Comparative characteristics of DNA polymorphisms of κ-casein gene (CSN3) in the horse and donkey

The aims of this study were to assess the genetic variability in the exon 1 of the κ-casein gene in four Italian horse populations (Italian Saddle horse, Italian Trotter, Italian Heavy Draught horse, and Murgese horse) and in a sample of Martina Franca donkey by estimating genotype, allele and haplotype frequencies, as well as several population genetic indices. Genotyping of the selected polymorphisms was performed using the PCR-RFLP technique with two restriction enzymes: PstI and BseYI aimed to discover the presence of c.-66A>G and c.-36C>A polymorphism, respectively. Both these loci were found to be polymorphic in horses with some differences depending on the breed. No genetic variability was observed in Martina Franca donkey breed. In the equine species no selective pressure for milk purpose was performed, therefore the polymorphisms at milk protein loci were mainly considered as result of natural selection or as indirect consequence of selection oriented to increase body size or to improve conformation. From this point of view these two single nucleotide polymorphisms and particularly the c.-36C>A one could be useful instruments for population studies

Analysis of a sequence nucleotide polymorphism of STAT5A gene in Garganica goat breed

STATs (signal transducer and activator of transcription) are a group of transcription factors that mediate actions of a variety of peptide hormones and cytokines within target cells (for example prolactin and growth hormone). In particular, STAT5A gene is a candidate marker for quantitative traits in farm animals. In this study, the STAT5A/Eco81I polymorphism was investigated with PCR-RFLP in a sample of Garganica goats. Garganica breed is an Italian goat breed that originates in the Gargano promontory, in Apulia region, by crossing the autochthonous population of goat with west European goats. Garganica breed show an exceptional ability to adapt to particularly difficult environments, as well as an extraordinary capacity to utilize poor pasture that would not otherwise be used. The investigated polymorphism is a substitution C→T at position 6852 within the exon 7 of the STAT5A gene. Only two out of three possible genotypes were identified in the population. The allelic frequencies of alleles C and T were 0.863 and 0.137 respectively and the population was kept in Hardy-Weinberg equilibrium. Moreover, some population genetic indices were also reported

Analysis of A Sequence Nucleotide Polymorphism of STAT5A Gene in Garganica Goat Breed

STATs (signal transducer and activator of transcription) are a group of transcription factors that mediate actions of a variety of peptide hormones and cytokines within target cells (for example prolactin and growth hormone). In particular, STAT5A gene is a candidate marker for quantitative traits in farm animals. In this study, the STAT5A/Eco81I polymorphism was investigated with PCR-RFLP in a sample of Garganica goats. Garganica breed is an Italian goat breed that originates in the Gargano promontory, in Apulia region, by crossing the autochthonous population of goat with west European goats. Garganica breed show an exceptional ability to adapt to particularly difficult environments, as well as an extraordinary capacity to utilize poor pasture that would not otherwise be used. The investigated polymorphism is a substitution C→T at position 6852 within the exon 7 of the STAT5A gene. Only two out of three possible genotypes were identified in the population. The allelic frequencies of alleles C and T were 0.863 and 0.137 respectively and the population was kept in Hardy-Weinberg equilibrium. Moreover, some population genetic indices were also reported

Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations

Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper, we present Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images

Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study

Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper we present GAN-DL, a Discriminator Learner based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images. We show that Wasserstein Generative Adversarial Networks combined with linear Support Vector Machines enable high-throughput compound screening based on raw images. We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines. In contrast to previous methods, our deep learning based approach does not require any annotation besides the one that is normally collected during the sample preparation process. We test our technique on the RxRx19a Sars-CoV-2 image collection. The dataset consists of fluorescent images that were generated to assess the ability of regulatory-approved or in late-stage clinical trials compound to modulate the in vitro infection from SARS-CoV-2 in both VERO and HRCE cell lines. We show that our technique can be exploited not only for classification tasks, but also to effectively derive a dose response curve for the tested treatments, in a self-supervised manner. Lastly, we demonstrate its generalization capabilities by successfully addressing a zero-shot learning task, consisting in the categorization of four different cell types of the RxRx1 fluorescent images collection

Predicting the 2000-m rowing ergometer performance from anthropometric, maximal oxygen uptake and 60-s mean power variables in national level young rowers

Many studies reported various relationships between 2000-m rowing performance and anthropometric as well as metabolic variables, however, little is known about 60-s mean power in elite youth athletes. The aim of this study was to develop different regression models to predict 2000-m rowing indoor performance time (t2000)using anthropometric variables, maximal oxygen uptake (VO2max) and mean power established during a 60-s all-out test (W60) in national elite youth rowers. Fifteen youth male Italian rowers (age: 15.7 \ub1 2.0 years; body height: 176.0 \ub1 8.0 cm; body mass: 71.2 \ub1 10.0 kg) performed an incremental maximal test, a 60-s all-out test and a 2000-m race simulation using a Concept2 rowing ergometer to assess VO2max, W60and t2000, respectively. The relationships of all variables with t2000 were investigated through Pearson\u2019s correlation. Multiple regression analyses were used to verify the best prediction model of 2000-m indoor rowing performance. The reliability of these models was expressed by R2 and the standard error of estimate. The results showed that t2000 was significantly correlated with all the examined variables, except for VO2max/body mass and age, and exhibited the significantly highest relationship with W60 (r = -0.943). The combination of anthropometric, VO2max and W60 variables was found to be the most reliable equation to predict t2000 (R2 = 0.94, SEE = 6.4). W60 measure should be considered when monitoring the rower\u2019s capability to perform high-intensity phases, important during the race\u2019s fast start and end. Not requiring expensive equipment and long duration, a 60-s all-out test could be considered a valuable tool for predicting 2000-m performance of elite youth rowers

Redox and autonomic responses to acute exercise-post recovery following Opuntia ficus-indica juice intake in physically active women

Background: The aim of this study was to investigate if the supplementation with Opuntia ficus-indica (OFI) juice may affect plasma redox balance and heart rate variability (HRV) parameters following a maximal effort test, in young physically active women. Methods: A randomized, double blind, placebo controlled and crossover study comprising eight women (23.25 ± 2.95 years, 54.13 ± 9.05 kg, 157.75 ± 0.66 cm and BMI of 21.69 ± 0.66 kg/m2) was carried out. A juice containing OFI diluted in water and a Placebo solution were supplied (170 ml; OFI = 50 ml of OFI juice + 120 ml of water; Placebo = 170 ml beverage without Vitamin C and indicaxanthin). Participants consumed the OFI juice or Placebo beverage every day for 3 days, before performing a maximal cycle ergometer test, and for 2 consecutive days after the test. Plasma hydroperoxides and total antioxidant capacity (PAT), Skin Carotenoid Score (SCS) and HRV variables (LF, HF, LF/HF and rMSSD) were recorded at different time points. Results: The OFI group showed significantly lower levels of hydroperoxides compared to the Placebo group in pretest, post-test and 48-h post-test. PAT values of the OFI group significantly increased compared to those of the Placebo group in pre-test and 48-h post-test. SCS did not differ between groups. LF was significantly lower in the OFI group 24-h after the end of the test, whereas rMSSD was significantly higher in the OFI group 48-h post-test. Conclusion: OFI supplementation decreased the oxidative stress induced by intense exercise and improved autonomic balance in physically active women

Relationship between wingate cycle test and 2000m rowing ergometer performance in youth athletes

During 2000m indoor rowing performances, the estimated aerobic and anaerobic contribution are 65-75% and 25-35%, respectively2. In considering that anaerobic power could be an important predictor of performance1, the aim of this study was to analyse the relationship between the power outputs during a Wingate anaerobic test (WAnT) on a cycling ergometer and a 2000m rowing ergometer performance in young rowers. In two separate days, 11 young (14.9±1.1yrs) male rowers performed a 2000m indoor rowing ergometer performance and a 30s WAnT on a cycling ergometer. WAnT peak power (PP) and mean power (MP), and 2000m time indoor rowing performance (t2000) were collected. Moreover, PP and MP were normalized with respect to body mass. Pearson correlation coefficients (r) were used to determine the association between t2000 and absolute and normalized PP and MP values. Absolute PP and MP were 888.1±133.2W and 548.5±74.4W, respectively. The relative picture for normalized values was 13.4±1.5 W·kg-1 and 8.2±0.6 W·kg-1. High associations emerged between t2000 (431.5±19.5s) and absolute PP (r=-0.900, P=0.05) values, whereas no significant relationship was observed for normalized PP (r=-0.585, P=0.058) and AP (r=-0.561, P=0.072) values. These findings indicate that PP and MP could be considered significant predictors of 2000m rowing ergometer performances, substantiating also the relevance of the anaerobic energy pathways to the 2,000m rowing performance