Electromagnetic radiation produced by avalanches in the magnetization reversal of Mn12-Acetate

Electromagnetic radiation produced by avalanches in the magnetization reversal of Mn12-Acetate has been measured. Short bursts of radiation have been detected, with intensity significantly exceeding the intensity of the black-body radiation from the sample. The model based upon superradiance from inversely populated spin levels has been suggested

Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

AbstractLameness causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods. Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2×2×80). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3 or 4/4) or not lame (score 1/4). Both models achieved sensitivity and specificity values around 80%, both in calibration and cross-validation. At the optimum values in the receiver operating characteristic curve, the false-positive rate was 28% in the parity 1 model, whereas in the parity 2 model it was about half (16%), which makes it more suitable for practical application; the model error rates were, 23 and 19%, respectively. Based on data registered automatically from one AMS farm, we were able to discriminate nonlame and lame cows, where partial least squares discriminant analysis achieved similar performance to the reference method

DATA FUSION APPROACHES IN SPECTROSCOPIC CHARACTERIZATION AND CLASSIFICATION OF PDO WINE VINEGARS

Spain is one of the major producers of high-quality wine vinegars having three protected designations of origin (a.k.a. PDOs): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". Their high prices due to their high quality and their high production costs explain the need for developing an adequate quality control technique and the interest in extensive characterization in order to capture the identity of each denomination. In this framework, methodologies based on non-targeted techniques, such as spectroscopies, are becoming popular in food authentication. Thus, for improving vinegar quality assessment, fusion of data blocks obtained from the same samples but different analytical techniques could be a good strategy, since the quantity and quality of sample knowledge could be enhanced providing new insights into the differentiation of vinegars. Therefore, the aim of this manuscript is the development of a multi-platform methodology and a model able to classify the Spanish wine vinegar PDOs. Sixty-five PDO wine vinegars were analyzed by four spectroscopic techniques: Fourier transform mid-infrared spectroscopy (MIR), near infrared spectroscopy (NIR), multidimensional fluorescence spectroscopy (EEM) and proton nuclear magnetic resonance (1H-NMR). Two different data fusion strategies were evaluated: Mid-level data fusion with different preprocessing, and Common Component and Specific Weights analysis multiblock method. Exploratory and classification analysis on the data from individual techniques were also performed and compared with data fusion models. The data fusion models improved the classification, providing a more efficient differentiation, than the models based on single methods, and supporting the approach to combine these methods to achieve synergies for an optimized PDO differentiation

Performance evaluation of deleteriousness prediction methods for intronic SNVs in next generation sequences

Introduction: Alterations in splicing sites (ss) are estimated to explain approximately 10% of human disease causal variants. Mutations outside the ss but affecting ?regulatory elements? can be up to 25%. Accurate deleteriousness prediction for intronic variants is crucial for diagnostic purposes. Many deleteriousness prediction methods have been developed, but their relative values are still unclear in practical applications. We comprehensively evaluated the predictive performance of two complementary deleteriousness-scoring methods using information from real patients. Material and Methods: We selected the dbscSNV (both ADA and RF scores) and SPIDEX algorithms, that study variants in splicing consensus regions or in regulatory regions respectively. The tools, either alone or in combination, were tested on 29294 gene intronic SNVs that have previously been characterised by ClinVar as either ?pathogenic? (430) or ?benign? (28864). The sensitivity, specificity and positive and negative predictive values were calculated. Moreover, we applied the algorithms to WES data from undiagnosed patients, and we analysed the mRNA sequence from genes that fitted the patient?s phenotype. Results: The highest sensitivity corresponds to dbscSNV with 96.55% while the best specificity is for SPIDEX with 95.78%. When considering the 3 scores (SPIDEX, dbscSNV ADA and RF Score), the sensitivity and specificity values were 60.7% and 94.6%. The Positive and Negative Predictive Value were 14.45% and 99.39%. The results for 20 undiagnosed cases are presented. Conclusions: Besides the low positive predictive value, the combination of both algorithms leads less than 1% of false negatives, so their routine use can be recommended for diagnostic purposes

Massive open star clusters using the VVV survey III: A young massive cluster at the far edge of the Galactic bar

Context: Young massive clusters are key to map the Milky Way's structure, and near-IR large area sky surveys have contributed strongly to the discovery of new obscured massive stellar clusters. Aims: We present the third article in a series of papers focused on young and massive clusters discovered in the VVV survey. This article is dedicated to the physical characterization of VVV CL086, using part of its OB-stellar population. Methods: We physically characterized the cluster using $JHK_S$ near-infrared photometry from ESO public survey VVV images, using the VVV-SkZ pipeline, and near-infrared $K$-band spectroscopy, following the methodology presented in the first article of the series. Results: Individual distances for two observed stars indicate that the cluster is located at the far edge of the Galactic bar. These stars, which are probable cluster members from the statistically field-star decontaminated CMD, have spectral types between O9 and B0V. According to our analysis, this young cluster ($1.0$ Myr $<$ age $< 5.0$ Myr) is located at a distance of $11^{+5}_{-6}$ kpc, and we estimate a lower limit for the cluster total mass of $(2.8^{+1.6}_{-1.4})\cdot10^3 {M}_{\odot}$. It is likely that the cluster contains even earlier and more massive stars.Comment: Accepted for publication as a Letter in A&

Experienced and inexperienced observers achieved relatively high within-observer agreement on video mobility scoring of dairy cows

AbstractAssessment of lameness prevalence and severity requires visual evaluation of thelocomotion of a cow. Welfare schemes including locomotion assessments are increasingly being adopted, and more farmers and their veterinarians might implement a locomotion-scoring routine together. However, high within-observer agreement is a prerequisite for obtaining valid mobility scorings, and within-observer agreement cannot be estimated in a barn, because the gait of cows is dynamic and may change between 2 occasions. The objective of this study was to estimate the within-observer agreement according to the observers’ educational background and experience with cattle, based on video recordings with very diverse types of gait. Groups of farmers, bovine veterinarians, first- and fourth-year veterinary students, researchers, and cattle-inexperienced sensory assessors evaluated mobility using a 5-point mobility score system developed specifically for walking cows (n=102 observers). The evaluation sessions were similar for all groups, lasted 75 min, and were organized as follows: introduction, test A, short training session, break, and test B. In total, video recordings of 22 cows were displayed twice in a random order (11 cows in each test × 2 replicates). Data were analyzed applying kappa coefficient, logistic regression, and testing for random effects of observers. The crude estimates of 95% confidence interval for weighted kappa in test A and B ranged, respectively, from 0.76 to 0.80 and 0.70 to 0.75. When adjusting for the fixed effects of video sample and gait scoring preferences, the probability of assigning the same mobility score twice to the same cow varied from 55% (sensory assessors) to 72% (fourth-year veterinary students). The random effect of the individual observers was negligible. That is, in general observers could categorize the mobility characteristics of cows quite well. Observers who preferred to assess the attributes back arch or the overall mobility score (based on uneven gait) had the highest agreement, respectively, 69 or 68%. The training session seemed insufficient to improve agreement. Nonetheless, even novice observers were able to achieve perfect agreement up to 60% of the 22 scorings with merely the experience obtained during the study (introduction and training session). The relatively small differences between groups, together with a high agreement, demonstrate that the new system is easy to follow compared with previously described scoring systems. The mobility score achieves sufficiently high within-observer repeatability to allow between-observer agreement estimates, which are reliable compared with other more-complex scoring systems. Consequently, the new scoring scale seems feasible for on-farm applications as a tool to monitor mobility within and between cows, for communication between farmers and veterinarians with diverse educational background, and for lamenessbenchmarking of herds

Effect of ZrO2 nanoparticles on thermophysical and rheological properties of three synthetic oils

This article presents an experimental study on some thermophysical properties (density, viscosity and adiabatic bulk modulus) of six nanolubricants based on synthetic oils and ZrO2 nanoparticles. Two-step method with ultrasonic disruptor was used to prepare the nanodispersions. The morphology, crystalline degree and elemental composition of nanoparticles were analyzed by electron microscopy. Visual observation, temporal variation of refractive index and dynamic light scattering were used to analyze the stability of the nanolubricants and the average size of the aggregates. The presence of new interactions between nanoparticles and base oils was studied through Fourier transform infrared spectrometer. Vibrating tube densimeters, rotational viscometer and rheometer equipped with cone-plate geometry were used within the temperature range from (278.15 to 373.15) K. The ability of some theoretical simple models to predict densities and viscosities of these nanolubricants as a function of temperature and nanoparticle concentration was also checked.This work was supported by Spanish Ministry of Economy and Competitiveness and the UE FEDER programme through ENE2014-55489-C2-1-R, ENE2014-55489-C2-2-R, ENE2017-86425- C2-1-R and ENE2017-86425-C2-2-R projects. Moreover, this work was funded by the Xunta de Galicia (AGRUP2015/11 and GRC ED431C 2016/001). D.C. was recipient of a postdoctoral fellowship from Xunta de Galicia (Spain).S