Recent advances in wearable sensors for animal health management

Veterinary telemedicine practicability: Analyzing Russian pet owners' feedback

Background and Aim

Previous research points to a growth rate of 17% for veterinary telemedicine. This study aimed to analyze pet owners' attitudes, feasibility, and socioeconomic impacts of introducing this growth technique to a particular demographic.

Materials and Methods

Five hundred population-representative respondents were utilized in the study. The ages ranged from 18 to 68 years. At the Russian State Agrarian University's veterinary hospital, respondents (pet owners) made contact (either in person or remotely). The survey inquired about participants' personal information, their pets, and veterinary telemedicine. Russia uses the ruble, issued by the Bank of Russia, as its currency. The required sample size of 385 for this study was determined using the Q test to ensure feasibility.

Results

79.2% of the participants had a positive outlook on telemedicine. Every fifth applicant turned down telemedicine, opting instead for personal vet appointments. 53.8% of respondents with prices under $14 were willing to pay for the service, whereas 17.8% (89 people) outright rejected it, and 93.8% of the paid customers belonged to the age group of 18-28. Pet owners with chronically ill animals merit special consideration.

Conclusion

Pet owners are generally open to veterinary telemedicine, but it remains underutilized. The study reveals directions for optimizing veterinary telemedicine and enhancing client and patient satisfaction. Despite limitations (less access to respondents/telemedicine), future approach is to investigate variables and invariable factors affecting this process.

Identification of Metabolites from Catharanthus roseus Leaves and Stem Extract, and In Vitro and In Silico Antibacterial Activity against Food Pathogens

The plant produced powerful secondary metabolites and showed strong antibacterial activities against food-spoiling bacterial pathogens. The present study aimed to evaluate antibacterial activities and to identify metabolites from the leaves and stems of Catharanthus roseus using NMR spectroscopy. The major metabolites likely to be observed in aqueous extraction were 2,3-butanediol, quinic acids, vindoline, chlorogenic acids, vindolinine, secologanin, and quercetin in the leaf and stem of the Catharanthus roseus. The aqueous extracts from the leaves and stems of this plant have been observed to be most effective against food spoilage bacterial strains, followed by methanol and hexane. However, leaf extract was observed to be most significant in terms of the content and potency of metabolites. The minimum inhibitory concentration (20 µg/mL) and bactericidal concentrations (35 g/mL) of leaf extract were observed to be significant as compared to the ampicillin. Molecular docking showed that chlorogenic acid and vindolinine strongly interacted with the bacterial penicillin-binding protein. The docking energies of chlorogenic acid and vindolinine also indicated that these could be used as food preservatives. Therefore, the observed metabolite could be utilized as a potent antibacterial compound for food preservation or to treat their illness, and further research is needed to perform.

TUBER: Time-aware UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network

This paper is a follow-up to a recent work by the authors on recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme for addressing sensor node and route failure issues in smart wireless livestock sensor networks. Time complexity and processing cost issues connected to the RUBER scheme are consequently treated in this article by proffering a time-aware UAV-based energy-efficient reconfigurable routing (TUBER) scheme. TUBER scheme employs a synchronized clustering-with-backup strategy, a minimum-hop neighborhood recovery mechanism, and a redundancy minimization technique. Comparative network performance of TUBER was investigated and evaluated with respect to RUBER and UAV-based energy-efficient reconfigurable routing (UBER) schemes. The metrics adopted for this comparative performance analysis are Cluster Survival Ratio (CSR), Network Stability (NST), Energy Dissipation Ratio (EDR), Network Coverage (COV), Packet Delivery Ratio (PDR), Fault Tolerance Index (FTI), Load Balancing Ratio (LBR), Routing Overhead (ROH), Average Routing Delay (ARD), Failure Detection Ratio (FDR), and Failure Recovery Ratio (FRR). With reference to best-obtained values, TUBER demonstrated improvements of 36.25%, 24.81%, 34.53%, 15.65%, 38.32%, 61.07%, 31.66%, 63.20%, 68.96%, 66.19%, and 78.63% over RUBER and UBER in terms of CSR, NST, EDR, COV, PDR, FTI, LBR, ROH, ARD, FDR, and FRR, respectively. These experimental results confirmed the relative effectiveness of TUBER against the compared routing schemes.

Revolutionizing cancer monitoring with carbon-based electrochemical biosensors

Cancer monitoring plays a critical role in improving patient outcomes by providing early detection, personalized treatment options, and treatment response tracking. Carbon-based electrochemical biosensors have emerged in recent years as a revolutionary technology with the potential to revolutionize cancer monitoring. These sensors are useful for clinical applications because of their high sensitivity, selectivity, rapid response, and compatibility with miniaturized equipment. This review paper gives an in-depth look at the latest developments and the possibilities of carbon-based electrochemical sensors in cancer surveillance. The essential principles of carbon-based electrochemical sensors are discussed, including their structure, operating mechanisms, and critical qualities that make them suited for cancer surveillance. Furthermore, we investigate their applicability in detecting specific cancer biomarkers, evaluating therapy responses, and detecting cancer recurrence early. Additionally, a comparison of carbon-based electrochemical sensor performance measures, including sensitivity, selectivity, accuracy, and limit of detection, is presented in contrast to existing monitoring methods and upcoming technologies. Finally, we discuss prospective tactics, future initiatives, and commercialization opportunities for improving the capabilities of these sensors and integrating them into normal clinical practice. The review highlights the potential impact of carbon-based electrochemical sensors on cancer diagnosis, treatment, and patient outcomes, as well as the importance of ongoing research, collaboration, and validation studies to fully realize their potential in revolutionizing cancer monitoring.

Using Sound Location to Monitor Farrowing in Sows

Precision Livestock Farming systems can help pig farmers prevent health and welfare issues around farrowing. Five sows were monitored in two field studies. A Sorama L642V sound camera, visualising sound sources as coloured spots using a 64-microphone array, and a Bascom XD10-4 security camera with a built-in microphone were used in a farrowing unit. Firstly, sound spots were compared with audible sounds, using the Observer XT (Noldus Information Technology), analysing video data at normal speed. This gave many false positives, including visible sound spots without audible sounds. In total, 23 of 50 piglet births were visible, but none were audible. The sow's behaviour changed when farrowing started. One piglet was silently crushed. Secondly, data were analysed at a 10-fold slower speed when comparing sound spots with audible sounds and sow behaviour. This improved results, but accuracy and specificity were still low. When combining audible sound with visible sow behaviour and comparing sound spots with combined sound and behaviour, the accuracy was 91.2%, the error was 8.8%, the sensitivity was 99.6%, and the specificity was 69.7%. We conclude that sound cameras are promising tools, detecting sound more accurately than the human ear. There is potential to use sound cameras to detect the onset of farrowing, but more research is needed to detect piglet births or crushing.

Unsupervised Domain Adaptation for Mitigating Sensor Variability and Interspecies Heterogeneity in Animal Activity Recognition

Animal activity recognition (AAR) using wearable sensor data has gained significant attention due to its applications in monitoring and understanding animal behavior. However, two major challenges hinder the development of robust AAR models: domain variability and the difficulty of obtaining labeled datasets. To address this issue, this study intensively investigates the impact of unsupervised domain adaptation (UDA) for AAR. We compared three distinct types of UDA techniques: minimizing divergence-based, adversarial-based, and reconstruction-based approaches. By leveraging UDA, AAR classifiers enable the model to learn domain-invariant features, allowing classifiers trained on the source domain to perform well on the target domain without labels. We evaluated the effectiveness of UDA techniques using dog movement sensor data and additional data from horses. The application of UDA across sensor positions (neck and back), sizes (middle-sized and large-sized), and gender (female and male) within the dog data, as well as across species (dog and horses), exhibits significant improvements in the classification performance and reduced the domain discrepancy. The results highlight the potential of UDA to mitigate the domain shift and enhance AAR in various settings and for different animal species, providing valuable insights for practical applications in real-world scenarios where labeled data is scarce.

Non-invasive estimation of in vivo optical properties and hemodynamic parameters of domestic animals: a preliminary study on horses, dogs, and sheep

Biosensors applied in veterinary medicine serve as a noninvasive method to determine the health status of animals and, indirectly, their level of welfare. Near infrared spectroscopy (NIRS) has been suggested as a technology with this application. This study presents preliminary in vivo time domain NIRS measurements of optical properties (absorption coefficient, reduced scattering coefficient, and differential pathlength factor) and hemodynamic parameters (concentration of oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin, and tissue oxygen saturation) of tissue domestic animals, specifically of skeletal muscle (4 dogs and 6 horses) and head (4 dogs and 19 sheep). The results suggest that TD NIRS in vivo measurements on domestic animals are feasible, and reveal significant variations in the optical and hemodynamic properties among tissue types and species. In horses the different optical and hemodynamic properties of the measured muscles can be attributed to the presence of a thicker adipose layer over the muscle in the Longissimus Dorsi and in the Gluteus Superficialis as compared to the Triceps Brachii. In dogs the absorption coefficient is higher in the head (temporalis musculature) than in skeletal muscles. The smaller absorption coefficient for the head of the sheep as compared to the head of dogs may suggest that in sheep we are indeed reaching the brain cortex while in dog light penetration can be hindered by the strongly absorbing muscle covering the cranium.

Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation

This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in 'shy feeder' cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector.

A MEMS-enabled portable gas chromatography injection system for trace analysis

Growing concerns about environmental conditions, public health, and disease diagnostics have led to the rapid development of portable sampling techniques to characterize trace-level volatile organic compounds (VOCs) from various sources. A MEMS-based micropreconcentrator (μPC) is one such approach that drastically reduces the size, weight, and power constraints offering greater sampling flexibility in many applications. However, the adoption of μPCs on a commercial scale is hindered by a lack of thermal desorption units (TDUs) that easily integrate μPCs with gas chromatography (GC) systems equipped with a flame ionization detector (FID) or a mass spectrometer (MS). Here, we report a highly versatile μPC-based, single-stage autosampler-injection unit for traditional, portable, and micro-GCs. The system uses μPCs packaged in 3D-printed swappable cartridges and is based on a highly modular interfacing architecture that allows easy-to-remove, gas-tight fluidic, and detachable electrical connections (FEMI). This study describes the FEMI architecture and demonstrates the FEMI-Autosampler (FEMI-AS) prototype (9.5 cm × 10 cm x 20 cm, ≈500 gms). The system was integrated with GC-FID, and the performance was investigated using synthetic gas samples and ambient air. The results were contrasted with the sorbent tube sampling technique using TD-GC-MS. FEMI-AS could generate sharp injection plugs (≈240 ms) and detect analytes with concentrations <15 ppb within 20 s and <100 ppt within 20 min of sampling time. With more than 30 detected trace-level compounds from ambient air, the demonstrated FEMI-AS, and the FEMI architecture significantly accelerate the adoption of μPCs on a broader scale.

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review

The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.

Fluid-based wearable sensors: a turning point in personalized healthcare

Nowadays, it has become very popular to develop wearable devices that can monitor biomarkers to analyze the health status of the human body more comprehensively and accurately. Wearable sensors, specially designed for home care services, show great promise with their ease of use, especially during pandemic periods. Scientists have conducted many innovative studies on new wearable sensors that can noninvasively and simultaneously monitor biochemical indicators in body fluids for disease prediction, diagnosis, and management. Using noninvasive electrochemical sensors, biomarkers can be detected in tears, saliva, perspiration, and skin interstitial fluid (ISF). In this review, biofluids used for noninvasive wearable sensor detection under four main headings, saliva, sweat, tears, and ISF-based wearable sensors, were examined in detail. This report analyzes nearly 50 recent articles from 2017 to 2023. Based on current research, this review also discusses the evolution of wearable sensors, potential implementation challenges, and future prospects.

Optimizing internal biosecurity on pig farms by assessing movements of farm staff

For internal biosecurity, it is important to separate different age groups in a pig farm and to stick to specific working lines when visiting the barns. Currently, there is no research on the movements of farm staff on pig farms. The objectives of this observational study were to assess movements of farm staff on pig farms, to assess risky movements and to investigate whether movements differ according to time (week of the batch farrowing system (BFS) and weekday vs. weekend) and unit (farrowing, gestation/insemination, nursery, and fattening unit). Five commercial sow farms participated and on each farm, an internal movement monitoring system was installed. Detection points were installed throughout the farm and workers had to wear a personal beacon. Movement data were collected from 1 December 2019 until 30 November 2020. The following sequence of movements was considered as safe: (1) dressing room, (2) farrowing, (3) gestation/insemination, (4) nursery, (5) fattening, (6) quarantine, and (7) cadaver storage. Movements in the opposite direction were considered as risk, unless a dressing room was visited in between. The total number of movements differed according to week of the BFS, and was highest in insemination and farrowing week. The percentage of risky movements was influenced by week of the BFS for two farms, and was highest around weaning. The percentage of risky movements varied between farms and ranged from 9 to 38%. There were more movements on a weekday compared to a weekend day. There were more movements towards the farrowing and gestation/insemination unit in insemination and farrowing week compared to other weeks of the BFS, but week of the BFS had no impact on movements towards nursery and fattening unit. This study showed that there were a lot of (risky) movements on pig farms and that these movements varied according to week of the BFS, day of the week, and unit. This study creates awareness, which could be a first step in optimizing working lines. Future research should focus on why certain risky movements occur and how these can be avoided to achieve better biosecurity and higher health status on farms.

Accidental laceration of the vaginal wall by an intravaginal thermometer as a calving detection device in a Japanese black cow

An intravaginal thermometer was inserted into a 59-month-old Japanese black cow to predict calving. After calving, the thermometer penetrated the vaginal wall and could not be removed by farm staff. Surgery to remove the thermometer was successful. The cow left the animal hospital without hospitalization. In the follow-up, the cow remained healthy on the farm for more than one year and is now pregnant. No symptoms related to damage to the vagina or infection developed. This is the first case report of a vaginal laceration caused by an intravaginal thermometer in a Japanese black cow. Insertional vaginal devices may cause vaginal lacerations in cattle.

Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors

The employment of machine learning algorithms to the data provided by wearable movement sensors is one of the most common methods to detect pets' behaviors and monitor their well-being. However, defining features that lead to highly accurate behavior classification is quite challenging. To address this problem, in this study we aim to classify six main dog activities (standing, walking, running, sitting, lying down, and resting) using high-dimensional sensor raw data. Data were received from the accelerometer and gyroscope sensors that are designed to be attached to the dog's smart costume. Once data are received, the module computes a quaternion value for each data point that provides handful features for classification. Next, to perform the classification, we used several supervised machine learning algorithms, such as the Gaussian naïve Bayes (GNB), Decision Tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM). In order to evaluate the performance, we finally compared the proposed approach's F-score accuracies with the accuracy of classic approach performance, where sensors' data are collected without computing the quaternion value and directly utilized by the model. Overall, 18 dogs equipped with harnesses participated in the experiment. The results of the experiment show a significantly enhanced classification with the proposed approach. Among all the classifiers, the GNB classification model achieved the highest accuracy for dog behavior. The behaviors are classified with F-score accuracies of 0.94, 0.86, 0.94, 0.89, 0.95, and 1, respectively. Moreover, it has been observed that the GNB classifier achieved 93% accuracy on average with the dataset consisting of quaternion values. In contrast, it was only 88% when the model used the dataset from sensors' data.

A Novel Improved YOLOv3-SC Model for Individual Pig Detection

Pork is the most widely consumed meat product in the world, and achieving accurate detection of individual pigs is of great significance for intelligent pig breeding and health monitoring. Improved pig detection has important implications for improving pork production and quality, as well as economics. However, most of the current approaches are based on manual labor, resulting in unfeasible performance. In order to improve the efficiency and effectiveness of individual pig detection, this paper describes the development of an attention module enhanced YOLOv3-SC model (YOLOv3-SPP-CBAM. SPP denotes the Spatial Pyramid Pooling module and CBAM indicates the Convolutional Block Attention Module). Specifically, leveraging the attention module, the network will extract much richer feature information, leading the improved performance. Furthermore, by integrating the SPP structured network, multi-scale feature fusion can be achieved, which makes the network more robust. On the constructed dataset of 4019 samples, the experimental results showed that the YOLOv3-SC network achieved 99.24% mAP in identifying individual pigs with a detection time of 16 ms. Compared with the other popular four models, including YOLOv1, YOLOv2, Faster-RCNN, and YOLOv3, the mAP of pig identification was improved by 2.31%, 1.44%, 1.28%, and 0.61%, respectively. The YOLOv3-SC proposed in this paper can achieve accurate individual detection of pigs. Consequently, this novel proposed model can be employed for the rapid detection of individual pigs on farms, and provides new ideas for individual pig detection.

Brief research report: Evaluation of photoplethysmographic heart rate monitoring for sheep under heat-stressed conditions

The objective of this study was to investigate the accuracy of a wearable photoplethysmography (PPG) sensor in monitoring heart rate (HR) of sheep housed in high-temperature environments. We hypothesized that the PPG sensor would be capable of differentiating low, normal, and high HR, but would struggle to produce exact HR estimates. The sensor was open source and comprised of a microprocessor (SparkFun® ThingPlus), a photoplethysmography sensor (SparkFun® MAX30101 &amp; MAX32664), and a data storage module (SD Card 16GB), all sewn into a nylon collar with hook-and-loop closure. Sheep (n=4) were divided into 2 groups and exposed to different thermal environments in a cross-over design. The collar was placed around the neck of the sheep during the data collection phase and the manual HR were collected twice a day using a stethoscope. Precision and accuracy of numeric heart rate estimates were analyzed in R software using Pearson correlation and root mean squared prediction errors. Random forest regression was used to classify HR based on low, medium, and high to determine opportunities to leverage the PPG sensors for HR classification. Sensitivity, specificity, and accuracy were measured to evaluate the classification approach. Our results indicated that the PPG-based sensor measured sheep HR with poor accuracy and with higher average estimates in comparison with manually measured with a stethoscope. Categorical classification of HR was also poor, with accuracies ranging from 32% to 49%. Additional work is needed focusing on data analytics, and signal optimization to further rely on PPG sensors for accurately measuring HR in sheep.

Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours

Simple Summary

Precision animal husbandry based on computer vision has developed promptly, especially in poultry farming. It is believed to improve animal welfare. To achieve the precise target detection and segmentation of geese, which can improve data acquisition, we newly built the world’s first goose instance segmentation dataset. Moreover, a high-precision detection and segmentation model was constructed, and the final mAP@0.5 of both target detection and segmentation reached 0.963. The evaluation of the model showed that the automated detection method proposed in this paper is feasible in a complex environment and can serve as a reference for the relevant development of the industry.

Abstract

With the rapid development of computer vision, the application of computer vision to precision farming in animal husbandry is currently a hot research topic. Due to the scale of goose breeding continuing to expand, there are higher requirements for the efficiency of goose farming. To achieve precision animal husbandry and to avoid human influence on breeding, real-time automated monitoring methods have been used in this area. To be specific, on the basis of instance segmentation, the activities of individual geese are accurately detected, counted, and analyzed, which is effective for achieving traceability of the condition of the flock and reducing breeding costs. We trained QueryPNet, an advanced model, which could effectively perform segmentation and extraction of geese flock. Meanwhile, we proposed a novel neck module that improved the feature pyramid structure, making feature fusion more effective for both target detection and instance individual segmentation. At the same time, the number of model parameters was reduced by a rational design. This solution was tested on 639 datasets collected and labeled on specially created free-range goose farms. With the occlusion of vegetation and litters, the accuracies of the target detection and instance segmentation reached 0.963 (mAP@0.5) and 0.963 (mAP@0.5), respectively.

Design and implementation of intelligent patient in-house monitoring system based on efficient XGBoost-CNN approach

Because of the scarcity of caregivers and the high cost of medical devices, it is difficult to keep track of the aging population and provide assistance. To avoid deterioration of health issues, continuous monitoring of personal health should be done prior to the intervention. If a problem is discovered, the IoT platform collects and presents the caretaker with graphical data. The death rates of older patients are reduced when projections are made ahead of time. Patients can die as a result of minor abnormalities in their ECG. The cardiac dysrhythmia/irregular heart rate is classified with several multilayer parameters using a deep convolutional neural network (CNN) approach in this paper. The key benefit of utilizing this CNN approach is that it can handle databases that have been purposefully oversampled. Using the XGBoost approach, these are oversampled to deal with difficulties like minority class and imbalance. XGBoost is a decision tree-based ensemble learning algorithm that uses a gradient boosting framework. It uses an artificial neural network and predicts the unstructured data in a structured manner. This CNN-based supervised learning model is tested and simulated on a real-time elderly heart patient IoT dataset. The proposed methodology has a recall value of 100%, an F1-Score of 94.8%, a precision of 98%, and an accuracy of 98%, which is higher than existing approaches like decision trees, random forests, and Support Vector Machine. The results reveal that the proposed model outperforms state-of-the-art methodologies and improves elderly heart disease patient monitoring with a low error rate.

Emerging bioanalytical sensors for rapid and close-to-real-time detection of priority abiotic and biotic stressors in aquaculture and culture-based fisheries

Abiotic stresses of various chemical contamination of physical, inorganic, organic and biotoxin origin and biotic stresses of bacterial, viral, parasitic and fungal origins are the significant constraints in achieving higher aquaculture production. Testing and rapid detection of these chemical and microbial contaminants are crucial in identifying and mitigating abiotic and biotic stresses, which has become one of the most challenging aspects in aquaculture and culture-based fisheries. The classical analytical techniques, including titrimetric methods, spectrophotometric, mass spectrometric, spectroscopic, and chromatographic techniques, are tedious and sometimes inaccessible when required. The development of novel and improved bioanalytical methods for rapid, selective and sensitive detection is a wide and dynamic field of research. Biosensors offer precise detection of biotic and abiotic stressors in aquaculture and culture-based fisheries within no time. This review article allows filling the knowledge gap for detection and monitoring of chemical and microbial contaminants of abiotic and biotic origin in aquaculture and culture-based fisheries using nano(bio-) analytical technologies, including nano(bio-)molecular and nano(bio-)sensing techniques.

UBER: UAV-Based Energy-Efficient Reconfigurable Routing Scheme for Smart Wireless Livestock Sensor Network

This paper addresses coverage loss and rapid energy depletion issues for wireless livestock sensor networks by proposing a UAV-based energy-efficient reconfigurable routing (UBER) scheme for smart wireless livestock sensor networking applications. This routing scheme relies on a dynamic residual energy thresholding strategy, robust cluster-to-UAV link formation, and UAV-assisted network coverage and recovery mechanism. The performance of UBER was evaluated using low, normal and high UAV altitude scenarios. Performance metrics employed for this analysis are network stability (NST), load balancing ratio (LBR), and topology fluctuation effect ratio (TFER). Obtained results demonstrated that operating with a UAV altitude of 230 m yields gains of 31.58%, 61.67%, and 75.57% for NST, LBR, and TFER, respectively. A comparative performance evaluation of UBER was carried out with respect to hybrid heterogeneous routing (HYBRID) and mobile sink using directional virtual coordinate routing (MS-DVCR). The performance indicators employed for this comparative analysis are energy consumption (ENC), network coverage (COV), received packets (RPK), SN failures detected (SNFD), route failures detected (RFD), routing overhead (ROH), and end-to-end delay (ETE). With regard to the best-obtained results, UBER recorded performance gains of 46.48%, 47.33%, 15.68%, 19.78%, 46.44%, 29.38%, and 58.56% over HYBRID and MS-DVCR in terms of ENC, COV, RPK, SNFD, RFD, ROH, and ETE, respectively. The results obtained demonstrated that the UBER scheme is highly efficient with competitive performance against the benchmarked CBR schemes.

Temperature thresholds and screening of febrile people by non-contact measurement of the face using infrared thermography - A methodology proposal

Recent outbreaks of infectious diseases such as Covid-19 that have fever as one of the symptoms drive the search for systems to track people with fever quickly and non-contact, also known as sanitary barriers. The use of non-contact infrared-based instruments, especially the infrared thermal imager, has widely spread. However, the screening process has presented low performance. This article addresses the choice of regions of interest on the human face for the analysis of the individual's fever, deals with the temperature thresholds used for this analysis, as well as the way to issue the recommendation to screen the person or not. The data collection and statistical analysis of temperatures of 198 volunteers allowed us to study and define the most appropriate face regions as targets for these barriers, as well as the temperature thresholds to be used for screening for each of these regions. Besides, the paper presents a probabilistic method based on the metrological quality of the sanitary barrier to the emission of recommendation for screening potentially febrile people. The developed method was tested in feverish and non-febrile volunteers, showing complete assertiveness in the tested cases.

Novel and Alternative Therapeutic Strategies for Controlling Avian Viral Infectious Diseases: Focus on Infectious Bronchitis and Avian Influenza

The growth of poultry farming has enabled higher spread of infectious diseases and their pathogens among different kinds of birds, such as avian infectious bronchitis virus (IBV) and avian influenza virus (AIV). IBV and AIV are a potential source of poultry mortality and economic losses. Furthermore, some pathogens have the ability to cause zoonotic diseases and impart human health problems. Antiviral treatments that are used often lead to virus resistance along with the problems of side effects, recurrence, and latency of viruses. Though target hosts are being vaccinated, the constant emergence and re-emergence of strains of these viruses cause disease outbreaks. The pharmaceutical industry is gradually focusing on plant extracts to develop novel herbal drugs to have proper antiviral capabilities. Natural therapeutic agents developed from herbs, essential oils (EO), and distillation processes deliver a rich source of amalgams to discover and produce new antiviral drugs. The mechanisms involved have elaborated how these natural therapeutics agents play a major role during virus entry and replication in the host and cause inhibition of viral pathogenesis. Nanotechnology is one of the advanced techniques that can be very useful in diagnosing and controlling infectious diseases in poultry. In general, this review covers the issue of the poultry industry situation, current infectious diseases, mainly IB and AI control measures and, in addition, the setup of novel therapeutics using plant extracts and the use of nanotechnology information that may help to control these diseases.

Dairy 4.0: Intelligent Communication Ecosystem for the Cattle Animal Welfare with Blockchain and IoT Enabled Technologies

An intelligent ecosystem with real-time wireless technology is now playing a key role in meeting the sustainability requirements set by the United Nations. Dairy cattle are a major source of milk production all over the world. To meet the food demand of the growing population with maximum productivity, it is necessary for dairy farmers to adopt real-time monitoring technologies. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in dairy cattle to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as animal health monitoring, animal location tracking, milk quality, and supply chain, feed monitoring and safety, etc., have been discussed briefly. Furthermore, generalized architecture has been proposed that can be directly applied in the future for breakthroughs in research and development linked to data gathering and the processing of applications through edge devices, robots, drones, and blockchain for building intelligent ecosystems. In addition, the article discusses the possibilities and challenges of implementing previous techniques for different activities in dairy cattle. High computing power-based wearable devices, renewable energy harvesting, drone-based furious animal attack detection, and blockchain with IoT assisted systems for the milk supply chain are the vital recommendations addressed in this study for the effective implementation of the intelligent ecosystem in dairy cattle.

The Future of Artificial Intelligence in Monitoring Animal Identification, Health, and Behaviour

Simple Summary

Due to climate change and human interference, many species are now without habitats and on the brink of extinction. Zoos and other conservation spaces allow for non-human animal preservation and public education about endangered species and ecosystems. Monitoring the health and well-being of animals in care, while providing species-specific environments, is critical for zoo and conservation staff. In order to best provide such care, keepers and researchers need to gather as much information as possible about individual animals and species as a whole. This paper focuses on existing technology to monitor animals, providing a review on the history of technology, including recent technological advancements and current limitations. Subsequently, we provide a brief introduction to our proposed novel computer software: an artificial intelligence software capable of unobtrusively and non-invasively tracking individuals’ location, estimating position, and analyzing behaviour. This innovative technology is currently being trained with orangutans at the Toronto Zoo and will allow for mass data collection, permitting keepers and researchers to closely monitor individual animal welfare, learn about the variables impacting behaviour and provide additional enrichment or interventions accordingly.

Abstract

With many advancements, technologies are now capable of recording non-human animals’ location, heart rate, and movement, often using a device that is physically attached to the monitored animals. However, to our knowledge, there is currently no technology that is able to do this unobtrusively and non-invasively. Here, we review the history of technology for use with animals, recent technological advancements, current limitations, and a brief introduction to our proposed novel software. Canadian tech mogul EAIGLE Inc. has developed an artificial intelligence (AI) software solution capable of determining where people and assets are within public places or attractions for operational intelligence, security, and health and safety applications. The solution also monitors individual temperatures to reduce the potential spread of COVID-19. This technology has been adapted for use at the Toronto Zoo, initiated with a focus on Sumatran orangutans (Pongo abelii) given the close physical similarity between orangutans and humans as great ape species. This technology will be capable of mass data collection, individual identification, pose estimation, behaviour monitoring and tracking orangutans’ locations, in real time on a 24/7 basis, benefitting both zookeepers and researchers looking to review this information.

Omics technologies in poultry health and productivity - part 2: future applications in the poultry industry

The increasing global demand for poultry products, together with the growing consumer concerns related to bird health and welfare, pose a significant challenge to the poultry industry. Therefore, the poultry industry is increasingly implementing novel technologies to optimize and enhance bird welfare and productivity. This second part of a bipartite review on omics technologies in poultry health and productivity highlights the implementation of specific diagnostic biomarkers based on omics-research in the poultry industry, as well as the potential integration of multi-omics in future poultry production. A general discussion of the use of multiple omics technologies in poultry research is provided in part 1. To date, approaches focusing on one or more omics type are widely used in poultry research, but the implementation of these omics techniques in poultry production is not expected in the near future. However, great potential lays in the development of diagnostic tests based on disease- or gut health-specific biomarkers, which are identified through omics research. As the cost of omics technologies is rapidly decreasing, implementation of multi-omics measurements in routine poultry monitoring systems might be feasible in the more distant future. Therefore, the opportunities, challenges and requirements to enable the integration of multi-omics-based monitoring of bird health and productivity in future poultry production are discussed.

Enzyme-embedded electrospun fiber sensor of hydrophilic polymer for fluorometric ethanol gas imaging in vapor phase

Non-invasive measurement of volatile organic compounds (VOCs) emitted from living organisms is a powerful technique for diagnosing health conditions or diseases in humans. Bio-based gas sensors are suitable for the sensitive and selective measurement of a target VOC from a complex mixture of VOCs. Conventional bio-based sensors are normally prepared as wet-type probes to maintain proteins such as enzymes in a stable state, resulting in limitations in the commercialization of sensors, their operating environment, and performance. In this study, we present an enzyme-based fluorometric electrospun fiber sensor (eFES) mesh as a gas-phase biosensor in dry form. The eFES mesh targeting ethanol was fabricated by simple one-step electrospinning of polyvinyl alcohol with an alcohol dehydrogenase and an oxidized form of nicotinamide adenine dinucleotide. The enzyme embedded in the eFES mesh worked actively in a dry state without pretreatment. Substrate specificity was also maintained, and the sensor responded well to ethanol with a sufficient dynamic range. Adjustment of the pH and coenzyme quantity in the eFES mesh also affected enzyme activity. The dry-form biosensor-eFES mesh-will open a new direction for gas-phase biosensors because of its remarkable performance and simple fabrication, which is advantageous for commercialization.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

Operationalizing "One Health" as "One Digital Health" Through a Global Framework That Emphasizes Fair and Equitable Sharing of Benefits From the Use of Artificial Intelligence and Related Digital Technologies

The operationalization of One Health (OH) through digitalization is a means to deploy digital technologies (including Artificial Intelligence (AI), big data and related digital technologies) to better capacitate us to deal with growing climate exigency and related threats to human, animal and plant health. With reference to the concept of One Digital Health (ODH), this paper considers how digital capabilities can help to overcome ‘operational brakes’ in OH through new and deeper insights, better predictions, and more targeted or precise preventive strategies and public health countermeasures. However, the data landscape is fragmented and access to certain types of data is increasingly restrictive as individuals, communities and countries seek to assert greater control over data taken from them. This paper proposes for a dedicated global ODH framework—centered on fairness and equity—to be established to promote data-sharing across all the key knowledge domains of OH and to devise data-driven solutions to challenges in the human-animal-ecosystems interface. It first considers the data landscape in relation to: (1) Human and population health; (2) Pathogens; (3) Animal and plant health; and (4) Ecosystems and biodiversity. The complexification from the application of advance genetic sequencing technology is then considered, with focus on current debates over whether certain types of data like digital (genetic) sequencing information (DSI) should remain openly and freely accessible. The proposed ODH framework must augment the existing access and benefit sharing (ABS) framework currently prescribed under the Nagoya Protocol to the Convention on Biological Diversity (CBD) in at least three different ways. First, the ODH framework should apply to all genetic resources and data, including DSI, whether from humans or non-humans. Second, the FAIRER principles should be implemented, with focus on fair and equitable benefit-sharing. Third, the ODH framework should adopt multilateral approaches to data sharing (such as through federated data systems) and to ABS. By operationalizing OH as ODH, we are more likely to be able to protect and restore natural habitats, secure the health and well-being of all living things, and thereby realize the goals set out in the post-2020 Global Biodiversity Framework under the CBD.

ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and Challenges of Confined and Extensive Precision Livestock Production

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board

Knowing the number of pigs on a large-scale pig farm is an important issue for efficient farm management. However, counting the number of pigs accurately is difficult for humans because pigs do not obediently stop or slow down for counting. In this study, we propose a camera-based automatic method to count the number of pigs passing through a counting zone. That is, using a camera in a hallway, our deep-learning-based video object detection and tracking method analyzes video streams and counts the number of pigs passing through the counting zone. Furthermore, to execute the counting method in real time on a low-cost embedded board, we consider the tradeoff between accuracy and execution time, which has not yet been reported for pig counting. Our experimental results on an NVIDIA Jetson Nano embedded board show that this “light-weight” method is effective for counting the passing-through pigs, in terms of both accuracy (i.e., 99.44%) and execution time (i.e., real-time execution), even when some pigs pass through the counting zone back and forth.

Applications of Smart Technology as a Sustainable Strategy in Modern Swine Farming

The size of the pork market is increasing globally to meet the demand for animal protein, resulting in greater farm size for swine and creating a great challenge to swine farmers and industry owners in monitoring the farm activities and the health and behavior of the herd of swine. In addition, the growth of swine production is resulting in a changing climate pattern along with the environment, animal welfare, and human health issues, such as antimicrobial resistance, zoonosis, etc. The profit of swine farms depends on the optimum growth and good health of swine, while modern farming practices can ensure healthy swine production. To solve these issues, a future strategy should be considered with information and communication technology (ICT)-based smart swine farming, considering auto-identification, remote monitoring, feeding behavior, animal rights/welfare, zoonotic diseases, nutrition and food quality, labor management, farm operations, etc., with a view to improving meat production from the swine industry. Presently, swine farming is not only focused on the development of infrastructure but is also occupied with the application of technological knowledge for designing feeding programs, monitoring health and welfare, and the reproduction of the herd. ICT-based smart technologies, including smart ear tags, smart sensors, the Internet of Things (IoT), deep learning, big data, and robotics systems, can take part directly in the operation of farm activities, and have been proven to be effective tools for collecting, processing, and analyzing data from farms. In this review, which considers the beneficial role of smart technologies in swine farming, we suggest that smart technologies should be applied in the swine industry. Thus, the future swine industry should be automated, considering sustainability and productivity.

Point of care diagnostics and non-invasive sampling strategy: a review on major advances in veterinary diagnostics

The use of point of care diagnostics (POCD) in animal diseases has steadily increased over the years since its introduction. Its potential application to diagnose infectious diseases in remote and resource limited settings have made it an ideal diagnostic in animal disease diagnosis and surveillance. The rapid increase in incidence of emerging infectious diseases requires urgent attention where POCD could be indispensable tools for immediate detection and early warning of a potential pathogen. The advantages of being rapid, easily affordable and the ability to diagnose an infectious disease on spot has driven an intense effort to refine and build on the existing technologies to generate advanced POCD with incremental improvements in analytical performance to diagnose a broad spectrum of animal diseases. The rural communities in developing countries are invariably affected by the burden of infectious animal diseases due to limited access to diagnostics and animal health personnel. Besides, the alarming trend of emerging and transboundary diseases with pathogen spill-overs at livestock-wildlife interfaces has been identified as a threat to the domestic population and wildlife conservation. Under such circumstances, POCD coupled with non-invasive sampling techniques could be successfully deployed at field level without the use of sophisticated laboratory infrastructures. This review illustrates the current and prospective POCD for existing and emerging animal diseases, the status of non-invasive sampling strategies for animal diseases, and the tremendous potential of POCD to uplift the status of global animal health care.

Breeding for disease resilience: opportunities to manage polymicrobial challenge and improve commercial performance in the pig industry

Disease resilience, defined as an animal's ability to maintain productive performance in the face of infection, provides opportunities to manage the polymicrobial challenge common in pig production. Disease resilience can deliver a number of benefits, including more sustainable production as well as improved animal health and the potential for reduced antimicrobial use. However, little progress has been made to date in the application of disease resilience in breeding programs due to a number of factors, including (1) confusion around definitions of disease resilience and its component traits disease resistance and tolerance, and (2) the difficulty in characterizing such a complex trait consisting of multiple biological functions and dynamic elements of rates of response and recovery from infection. Accordingly, this review refines the definitions of disease resistance, tolerance, and resilience based on previous studies to help improve the understanding and application of these breeding goals and traits under different scenarios. We also describe and summarize results from a "natural disease challenge model" designed to provide inputs for selection of disease resilience. The next steps for managing polymicrobial challenges faced by the pig industry will include the development of large-scale multi-omics data, new phenotyping technologies, and mathematical and statistical methods adapted to these data. Genome editing to produce pigs resistant to major diseases may complement selection for disease resilience along with continued efforts in the more traditional areas of biosecurity, vaccination and treatment. Altogether genomic approaches provide exciting opportunities for the pig industry to overcome the challenges provided by hard-to-manage diseases as well as new environmental challenges associated with climate change.

Modular E-Collar for Animal Telemetry: An Animal-Centered Design Proposal

Animal telemetry is a subject of great potential and scientific interest, but it shows design-dependent problems related to price, flexibility and customization, autonomy, integration of elements, and structural design. The objective of this paper is to provide solutions, from the application of design, to cover the niches that we discovered by reviewing the scientific literature and studying the market. The design process followed to achieve the objective involved a development based on methodologies and basic design approaches focused on the human experience and also that of the animal. We present a modular collar that distributes electronic components in several compartments, connected, and powered by batteries that are wirelessly recharged. Its manufacture is based on 3D printing, something that facilitates immediacy in adaptation and economic affordability. The modularity presented by the proposal allows for adapting the size of the modules to the components they house as well as selecting which specific modules are needed in a project. The homogeneous weight distribution is transferred to the comfort of the animal and allows for a better integration of the elements of the collar. This device substantially improves the current offer of telemetry devices for farming animals, thanks to an animal-centered design process.

Review: Precision Livestock Farming technologies in pasture-based livestock systems

Precision Livestock Farming (PLF) encompasses the combined application of single technologies or multiple tools in integrated systems for real-time and individual monitoring of livestock. In grazing systems, some PLF applications could substantially improve farmers' control of livestock by overcoming issues related to pasture utilisation and management, and animal monitoring and control. A focused literature review was carried out to identify technologies already applied or at an advanced stage of development for livestock management in pastures, specifically cattle, sheep, goats, pigs, poultry. Applications of PLF in pasture-based systems were examined for cattle, sheep, goats, pigs, and poultry. The earliest technology applied to livestock was the radio frequency identification tag, allowing the identification of individuals, but also for retrieving important information such as maternal pedigree. Walk-over-weigh platforms were used to record individual and flock weights. Coupled with automatic drafting systems, they were tested to divide the animals according to their needs. Few studies have dealt with remote body temperature assessment, although the use of thermography is spreading to monitor both intensively reared and wild animals. Global positioning system and accelerometers are among the most applied technologies, with several solutions available on the market. These tools are used for several purposes, such as animal location, theft prevention, assessment of activity budget, behaviour, and feed intake of grazing animals, as well as for reproduction monitoring (i.e., oestrus, calving, or lambing). Remote sensing by satellite images or unmanned aerial vehicles (UAVs) seems promising for biomass assessment and herd management based on pasture availability, and some attempts to use UAVs to monitor, track, or even muster animals have been reported recently. Virtual fencing is among the upcoming technologies aimed at grazing management. This system allows the management of animals at pasture without physical fences but relies on associative learning between audio cues and an electric shock delivered if the animal does not change direction after the acoustic warning. Regardless of the different technologies applied, some common constraints have been reported on the application of PLF in grazing systems, especially when compared with indoor or confined livestock systems. Battery lifespan, transmission range, service coverage, storage capacity, and economic affordability were the main factors. However, even if the awareness of the existence and the potential of these upcoming tools are still limited, farmers' and researchers' demands are increasing, and positive outcomes in terms of rangeland conservation, animal welfare, and labour optimisation are expected from the spread of PLF in grazing systems.

Graphene Oxide Based Electrochemical Genosensor for Label Free Detection of Mycobacterium tuberculosis from Raw Clinical Samples

Background

Mycobacterium tuberculosis’ rapid detection is still a formidable challenge to have control over the lethal disease. New diagnostic methods such as LED fluorescence microscopy, Genexpert, Interferon Gamma Release Assay (IGRA) are limited on efficacy spectrum owing to their high cost, time-intensive and laborious nature, in addition their low sensitivity hinders their robustness and portability. Electroanalytical methods are now being considered as an excellent alternative, being currently employed for efficient detection of the analytes with the potential of being portable. This report suggests label-free electrochemical detection of Mycobacterium tuberculosis (Mtb) via its marker, insertion sequence (IS6110).

Methods

In this pursuit, graphene oxide-chitosan nanocomposite (GO-CHI), a biocompatible matrix, having a large electroactive area with an overall positively charged surface, is fabricated and characterized. The obtained GO-CHI nanocomposite is then immobilized on the ITO surface to form a positively functionalized electrochemical sensor for the detection of Mtb. DNA probe, specific for the IS6110, was electrostatically anchored on a positively charged electrode surface and the resistance of charge transfer was investigated for the sensitive and specific (complementary vs non-complementary) detection of Mtb by cyclic voltammetry and differential pulse voltammetry techniques.

Results

The cyclic voltammetry was found to be diffusion controlled facilitating the absorption of analyte on the electrode surface. The label-free “genosensor” was found to detect a hybridization efficiency with a limit of detection of 3.4 pM, and correlation coefficient R²=0.99 when analysed over a range of concentrations of DNA from 7.86 pM to 94.3pM. The genosensor was also able to detect target DNA from raw sputum samples of clinical isolates without DNA purification.

Conclusion

This electrochemical genosensor provides high sensitivity and specificity; thus offering a promising platform for clinical diagnosis of TB and other infectious diseases in general.

Exploring Dry-Film FTIR Spectroscopy to Characterize Milk Composition and Subclinical Ketosis throughout a Cow's Lactation

The use of technologies for measurements of health parameters of individual cows may ensure early detection of diseases and maximization of individual cow and herd potential. In the present study, dry-film Fourier transform infrared spectroscopy (FTIR) was evaluated for the purpose of detecting and quantifying milk components during cows’ lactation. This was done in order to investigate if these systematic changes can be used to identify cows experiencing subclinical ketosis. The data included 2329 milk samples from 61 Norwegian Red dairy cows collected during the first 100 days in milk (DIM). The resulting FTIR spectra were used for explorative analyses of the milk composition. Principal component analysis (PCA) was used to search for systematic changes in the milk during the lactation. Partial least squares regression (PLSR) was used to predict the fatty acid (FA) composition of all milk samples and the models obtained were used to evaluate systematic changes in the predicted FA composition during the lactation. The results reveal that systematic changes related to both gross milk composition and fatty acid features can be seen throughout lactation. Differences in the predicted FA composition between cows with subclinical ketosis and normal cows, in particular C14:0 and C18:1cis9, showed that dietary energy deficits may be detected by deviations in distinct fatty acid features.

Does Smart Farming Improve or Damage Animal Welfare? Technology and What Animals Want

“Smart” or “precision” farming has revolutionized crop agriculture but its application to livestock farming has raised ethical concerns because of its possible adverse effects on animal welfare. With rising public concern for animal welfare across the world, some people see the efficiency gains offered by the new technology as a direct threat to the animals themselves, allowing producers to get “more for less” in the interests of profit. Others see major welfare advantages through life-long health monitoring, delivery of individual care and optimization of environmental conditions. The answer to the question of whether smart farming improves or damages animal welfare is likely to depend on three main factors. Firstly, much will depend on how welfare is defined and the extent to which politicians, scientists, farmers and members of the public can agree on what welfare means and so come to a common view on how to judge how it is impacted by technology. Defining welfare as a combination of good health and what the animals themselves want provides a unifying and animal-centered way forward. It can also be directly adapted for computer recognition of welfare. A second critical factor will be whether high welfare standards are made a priority within smart farming systems. To achieve this, it will be necessary both to develop computer algorithms that can recognize welfare to the satisfaction of both the public and farmers and also to build good welfare into the control and decision-making of smart systems. What will matter most in the end, however, is a third factor, which is whether smart farming can actually deliver its promised improvements in animal welfare when applied in the real world. An ethical evaluation will only be possible when the new technologies are more widely deployed on commercial farms and their full social, environmental, financial and welfare implications become apparent.

On the move: sloths and their epibionts as model mobile ecosystems

Sloths are unusual mobile ecosystems, containing a high diversity of epibionts living and growing in their fur as they climb slowly through the canopies of tropical forests. These epibionts include poorly studied algae, arthropods, fungi, and bacteria, making sloths likely reservoirs of unexplored biodiversity. This review aims to identify gaps and eliminate misconceptions in our knowledge of sloths and their epibionts, and to identify key questions to stimulate future research into the functions and roles of sloths within a broader ecological and evolutionary context. This review also seeks to position the sloth fur ecosystem as a model for addressing fundamental questions in metacommunity and movement ecology. The conceptual and evidence-based foundation of this review aims to serve as a guide for future hypothesis-driven research into sloths, their microbiota, sloth health and conservation, and the coevolution of symbioses in general.

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

Understanding intake on pastures: how, why, and a way forward

An assessment of dietary intake is a critical component of animal nutrition. Consumption of feed resources is the basis upon which feeding strategies and grazing management are based. Yet, as far back as 1948, researchers have lauded the trials and tribulations of estimation of the phenomenon, especially when focused on grazing animals and pasture resources. The grazing environment presents a unique situation in which the feed resource is not provided to the animal but, rather, the animal operates as the mechanism of harvest. Therefore, tools for estimation must be developed, validated, and applied to the scenario. There are a plethora of methods currently in use for the estimation of intake, ranging from manual measurement of herbage disappearance to digital technologies and sensors, each of which come with its share of advantages and disadvantages. In order to more firmly grasp these concepts and provide a discussion on the future of this estimation, the Forages and Pastures Symposium at the 2020 ASAS-CSAS-WSASAS Annual Meeting was dedicated to this topic. This review summarizes the presentations in that symposium and offers further insight into where we have come from and where we are going in the estimation of intake for grazing livestock.