Estimating body weight and body condition score of mature beef cows using depth images.

Obtaining accurate body weight (BW) is crucial for management decisions yet can be a challenge for cow-calf producers. Fast-evolving technologies such as depth sensing have been identified as low-cost sensors for agricultural applications but have not been widely validated for U.S. beef cattle. This study aimed to (1) estimate the body volume of mature beef cows from depth images, (2) quantify BW and metabolic weight (MBW) from image-projected body volume, and (3) classify body condition scores (BCS) from image-obtained measurements using a machine-learning-based approach. Fifty-eight crossbred cows with a mean BW of 410.0 ±â€…60.3 kg and were between 4 and 6 yr of age were used for data collection between May and December 2021. A low-cost, commercially available depth sensor was used to collect top-view depth images. Images were processed to obtain cattle biometric measurements, including MBW, body length, average height, maximum body width, dorsal area, and projected body volume. The dataset was partitioned into training and testing datasets using an 80%:20% ratio. Using the training dataset, linear regression models were developed between image-projected body volume and BW measurements. Results were used to test BW predictions for the testing dataset. A machine-learning-based multivariate analysis was performed with 29 algorithms from eight classifiers to classify BCS using multiple inputs conveniently obtained from the cows and the depth images. A feature selection algorithm was performed to rank the relevance of each input to the BCS. Results demonstrated a strong positive correlation between the image-projected cow body volume and the measured BW (r = 0.9166). The regression between the cow body volume and the measured BW had a co-efficient of determination (R2) of 0.83 and a 19.2 ±â€…13.50 kg mean absolute error (MAE) of prediction. When applying the regression to the testing dataset, an increase in the MAE of the predicted BW (22.7 ±â€…13.44 kg) but a slightly improved R2 (0.8661) was noted. Among all algorithms, the Bagged Tree model in the Ensemble class had the best performance and was used to classify BCS. Classification results demonstrate the model failed to predict any BCS lower than 4.5, while it accurately classified the BCS with a true prediction rate of 60%, 63.6%, and 50% for BCS between 4.75 and 5, 5.25 and 5.5, and 5.75 and 6, respectively. This study validated using depth imaging to accurately predict BW and classify BCS of U.S. beef cow herds.

Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques.

Individual feedlot beef cattle identification represents a critical component in cattle traceability in the supply food chain. It also provides insights into tracking disease trajectories, ascertaining ownership, and managing cattle production and distribution. Animal biometric solutions, e.g., identifying cattle muzzle patterns (unique features comparable to human fingerprints), may offer noninvasive and unique methods for cattle identification and tracking, but need validation with advancement in machine learning modeling. The objectives of this research were to (1) collect and publish a high-quality dataset for beef cattle muzzle images, and (2) evaluate and benchmark the performance of recognizing individual beef cattle with a variety of deep learning models. A total of 4923 muzzle images for 268 US feedlot finishing cattle (>12 images per animal on average) were taken with a mirrorless digital camera and processed to form the dataset. A total of 59 deep learning image classification models were comparatively evaluated for identifying individual cattle. The best accuracy for identifying the 268 cattle was 98.7%, and the fastest processing speed was 28.3 ms/image. Weighted cross-entropy loss function and data augmentation can increase the identification accuracy of individual cattle with fewer muzzle images for model development. In conclusion, this study demonstrates the great potential of deep learning applications for individual cattle identification and is favorable for precision livestock management. Scholars are encouraged to utilize the published dataset to develop better models tailored for the beef cattle industry.

Perches as Cooling Devices for Reducing Heat Stress in Caged Laying Hens: A Review.

Heat stress is one of the most detrimental environmental challenges affecting the biological process and the related production performance of farm animals, especially in poultry. Commercial laying hens have been bred (selected) for high egg production, resulting in increased sensitivity to heat stress due to breeding-linked metabolic heat production. In addition, laying hens are prone to heat stress due to their inadequate species-specific cooling mechanisms resulting in low heat tolerance. In addition, hens have no sweat glands and feathering covers almost their entire body to minimize body heat loss. The poultry industry and scientists are developing cooling methods to prevent or reduce heat stress-caused damage to chicken health, welfare, and economic losses. We have designed and tested a cooling system using perches, in which chilled water (10 °C) circulates through a conventional perch passing through the layer cages to offer the cooling potential to improve hen health, welfare, and performance during acute and chronic periods of heat stress (35 °C). This review summarizes the outcomes of a multi-year study using the designed cooled perch system. The results indicate that conducting heat from perching hens directly onto the cooled perch system efficiently reduces heat stress and related damage in laying hens. It provides a novel strategy: perches, one key furnishment in cage-free and enriched colony facilities, could be modified as cooling devices to improve thermal comfort for hens during hot seasons, especially in the tropical and subtropical regions.

Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition.

Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive behaviors under various forage characteristics. The results show that the amplitude and duration of the bite, chew, and chew-bite sounds were mostly larger for tall forages (tall fescue and alfalfa) compared to their counterparts. The long short-term memory network using a filtered dataset with balanced duration and imbalanced audio files offered better performance than its counterparts. The best classification performance was over 0.93, and the best and poorest performance difference was 0.4-0.5 under different forage species and heights. In conclusion, the deep learning technique could classify the dairy cow ingestive behaviors but was unable to differentiate between them under some forage characteristics using acoustic signals. Thus, while the developed tool is useful to support precision dairy cow management, it requires further improvement.

[Analysis on the status of health literacy and its influencing factors among residents aged 15-69 years old in Haidian District of Beijing City in 2018].

OBJECTIVE: To understand the status of health literacy and its influencing factors among the residents in Haidian District of Beijing, and to provide references for targeted health education intervention. METHODS: A multi-staged probability proportionate to size sampling(PPS) sampling method was used to collect 7034 residents that aged 15-69 years old in Haidian District in 2018. RESULTS: The standardized rate of health literacy among the residents of Haidian District was 28. 56%. The standardized health literacy rate of basic health knowledge and concept literacy, health lifestyle and behavior literacy, basic health skill were 35. 79%, 30. 90% and 36. 39%, respectively. The standardized health literacy rate of 6 health literacy issues from high to low were safety and first aid(66. 39%), scientific health perspectives(51. 24%), infectious diseases prevention and treatment(39. 78%), health information(30. 25%), chronic disease control and prevention(13. 33%), and basic medical care(11. 23%), respectively. The result of multiple logistic regression showed that aged between 30-39 years old, female, high school education and above, the teacher, medical and government staff, staff of other institutions, staff of other enterprises, other employees and the annual income of the family>45000 RMB were protective factors for health literacy. Aged between 50-69 years old was risk factor for health literacy. CONCLUSION: The level of health literacy in Haidian District was low. Various forms of intervention activities should be carried out to improve the residents' health literacy, especially focused on health lifestyle and behavior literacy, chronic disease control and prevention and basic medical care.

Recent advances in the biotoxicity of metal oxide nanoparticles: Impacts on plants, animals and microorganisms.

The contact between metal oxide nanoparticles (NPs) and human is more and more close with their wide applications. The inputs of metal oxide NPs to the environment are also growing every year, which causes potential environmental and human health risks. They are toxic to animals, microorganisms and plants at high concentrations, and they show different mechanisms of toxicity to different species. In addition, under complex environmental conditions, their toxic effects are often unpredictable. We have integrated the recent studies on the biotoxicity of metal oxide NPs from 2015-present, and clarified their toxic mechanism, as well as the toxic harm. It lays a foundation for further studying the toxicity and ecological risk of metal oxide NPs.

Factors Affecting Trailer Thermal Environment Experienced by Market Pigs Transported in the US.

Extreme weather conditions challenge pig thermoregulation during transport and are addressed by the National Pork Board (NPB) Transport Quality Assurance® (TQA) program that provides guidelines for trailer boarding, bedding, and misting. These guidelines are widely applied, yet very little is known about the microenvironment within the trailer. In this study, TQA guidelines (V4) were evaluated via extensive thermal environment measurements during transport in order to evaluate spatial variability and implications on ventilation pattern. Effects of trailer management strategies including bedding, boarding, and misting were examined and the trailer was monitored for interior temperature rise and THI responses within six separate zones. The trailer thermal environment was not uniformly distributed in the colder trips with the top front and bottom zones were the warmest, indicating these zones had the majority of outlet openings and experienced air with accumulated sensible and latent heat of the pigs. Relatively enhanced thermal environment uniformity was observed during hot trips, suggesting that ventilation patterns and ventilation rate were different for colder vs. warmer weather conditions. Misting applied prior to transport cooled interior air temperature, but also created high THI conditions in some cases. Neither boarding and bedding combinations in the TQA nor boarding position showed impacts on trailer interior temperature rise or spatial distribution of temperature inside the trailer.

Characteristics of Trailer Thermal Environment during Commercial Swine Transport Managed under U.S. Industry Guidelines.

Transport is a critical factor in modern pork production and can seriously affect swine welfare. While previous research has explored thermal conditions during transport, the impact of extreme weather conditions on the trailer thermal environment under industry practices has not been well documented; and the critical factors impacting microclimate are not well understood. To assess the trailer microclimate during transport events, an instrumentation system was designed and installed at the central ceiling level, pig level and floor-level in each of six zones inside a commercial swine trailer. Transport environmental data from 34 monitoring trips (approximately 1-4 h in duration each) were collected from May, 2012, to February, 2013, with trailer management corresponding to the National Pork Board Transport Quality Assurance (TQA) guidelines in 31 of these trips. According to the TQA guidelines, for outdoor temperature ranging from 5 °C (40 °F) to 27 °C (80 °F), acceptable thermal conditions were observed based on the criteria that no more than 10% of the trip duration was above 35 °C (95 °F) or below 0 °C (32 °F). Recommended bedding, boarding and water application were sufficient in this range. Measurements support relaxing boarding guidelines for moderate outdoor conditions, as this did not result in less desirable conditions. Pigs experienced extended undesirable thermal conditions for outdoor temperatures above 27 °C (80 °F) or below 5 °C (40 °F), meriting a recommendation for further assessment of bedding, boarding and water application guidelines for extreme outdoor temperatures. An Emergency Livestock Weather Safety Index (LWSI) condition was observed inside the trailer when outdoor temperature exceeded 10 °C (50 °F); although the validity of LWSI to indicate heat stress for pigs during transport is not well established. Extreme pig surface temperatures in the rear and middle zones of the trailer were more frequently experienced than in the front zones, and the few observations of pigs dead or down upon arrival were noted in these zones. Observations indicate that arranging boarding placement may alter the ventilation patterns inside the trailer.

[Determination of fifty-five elements in lung carcinomatous tissues and their pericarcinomatous tissues by atomic spectrometry].

A method for determining 55 elements in human lung tissue was developed. Mixed acid (HNO3:HCl04) was added into samples, which were digested at room temperature over night, then heated at 180 degrees C. Arsenic and selenium in lung tissue were determined by hydride generation atomic fluorescence spectrometry (HG-AFS), potassium, sodium, calcium and magnesium were determined by atomic absorption spectrometer (AAS), while the rest of forty-eight elements were determined by inductively coupled plasma mass spectrometry (ICP-MS), respectively. Reference materials of GBW(E)080193 bovine hepar and GBWO9101 human hair were analyzed by the described method. The measured element values in two reference materials accorded with their reference values. The recovery rates for most of the studied elements were 90%-110%. The precisions of the method were 1.7%-10.0%. The concentrations of seventeen elements in the carcinomatous tissues were remarkably different from those in the pericarcinomatous tissues. The method is rapid, simple and accurate.