Kit per audiometria vocale prassica

Un kit per audiometria vocale prassica comprende una pluralit\ue0 di oggetti aventi forma di anello ciascuno di un colore diverso scelto dal gruppo comprendente il giallo, il blu, il verde, il rosso e l\u2019arancione, una pluralit\ue0 di oggetti aventi forma diversa da un anello, ciascuno di un colore diverso scelto dal gruppo comprendente il giallo, il blu, il verde, il rosso e l\u2019arancione ed una serie di comandi (9) definenti una serie di azioni che un soggetto deve svolgere interagendo con detta pluralit\ue0 di oggetti a forma di anello e detta pluralit\ue0 di oggetti aventi forma diversa da un anello

VTMR, a new speech audiometry test with verbal tasks and motor responses

Objectives: The aim of this study was to design a complementary speech audiometry test using verbal tasks and motor responses (VTMR) to assess the ability of a subject to understand and perform simple motor tasks with 3-dimensional objects, to describe its construction, and to show the preliminary results of a pilot study on the Italian version of the test. Methods: The items used in the test setting included 1 base, 1 hammer, 1 wooden structure with 4 sticks, and 5 rings of different colors and 20 lists with 5 verbal tasks per list. The VTMR test and bisyllabic speech audiometry were evaluated in normal-hearing subjects with and without cognitive impairment and in subjects with sensorineural hearing loss. Results: All normal-hearing subjects without cognitive impairment performed the VTMR tasks (100%) correctly at 35 dB sound pressure level. In subjects with sensorineural hearing loss, the percentage of correct answers was significantly higher for the VTMR test than for bisyllabic speech audiometry above 50 dB sound pressure level. This percentage was higher for the VTMR also in normal-hearing subjects with poor cognitive skills. Conclusions: The VTMR might make it easier to check patients\u2019 ability to understand verbal commands than does traditional speech audiometry, in particular in those patients with poor test-taking skills

Use of the multivariate discriminant analysis for genome-wide association studies in cattle

Genome-wide association studies (GWAS) are traditionally carried out by using the single marker regression model that, if a small number of individuals is involved, often lead to very few associations. The Bayesian methods, such as BayesR, have obtained encouraging results when they are applied to the GWAS. However, these approaches, require that an a priori posterior inclusion probability threshold be fixed, thus arbitrarily affecting the obtained associations. To partially overcome these problems, a multivariate statistical algorithm was proposed. The basic idea was that animals with different phenotypic values of a specific trait share different allelic combinations for genes involved in its determinism. Three multivariate techniques were used to highlight the differences between the individuals assembled in high and low phenotype groups: the canonical discriminant analysis, the discriminant analysis and the stepwise discriminant analysis. The multivariate method was tested both on simulated and on real data. The results from the simulation study highlighted that the multivariate GWAS detected a greater number of true associated single nucleotide polymorphisms (SNPs) and Quantitative trait loci (QTLs) than the single marker model and the Bayesian approach. For example, with 3000 animals, the traditional GWAS highlighted only 29 significantly associated markers and 13 QTLs, whereas the multivariate method found 127 associated SNPs and 65 QTLs. The gap between the two approaches slowly decreased as the number of animals increased. The Bayesian method gave worse results than the other two. On average, with the real data, the multivariate GWAS found 108 associated markers for each trait under study and among them, around 63% SNPs were also found in the single marker approach. Among the top 118 associated markers, 76 SNPs harbored putative candidate genes

Genomic investigation of milk production in Italian buffalo

The aim of this study was to test the feasibility of genomic selection in the Italian Mediterranean water buffalo, which is farmed mainly in the south Italy for milk, and mozzarella, production. A total of 498 animals were genotyped at 49,164 loci. Test day records (80,417) of milk (MY), fat (FY) and protein (PY) yields from 4127 cows, born between 1975 and 2009, were analysed in a three-trait model. Cows born in 2008 and 2009 with phenotypes and genotypes were selected as validation animals (n = 50). Variance components (VC) were estimated with BLUP and ssGBLUP. Heritabilities for BLUP were 0.25 ± 0.02 (MY), 0.16 ± 0.01 (FY) and 0.25 ± 0.01 (PY). Breeding values were computed using BLUP and ssGBLUP, using VC estimated from BLUP. ssGBLUP was applied in five scenarios, each with a different number of genotypes available: (A) bulls (35); (B) validation cows (50); (C) bulls and validation cows (85); (D) all genotyped cows (463); (E) all genotypes (498). Model validation was performed using the LR method: correlation, accuracy, dispersion, and bias statistics were calculated. Average correlations were 0.71 ± 0.02 and 0.82 ± 0.01 for BLUP and ssGBLUP-E, respectively. Accuracies were also higher in ssGBLUP-E (0.75 ± 0.03) compared to BLUP (0.57 ± 0.03). The best dispersions (i.e. closer to 1) were found for ssGBLUP-C. The use of genotypes only for the 35 bulls did not change the validation values compared to BLUP. Results of the present study, even if based on small number of animals, showed that the inclusion of genotypes of females can improve breeding values accuracy in the Italian Buffalo.Highlights The genotypes of males did not improve the predictions. Genotypes of females improve breeding values accuracy. Slight increase in prediction accuracy with weighted ssGBLUP