Rapid identification and differentiation of Mycobacterium avium subspecies paratuberculosis types by use of real-time PCR and high-resolution melt analysis of the MAP1506 locus

High-resolution melt (HRM) analysis can identify sequence polymorphisms by comparing the melting curves of amplicons generated by real-time PCR amplification. We describe the application of this technique to identify Mycobacterium avium subspecies paratuberculosis types I, II, and III. The HRM approach was based on type-specific nucleotide sequences in MAP1506, a member of the PPE (proline-proline-glutamic acid) gene family.

Effect of room temperature transport vials on DNA quality and phylogenetic composition of faecal microbiota of elderly adults and infants

Background: Alterations in intestinal microbiota have been correlated with a growing number of diseases. Investigating the faecal microbiota is widely used as a non-invasive and ethically simple proxy for intestinal biopsies. There is an urgent need for collection and transport media that would allow faecal sampling at distance from the processing laboratory, obviating the need for same-day DNA extraction recommended by previous studies of freezing and processing methods for stool. We compared the faecal bacterial DNA quality and apparent phylogenetic composition derived using a commercial kit for stool storage and transport (DNA Genotek OMNIgene GUT) with that of freshly extracted samples, 22 from infants and 20 from older adults. Results: Use of the storage vials increased the quality of extracted bacterial DNA by reduction of DNA shearing. When infant and elderly datasets were examined separately, no differences in microbiota composition were observed due to storage. When the two datasets were combined, there was a difference according to a Wilcoxon test in the relative proportions of Faecalibacterium, Sporobacter, Clostridium XVIII, and Clostridium XlVa after 1 week's storage compared to immediately extracted samples. After 2 weeks' storage, Bacteroides abundance was also significantly different, showing an apparent increase from week 1 to week 2. The microbiota composition of infant samples was more affected than that of elderly samples by storage, with significantly higher Spearman distances between paired freshly extracted and stored samples (p

Near-infrared polarimetry in the galactic center. On properties of Sagittarius A* and a dusty S-cluster object

Near-infrared polarimetry observation is a powerful tool to study the central sources at the center of the Milky Way. My aim of this thesis is to analyze the polarized emission present in the central few light years of the Galactic Center region, in particular the non-thermal polarized emission of Sagittarius~A* (Sgr~A*), the electromagnetic manifestation of the super-massive black hole, and the polarized emission of an infrared-excess source in the literature referred to as DSO/G2. This source is in orbit about Sgr~A*. In this thesis I focus onto the Galactic Center observations at $\lambda=2.2~\mu m$ ($K_\mathrm{s}$-band) in polarimetry mode during several epochs from 2004 to 2012. The near-infrared polarized observations have been carried out using the adaptive optics instrument NAOS/CONICA and Wollaston prism at the Very Large Telescope of ESO (European Southern Observatory). Linear polarization at 2.2 $\mu m$, its flux statistics and time variation, can be used to constrain the physical conditions of the accretion process onto the central super-massive black hole. I present a statistical analysis of polarized $K_\mathrm{s}$-band emission from Sgr~A* and investigate the most comprehensive sample of near-infrared polarimetric light curves of this source up to now. I find several polarized flux excursions during the years and obtain an exponent of about 4 for the power-law fitted to polarized flux density distribution of fluxes above 5~mJy. Therefore, this distribution is closely linked to the single state power-law distribution of the total $K_\mathrm{s}$-band flux densities reported earlier by us. I find polarization degrees of the order of 20\%$\pm$10\% and a preferred polarization angle of $13^o\pm15^o$. Based on simulations of polarimetric measurements given the observed flux density and its uncertainty in orthogonal polarimetry channels, I find that the uncertainties of polarization parameters under a total flux density of $\sim 2\,{\mathrm{mJy}}$ are probably dominated by observational uncertainties. At higher flux densities there are intrinsic variations of polarization degree and angle within rather well constrained ranges. Since the emission is most likely due to optically thin synchrotron radiation, the obtained preferred polarization angle is very likely reflecting the intrinsic orientation of the Sgr~A* system i.e. an accretion disk or jet/wind scenario coupled to the super-massive black hole. Our polarization statistics show that Sgr~A* must be a stable system, both in terms of geometry, and the accretion process. I also investigate an infrared-excess source called G2 or Dusty S-cluster Object (DSO) moving on a highly eccentric orbit around the Galaxy's central black hole, Sgr~A*. I use for the first time the near-infrared polarimetric imaging data to determine the nature and the properties of DSO and obtain an improved $K_\mathrm{s}$-band identification of this source in median polarimetry images of different observing years. The source starts to deviate from the stellar confusion in 2008 data and it does not show a flux density variability based on our data set. Furthermore, I measure the polarization degree and angle of this source and conclude based on the simulations on polarization parameters that it is an intrinsically polarized source with a varying polarization angle as it approaches Sgr~A* position. I use the interpretation of the DSO polarimetry measurements to assess its possible properties.

South Carolina 2017 List of Reportable Conditions

This is a list of diseases and conditions that must, by law, be reported by physicians and health care professionals to their local public health department.

Self-care in older people with functional deficit: needs of long-term care.

The group of 65-year-olds is becoming more numerous and with greater needs for health care. So, is necessary the reflection about new models of provision, organization, and allocation of health resources. According to the United Nations Organization, 2015, in 2050 elderly people will reach two million people (20% of the world’s population), what mean that the number of people over 60 years old will exceed a population of young people under 15 years. Parallel to aging, less healthy lifestyles have contributed to the prevalence of chronic diseases, especially cerebrovascular diseases. Hypertension and diabetes mellitus are risk factors and increase predisposition to other diseases. With aging, there is an increased risk for developing chronic, oncological and degenerative diseases, which account for more than 50% of the burden of diseases, with profound implications on independency, use of health care and services.

A single-sided NMR approach to study structural differences of bovine articular tissue

This thesis work has been developed in collaboration between the Department of Physics and Astronomy of the University of Bologna and the IRCCS Rizzoli Orthopedic Institute during an internship period. The study aims to investigate the sensitivity of single-sided NMR in detecting structural differences of the articular cartilage tissue and their correlation with mechanical behavior. Suitable cartilage indicators for osteoarthritis (OA) severity (e.g., water and proteoglycans content, collagen structure) were explored through four NMR parameters: T2, T1, D, and Slp. Structural variations of the cartilage among its three layers (i.e., superficial, middle, and deep) were investigated performing several NMR pulses sequences on bovine knee joint samples using the NMR-MOUSE device. Previously, cartilage degradation studies were carried out, performing tests in three different experimental setups. The monitoring of the parameters and the best experimental setup were determined. An NMR automatized procedure based on the acquisition of these quantitative parameters was implemented, tested, and used for the investigation of the layers of twenty bovine cartilage samples. Statistical and pattern recognition analyses on these parameters have been performed. The results obtained from the analyses are very promising: the discrimination of the three cartilage layers shows very good results in terms of significance, paving the way for extensive use of NMR single-sided devices for biomedical applications. These results will be also integrated with analyses of tissue mechanical properties for a complete evaluation of cartilage changes throughout OA disease. The use of low-priced and mobile devices towards clinical applications could concern the screening of diseases related to cartilage tissue. This could have a positive impact both economically (including for underdeveloped countries) and socially, providing screening possibilities to a large part of the population.

Evaluation of seismic losses of RC buildings: the case of the industrial areas after the 2012 Emilia Earthquake

The topic of seismic loss assessment not only incorporates many aspects of the earthquake engineering, but also entails social factors, public policies and business interests. Because of its multidisciplinary character, this process may be complex to challenge, and sound discouraging to neophytes. In this context, there is an increasing need of deriving simplified methodologies to streamline the process and provide tools for decision-makers and practitioners. This dissertation investigates different possible applications both in the area of modelling of seismic losses, both in the analysis of observational seismic data. Regarding the first topic, the PRESSAFE-disp method is proposed for the fast evaluation of the fragility curves of precast reinforced-concrete (RC) structures. Hence, a direct application of the method to the productive area of San Felice is studied to assess the number of collapses under a specific seismic scenario. In particular, with reference to the 2012 events, two large-scale stochastic models are outlined. The outcomes of the framework are promising, in good agreement with the observed damage scenario. Furthermore, a simplified displacement-based methodology is outlined to estimate different loss performance metrics for the decision-making phase of the seismic retrofit of a single RC building. The aim is to evaluate the seismic performance of different retrofit options, for a comparative analysis of their effectiveness and the convenience. Finally, a contribution to the analysis of the observational data is presented in the last part of the dissertation. A specific database of losses of precast RC buildings damaged by the 2012 Earthquake is created. A statistical analysis is performed, allowing deriving several consequence functions. The outcomes presented may be implemented in probabilistic seismic risk assessments to forecast the losses at the large scale. Furthermore, these may be adopted to establish retrofit policies to prevent and reduce the consequences of future earthquakes in industrial areas.

Statistical learning of random probability measures

The study of random probability measures is a lively research topic that has attracted interest from different fields in recent years. In this thesis, we consider random probability measures in the context of Bayesian nonparametrics, where the law of a random probability measure is used as prior distribution, and in the context of distributional data analysis, where the goal is to perform inference given avsample from the law of a random probability measure. The contributions contained in this thesis can be subdivided according to three different topics: (i) the use of almost surely discrete repulsive random measures (i.e., whose support points are well separated) for Bayesian model-based clustering, (ii) the proposal of new laws for collections of random probability measures for Bayesian density estimation of partially exchangeable data subdivided into different groups, and (iii) the study of principal component analysis and regression models for probability distributions seen as elements of the 2-Wasserstein space. Specifically, for point (i) above we propose an efficient Markov chain Monte Carlo algorithm for posterior inference, which sidesteps the need of split-merge reversible jump moves typically associated with poor performance, we propose a model for clustering high-dimensional data by introducing a novel class of anisotropic determinantal point processes, and study the distributional properties of the repulsive measures, shedding light on important theoretical results which enable more principled prior elicitation and more efficient posterior simulation algorithms. For point (ii) above, we consider several models suitable for clustering homogeneous populations, inducing spatial dependence across groups of data, extracting the characteristic traits common to all the data-groups, and propose a novel vector autoregressive model to study of growth curves of Singaporean kids. Finally, for point (iii), we propose a novel class of projected statistical methods for distributional data analysis for measures on the real line and on the unit-circle.

Neurogeometry of stereo vision

This work aims to develop a neurogeometric model of stereo vision, based on cortical architectures involved in the problem of 3D perception and neural mechanisms generated by retinal disparities. First, we provide a sub-Riemannian geometry for stereo vision, inspired by the work on the stereo problem by Zucker (2006), and using sub-Riemannian tools introduced by Citti-Sarti (2006) for monocular vision. We present a mathematical interpretation of the neural mechanisms underlying the behavior of binocular cells, that integrate monocular inputs. The natural compatibility between stereo geometry and neurophysiological models shows that these binocular cells are sensitive to position and orientation. Therefore, we model their action in the space R3xS2 equipped with a sub-Riemannian metric. Integral curves of the sub-Riemannian structure model neural connectivity and can be related to the 3D analog of the psychophysical association fields for the 3D process of regular contour formation. Then, we identify 3D perceptual units in the visual scene: they emerge as a consequence of the random cortico-cortical connection of binocular cells. Considering an opportune stochastic version of the integral curves, we generate a family of kernels. These kernels represent the probability of interaction between binocular cells, and they are implemented as facilitation patterns to define the evolution in time of neural population activity at a point. This activity is usually modeled through a mean field equation: steady stable solutions lead to consider the associated eigenvalue problem. We show that three-dimensional perceptual units naturally arise from the discrete version of the eigenvalue problem associated to the integro-differential equation of the population activity.

Neurogeometry of reaching

The purpose of the thesis is to develop a model for the functional behaviour of neurons in the primary motor cortex (M1) responsible for arm reaching movements. From Georgopoulos neurophysiological data, we provide a first bundle structure compatible with the hypercolumnar organization and with the position-direction selectivity of motor cortical cells. We then extend this model to encode the direction of arm movement which varies in time, as experimentally measured by Hatsopoulos by introducing the notion of movement fragments. We provide a sub-Riemannian model which describes the time-dependent directional selectivity of cells though integral curves of the geometric structure we set up. The sub-Riemannian distance we define allows to implement a grouping algorithm able to detect a set of hand motor trajectories. These paths, identified by using a kernel defined in terms of kinematic variables, are compatible with the motor primitives obtained from neurophysiological results by spectral analysis applied directly on cortical variables. In a second part of the work, we propose geodesics in this space as an alternative model of models for arm movement trajectories. We define a special class of curves, called admissible, on which to study the geodesics problem: we provide a connectivity property in terms of admissible paths and the existence of normal length minimizers. Admissible geodesics are used as a model of reaching paths, finding a first validation through Flash and Hogan minimizing trajectories.

Deep learning and embeddings for problems of computational biology

The development of Next Generation Sequencing promotes Biology in the Big Data era. The ever-increasing gap between proteins with known sequences and those with a complete functional annotation requires computational methods for automatic structure and functional annotation. My research has been focusing on proteins and led so far to the development of three novel tools, DeepREx, E-SNPs&GO and ISPRED-SEQ, based on Machine and Deep Learning approaches. DeepREx computes the solvent exposure of residues in a protein chain. This problem is relevant for the definition of structural constraints regarding the possible folding of the protein. DeepREx exploits Long Short-Term Memory layers to capture residue-level interactions between positions distant in the sequence, achieving state-of-the-art performances. With DeepRex, I conducted a large-scale analysis investigating the relationship between solvent exposure of a residue and its probability to be pathogenic upon mutation. E-SNPs&GO predicts the pathogenicity of a Single Residue Variation. Variations occurring on a protein sequence can have different effects, possibly leading to the onset of diseases. E-SNPs&GO exploits protein embeddings generated by two novel Protein Language Models (PLMs), as well as a new way of representing functional information coming from the Gene Ontology. The method achieves state-of-the-art performances and is extremely time-efficient when compared to traditional approaches. ISPRED-SEQ predicts the presence of Protein-Protein Interaction sites in a protein sequence. Knowing how a protein interacts with other molecules is crucial for accurate functional characterization. ISPRED-SEQ exploits a convolutional layer to parse local context after embedding the protein sequence with two novel PLMs, greatly surpassing the current state-of-the-art. All methods are published in international journals and are available as user-friendly web servers. They have been developed keeping in mind standard guidelines for FAIRness (FAIR: Findable, Accessible, Interoperable, Reusable) and are integrated into the public collection of tools provided by ELIXIR, the European infrastructure for Bioinformatics.

Characterisation of RNase Y in H. pylori: a non-essential enzyme involved in the processing of cag-PAI non coding RNA 1 (CncR1) sRNA

The importance of Helicobacter pylori as a human pathogen is underlined by the plethora of diseases it is responsible for. The capacity of H. pylori to adapt to the restricted host-associated environment andto evade the host immune response largely depends on a streamlined signalling network. The peculiar H. pylori small genome size combined with its paucity of transcriptional regulators highlights the relevance of post-transcriptional regulatory mechanisms as small non-coding RNAs (sRNAs). However, among the 8 RNases represented in H. pylori genome, a regulator guiding sRNAs metabolism is still not well studied. We investigated for the first time the physiological role in H. pylori G27 strain of the RNase Y enzyme. In the first line of research we provide a comprehensive characterization of the RNase Y activity by analysing its genomic organization and the factors that orchestrate its expression. Then, based on bioinformatic prediction models, we depict the most relevant determinants of RNase Y function, demonstrating a correlation of both structure and domain organization with orthologues represented in Gram-positive bacteria. To unveil the post-transcriptional regulatory effect exerted by the RNase Y, we compared the transcriptome of an RNase Y knock-out mutant to the parental wild type strain by RNA-seq approach. In the second line of research we characterized the activity of this single strand specific endoribonuclease on cag-PAI non coding RNA 1 (CncR1) sRNA. We found that deletion or inactivation of RNase Y led to the accumulation of a 3’-extended CncR1 (CncR1-L) transcript over time. Moreover, beneath its increased half-life, CncR1-L resembled a CncR1 inactive phenotype. Finally, we focused on the characterization of the in vivo interactome of CncR1. We set up a preliminary MS2-affinity purification coupled with RNA-sequencing (MAPS) approach and we evaluated the enrichment of specific targets, demonstrating the suitability of the technique in the H. pylori G27 strain.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.

Histoplasma capsulatum Cell Wall beta-Glucan Induces Lipid Body Formation through CD18, TLR2, and Dectin-1 Receptors: Correlation with Leukotriene B(4) Generation and Role in HIV-1 Infection

Histoplasma capsulatum (Hc) is a facultative, intracellular parasite of worldwide significance. Infection with Hc produces a broad spectrum of diseases and may progress to a life-threatening systemic disease, particularly in individuals with HIV infection. Resolution of histoplasmosis is associated with the activation of cell-mediated immunity, and leukotriene B(4) plays an important role in this event. Lipid bodies (LBs) are increasingly being recognized as multifunctional organelles with roles in inflammation and infection. In this study, we investigated LB formation in histoplasmosis and its putative function in innate immunity. LB formation in leukocytes harvested from Hc-infected C57BL/6 mice peaks on day 2 postinfection and correlates with enhanced generation of lipid mediators, including leukotriene B(4) and PGE(2). Pretreatment of leukocytes with platelet-activating factor and BLT1 receptor antagonists showed that both lipid mediators are involved in cell signaling for LB formation. Alveolar leukocytes cultured with live or dead Hc also presented an increase in LB numbers. The yeast alkali-insoluble fraction 1, which contains mainly beta-glucan isolated from the Hc cell wall, induced a dose- and time-dependent increase in LB numbers, indicating that beta-glucan plays a signaling role in LB formation. In agreement with this hypothesis, beta-glucan-elicited LB formation was inhibited in leukocytes from 5-LO(-/-), CD18(low) and TLR2(-/-) mice, as well as in leukocytes pretreated with anti-Dectin-1 Ab. Interestingly, human monocytes from HIV-1-infected patients failed to produce LBs after beta-glucan stimulation. These results demonstrate that Hc induces LB formation, an event correlated with eicosanoid production, and suggest a role for these lipid-enriched organelles in host defense during fungal infection. The Journal of Immunology, 2009, 182: 4025-4035.

Agricultural development and arthropod-borne diseases: a review

A review is presented of the interrelationships between arthropod vectors, the diseases they transmit and agricultural development. Particular attention is given to the effects of deforestation, livestock development and irrigation on the abundance of vectors and changing patterns of diseases such as malaria, trypanosomiases, leishmaniasis, Chagas' and some arboviral infections. The question as whether keeping livestock diverts biting away from people and reduces diseases such as malaria - that is zooprophylaxis, or whether the presence of cattle actually increases biting populations is discussed.