Elisabetta Manca

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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.

Porcine fat traits depend mostly on the interaction between nutritional and genetic factors. However, the pathways and biological processes influenced by this interaction are still poorly known in pigs, although they can have a huge impact on meat quality traits. The present research provides new knowledge insight into the effect of four diets (D1 = standard diet; D2 = linseed supplementation; D3 = linseed, vitamin E and selenium supplementation; D4 = linseed and plant-derived polyphenols supplementation) on the expression of 24 candidate genes selected for their role in lipid and energy metabolism. The data indicated that 10 out of 24 genes were differentially expressed among diets, namely ACACA, ADIPOQ, ADIPOR1, CHREBP (MLXPL), ELOVL6, FASN, G6PD, PLIN2, RXRA and SCD. Results from the univariate analysis displayed an increased expression of ACACA, ADIPOQ, ADIPOR1, CHREBP, ELOVL6, FASN, PLIN2, RXRA and SCD in D4 compared to D2. Similarly, ACACA, ADIPOQ, ADIPOR1, ELOVL6 and SCD were highly expressed in D4 compared to D3, while no differences were observed in D2-D3 comparison. Moreover, an increased expression of G6PD and ELOVL6 genes in D4 compared to D1 was observed. Results from the multivariate analysis confirmed that D2 was not different from D3 and that ACACA, SCD and FASN expression made D4 different from D2 and D3. Comparing D4 and D1, the expression levels of ELOVL6 and ACACA were the most influenced. This research provides evidence that the addition of both n-3 PUFA and polyphenols, derived from linseed, grape-skin and oregano supplementation in the diets, stimulates the expression of genes involved in lipogenesis and in oxidative processes. Results evidenced a greater effect on gene expression of the diet added with both plant extracts and n-3 PUFA, resulting in an increased expression of genes coding for fatty acid synthesis, desaturation and elongation in pig Longissimus thoracis muscle.

<h2>ABSTRACT</h2> Many of the metrics used to evaluate farm performance are only partial indicators of farm operations, which are assumed to be best predictors of the whole farm efficiency. The main objective of this work was to identify aggregated multiple indexes of profitability using common partial indicators that are routinely available from individual farms to better support the short-term decision-making processes of the cattle-feeding process. Data were collected from face-to-face interviews with farmers from 90 dairy farms in Italy and used to calculate 16 partial indicators that covered almost all indicators currently used to target feeding and economic efficiency in dairy farms. These partial indicators described feed efficiency, energy utilization, feed costs, milk-to-feed price ratio, income over feed costs, income equal feed cost, money-corrected milk, and bargaining power for feed costs. Calculations of feeding costs were based on lactating cows or the whole herd, and income from milk deliveries was determined with or without considering the milk quality payment. Multivariate factor analysis was then applied to the 16 partial indicators to determine simplified and latent structures. The results indicated that 5 factors explained 70% of the variability. Each of the original partial indicator was associated with all factors in different proportions, as indicated by loading scores from the multivariate factor analysis. Based on the loading scores, we labeled these 5 factors as "economic efficiency," "energy utilization," "break-even point," "milk-to-feed price," and "bargaining power of the farm," in decreasing order of explained communality. The first 3 factors shared 83% of the total communality. Feed efficiency was similarly associated with factor 1 (53% loading) and factor 2 (66% loading). Only factor 4 was significantly affected by farm location. Milk production and herd size had significant effects on factor 1 and factor 2. Our multivariate approach eliminated the problem of multicollinearity of partial indicators, providing simple and effective descriptions of farm feeding economics. The proposed method allowed the evaluation, benchmarking, and ranking of dairy herd performance at the level of single farms and at territorial level with high opportunity to be used or replicated in other areas.

$W$ bosons are produced at LHC from a forward-backward symmetric initial state. Their decay to a charged lepton and a neutrino has a strong spin analysing power. The combination of these effects results in characteristic distributions of the pseudorapidity of the leptons decaying from $W^+$ and $W^-$ of different helicity. This observation may open the possibility to measure precisely the $W^+$ and $W^-$ rapidity distributions for the two transverse polarisation states of $W$ bosons produced at small transverse momentum.

With the development of precision dairy farming, data on individual daily milk production are easily available in many herds. The aim of the present research was to develop an algorithm able to early detect dairy cows with a potential persistent lactation using daily production data. In this study, 2295 lactations belonging to primiparous (1015) and multiparous (1280) Holstein cows from two different farms equipped with the Afimilk system were used. Based on daily milk yield at 305 days in milk (DIM), animals were grouped into three production classes: low (LC) with milk yield <20 kg, middle (MC) with milk yield between 20 kg and 32 kg, and high (HC) with milk yield >32 kg, respectively. Lactations of MC or HC were considered as suitable for becoming long lactations. Four different models (Wood, Ali & Schaeffer, Legendre polynomials and 4th degree polynomials) were fitted to individual lactations by using the first 90, 120 and 150 DIM. Estimated model parameters were considered as variables in two multivariate discriminant techniques. The canonical discriminant analysis was used to test for possible differences between the extreme classes LC and HC. The discriminant analysis was performed to assign animals to the two production classes. The canonical discriminant analysis significantly separated LC from HC both for primiparous and multiparous cows. Among the different lactation models, the 4th degree polynomial was the most precise when the discriminant analysis was used to assign animals to the two production classes. In particular, by using the data of the first 150 DIM, the percentage of LC lactations incorrectly assigned to HC was 5% for primiparous and 7% for multiparous. Errors slightly increased when data of 120 (6% and 8% for primiparous and multiparous) and 90 (7% and 12% for primiparous and multiparous) DIM were used. The entire procedure could be automated by implementing, for example, the Afifarm’s report with a statistical computer software and it could be applied at farm level or using data from different associated farms. In practice, a historical database with previous complete lactations should be firstly created. As a new lactation proceeds, the recorded milk production data are fitted by using the 4th degree polynomial model and the estimated parameters submitted to the discriminant analysis. The lactation will be assigned to LC or HC.

Abstract The parton distribution functions (PDFs) of the proton play a role in determining the lineshape of Z and W bosons produced at the LHC. In particular, the mode of the gauge boson virtuality is shifted with respect to the pole due to the dependence of the partonic luminosity on the boson virtuality. The knowledge of this shift contributes to the systematic uncertainty for a direct measurement of the boson mass. A detailed study of the shift and of its systematic uncertainty due to the limited knowledge of the PDFs is obtained using a tree-level model of Z and W boson production in proton-proton collisions at $$\sqrt{s}=13~\hbox {TeV}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt><mml:mo>=</mml:mo><mml:mn>13</mml:mn><mml:mspace /><mml:mtext>TeV</mml:mtext></mml:mrow></mml:math> . A Monte Carlo simulation is further used to validate the tree-level model and study the dependence of the shift on the transverse momentum of the gauge bosons. The tree-level calculation is found to provide a good description of the shift. The systematic uncertainty on the lineshape due to the PDFs is estimated to be below one MeV in the phase-space relevant for a future high-precision mass measurement of the gauge boson masses at the LHC.

An index currently used to evaluate feed efficiency in cattle is the residual feed intake (RFI) whose heritability is around 0.20–0.40. Genome wide association studies (GWAS) can contribute to breeding programs aimed at improving RFI by detecting genomic regions and candidate genes that regulate it. However, the detection of significant SNP in GWAS with high density SNP platforms is often hampered by the severity of Bonferroni's p-value correction for multiple testing, due to huge number of tests. The pre-selection of markers could be an option to mitigate this problem. In the present research, a multivariate approach was used to select a pool of markers that could have any chances to be associated with RFI. Data consisted of 1092 Brown Swiss young bulls genotyped with the Illumina's 50K BeadChip. Animals were divided into two groups, according to RFI: high RFI (HRFI) for RFI > 0.5 standard deviations from the mean RFI; low RFI (LRFI) for animals with RFI < – 0.5 standard deviations from the mean. The two groups consisted of 266 and 280 animals, for LRFI and HRFI, respectively. Individuals that did not belong to the two groups were discarded.Three multivariate discriminant techniques were applied to data. The stepwise discriminant analysis was used to select 152 genome-wide most discriminant markers that were retained for the further analyses. The canonical discriminant analysis significantly separated the LRFI from the HRFI group, and the extracted canonical function was able to correctly assign 92% of animals to the correct group. Canonical coefficients associated to the 152 SNP in the canonical function were useful to rank markers according to their discriminant power. The ability of the selected SNP in depicting the RFI profile of calves was tested by developing a k-means cluster analysis that correctly classified 84% of individuals. For instance, a GWAS was also developed by regressing RFI phenotypes on SNP covariates. After p-values were corrected for multiple testing, no significant marker was obtained by using all original variables (41,183). When only the selected 152 SNP were used, 5 significant markers were obtained.

Exploiting the large number of collected events and the symmetries in the W production and decay at LHC, it is possible to measure the W double differential cross section of transverse momentum and rapidity and the angular coefficients. With the proposed method a determination of the integrated spectrum of transverse momentum of W bosons is possible with an unprecedented granularity and increased precision with respect to the state-of-the-art generators. This enables the possibility to perform a theory-agnostic measurement of the W mass with competitive uncertainty by constraining the W production directly on data. This paper shows preliminary results derived on a subset of events collected by CMS during Run 2, for an integrated luminosity of 36 fb-1.

The employment of fossil fuels is spread in a wide range of different sectors, such as the residential and the industrial ones, including the transportation as well, not only to generate power but also to generate heat or cold. This high dependency covers many aspects of the modern world in such a deep way that the exploitation of fossil sources has become huge. Nevertheless, it is well known that the combustion of fossil fuels gives as byproduct the emission of CO2 which in turn is reckoned to be the main cause of global warming. The climate has been submitted to more changes in the last few decades than during the whole life of the planet, with huge consequences for the environment. Given this framework, it is normal that the research has finally moved towards new and clean power generation devices. Among them, fuel cells are thought to potentially become a good alternative to traditional power generators, being able to exploit the oxidation of the fuel to produce directly electricity while being noiseless and clean. Among the different typologies of fuel cells, this work aims to analyze the so called Proton Exchange Membrane Fuel Cells (PEMFCs), whose name is given by the material which composes their electrolyte. The material is indeed formed by a polymeric membrane which is able to assure good mobility to the positive ions, while being impermeable to other species. In fact these cells, work through the oxidation of hydrogen and thus the separation of the electrons, whose flow gives electricity, and the protons that go through the membrane to react with oxygen and produce water as byproduct. In this way, the enthalpy of the oxidation of hydrogen to produce water is used to produce electricity, whilst no combustion occurs. One of the main features of these devices is that they need to work at low temperature, and thus they allow fast start-ups and shut-downs, which makes them ideal for automotive applications. The main goal of this work is indeed to analyze how the durability of the fuel cells is affected by the submission of a load that recalls the traffic conditions. This aspect is investigated through the performance of durability tests and the exploitation of the most typical fuel cells diagnostic tools: polarization curves and the electrochemical impedance spectroscopy.