Estimation of gestating sows' welfare status based on machine learning methods and behavioral data.

Estimating the welfare status at an individual level on the farm is a current issue to improve livestock animal monitoring. New technologies showed opportunities to analyze livestock behavior with machine learning and sensors. The aim of the study was to estimate some components of the welfare status of gestating sows based on machine learning methods and behavioral data. The dataset used was a combination of individual and group measures of behavior (activity, social and feeding behaviors). A clustering method was used to estimate the welfare status of 69 sows (housed in four groups) during different periods (sum of 2 days per week) of gestation (between 6 and 10 periods, depending on the group). Three clusters were identified and labelled (scapegoat, gentle and aggressive). Environmental conditions and the sows' health influenced the proportion of sows in each cluster, contrary to the characteristics of the sow (age, body weight or body condition). The results also confirmed the importance of group behavior on the welfare of each individual. A decision tree was learned and used to classify the sows into the three categories of welfare issued from the clustering step. This classification relied on data obtained from an automatic feeder and automated video analysis, achieving an accuracy rate exceeding 72%. This study showed the potential of an automatic decision support system to categorize welfare based on the behavior of each gestating sow and the group of sows.

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms.

Precision feeding is a strategy for supplying an amount and composition of feed as close that are as possible to each animal's nutrient requirements, with the aim of reducing feed costs and environmental losses. Usually, the nutrient requirements of gestating sows are provided by a nutrition model that requires input data such as sow and herd characteristics, but also an estimation of future farrowing performances. New sensors and automatons, such as automatic feeders and drinkers, have been developed on pig farms over the last decade, and have produced large amounts of data. This study evaluated machine-learning methods for predicting the daily nutrient requirements of gestating sows, based only on sensor data, according to various configurations of digital farms. The data of 73 gestating sows was recorded using sensors such as electronic feeders and drinker stations, connected weight scales, accelerometers, and cameras. Nine machine-learning algorithms were trained on various dataset scenarios according to different digital farm configurations (one or two sensors), to predict the daily metabolizable energy and standardized ileal digestible lysine requirements for each sow. The prediction results were compared to those predicted by the InraPorc model, a mechanistic model for the precision feeding of gestating sows. The scenario predictions were also evaluated with or without the housing conditions and sow characteristics at artificial insemination usually integrated into the InraPorc model. Adding housing and sow characteristics to sensor data improved the mean average percentage error by 5.58% for lysine and by 2.22% for energy. The higher correlation coefficient values for lysine (0.99) and for energy (0.95) were obtained for scenarios involving an automatic feeder system (daily duration and number of visits with or without consumption) only. The scenarios including an automatic feeder combined with another sensor gave good performance results. For the scenarios using sow and housing characteristics and automatic feeder only, the root mean square error was lower with gradient tree boosting (0.91 MJ/d for energy and 0.08 g/d for lysine) compared with those obtained using linear regression (2.75 MJ/d and 1.07 g/d). The results of this study show that the daily nutrient requirements of gestating sows can be predicted accurately using data provided by sensors and machine-learning methods. It paves the way for simpler solutions for precision feeding.

New technologies, such as sensors and automatons, are being developed in agriculture to reduce workload or help farmers make management decisions. The most common approach to the analysis of the huge amount of data generated by these technologies is to use machine-learning algorithms, to detect health or welfare problems for example. The hypothesis was that these automatically collected data and algorithms could also serve to predict the nutrient requirements of gestating sows, usually calculated based on complex models that require a lot of on-farm input data. The predictions of 22 scenarios were compared based on different combinations of sensor data, with the prediction of a nutritional model for gestating sows. The results of nine algorithms applied to the different scenarios were also compared. The results suggested that feeder data, alone or in combination with another sensor, predicted nutrient requirements with high accuracy. Data from other sensors combined with additional information about the sow (i.e., age and body weight) also led to high prediction accuracy. The difference between the algorithms evaluated was relatively significant, but all showed acceptable prediction results, especially non-linear algorithms. In conclusion, this work demonstrated the possibility of accurately predicting daily nutrient requirements for each sow using sensor data and machine-learning algorithms.

Effects of thermal conditions on gestating sows' behaviors and energy requirements.

Room temperature and individual behavior may influence the energy requirements of gestating sows. These factors are not yet integrated on a daily and individual basis in the calculation of these requirements. The objective of this study was to quantify the effect of temperatures on the sows' behaviors, especially on the level of physical activity, and on the energy requirements of gestating sows. Over four consecutive weeks, the temperature of two gestation rooms was maintained at medium temperatures (16.7 °C and 18.5 °C, respectively, for room 1 and room 2) for the first and third week, at low temperatures (14.4 °C and 15.3 °C) for the second week, and at high temperatures (31.6 °C and 31.9 °C) for the fourth week. Individual behavior was manually recorded based on videos and the data used to estimate the physical activity and social interactions of 37 gestating sows separated into two groups. The videos were analyzed over two periods of 5 h ("Feeding period" from 2300 to 0400 hours, "Resting period" from 1330 to 1830 hours). The energy requirements were calculated by the InraPorc model, modified for gestating sows, on the basis of a thermo-neutral situation and an average activity of 4 h standing per day for all the sows. The sows of one group were less active in high than low temperatures (83 vs. 103 min standing or walking over 5 h, P < 0.001). Isolation for high temperatures or huddling for low temperatures could be observed when sows were lying down. The sows spent more time lying laterally with high temperatures than low temperatures (66% vs. 52% of time spent lying, respectively, P < 0.001). Both groups reacted differently to high temperatures, in one the sows changed their activity (lying more) whereas in the other they drank more water compared to medium temperatures (11 vs. 8.5 L/d, P = 0.01). In one group, with high temperatures the sows were fed above their requirements (they should have received 110 g of feed per day per sow less, P < 0.001) and with low temperatures the same group should have received 50 g/d per sow more to fulfill their requirements. For the second group of sows, the temperatures did not significantly affect the feed requirements. In conclusion, daily ambient temperature and individual physical activity seem to be relevant information to add in nutritional models to improve precision feeding.

Ambient temperature may influence the energy requirement of gestating sows, but this factor is not yet integrated daily in the calculation of this requirement. The objective of this study was to quantify the effect of temperatures on sow's behavior, physical activity, and energy requirements on gestating sows. The 37 gestating sows were housed in two groups for which the temperature of each room was maintained at different temperatures during four consecutive weeks: the first and third weeks at 18 °C on average (medium temperature), the second week at 15.5 °C (low temperature), and the last one at 32 °C (high temperature). The sows modified their behavior regarding the room temperature even though these changes differed regarding the group of sows. Compared to medium temperature, high temperatures may induce an increase of water consumption or of the time spent lying, and of the rectal temperature of some sows. Low temperatures may induce huddling and/or an increase in aggressiveness. Low and high temperatures seem to impact energy costs even though it depends on the group of sows. Therefore, ambient temperature and individual activity are relevant information to add into nutritional models to improve their accuracy of energy requirement prediction.

Ultrasound guidance for urgent arterial and venous catheterisation: randomised controlled study.

BACKGROUND: Haemodynamically unstable patients often require arterial and venous catheter insertion urgently. We hypothesised that ultrasound-guided arterial and venous catheterisation would reduce mechanical complications. METHODS: We performed a prospective RCT, where patients requiring both urgent arterial and venous femoral catheterisation were randomised to either ultrasound-guided or landmark-guided catheterisation. Complications and characteristics of catheter insertion (procedure duration, number of punctures, and procedure success) were recorded at the time of insertion (immediate complications). Late complications were investigated by ultrasound examination performed between the third and seventh days after randomisation. Primary outcome was the proportion of patients with at least one mechanical complication (immediate or late), by intention-to-treat analysis. Secondary outcomes included success rate, procedure time, and number of punctures. RESULTS: We analysed 136 subjects (102 [75%] male; age range: 27-62 yr) by intention to treat. The proportion of subjects with one or more complications was lower in 22/67 (33%) subjects undergoing ultrasound-guided catheterisation compared with landmark-guided catheterisation (40/69 [58%]; odds ratio: 0.35 [95% confidence interval: 0.18-0.71]; P=0.003). Ultrasound-guided catheterisation reduced both immediate (27%, compared with 51% in the landmark approach group; P=0.004) and late (10%, compared with 23% in the landmark approach group; P=0.047) complications. Ultrasound guidance also reduced the proportion of patients who developed deep vein thrombosis (4%, compared with 22% following landmark approach; P=0.012), and achieved a higher procedural success rate (96% vs 78%; P=0.004). CONCLUSIONS: An ultrasound-guided approach reduced mechanical complications after urgent femoral arterial and venous catheterisation, while increasing procedural success. CLINICAL TRIAL REGISTRATION: NCT02820909.

Correlation between laboratory coagulation testing and thromboelastometry is modified during management of trauma patients.

BACKGROUND: Thromboelastometry (ROTEM, Pentapharm GmbH, Munich, Germany) is increasingly being used to make a diagnosis of coagulopathy and to guide hemostatic therapy (HT). Although ROTEM parameters and standard laboratory test (SLT) correlated well before administration of HT, it is not known if this correlation persists after hemostatic resuscitation. METHODS: A retrospective analysis of prospectively collected data from a trauma registry (2011-2014) was performed. All patients having a ROTEM analysis were included. ROTEM parameters (clotting time and clot amplitude at 5 minutes) were determined after activation with tissue factor (EXTEM) or platelet inhibition with cytochalasin D (FIBTEM). Spearman rank correlation coefficient was calculated for the correlation between SLT and thromboelastometry parameters, and thresholds were determined with receiver operating characteristic (ROC) curve analysis for the diagnosis of an international normalized ratio (INR) greater than 1.5, fibrinogen 1.5 g/L or less, and platelet count of less than 100.10/L. RESULTS: Of the 358 patients included, 533 thromboelastometry results were obtained (335 at admission, 198 during care). Correlation between INR and EXTEM-clotting time was good at admission (r = 0.617) in the whole cohort but decreased in the subgroup of patients having an Injury Severity Score of less than 25 (r = 0.399) or a base excess of less than 6 mmol/L (r = 0.489). During care, correlation was impaired after the administration of fibrinogen concentrates in the whole cohort (r = 0.430), as well as in the subgroup of patients having an Injury Severity Score greater than 24 (r = 0.465). As well, for the diagnosis of increased INR, sensitivity and the area under the ROC curve decreased from 75% and 0.894 (no treatment) to 20% and 0.653 (fibrinogen concentrate). Areas under the ROC curve for the prediction of a fibrinogen or platelet decrease were not significantly altered regardless of the treatment group. CONCLUSIONS: A decrease in the correlation between SLTs and ROTEM parameters was observed at admission or during care, which could be in relation with injury severity, base deficit, or the administration of blood products, particularly fibrinogen concentrate. Further work will be necessary to better understand which tool is the most suitable for guiding HT. LEVEL OF EVIDENCE: Therapeutic study, level IV; diagnostic study, level IV.

Determinants of 6-month maternal satisfaction with breastfeeding experience in a multicenter prospective cohort study.

Although a personally defined experience, successful breastfeeding is usually measured with regard to duration. This study investigated the determinants of maternal satisfaction with breastfeeding experience for 907 mothers enrolled in a prospective cohort study. Despite a median breastfeeding duration (18 weeks) that fell short of recommendations, 822 mothers (90.6%) rated their breastfeeding experience as very or fairly satisfactory. Anticipated breastfeeding duration was a determinant of satisfaction only for women who actually breastfeed < 2 months; in this subgroup of mothers, satisfaction rates ranged from 84.6% for those who anticipated breastfeeding < 2 months to 69.8% for those who anticipated breastfeeding > 4 months (P = .01). Smoking during pregnancy and experiencing breastfeeding difficulties after discharge were independently associated with decreased satisfaction. Eliciting the mother's expectations regarding breastfeeding duration may help the lactation consultant in providing appropriate guidance. Future studies should assess maternal satisfaction using validated instruments.