Relationships between running economy and mechanics in middle-distance runners

Running economy (RE), i.e. the oxygen consumption at a given submaximal speed, is an important determinant of endurance running performance. So far, investigators have widely attempted to individuate the factors affecting RE in competitive athletes, focusing mainly on the relationships between RE and running biomechanics. However, the current results are inconsistent and a clear mechanical profile of an economic runner has not been yet established. The present work aimed to better understand how the running technique influences RE in sub-elite middle-distance runners by investigating the biomechanical parameters acting on RE and the underlying mechanisms. Special emphasis was given to accounting for intra-individual variability in RE at different speeds and to assessing track running rather than treadmill running. In Study One, a factor analysis was used to reduce the 30 considered mechanical parameters to few global descriptors of the running mechanics. Then, a biomechanical comparison between economic and non economic runners and a multiple regression analysis (with RE as criterion variable and mechanical indices as independent variables) were performed. It was found that a better RE was associated to higher knee and ankle flexion in the support phase, and that the combination of seven individuated mechanical measures explains ∼72% of the variability in RE. In Study Two, a mathematical model predicting RE a priori from the rate of force production, originally developed and used in the field of comparative biology, was adapted and tested in competitive athletes. The model showed a very good fit (R2=0.86). In conclusion, the results of this dissertation suggest that the very complex interrelationships among the mechanical parameters affecting RE may be successfully dealt with through multivariate statistical analyses and the application of theoretical mathematical models. Thanks to these results, coaches are provided with useful tools to assess the biomechanical profile of their athletes. Thus, individual weaknesses in the running technique may be identified and removed, with the ultimate goal to improve RE.

Mitochondrial dysfunction in hereditary optic neuropathies

MITOCHONDRIAL DYSFUNCTION IN HEREDITARY OPTIC NEUROPATHIES Mitochondrial pathologies are a heterogeneous group of clinical manifestations characterized by oxidative phosphorylation impairment. At the beginning of their recognition mitochondrial pathologies were regarded as rare disorders but indeed they are more frequent than originally thought. Due to the unique mitochondria peculiarities mitochondrial pathologies can be caused by mutations in both mitochondrial and nuclear genomes. The poor knowledge of pathologic mechanism of these disorders has not allowed a real development of the “mitochondrial medicine”, that is currently limited to symptoms mitigation. Leber hereditary optic neuropathy (LHON) was the first pathology to be linked to a point mutation in the mtDNA. The mechanism by which point mutations in mitochondrial gene encoding Complex I subunits leads to optic nerve degeneration is still unknown, although is well accepted that other genetic or environmental factors are involved in the modulation of pathology, where a pivotal role is certainly played by oxidative stress. We studied the relationship between the Ala16Val dimorphism in the mitochondrial targeting sequence of nuclear gene SOD2 and the 3460/ND1 LHON mutation. Our results show that, in control population, the heterozygous SOD2 genotype is associated to a higher activity and quantity of MnSOD, particularly with respect to Val homozygotes. Furthermore, we demonstrated that LHON patients harboring at least one Ala allele are characterized by an increased MnSOD activity with respect to relative control population. Since the ATP synthesis rate – severely reduced in LHON patients lymphocytes - is not affected by the SOD2 genotype, we concluded that SOD2 gene could modulate the pathogenicity of LHON mutations through a mechanism associated to an increase of reactive oxygen species production. Autosomal dominant optic atrophy (ADOA) is a pathology linked to mutations in nuclear gene encoding Opa1, a dynamin-related protein localized in the mitochondrial matrix. Although the clinical course is slightly different, the endpoint of ADOA is exactly the same of LHON: optic nerve degeneration with specific involvement of retinal ganglion cells. Opa1 is a relatively new protein, whose major role is the regulation of mitochondrial fusion. Mitochondrial morphology is the results of the equilibrium between two opposite force: fusion and fission, two processes that have to be finely regulated in order to preserve mitochondrial and cellular physiology. We studied fibroblasts deriving from ADOA patients characterized by a new deletion in the GTPase domain of the OPA1 gene. The biochemical characterization of ADOA and control fibroblasts has concerned the evaluation of ATP synthesis rate, mitochondrial membrane potential in different metabolic conditions and the morphological status of mitochondria. Regarding ATP synthesis rate we did not find significant differences between ADOA and control fibroblasts even though a trend toward increased reduction in ADOA samples is observed when fibroblasts are grown in absence of glucose or in the medium containing gramicidin. Furthermore, we found that also in ADOA fibroblasts membrane potential is actively maintained by proton pumping of fully functional respiratory chain complexes. Our results indicate that the mutation found in the pedigree analyzed acts primary impairing the mitochondrial fusion without affecting the energy production, supporting the notion that cell function is tightly linked to mitochondrial morphology. Mitochondrial dysfunctions are acquiring great attention because of their recognized relevance not only in aging but also in age-related pathologies including cancer, cardiovascular disease, type II diabetes, and neurodegenerative disorders. The involvement of mitochondria in such detrimental pathologies that, currently, have become so common enhances the necessity of standardization of therapeutic strategies capable of rescuing the normal mitochondrial function. In order to propose an alternative treatment for energy deficiency-disorders we tested the effect of substrates capable to stimulate the substrate-level phosphorylation on viability and energy availability in different experimental models grown under different metabolic conditions. In fibroblasts, the energy defect was achieved by culturing cells in presence of oligomycin, an inhibitor of ATP synthase complex. NARP cybrids have been used as model of mitochondrial pathology. Cell viability and ATP content have been considered as parameters to assay the capability of exogenous substrate to rescue energy failure. Our results suggest that patients suffering for some forms of ATP synthase deficiency, or characterized by a deficiency in energy production, might benefit from dietary or pharmacological treatment based on supplementation of α-ketoglutarate and aspartate.

Molecular bases, pathogenic mechanisms and possible therapeutic approach in Leber's Hereditary Optic Neuropathy

Leber’s hereditary optic neuropathy (LHON) is a mitochondrial disease characterized by a rapid loss of central vision and optic atrophy, due to the selective degeneration of retinal ganglion cells. The age of onset is around 20, and the degenerative process is fast and usually the second eye becomes affected in weeks or months. Even if this pathology is well known and has been well characterized, there are still open questions on its pathophysiology, such as the male prevalence, the incomplete penetrance and the tissue selectivity. This maternally inherited disease is caused by mutations in mitochondrial encoded genes of NADH ubiquinone oxidoreductase (complex I) of the respiratory chain. The 90% of LHON cases are caused by one of the three common mitochondrial DNA mutations (11778/ND4, 14484/ND6 and 3460/ND1) and the remaining 10% is caused by rare pathogenic mutations, reported in literature in one or few families. Moreover, there is also a small subset of patients reported with new putative pathogenic nucleotide changes, which awaits to be confirmed. We here clarify some molecular aspects of LHON, mainly the incomplete penetrance and the role of rare mtDNA mutations or variants on LHON expression, and attempt a possible therapeutic approach using the cybrids cell model. We generated novel structural models for mitochondrial encoded complex I subunits and a conservation analysis and pathogenicity prediction have been carried out for LHON reported mutations. This in-silico approach allowed us to locate LHON pathogenic mutations in defined and conserved protein domains and can be a useful tool in the analysis of novel mtDNA variants with unclear pathogenic/functional role. Four rare LHON pathogenic mutations have been identified, confirming that the ND1 and ND6 genes are mutational hot spots for LHON. All mutations were previously described at least once and we validated their pathogenic role, suggesting the need for their screening in LHON diagnostic protocols. Two novel mtDNA variants with a possible pathogenic role have been also identified in two independent branches of a large pedigree. Functional studies are necessary to define their contribution to LHON in this family. It also been demonstrated that the combination of mtDNA rare polymorphic variants is relevant in determining the maternal recurrence of myoclonus in unrelated LHON pedigrees. Thus, we suggest that particular mtDNA backgrounds and /or the presence of specific rare mutations may increase the pathogenic potential of the primary LHON mutations, thereby giving rise to the extraocular clinical features characteristic of the LHON “plus” phenotype. We identified the first molecular parameter that clearly discriminates LHON affected individuals from asymptomatic carriers, the mtDNA copy number. This provides a valuable mechanism for future investigations on variable penetrance in LHON. However, the increased mtDNA content in LHON individuals was not correlated to the functional polymorphism G1444A of PGC-1 alpha, the master regulator of mitochondrial biogenesis, but may be due to gene expression of genes involved in this signaling pathway, such as PGC-1 alpha/beta and Tfam. Future studies will be necessary to identify the biochemical effects of rare pathogenic mutations and to validate the novel candidate mutations here described, in terms of cellular bioenergetic characterization of these variants. Moreover, we were not able to induce mitochondrial biogenesis in cybrids cell lines using bezafibrate. However, other cell line models are available, such as fibroblasts harboring LHON mutations, or other approaches can be used to trigger the mitochondrial biogenesis.

Comparing Different Approaches for Clustering Categorical Data

There are different ways to do cluster analysis of categorical data in the literature and the choice among them is strongly related to the aim of the researcher, if we do not take into account time and economical constraints. Main approaches for clustering are usually distinguished into model-based and distance-based methods: the former assume that objects belonging to the same class are similar in the sense that their observed values come from the same probability distribution, whose parameters are unknown and need to be estimated; the latter evaluate distances among objects by a defined dissimilarity measure and, basing on it, allocate units to the closest group. In clustering, one may be interested in the classification of similar objects into groups, and one may be interested in finding observations that come from the same true homogeneous distribution. But do both of these aims lead to the same clustering? And how good are clustering methods designed to fulfil one of these aims in terms of the other? In order to answer, two approaches, namely a latent class model (mixture of multinomial distributions) and a partition around medoids one, are evaluated and compared by Adjusted Rand Index, Average Silhouette Width and Pearson-Gamma indexes in a fairly wide simulation study. Simulation outcomes are plotted in bi-dimensional graphs via Multidimensional Scaling; size of points is proportional to the number of points that overlap and different colours are used according to the cluster membership.

Internationalization intentions: micro-foundations and psychological distance perceptions in immigrant and non-immigrant entrepreneurs

This dissertation project aims at shedding light on the micro-foundations of international entrepreneurship, focusing on the pre-internationalization phase and taking an individual-level perspective. Three research questions are investigated building on a cognitive model of internationalization intentions. First, what are the antecedents to internationalization intentions, i.e. desirability and feasibility, and how they interact with psychological distance towards internationalization options. Second, what is the role of previous entrepreneurs’ experience on such antecedents, in particular for immigrant vs. non-immigrant entrepreneurs. Third, how are these antecedent elements influenced by entrepreneurs’ individual-level motivations and goals. Using a new data set from 140 independent, non-internationalized, high-tech SMEs and their 169 owners, a variety of analytical techniques are used to investigate the research questions, such as structural equation modeling, hierarchical regression and a "laddering" technique. This project advances our theoretical understanding of internationalization and international entrepreneurship and has relevant implications for entrepreneurs and policy-makers.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Improving the cosmic distance ladder. Distance and structure of the Large Magellanic Cloud

The Large Magellanic Cloud (LMC) is widely considered as the first step of the cosmological distance ladder, since it contains many different distance indicators. An accurate determination of the distance to the LMC allows one to calibrate these distance indicators that are then used to measure the distance to far objects. The main goal of this thesis is to study the distance and structure of the LMC, as traced by different distance indicators. For these purposes three types of distance indicators were chosen: Classical Cepheids,``hot'' eclipsing binaries and RR Lyrae stars. These objects belong to different stellar populations tracing, in turn, different sub-structures of the LMC. The RR Lyrae stars (age >10 Gyr) are distributed smoothly and likely trace the halo of the LMC. Classical Cepheids are young objects (age 50-200 Myr), mainly located in the bar and spiral arm of the galaxy, while ``hot'' eclipsing binaries mainly trace the star forming regions of the LMC. Furthermore, we have chosen these distance indicators for our study, since the calibration of their zero-points is based on fundamental geometric methods. The ESA cornerstone mission Gaia, launched on 19 December 2013, will measure trigonometric parallaxes for one billion stars with an accuracy of 20 micro-arcsec at V=15 mag, and 200 micro-arcsec at V=20 mag, thus will allow us to calibrate the zero-points of Classical Cepheids, eclipsing binaries and RR Lyrae stars with an unprecedented precision.