the capability of GNSS-R for global AGB/CH retrievals as well as different land cover areas

A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval

In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega (τ−w) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m2, respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m2, respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring

Precision Landing Test and Simulation of the Agricultural UAV on Apron

Unmanned aerial vehicle (UAV) has been used to assist agricultural production. Precision landing control of UAV is critical for application of it in some specific areas such as greenhouses or livestock/poultry houses. For controlling UAV landing on a fixed or mobile apron/platform accurately, this study proposed an automatic method and tested it under three scenarios: (1) UAV landing at high operating altitude based on the GPS signal of the mobile apron; (2) UAV landing at low operating altitude based on the image recognition on the mobile apron; and (3) UAV landing progress control based on the fixed landing device and image detection to achieve a stable landing action. To verify the effectiveness of the proposed control method, apron at both stationary and mobile (e.g., 3 km/h moving speed) statuses were tested. Besides, a simulation was conducted for the UAV landing on a fixed apron by using a commercial poultry house as a model (135 L × 15 W × 3 H m). Results show that the average landing errors in high altitude and low altitude can be controlled within 6.78 cm and 13.29 cm, respectively. For the poultry house simulation, the landing errors were 6.22 ± 2.59 cm, 6.79 ± 3.26 cm, and 7.14 ± 2.41cm at the running speed of 2 km/h, 3 km/h, and 4 km/h, respectively. This study provides the basis for applying the UAV in agricultural facilities such as poultry or animal houses where requires a stricter landing control than open fields

Monitoring Behaviors of Broiler Chickens at Different Ages with Deep Learning

Animal behavior monitoring allows the gathering of animal health information and living habits and is an important technical means in precision animal farming. To quickly and accurately identify the behavior of broilers at different days, we adopted different deep learning behavior recognition models. Firstly, the top-view images of broilers at 2, 9, 16 and 23 days were obtained. In each stage, 300 images of each of the four broilers behaviors (i.e., feeding, drinking, standing, and resting) were segmented, totaling 4800 images. After image augmentation processing, 10,200 images were generated for each day including 8000 training sets, 2000 validation sets, and 200 testing sets. Finally, the performance of different convolutional neural network models (CNN) in broiler behavior recognition at different days was analyzed. The results show that the overall performance of the DenseNet-264 network was the best, with the accuracy rates of 88.5%, 97%, 94.5%, and 90% when birds were 2, 9, 16 and 23 days old, respectively. In addition, the efficient channel attention was introduced into the DenseNet-264 network (ECA-DenseNet-264), and the results (accuracy rates: 85%, 95%, 92%, 89.5%) confirmed that the DenseNet-264 network was still the best overall. The research results demonstrate that it is feasible to apply deep learning technology to monitor the behavior of broilers at different days

Automatic detection of brown hens in cage-free houses with deep learning methods

ABSTRACT: Computer vision technologies have been tested to monitor animals’ behaviors and performance. High stocking density and small body size of chickens such as broiler and cage-free layers make effective automated monitoring quite challenging. Therefore, it is critical to improve the accuracy and robustness of laying hens clustering detection. In this study, we established a laying hens detection model YOLOv5-C3CBAM-BiFPN, and tested its performance in detecting birds on open litter. The model consists of 3 parts: 1) the basic YOLOv5 model for feature extraction and target detection of laying hens; 2) the convolution block attention module integrated with C3 module (C3CBAM) to improve the detection effect of targets and occluded targets; and 3) bidirectional feature pyramid network (BiFPN), which is used to enhance the transmission of feature information between different network layers and improve the accuracy of the algorithm. In order to better evaluate the effectiveness of the new model, a total of 720 images containing different numbers of laying hens were selected to construct complex datasets with different occlusion degrees and densities. In addition, this paper also compared the proposed model with a YOLOv5 model that combined other attention mechanisms. The test results show that the improved model YOLOv5-C3CBAM-BiFPN achieved a precision of 98.2%, a recall of 92.9%, a mAP (IoU = 0.5) of 96.7%, a classification rate 156.3 f/s (frames per second), and a F1 (F1 score) of 95.4%. In other words, the laying hen detection method based on deep learning proposed in the present study has excellent performance, can identify the target accurately and quickly, and can be applied to real-time detection of laying hens in real-world production environment

Evaluation of the ZWD/ZTD Values Derived from MERRA-2 Global Reanalysis Products Using GNSS Observations and Radiosonde Data

Tropospheric delay is one of the main errors affecting high-precision positioning and navigation and is a key parameter of water vapor detection in the Global Navigation Satellite System (GNSS). The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is the latest generation of reanalysis data collected by the National Aeronautics and Space Administration (NASA), which can be used to calculate tropospheric delay products with high spatial and temporal resolution. However, there is no report analyzing the accuracy of the zenith tropospheric delay (ZTD) and zenith wet delay (ZWD) calculated from MERRA-2 data. This paper evaluates the performance of the ZTD and ZWD values derived from global MERRA-2 data using global radiosonde data and International GNSS Service (IGS) precise ZTD products. The results are as follows: (1) Taking the precision ZTD products of 316 IGS stations from around the world from 2015 to 2017 as the reference, the average root mean square (RMS) of the ZTD values calculated from the MERRA-2 data is better than 1.35 cm, and the accuracy difference between different years is small. The bias and RMS of the ZTD values show certain seasonal variations, with a higher accuracy in winter and a lower accuracy in summer, and the RMS decreases from the equator to the poles. However, those of the ZTD values do not show obvious variations according to elevation. (2) Relative to the radiosonde data, the RMS of the ZWD and ZTD values calculated from the MERRA-2 data are better than 1.37 cm and 1.45 cm, respectively. Furthermore, the bias and RMS of the ZWD and ZTD values also show some temporal and spatial characteristics, which are similar to the test results of the IGS stations. It is suggested that MERRA-2 data can be used for global tropospheric vertical profile model construction because of their high accuracy and good stability in the global calculation of the ZWD and ZTD

Detecting broiler chickens on litter floor with the YOLOv5-CBAM deep learning model

For commercial broiler production, about 20,000–30,000 birds are raised in each confined house, which has caused growing public concerns on animal welfare. Currently, daily evaluation of broiler wellbeing and growth is conducted manually, which is labor-intensive and subjectively subject to human error. Therefore, there is a need for an automatic tool to detect and analyze the behaviors of chickens and predict their welfare status. In this study, we developed a YOLOv5-CBAM-broiler model and tested its performance for detecting broilers on litter floor. The proposed model consisted of two parts: (1) basic YOLOv5 model for bird or broiler feature extraction and object detection; and (2) the convolutional block attention module (CBAM) to improve the feature extraction capability of the network and the problem of missed detection of occluded targets and small targets. A complex dataset of broiler chicken images at different ages, multiple pens and scenes (fresh litter versus reused litter) was constructed to evaluate the effectiveness of the new model. In addition, the model was compared to the Faster R-CNN, SSD, YOLOv3, EfficientDet and YOLOv5 models. The results demonstrate that the precision, recall, F1 score and an [email protected] of the proposed method were 97.3%, 92.3%, 94.7%, and 96.5%, which were superior to the comparison models. In addition, comparing the detection effects in different scenes, the YOLOv5-CBAM model was still better than the comparison method. Overall, the proposed YOLOv5-CBAM-broiler model can achieve real-time accurate and fast target detection and provide technical support for the management and monitoring of birds in commercial broiler houses

A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution

The proper spatial distribution of chickens is an indication of a healthy flock. Routine inspections of broiler chicken floor distribution are done manually in commercial grow-out houses every day, which is labor intensive and time consuming. This task requires an efficient and automatic system that can monitor the chicken’s floor distributions. In the current study, a machine vision-based method was developed and tested in an experimental broiler house. For the new method to recognize bird distribution in the images, the pen floor was virtually defined/divided into drinking, feeding, and rest/exercise zones. As broiler chickens grew, the images collected each day were analyzed separately to avoid biases caused by changes of body weight/size over time. About 7000 chicken areas/profiles were extracted from images collected from 18 to 35 days of age to build a BP neural network model for floor distribution analysis, and another 200 images were used to validate the model. The results showed that the identification accuracies of bird distribution in the drinking and feeding zones were 0.9419 and 0.9544, respectively. The correlation coefficient (R), mean square error (MSE), and mean absolute error (MAE) of the BP model were 0.996, 0.038, and 0.178, respectively, in our analysis of broiler distribution. Missed detections were mainly caused by interference with the equipment (e.g., the feeder hanging chain and water line); studies are ongoing to address these issues. This study provides the basis for devising a real-time evaluation tool to detect broiler chicken floor distribution and behavior in commercial facilities