Elucidating the role of endothelial αvβ3-integrin in tumour growth and angiogenesis

Angiogenesis, the formation of new vessels from pre-existing ones, is essential for primary tumour growth as well as for metastasis, and endothelial cells play a central role in this process: they drive blood vessel formation in response to signals from the local environment by a mechanism that is integrin-dependent. αvβ3-integrin seemingly poses an ideal anti-angiogenic target. Its expression is vastly up-regulated in neo-angiogenic vessels, while its expression in quiescent vasculature is minimal. However, anti-angiogenic therapy targeting αvβ3-integrin has proven somewhat disappointing. In part, this may relate to the fact that αvβ3-integrin is not expressed solely by endothelial cells, but across a wide range of cell types that each contribute to angiogenesis. In this thesis, I describe my studies on understanding the role of αvβ3-integrin as expressed specifically by endothelial cells in tumour growth and angiogenesis using endothelial specific β3-integrin deficient mice. I have shown that inducible deletion of endothelial β3-integrin inhibits tumour growth and angiogenesis preventatively, while its constitutive deletion is ineffective; furthermore, I have found that even the inducible deletion does not alter angiogenesis in already established tumours. The findings described in this thesis re-establish αvβ3-integrin as good antiangiogenic target, but imply that timing and length of inhibition are critical factors to be considered when targeting endothelial β3-integrin-expression

The Mathematical description of the lactation curve of ruminants: issues and perspectives

Test day records of milk production traits in the main livestock species reared for milk production were analyzed with several empirical mathematical models, with aim to identify suitable functions to fit different types of lactation curve shape. In the first chapter the modelling of extended lactations in dairy cows were analyzed. Several empirical mathematical models currently used to fit lactations with a function developed specifically for long patterns were compared. The second experimental contribution has approached a common problem when small ruminant lactation curves are modelled. In the specific case of the Sarda goat, the partial overlapping of altitude of location of flock with the partition into three different genetic subpopulation represents a further peculiarity. The third study was a investigation on the peculiar situation of the Massese breed ewes, where the intensification of reproductive cycle results in an alternate sequence of long and short lactations for each ewe. A frequent issue in modelling lactation curves is represented by relevant occurrence of atypical shapes, i.e. those that lacks of the peak yield and consist of a monotonically decreasing pattern

Multivariate meta-analysis of QTL mapping studies

A large number of quantitative trait loci (QTLs) for milk production and quality traits in dairy cattle has been reported in literature. The large amount of information available could be exploited by meta-analyses to draw more general conclusions from results obtained in different experimental conditions (animals, statistical methodologies). QTL meta-analyses have been carried out to estimate the distribution of QTL effects in livestock and to find consensus on QTL position. In this study, multivariate dimension reduction techniques are used to analyse a database of dairy cattle QTL published results, in order to extract latent variables able to characterise the research. A total of 92 papers by 72 authors were found on 25 scientific Journals for the period January 1995-February 2008. More than thirty parameters were picked up from the articles. To overcome the problem of different map location, the flanking markers were mapped on release 4.1 of the Bos taurus genome sequence (www.ensembl. org). Their position was retrieved from public databases and, when absent, was calculated in silico by blasting (http://blast.wustl.edu/) the markers’ nucleotide sequence against the genomic sequence. Records were discarded if flanking markers or P-values were not available. After these edits, the final archive consisted of 1,162 records. Seven selected variables were analysed both with the Factor Analysis (FA), combined with the varimax rotation technique, and Principal Component Analysis (PCA). FA was able to explain 68% of the original variability with 3 latent factors: the first factor extracted was highly associated (factor loading of 0.98) to marker location along the chromosome and could be considered as a marker map index; the second factor showed factor loadings of 0.74 and 0.84 related to the variable number of animals involved and year of the experiment, respectively, and it can be regarded as an indicator of the dimension of the study; the third factor was correlated to the significance level of the statistical test (0.78), number of families (0.63), and, negatively, to the marker density (-0.43). It can be named as index of power of the experiment. Same patterns can be observed in the eigenvectors of PCA. Four PCs were able to explain about 80% of the original variance. The first two PCs basically underlined accurately the same structure found with the first two factors in FA, whereas PC3 and PC4 summarized the structure of F3. The score that each QTL gets on each Factor or PC could be useful to classify the original QTL records and make them more comparable once that the redundancy of information has been removed

The Mathematical description of lactation curves in dairy cattle

This review gives an overview of the mathematical modelling of lactation curves in dairy cattle. Over the last ninety years, the development of this field of study has followed the main requirements of the dairy cattle industry. Non-linear parametric functions have represented the preferred tools for modelling average curves of homogeneous groups of animals, with the main aim of predicting yields for management purposes. The increased availability of records per individual lactations and the genetic evaluation based on test day records has shifted the interest of modellers towards more flexible and general linear functions, as polynomials or splines. Thus the main interest of modelling is no longer the reconstruction of the general pattern of the phenomenon but the fitting of individual deviations from an average curve. Other specific approaches based on the modelling of the correlation structure of test day records within lactation, such as mixed linear models or principal component analysis, have been used to test the statistical significance of fixed effects in dairy experiments or to create new variables expressing main lactation curve traits. The adequacy of a model is not an absolute requisite, because it has to be assessed according to the specific purpose it is used for. Occurrence of extended lactations and of new productive and functional traits to be described and the increase of records coming from automatic milking systems likely will represent some of the future challenges for the mathematical modelling of the lactation curve in dairy cattle

Pengaruh Model Pembelajaran Predict, Observe and Explain Terhadap Hasil Belajar Siswa Kelas VII SMPN 15 Sigi

Penelitian ini dilakukan dengan tujuan untuk mengetahui pengaruh model pembelajaran Predict, Observe and Explain terhadap hasil belajar siswa kelas VII  SMP Negeri 15 Sigi. Jenis penelitian ini merupakan eksperimen kuasi dengan desain non randomized pretest-posttest control group design. Populasi penelitian ini adalah seluruh siswa kelas VII SMP Negeri 15 Sigi. Teknik Sampling yang digunakan pada penelitian ini adalah Purposive Sampling dengan sampel penelitian adalah kelas VII A sebagai kelompok eksperimen dan kelas VII C sebagai kelompok kontrol. Instrumen hasil belajar siswa berupa tes pilihan ganda yang telah divalidasi melalui validitas kontruksi. Hasil belajar siswa menunjukkan bahwa skor rata-rata kelompok eksperimen sebesar 16,5 dan kelompok kontrol sebesar 12,66. Analisa data Uji Hipotesis menggunakan Uji-t dua pihak, diperoleh  = 4,03 dan  = 2,02 pada taraf nyata α = 0,05. Ini berarti bahwa nilai  berada diluar daerah penerimaan . Sehingga dapat disimpulkan bahwa ada pengaruh model pembelajaran Predict, Observe and Explain terhadap hasil belajar siswa kelas VII  SMP Negeri 15 Sigi

Modelling extended lactation curves for milk production traits in Italian Holsteins

Test day records of milk production traits (milk yield, fat and protein percentage, and somatic cell score) of 45,132 Italian Holstein cows were analyzed with seven mathematical models in order to assess the main features of lactations of different length. Lactations curves were grouped according to parity (1, 2, and 3) and lactation length (1<350d; 2=from 351 to 450d; 3=from 451 to 650d; 4=651 to 1000d). Models with a larger number of parameters showed better fitting performances for all classes of length for milk yield, whereas poor fitting was observed for fat and protein percentages and SCS in the 651-1000d class. In lactation with length>650d, peak yield was about 31, 37, and 39 kg for first, second, and third parity respectively; peak was predicted at around 60 and 40 days for younger and older animals respectively. The asymptotic level of production was below 10 kg

Movements assessment and analysis for IMU based wearable networks

Movements assessment and analysis based on Inertial Measurement Units (IMUs) is becoming a common feature for many kinds of applications, such as industrial, videogames, sports and medical. The approach predominantly used is the processing of sensors output as a waveform in which a pattern can be recognized or the initial and the final positions of the movement detected. Not only does this approach lack in precision of the measurement but also yields results that are not readily understandable, especially visually. On the other hand, visual interaction alone can be restricted by the difficulties in capturing some body positions and evaluating them. Using six degrees of freedom IMUs and similarly to techniques that rely on optical devices, but obviously without employing them, we tried to view the sensors output as a representation of the movement in space, in order to obtain the actual trajectory of the whole movement. We also gave a suitable representation of the movements and efficient analysis and comparison techniques for evaluating them. In this thesis we will describe how our approach was realized, the experiments performed and the solutions proposed, even comparing them with other solutions proposed in literature. ----------------------------------------------------------------------------------------------------- La valutazione e l’analisi di movimenti basata su dispositivi di misurazione inerziale (IMUs) sta diventando caratteristica comune in molti tipi di impieghi, come quelli industriali, video-ludici, sportivi e medici. L’approccio maggiormente utilizzato è il processamento dell’output dei sensori visto come una forma d’onda nella quale riconoscere un pattern o individuare le pose iniziali e finali del movimento. Questo approccio non solo è carente nella precisione della misurazione ma fornisce risultati di difficile lettura e comprensione, specialmente dal punto di vista visivo. D’altro canto la sola interazione visuale può essere limitata dalla difficoltà nel rilevare alcune posizioni del corpo e quindi valutarle. Utilizzando IMUs a sei gradi di libertà e in maniera simile alle tecniche basate su dispositivi ottici, ma ovviamente senza impiegarli, abbiamo cercato di interpretate l’output dei sensori come rappresentazione del movimento nello spazio, al fine di ottenere la reale traiettoria dell’intero movimento. Inoltre, abbiamo fornito un’adeguata rappresentazione dei movimenti e efficienti tecniche per la loro analisi e il loro confronto. In questa tesi descriveremo come il nostro approccio è stato realizzato, gli esperimenti condotti e le soluzioni proposte, effettuando anche dei confronti con altre soluzioni proposte in letteratura

Analysis of genetic correlations between multivariate measures of lactation persistency and somatic cell score in Italian Simmental cattle

Genetic relationships between lactation curve traits and Somatic Cell Count are of great interest for dairy cattle breeding. Factor Analysis (MFA) and Principal Component Analysis (PCA) can be used to extract from the correlation matrix of milk test day records new unobservable (latent) variables that can be related to lactation curve shape. Previous researches report that MFA is particularly able to extract two latent variables related with level of production in early lactation (PEL) and lactation persistency (PERS), respectively, whereas PCA yields a leading component related to the average level of production (AVY) for the whole lactation and a second component negatively related with tests of early lactation and positively with tests of the second part of lactation (SLOPE). Aim of this work was to estimate genetic correlations between lactation curve shape traits and Somatic Cell Score (SCS). MFA and PCA were carried out on a data set of 16,020 lactations of Italian Simmental cows, each with six TD records for milk yield recorded with the A4 scheme. Genetic parameters were estimated with a bivariate animal model that included fixed effects of herd-test date, parity*age*lactation stage (only parity*age for lactation curve traits), calving season, and random effects of additive genetic and permanent environment. Heritability estimates were moderate for lactation curve traits (0.15, 0.15, 0.21 and 0.09 for PEL, PERS, AVY and SLOPE, respectively) and low for SCS (0.09). Correlations between lactation curve traits and SCS were favourable, i.e. negative, except for the level of production in early lactation. In particular, the genetic improvement of lactation persistency result in a contemporary reduction of SCS (rg -0.55 and -0.51 with PERS and SLOPE, respectively) whereas the increase of level of production in early lactation can lead to a moderate increase of SCS (rg 0.13). Finally, the two measures of persistency could be used for different selection strategies: the use of PERS may allow for the increase of persistency together with the total lactation yield whereas the use of SLOPE may result in an improvement of the lactation curve shape without modifying total lactation yield

Kernel learning for ligand-based virtual screening: discovery of a new PPARgamma agonist

Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results