Environmental influence on the phenotype: Morphological variation in human dentition

This work is about the role that environment plays in the production of evolutionary significant variations. It starts with an historical introduction about the concept of variation and the role of environment in its production. Then, I show how a lack of attention to these topics may lead to serious mistakes in data interpretation. A statistical re-analysis of published data on the effects of malnutrition on dental eruption, shows that what has been interpreted as an increase in the mean value, is actually linked to increase of variability. In Chapter 3 I present the topic of development as a link between variability and environmental influence, giving a review of the possible mechanisms by which development influences evolutionary dynamics. Chapter 4 is the core chapter of the thesis; I investigated the role of environment in the development of dental morphology. I used dental hypoplasia as a marker of stress, characterizing two groups. Comparing the morphology of upper molars in the two groups, three major results came out: (i) there is a significant effect of environmental stressors on the overall morphology of upper molars; (ii) the developmental response increases morphological variability of the stressed population; (iii) increase of variability is directional: stressed individuals have increased cusps dimensions and number. I also hypothesized the molecular mechanisms that could be responsible of the observed effects. In Chapter 5, I present future perspectives for developing this research. The direction of dental development response is the same direction of the trend in mammalian dental evolution. Since malnutrition triggers the developmental response, and this particular kind of stressor must have been very common in our class evolutionary history, I propose the possibility that environmental stress actively influenced mammals evolution. Moreover, I discuss the possibility of reconsidering the role of natural selection in the evolution of dental morphology.

Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.

Applicability of Process Mining Techniques in Business Environments

This thesis analyses problems related to the applicability, in business environments, of Process Mining tools and techniques. The first contribution is a presentation of the state of the art of Process Mining and a characterization of companies, in terms of their "process awareness". The work continues identifying circumstance where problems can emerge: data preparation; actual mining; and results interpretation. Other problems are the configuration of parameters by not-expert users and computational complexity. We concentrate on two possible scenarios: "batch" and "on-line" Process Mining. Concerning the batch Process Mining, we first investigated the data preparation problem and we proposed a solution for the identification of the "case-ids" whenever this field is not explicitly indicated. After that, we concentrated on problems at mining time and we propose the generalization of a well-known control-flow discovery algorithm in order to exploit non instantaneous events. The usage of interval-based recording leads to an important improvement of performance. Later on, we report our work on the parameters configuration for not-expert users. We present two approaches to select the "best" parameters configuration: one is completely autonomous; the other requires human interaction to navigate a hierarchy of candidate models. Concerning the data interpretation and results evaluation, we propose two metrics: a model-to-model and a model-to-log. Finally, we present an automatic approach for the extension of a control-flow model with social information, in order to simplify the analysis of these perspectives. The second part of this thesis deals with control-flow discovery algorithms in on-line settings. We propose a formal definition of the problem, and two baseline approaches. The actual mining algorithms proposed are two: the first is the adaptation, to the control-flow discovery problem, of a frequency counting algorithm; the second constitutes a framework of models which can be used for different kinds of streams (stationary versus evolving).

Exploring the biofilm of Streptococcus agalactiae to identify virulence factors

Streptococcus agalactiae, also known as Group B Streptococcus (GBS) is the primary colonizer of the anogenital mucosa of up to 40% of healthy women and an important cause of invasive neonatal infections worldwide. Among the 10 known capsular serotypes, GBS type III accounts for 30-76% of the cases of neonatal meningitis. Biofilms are dense aggregates of surface-adherent microorganisms embedded in an exopolysaccharide matrix. Centers for Disease Control and Prevention estimate that 65% of human bacterial infections involve biofilms (Post et al., 2004). In recent years, the ability of GBS to form biofilm attracted attention for its possible role in fitness and/or virulence. Here, a new in vitro biofilm formation protocol was developed to guarantee more stringent conditions, to better discriminate between strong-, low- and non- biofilm forming strains and reduce ambiguous data interpretation. This protocol was applied to screen the in vitro biofilm formation ability of more than 350 GBS clinical isolates from pregnant women and neonatal infections belonging to different serotype, in relation to media composition and pH. The results showed the enhancement of GBS biofilm formation in acidic condition and identified a subset of isolates belonging to serotypes III and V that forms strong biofilms in these conditions. Interestingly, the best biofilm formers belonged to the serotype III hypervirulent clone ST-17.It was also found that pH 5.0 induces down-regulation of the capsule but that this reduction is not enough by itself to ensure biofilm formation. Moreover, the ability of proteinase K to strongly inhibit biofilm formation and to disaggregate mature biofilms suggested that proteins play an essential role in promoting GBS biofilm formation and contribute to the biofilm structural stability. Finally, a set of proteins potentially expressed during the GBS in vitro biofilm formation were identified by mass spectrometry.

Integrating structural variant calling, annotation and prioritization into whole genome analysis workflows: a practical application in the molecular diagnosis of neurodevelopmental disorders

Background: WGS is increasingly used as a first-line diagnostic test for patients with rare genetic diseases such as neurodevelopmental disorders (NDD). Clinical applications require a robust infrastructure to support processing, storage and analysis of WGS data. The identification and interpretation of SVs from WGS data also needs to be improved. Finally, there is a need for a prioritization system that enables downstream clinical analysis and facilitates data interpretation. Here, we present the results of a clinical application of WGS in a cohort of patients with NDD. Methods: We developed highly portable workflows for processing WGS data, including alignment, quality control, and variant calling of SNVs and SVs. A benchmark analysis of state-of-the-art SV detection tools was performed to select the most accurate combination for SV calling. A gene-based prioritization system was also implemented to support variant interpretation. Results: Using a benchmark analysis, we selected the most accurate combination of tools to improve SV detection from WGS data and build a dedicated pipeline. Our workflows were used to process WGS data from 77 NDD patient-parent families. The prioritization system supported downstream analysis and enabled molecular diagnosis in 32% of patients, 25% of which were SVs and suggested a potential diagnosis in 20% of patients, requiring further investigation to achieve diagnostic certainty. Conclusion: Our data suggest that the integration of SNVs and SVs is a main factor that increases diagnostic yield by WGS and show that the adoption of a dedicated pipeline improves the process of variant detection and interpretation.

New insights into methodology and interpretation of osteoarthritis. The study of Frassetto identified skeletal collection.

Osteoarthritis (OA) or degenerative joint disease (DJD) is a pathology which affects the synovial joints and characterised by a focal loss of articular cartilage and subsequent bony reaction of the subcondral and marginal bone. Its etiology is best explained by a multifactorial model including: age, sex, genetic and systemic factors, other predisposing diseases and functional stress. In this study the results of the investigation of a modern identified skeletal collection will be presented. In particular, we will focus on the relationship between the presence of OA at various joints. The joint modifications have been analysed using a new methodology that allows the scoring of different degrees of expression of the features considered. Materials and Methods The sample examined comes from the Sassari identified skeletal collection (part of “Frassetto collections”). The individuals were born between 1828 and 1916 and died between 1918 and 1932. Information about sex and age is known for all the individuals. The occupation is known for 173 males and 125 females. Data concerning the occupation of the individuals indicate a preindustrial and rural society. OA has been diagnosed when eburnation (EB) or loss of morphology (LM) were present, or when at least two of the following: marginal lipping (ML), esostosis (EX) or erosion (ER), were present. For each articular surface affected a “mean score” was calculated, reflecting the “severity” of the alterations. A further “score” was calculated for each joint. In the analysis sexes and age classes were always kept separate. For the statistical analyses non parametric test were used. Results The results show there is an increase of OA with age in all the joints analyzed and in particular around 50 years and 60 years. The shoulder, the hip and the knee are the joints mainly affected with ageing while the ankle is the less affected; the correlation values confirm this result. The lesion which show the major correlation with age is the ML. In our sample males are more frequently and more severely affected by OA than females, particularly at the superior limbs, while hip and knee are similarly affected in the two sexes. Lateralization shows some positive results in particular in the right shoulder of males and in various articular surfaces especially of the superior limb of both males and females; articular surfaces and joints are quite always lateralized to the right. Occupational analyses did not show remarkable results probably because of the homogeneity of the sample; males although performing different activities are quite all employed in stressful works. No highest prevalence of knee and hip OA was found in farm-workers respect to the other males. Discussion and Conclusion In this work we propose a methodology to score the different features, necessary to diagnose OA, that allows the investigation of the severity of joint degeneration. This method is easier than the one proposed by Buikstra and Ubelaker (1994), but in the same time allows a quite detailed recording of the features. Epidemiological results can be interpreted quite simply and they are in accordance with other studies; more difficult is the interpretation of the occupational results because many questions concerning the activities performed by the individuals of the collection during their lifespan cannot be solved. Because of this, caution is suggested in the interpretation of bioarcheological specimens. With this work we hope to contribute to the discussion on the puzzling problem of the etiology of OA. The possibility of studying identified skeletons will add important data to the description of osseous features of OA, enriching the medical documentation, based on different criteria. Even if we are aware that the clinical diagnosis is different from the palaeopathological one we think our work will be useful in clarifying some epidemiological as well as pathological aspects of OA.