The objectives of this study were to validate the application of Bluetooth technology to determine maternal pedigree and to determine ewe-lamb spatial relationships in extensive farming systems. A total of 35 first-cross Merino ewes (Merino × Border Leicester and East Friesian) and 23 of their lambs aged 1 to 3 wk were fitted with activity monitors equipped with Bluetooth (BT) technology (ActiGraph wGT3X-BT) by means of halters and collars, respectively. The BT devices on lambs were programmed to receive wireless signals once every minute from nearby BT units on ewes, which were programmed as beacons sending BT signals 4 times every second. Ewes and lambs fitted with sensors were dispatched into the paddocks, and after 10 d, the sensor units were retrieved and the BT signals received by lambs were downloaded using the ActiGraph software. The maternal pedigree of the lambs was determined as the ewe from which the lamb received the most BT signals. The distance between the lamb receiving the signal and the ewe sending the signal was estimated from the strength of BT signal received. The pedigree determined by BT was compared with the pedigree determined by DNA profiling and verification. The results showed that the accuracy of maternal pedigree determined by BT signals reached 100% within the first 15 min of returning animals to pasture of ewes and lambs fitted with sensors. Maternal signals (counts/d) received by 1-, 2-, and 3-wk-old lambs were 617 ± 102, 603 ± 54, and 498 ± 36, respectively, and the corresponding nonmaternal signals received were 140 ± 27, 106 ± 30, and 155 ± 39, respectively. Maternal signals received during the dark period were significantly higher than the maternal signals received during the light period ( < 0.05). Maternal signals received during the light period by 3-wk-old lambs were significantly lower when compared with those received by 1- and 2-wk-old lambs. Over 90% of the BT signals received from within 2 m of the lamb were from its mother. The maternal BT signals expressed as a portion of total BT signals decreased with increasing distance from the lamb. The results show that BT wireless networking is a fast and reliable method for the determination of maternal pedigree of lambs in extensive farming systems. In addition, wireless BT technology is also useful in determining mother-offspring spatial relationships.

BACKGROUND: Cocoa, one of the richest dietary sources of polyphenols has been studied for its health promoting effects, but how long-term consumption of cocoa affects age-associated health and lifespan is not well defined. OBJECTIVE: The objective of this study was to determine the effects of long-term cocoa consumption on age-associated health and lifespan in C. elegans METHODS: The standard E. coli OP50 diet of wild type C. elegans was supplemented with cocoa powder starting from L1 stage until they die. Body length and area were measured as indicators of worm nutrition. Age associated health was determined at different stages of life as day 4, day 8 and day 12 using worm locomotion, thermotolerance, cognition and mitochondrial function. In addition, lifespan was evaluated. RESULTS: Cocoa improved age-associated decline in neuromuscular function. Both mean and median lifespan were extended by cocoa supplementation. However, maximum lifespan was not affected. Cocoa showed beneficial effects on thermotolerance at all ages (more prominent effects at young (day 4) and middle (day 8) age). Further, consumption of cocoa improved age-related learning deficits, short-term memory loss and mitochondrial dysfunction. CONCLUSIONS: Long-term cocoa consumption seemed to improve age-associated health and extends lifespan in C. elegans

The use of many psychotropic drugs (PDs) is associated with increased caloric intake, significant weight gain, and metabolic disorders. The nematode Caenorhabditis elegans (C. elegans) has been used to study the effects of PDs on food intake. However, little is known about PDs effects on the body fat of C. elegans . In C. elegans , feeding behavior and fat metabolism are regulated through independent mechanisms. This study aims to evaluate the body fat and food intake of C. elegans in response to treatment olanzapine and fluoxetine. Here we report that, with careful consideration to the dosage used, administration of fluoxetine and olanzapine increases body fat and food intake in C. elegans .

Context. Lamb loss and dyctocia are two major challenges in extensive farming systems. While visual observation can be impractical due to the large sizes of paddocks, number of animals and high labour cost, wearable sensors can be used to monitor the behaviour of ewes as there might be changes in their activities prior to lambing. This provides sufficient time for the farm manager to nurse those ewes that are at risk of dyctocia. Aim. The objective of this study was to determine whether the behaviour of a pregnant ewe could predict the time of parturition. Methods. Two separate trials were conducted: the first trial (T1), with 32 ewes, included human/video observations, and the second trial (T2), with 165 ewes, conducted with no humans present, to emulate real extensive farming settings. The ewes were fitted with tri-axial accelerometer sensors by means of halters. Three-dimensional movement data were collected for a period of at least 7 and 14 days in T1 and T2 respectively. The sensor units were retrieved, and their data downloaded using ActiGraph software. Ewe behaviour was determined through support vector machine learning (SVM) algorithm, including licking, grazing, rumination, walking, and idling. The behaviours of ewes predicted by analysis of sensor data were compared with behaviours determined using visual observation (video recordings), with time synchronisation to validate the results. Deep learning and neural-network algorithms were used to predict lambing time. Key results. The concordance percentages between visual observation and sensor data were 90 ± 11, 81 ± 15, 95 ± 10, 96 ± 6, and 93 ± 8% ± s.d. for grazing, licking, rumination, idling, and walking respectively. The deep-learning model predicted the time of lambing with 90% confidence via a quantile regression method, which can be interpereted as 90% prediction intervals, and shows that the time of lambing can be predicted with reasonable confidence approximately 240 h before the actual lambing events. Conclusion. It was possible to predict the time of parturition up to 10 days before lambing. Implications. The behaviour of ewes around lambing time has a direct effect on the survival of the lambs and therefore plays an important part in animal management. This knowledge could improve the productivity of sheep and considerably decrease lamb mortality rates.

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