Use of a partial least squares regression model to predict Test Day of milk, fat and protein yields in dairy goats

A model able to predict missing test day data for milk, fat and protein yields on the basis of few recorded tests was proposed, based on the partial least squares (PLS) regression technique, a multivariate method that is able to solve problems related to high collinearity among predictors. A data set of 1731 lactations of Sarda breed dairy Goats was split into two data sets, one for model estimation and the other for the evaluation of PLS prediction capability. Eight scenarios of simplified recording schemes for fat and protein yields were simulated. Correlations among predicted and observed test day yields were quite high (from 050 to 088 and from 053 to 096 for fat and protein yields, respectively, in the different scenarios). Results highlight great flexibility and accuracy of this multivariate technique

Effect of normalisation on detection of differentially expressed genes in cDNA microarray data analysis

Four different normalisation techniques were applied for the corrections of fluorescence data generated by a cDNA microarray experiment. Correction for inaccurate signals and possible bias induced by fluorescence intensity, background intensity and dye effect were used in different combinations. Results of the present study highlight a pronounced role for the normalisation techniques in the absolute number of genes different expressed and a low concordance between different methods. Moreover, a significant effect of the dependent variable used, mean or median fluorescence intensity, was observed

Effect of normalisation on detection of differentially expressed genes in cDNA microarray data analysis

Four different normalisation techniques were applied for the corrections of fluorescence data generated by a cDNA microarray experiment. Correction for inaccurate signals and possible bias induced by fluorescence intensity, background intensity and dye effect were used in different combinations. Results of the present study highlight a pronounced role for the normalisation techniques in the absolute number of genes different expressed and a low concordance between different methods. Moreover, a significant effect of the dependent variable used, mean or median fluorescence intensity, was observed

QTL detection for a medium density SNP panel: comparison of different LD and LA methods

Background: New molecular technologies allow high throughput genotyping for QTL mapping with dense genetic maps. Therefore, the interest of linkage analysis models against linkage disequilibrium could be questioned. As these two strategies are very sensitive to marker density, experimental design structures, linkage disequilibrium extent and QTL effect, we propose to investigate these parameters effects on QTL detection.[br/] Methods: The XIIIth QTLMAS workshop simulated dataset was analysed using three linkage disequilibrium models and a linkage analysis model. Interval mapping, multivariate and interaction between QTL analyses were performed using QTLMAP.[br/] Results: The linkage analysis models identified 13 QTL, from which 10 mapped close of the 18 which were simulated and three other positions being falsely mapped as containing a QTL. Most of the QTLs identified by interval mapping analysis are not clearly detected by any linkage disequilibrium model. In addition, QTL effects are evolving during the time which was not observed using the linkage disequilibrium models.[br/] Conclusions: Our results show that for such a marker density the interval mapping strategy is still better than using the linkage disequilibrium only. While the experimental design structure gives a lot of power to both approaches, the marker density and informativity clearly affect linkage disequilibrium efficiency for QTL detection

Fit of different linear models to the lactation curve of Italian water buffalo

Mathematical modelling of lactation curve by suitable functions of time, widely used in the dairy cattle industry, can represent also for buffaloes a fundamental tool for management and breeding decision, where average curves are considered, and for genetic evaluation by random regression models, where individual patterns are fitted. Average lactation curves of Italian Buffalo cows have been fitted with good results (Catillo et al., 2002) whereas there is a lack of information on individual fitting.Aim of the present work is to check performances of some of the most currently used empirical models in fitting both average and individual lactation curves of Italian water buffaloes

Effects of Hybrid and Maturity Stage on in Vitro Rumen Digestibility of Immature Corn Grain

This study aimed to evaluate the influences of hybrids (HYB) and maturity stage (SAMP) on in vitro rumen digestibility of immature corn grain. Four HYB (Gigantic, Y43, Klips and 9575) from the FAO group 700 were grown under identical agronomic conditions. First sampling (T1) was done after 95 days from seedling and then 4, 8, 13, 18 and 27 days later (T2 to T6). In vitro starch digestibility (STD_7h) and gas production (72 h) were measured. Whole plant and grain dry matter (WP_DM and GR_DM, respectively) and zein content were significantly affected (P<0.01) by HYB and SAMP. Starch content was significantly affected by HYB, SAMP and their interaction. It increased from T1 to T4 (from 67.47 to 72.82% of GR_DM) and then tended to plateau. Concurrently, STD_7h significantly decreased with advancing SAMP and was also affected by HYB. With advancing maturity, total volatile fatty acids (VFA) significantly decreased, with an increase of acetate and a decrease of propionate molar proportion (P<0.01). Gas production rate (GP_c) was significantly affected by HYB, SAMP and HYB×SAMP. Whole plant grain DM correlated (P<0.01) positively with grain starch content (r=0.60 and 0.64) but negatively with STD_7h (r=-0.39 and r=-0.63) and VFA concentration (r=-0.59 and -0.75). Zein percentage in crude protein negatively affected (P<0.01) total DM (r=-0.65,), STD_7h (r=-0.73) and GP_c (r=- 0.68). Results suggest that genotypes and maturity stages influence DM and rumen starch digestibility of immature corn grain and in this respect zein can play a significant role

RRAML: Reinforced Retrieval Augmented Machine Learning

The emergence of large language models (LLMs) has revolutionized machine learning and related fields, showcasing remarkable abilities in comprehending, generating, and manipulating human language. However, their conventional usage through API-based text prompt submissions imposes certain limitations in terms of context constraints and external source availability. To address these challenges, we propose a novel framework called Reinforced Retrieval Augmented Machine Learning (RRAML). RRAML integrates the reasoning capabilities of LLMs with supporting information retrieved by a purpose-built retriever from a vast user-provided database. By leveraging recent advancements in reinforcement learning, our method effectively addresses several critical challenges. Firstly, it circumvents the need for accessing LLM gradients. Secondly, our method alleviates the burden of retraining LLMs for specific tasks, as it is often impractical or impossible due to restricted access to the model and the computational intensity involved. Additionally we seamlessly link the retriever's task with the reasoner, mitigating hallucinations and reducing irrelevant, and potentially damaging retrieved documents. We believe that the research agenda outlined in this paper has the potential to profoundly impact the field of AI, democratizing access to and utilization of LLMs for a wide range of entities

Service discovery and negotiation with COWS

To provide formal foundations to current (web) services technologies, we put forward using COWS, a process calculus for specifying, combining and analysing services, as a uniform formalism for modelling all the relevant phases of the life cycle of service-oriented applications, such as publication, discovery, negotiation, deployment and execution. In this paper, we show that constraints and operations on them can be smoothly incorporated in COWS, and propose a disciplined way to model multisets of constraints and to manipulate them through appropriate interaction protocols. Therefore, we demonstrate that also QoS requirement specifications and SLA achievements, and the phases of dynamic service discovery and negotiation can be comfortably modelled in COWS. We illustrate our approach through a scenario for a service-based web hosting provider

A Calculus for Orchestration of Web Services

Service-oriented computing, an emerging paradigm for distributed computing based on the use of services, is calling for the development of tools and techniques to build safe and trustworthy systems, and to analyse their behaviour. Therefore, many researchers have proposed to use process calculi, a cornerstone of current foundational research on specification and analysis of concurrent, reactive, and distributed systems. In this paper, we follow this approach and introduce CWS, a process calculus expressly designed for specifying and combining service-oriented applications, while modelling their dynamic behaviour. We show that CWS can model all the phases of the life cycle of service-oriented applications, such as publication, discovery, negotiation, orchestration, deployment, reconfiguration and execution. We illustrate the specification style that CWS supports by means of a large case study from the automotive domain and a number of more specific examples drawn from it