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Among the dairy sector's current concerns, the as-sessment of global animal health status is a complex challenge. Its multidimensionality means that global monitoring tools are rarely considered. Instead, specific disease detection is often studied separately and, due to financial and ethical issues, uses small-scale data sets focusing on few biomarkers. Several studies have already been conducted using milk Fourier transform mid-infrared (FT-MIR) spectroscopy to detect mastitis and lameness or to quantify health-related biomarkers in milk or blood. Those studies are relevant but they focus mainly on one biomarker or disease. To solve this issue and the small-scale data set, in this study, we proposed a holistic approach using big data obtained from milk recording, including milk yield, somatic cell count, and 27 FT-MIR-based predictors related to milk composition and animal health status. Using 740,454 records collected from 114,536 first-parity Holstein cows in southern Belgium, we performed repeated unsupervised learning algorithms based on Ward's agglomerative hierarchical clustering method to find potential interesting patterns. A divide-and-conquer approach was used to overcome the limitation of computational resources in clustering a relatively large data set. Five groups of records were identified. Differences observed in the fourth group suggested a relationship to metabolic disorders. The fifth group seemed to be related to mastitis. In a second step, we performed a partial least squares discriminant analysis (PLS-DA) to predict the probability of belonging to those specific groups for the entire data set. The obtained global ac-curacy was 0.77 and the balanced accuracy (i.e., the mean between sensitivity and specificity) of discrimi-nating the fourth and fifth groups was 0.88 and 0.96, respectively. Then, a validation of the interpretation of those groups was performed using 204 milk and blood reference records. The predicted probability associated with the metabolic disorders issue had significant cor-relations of 0.54 with blood beta-hydroxybutyrate, 0.44 with blood nonesterified fatty acids, -0.32 with blood glucose, -0.23 with milk glucose-6-phosphate, and 0.38 with milk isocitrate. In contrast, the predicted prob-ability of belonging to the mastitis group had correla-tions of 0.69 with milk lactate dehydrogenase, 0.46 with milk N-acetyl-beta-D-glucosaminidase, -0.18 with milk free glucose, and 0.16 with milk glucose-6-phosphate. Consequently, these results suggest that the obtained quantitative traits indirectly reflect some of the main health disorders in dairy farming and could be used to monitor dairy cows on a large scale. By using unsuper-vised learning on large-scale milk recording data and then validating the pattern using reference laboratory measures, we propose a new approach to quickly assess dairy cow health status.

Introduction Hippocampal volume is a core biomarker of Alzheimer's disease (AD). However, its contribution over the standard diagnostic workup is unclear. Methods Three hundred fifty-six patients, under clinical evaluation for cognitive impairment, with suspected AD and Mini–Mental State Examination ≥20, were recruited across 17 European memory clinics. After the traditional diagnostic workup, diagnostic confidence of AD pathology (DCAD) was estimated by the physicians in charge. The latter were provided with the results of automated hippocampal volumetry in standardized format and DCAD was reassessed. Results An increment of one interquartile range in hippocampal volume was associated with a mean change of DCAD of −8.0% (95% credible interval: [−11.5, −5.0]). Automated hippocampal volumetry showed a statistically significant impact on DCAD beyond the contributions of neuropsychology, 18F-fluorodeoxyglucose positron emission tomography/single-photon emission computed tomography, and cerebrospinal fluid markers (−8.5, CrI: [−11.5, −5.6]; −14.1, CrI: [−19.3, −8.8]; −10.6, CrI: [−14.6, −6.1], respectively). Discussion There is a measurable effect of hippocampal volume on DCAD even when used on top of the traditional diagnostic workup. © 2017 the Alzheimer's Association

Metabolic diseases, such as ketosis and milk fever, are among the most common diseases affecting dairy cattle. Genetic improvement of ability to resist metabolic diseases can be achieved by direct selection with genetic evaluation based on clinically observed traits, or by indirect selection based on indicators or predictors of metabolic diseases. The most prevalent metabolic diseases in dairy cattle, for which genetic parameters have been published, are ketosis, displaced abomasum, milk fever, and tetany. In this review we present genetic parameters for these metabolic diseases, give a status of genetic and genomic evaluations, and discuss possible indicator traits.

Body condition score (BCS) is a subjective measure of the amount of metabolizable energy stored in a live animal. Change in BCS of dairy cows is considered to be an indicator of the extent and the duration of postpartum negative energy balance. Although change in BCS over lactation is lowly heritable, heritability estimates of level of BCS range from 0.20 to 0.50. Also, BCS tends to be more heritable in mid-lactation indicating that genetic differences are more related to how well cows recover from the negative energy balance state. BCS measurements are generally highly correlated within and between lactations. Genetic correlations with BCS are unfavorable for milk, fat, and protein yield, suggesting that genetically superior producers tend to have lower BCS, especially during the lactation. Genetic correlations are generally moderate and favorable with fertility indicating that cows with higher levels of BCS would have a greater chance to conceive after insemination and fewer number of days when not pregnant. Because direct selection to improve fertility might be complicated by several factors, selection for higher levels of BCS, especially in mid-lactation, appears to be a good option to indirectly improve fertility in dairy cows.

OBJECTIVE: The most widely studied positron emission tomography ligand for in vivo beta-amyloid imaging is (11)C-Pittsburgh compound B ((11)C-PIB). Its availability, however, is limited by the need for an on-site cyclotron. Validation of the (18)F-labeled PIB derivative (18)F-flutemetamol could significantly enhance access to this novel technology. METHODS: Twenty-seven patients with early-stage clinically probable Alzheimer disease (AD), 20 with amnestic mild cognitive impairment (MCI), and 15 cognitively intact healthy volunteers (HVs) above and 10 HVs below 55 years of age participated. The primary endpoint was the efficacy of blinded visual assessments of (18)F-flutemetamol scans in assigning subjects to a raised versus normal uptake category, with clinical diagnosis as the standard of truth (SOT). As secondary objectives, we determined the correlation between the regional standardized uptake value ratios (SUVRs) for (18)F-flutemetamol and its parent molecule (11)C-PIB in 20 of the AD subjects and 20 of the MCI patients. We also determined test-retest variability of (18)F-flutemetamol SUVRs in 5 of the AD subjects. RESULTS: Blinded visual assessments of (18)F-flutemetamol scans assigned 25 of 27 scans from AD subjects and 1 of 15 scans from the elderly HVs to the raised category, corresponding to a sensitivity of 93.1% and a specificity of 93.3% against the SOT. Correlation coefficients between cortical (18)F-flutemetamol SUVRs and (11)C-PIB SUVRs ranged from 0.89 to 0.92. Test-retest variabilities of regional SUVRs were 1 to 4%. INTERPRETATION: (18)F-Flutemetamol performs similarly to the (11)C-PIB parent molecule within the same subjects and provides high test-retest replicability and potentially much wider accessibility for clinical and research use.

Precise brain regions are activated when a subject gives a judgment on himself. Those are the medial parietal cortex, essentially related to episodic memory processing, and the ventromedial prefrontal cortex, recruited for evaluating the personal valence of an information. These regions are not activated in Alzheimer's disease. The decrease of awareness for own deficits in a patient with Alzheimer's disease would depend on a reduction of episodic memory capacities and a worsening of judgment for self significance.