•Effect of seaweed and rapeseed feeding on bovine milk minerals was investigated.•Feeding seaweed increased milk I and reduced milk Ca and Co contents.•Feeding rapeseed reduced milk Mn and I ...contents.•Seaweed increases milk I but care is needed to avoid excess milk I content.•I supplementation is recommended in rapeseed-fed cows to maintain I content.
Sixteen multiparous Holstein cows in four blocks of 4 × 4 Latin square over 4-week experimental periods were used to study the effects of seaweed (Saccharina latissima) supplement (with/without) and protein source (rapeseed meal (RSM)/wheat distiller’s grain (WDG)) on milk mineral concentrations. Dietary treatments did not affect milk production and basic composition. Feeding seaweed slightly decreased milk Ca and Cu concentrations; whilst increased (by 3.3-fold) milk iodine (I) concentration, due to a higher dietary I supply. Substitution of WDG with RSM increased feed-to-milk transfer of Ca, Na, and Se and decreased that of Mg, P, Fe, and Mn; but only reduced milk Mn and I concentrations (the latter by 27 % as a potential result of increased glucosinolate intake). Seaweed supplement can improve milk I content when cows’ I supply/availability is limited, but care should be taken to avoid excess milk I contents that may pose nutritional risks for young children.
Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, ...early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy, autism spectrum disorders). Previously, novel wearable technology has shown promise for offering efficient, scalable and automated methods for movement assessment in adults. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscope data collection during movements. Using this suit, we first recorded play sessions of 22 typically developing infants of approximately 7 months of age. These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group. A machine learning algorithm, based on deep convolutional neural networks (CNNs) was then trained for automatic detection of posture and movement classes using the data and annotations. Our experiments show that the setup can be used for quantitative tracking of infant movement activities with a human equivalent accuracy, i.e., it meets the human inter-rater agreement levels in infant posture and movement classification. We also quantify the ambiguity of human observers in analyzing infant movements, and propose a method for utilizing this uncertainty for performance improvements in training of the automated classifier. Comparison of different sensor configurations also shows that four-limb recording leads to the best performance in posture and movement classification.
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