Advantages of GPU-accelerated approach for solving the Parker equation in the heliosphere

The increasing of experimental observations' accuracy and model complexity requires the development of a new class of numerical solvers. In this work, we present a GPU-accelerated approach for solving the Parker equation in the heliosphere using a stochastic differential equation (SDE) approach. The presented method was applied to a generic system of SDE using the CUDA programming language. Our approach achieves significant speedup compared to a CPU implementation, allowing us to efficiently solve for the modulated spectra of charged particles in the heliosphere. We demonstrate the accuracy and efficiency of our method through numerical experiments on a realistic model of the heliosphere

Relationship between milk urea, blood plasma urea and body condition score in primiparous browsing goats with different milk yield level

Abstract. The aim of this study was to investigate the relationships among milk urea, blood plasma urea, milk yield and body condition score (BCS) in primiparous goats fed at pasture. Ninety goats of Sarda breed were used and, on the basis of their yield level, divided in three groups of 30 animals each, low (LY), intermediate (IY) and high milk yield (HY). Daily milk yield, milk protein content, milk urea, plasma total protein and albumin, plasma urea and BCS were measured at monthly intervals from 45 days in milking (45 DIM) to 165 DIM. Milk yield level affected protein concentration of milk and plasma, whereas albumin showed no variation. Plasma and milk urea showed a high correlation (P<0.001) despite of the yield level; plasma urea was always lower than milk urea. BCS decreased on 75 DIM and again after 135 DIM, and it was not affected by the milk yield level. Because milk urea and plasma urea were closely correlated and not influenced by the yield level, the study pointed out that measurement of milk urea could be utilized to evaluate urea metabolism also for browsing goats

Atorvastatin combined to interferon to verify the efficacy (ACTIVE) in relapsing-remitting active multiple sclerosis patients: a longitudinal controlled trial of combination therapy.

A large body of evidence suggests that, besides their cholesterol-lowering effect, statins exert anti-inflammatory action. Consequently, statins may have therapeutic potential in immune-mediated disorders such as multiple sclerosis. Our objectives were to determine safety, tolerability and efficacy of low-dose atorvastatin plus high-dose interferon beta-1a in multiple sclerosis patients responding poorly to interferon beta-1a alone. Relapsing–remitting multiple sclerosis patients, aged 18–50 years, with contrast-enhanced lesions or relapses while on therapy with interferon beta-1a 44 mg (three times weekly) for 12 months, were randomized to combination therapy (interferon+atorvastatin 20mg per day; group A) or interferon alone (group B) for 24 months. Patients underwent blood analysis and clinical assessment with the Expanded Disability Status Scale every 3 months, and brain gadolinium-enhanced magnetic resonance imaging at screening, and 12 and 24 months thereafter. Primary outcome measure was contrast-enhanced lesion number. Secondary outcome measures were number of relapses, EDSS variation and safety laboratory data. Forty-five patients were randomized to group A (n 1⁄4 21) or B (n 1⁄4 24). At 24 months, group A had significantly fewer contrast-enhanced lesions versus baseline (p 1⁄4 0.007) and significantly fewer relapses versus the two pre-randomization years (p < 0.001). At survival analysis, the risk for a 1-point EDSS increase was slightly higher in group B than in group A (p 1⁄4 0.053). Low-dose atorvastatin may be beneficial, as add-on therapy, in poor responders to high-dose interferon beta-1a alone

Predictive formulas for different measures of cheese yield using milk composition from individual goat samples

The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CYCURD), total solids (%CYSOLIDS), and water retained (%CYWATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R2VAL) and the root mean square error of validation (RMSEVAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R2VAL = 0.65, 0.96, and 0.23 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively). The WAV procedure showed R2VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CYCURD and %CYWATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production

Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk

The objectives of this study were to explore the use of Fourier-transform infrared (FITR) spectroscopy on 458 goat milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. Calibration equations were developed using a Bayesian approach with three different scenarios: i) a random cross-validation (CV) [80% calibration (CAL); 20% validation (VAL) set], ii) a stratified CV [(SCV), 13 farms used as CAL, and the remaining one as VAL set], and iii) a SCV where 20% of the goats randomly selected from the VAL farm were included in the CAL set (SCV80). The best prediction performance was obtained for cheese yield solids, justifying for its practical application at population level. Overall results were similar to or outperformed those reported for bovine milk. Our results suggest considering specific procedures for calibration development to propose reliable tools applicable along the dairy goat chain

Early Life Microbiota Colonization at Six Months of Age: A Transitional Time Point

Background: Early life gut microbiota is involved in several biological processes, particularly metabolism, immunity, and cognitive neurodevelopment. Perturbation in the infant's gut microbiota increases the risk for diseases in early and later life, highlighting the importance of understanding the connections between perinatal factors with early life microbial composition. The present research paper is aimed at exploring the prenatal and postnatal factors influencing the infant gut microbiota composition at six months of age. Methods: Gut microbiota of infants enrolled in the longitudinal, prospective, observational study "A.MA.MI" (Alimentazione MAmma e bambino nei primi MIlle giorni) was analyzed. We collected and analyzed 61 fecal samples at baseline (meconium, T0); at six months of age (T2), we collected and analyzed 53 fecal samples. Samples were grouped based on maternal and gestational weight factors, type of delivery, type of feeding, time of weaning, and presence/absence of older siblings. Alpha and beta diversities were evaluated to describe microbiota composition. Multivariate analyses were performed to understand the impact of the aforementioned factors on the infant's microbiota composition at six months of age. Results: Different clustering hypotheses have been tested to evaluate the impact of known metadata factors on the infant microbiota. Neither maternal body mass index nor gestational weight gain was able to determine significant differences in infant microbiota composition six months of age. Concerning the type of feeding, we observed a low alpha diversity in exclusive breastfed infants; conversely, non-exclusively breastfed infants reported an overgrowth of Ruminococcaceae and Flavonifractor. Furthermore, we did not find any statistically significant difference resulting from an early introduction of solid foods (before 4 months of age). Lastly, our sample showed a higher abundance of clostridial patterns in firstborn babies when compared to infants with older siblings in the family. Conclusion: Our findings showed that, at this stage of life, there is not a single factor able to affect in a distinct way the infants' gut microbiota development. Rather, there seems to be a complex multifactorial interaction between maternal and neonatal factors determining a unique microbial niche in the gastrointestinal tract

Prenatal and postnatal determinants in shaping offspring's microbiome in the first 1000 days: Study protocol and preliminary results at one month of life

Background: Fetal programming during in utero life defines the set point of physiological and metabolic responses that lead into adulthood; events happening in "the first 1,000 days" (from conception to 2-years of age), play a role in the development of non-communicable diseases (NCDs). The infant gut microbiome is a highly dynamic organ, which is sensitive to maternal and environmental factors and is one of the elements driving intergenerational NCDs' transmission. The A.MA.MI (Alimentazione MAmma e bambino nei primi MIlle giorni) project aims at investigating the correlation between several factors, from conception to the first year of life, and infant gut microbiome composition. We described the study design of the A.MA.MI study and presented some preliminary results. Methods: A.MA.MI is a longitudinal, prospective, observational study conducted on a group of mother-infant pairs (n = 60) attending the Neonatal Unit, Fondazione IRCCS Policlinico San Matteo, Pavia (Italy). The study was planned to provide data collected at T0, T1, T2 and T3, respectively before discharge, 1,6 and 12 months after birth. Maternal and infant anthropometric measurements were assessed at each time. Other variables evaluated were: Pre-pregnancy/gestational weight status (T0), maternal dietary habits/physical activity (T1-T3); infant medical history, type of feeding, antibiotics/probiotics/supplements use, environment exposures (e.g cigarette smoking, pets, environmental temperature) (T1-T3). Infant stool samples were planned to be collected at each time and analyzed using metagenomics 16S ribosomal RNA gene sequence-based methods. Results: Birth mode (cesarean section vs. vaginal delivery) and maternal pre pregnancy BMI (BMI &lt; 25 Kg/m2 vs. BMI ≥ 25 Kg/m2), significant differences were found at genera and species levels (T0). Concerning type of feeding (breastfed vs. formula-fed), gut microbiota composition differed significantly at genus and species level (T1). Conclusion: These preliminary and explorative results confirmed that pre-pregnancy, mode of delivery and infant factors likely impact infant microbiota composition at different levels. Trial registration: ClinicalTrials.gov identifier: NCT04122612

Goat farm variability affects milk Fourier-transform infrared spectra used for predicting coagulation properties

Peer-ReviewedDriven by the large amount of goat milk destined for cheese production, and to pioneer the goat cheese industry, the objective of this study was to assess the effect of farm in predicting goat milk-coagulation and curd-firmness traits via Fourier-transform infrared spectroscopy. Spectra from 452 Sarda goats belonging to 14 farms in central and southeast Sardinia (Italy) were collected. A Bayesian linear regression model was used, estimating all spectral wavelengths' effects simultaneously. Three traditional milk-coagulation properties [rennet coagulation time (min), time to curd firmness of 20 mm (min), and curd firmness 30 min after rennet addition (mm)] and 3 curd-firmness measures modeled over time [rennet coagulation time estimated according to curd firmness change over time (RCTeq), instant curd-firming rate constant, and asymptotical curd firmness] were considered. A stratified cross validation (SCV) was assigned, evaluating each farm separately (validation set; VAL) and keeping the remaining farms to train (calibration set) the statistical model. Moreover, a SCV, where 20% of the goats randomly taken (10 replicates per farm) from the VAL farm entered the calibration set, was also considered (SCV80). To assess model performance, coefficient of determination (R2VAL) and the root mean squared error of validation were recorded. The R2VAL varied between 0.14 and 0.45 (instant curd-firming rate constant and RCTeq, respectively), albeit the standard deviation was approximating half of the mean for all the traits. Although average results of the 2 SCV procedures were similar, in SCV80, the maximum R2VAL increased at about 15% across traits, with the highest observed for time to curd firmness of 20 mm (20%) and the lowest for RCTeq (6%). Further investigation evidenced important variability among farms, with R2VAL for some of them being close to 0. Our work outlined the importance of considering the effect of farm when developing Fourier-transform infrared spectroscopy prediction equations for coagulation and curd-firmness traits in goats.Università degli Studi di Sassar