Geometrical Methods in Multivariate Risk Management: Algorithms and Applications

This thesis builds a framework for evaluating downside risk from multivariate data via a special class of risk measures (RM). The peculiarity of the analysis lies in getting rid of strong data distributional assumptions and in orientation towards the most critical data in risk management: those with asymmetries and heavy tails. At the same time, under typical assumptions, such as the ellipticity of the data probability distribution, the conformity with classical methods is shown. The constructed class of RM is a multivariate generalization of the coherent distortion RM, which possess valuable properties for a risk manager. The design of the framework is twofold. The first part contains new computational geometry methods for the high-dimensional data. The developed algorithms demonstrate computability of geometrical concepts used for constructing the RM. These concepts bring visuality and simplify interpretation of the RM. The second part develops models for applying the framework to actual problems. The spectrum of applications varies from robust portfolio selection up to broader spheres, such as stochastic conic optimization with risk constraints or supervised machine learning.

Assessing landscape functional connectivity in a forest carnivore using path selection functions

Context Understanding connectivity patterns in relation to habitat fragmentation is essential to landscape management. However, connectivity is often judged from expert opinion or species occurrence patterns, with very few studies considering the actual movements of individuals. Path selection functions provide a promising tool to infer functional connectivity from animal movement data, but its practical application remains scanty. Objectives We aimed to describe functional connectivity patterns in a forest carnivore using path-level analysis, and to explore how connectivity is affected by land cover patterns and road networks. Methods We radiotracked 22 common genets in a mixed forest-agricultural landscape of southern Portugal. We developed path selection functions discriminating between observed and random paths in relation to landscape variables. These functions were used together with land cover information to map conductance surfaces. Results Genets moved preferentially within forest patches and close to riparian habitats. Functional connectivity declined with increasing road density, but increased with the proximity of culverts, viaducts and bridges. Functional connectivity was favoured by large forest patches, and by the presence of riparian areas providing corridors within open agricultural land. Roads reduced connectivity by dissecting forest patches, but had less effect on riparian corridors due to the presence of crossing structures. Conclusions Genet movements were jointly affected by the spatial distribution of suitable habitats, and the presence of a road network dissecting such habitats and creating obstacles in areas otherwise permeable to animal movement. Overall, the study showed the value of path-level analysis to assess functional connectivity patterns in human-modified landscapes.

Methods for objective and interpretative evaluation of function and motor control: gait in perturbed condition and in the aquatic therapy

Gait analysis allows to characterize motor function, highlighting deviations from normal motor behavior related to an underlying pathology. The widespread use of wearable inertial sensors has opened the way to the evaluation of ecological gait, and a variety of methodological approaches and algorithms have been proposed for the characterization of gait from inertial measures (e.g. for temporal parameters, motor stability and variability, specific pathological alterations). However, no comparative analysis of their performance (i.e. accuracy, repeatability) was available yet, in particular, analysing how this performance is affected by extrinsic (i.e. sensor location, computational approach, analysed variable, testing environmental constraints) and intrinsic (i.e. functional alterations resulting from pathology) factors. The aim of the present project was to comparatively analyze the influence of intrinsic and extrinsic factors on the performance of the numerous algorithms proposed in the literature for the quantification of specific characteristics (i.e. timing, variability/stability) and alterations (i.e. freezing) of gait. Considering extrinsic factors, the influence of sensor location, analyzed variable, and computational approach on the performance of a selection of gait segmentation algorithms from a literature review was analysed in different environmental conditions (e.g. solid ground, sand, in water). Moreover, the influence of altered environmental conditions (i.e. in water) was analyzed as referred to the minimum number of stride necessary to obtain reliable estimates of gait variability and stability metrics, integrating what already available in the literature for over ground gait in healthy subjects. Considering intrinsic factors, the influence of specific pathological conditions (i.e. Parkinson’s Disease) was analyzed as affecting the performance of segmentation algorithms, with and without freezing. Finally, the analysis of the performance of algorithms for the detection of gait freezing showed how results depend on the domain of implementation and IMU position.

Machine Learning methods for hepatocellular malignancies segmentation and MVI prediction

The aim of this thesis project is to automatically localize HCC tumors in the human liver and subsequently predict if the tumor will undergo microvascular infiltration (MVI), the initial stage of metastasis development. The input data for the work have been partially supplied by Sant'Orsola Hospital and partially downloaded from online medical databases. Two Unet models have been implemented for the automatic segmentation of the livers and the HCC malignancies within it. The segmentation models have been evaluated with the Intersection-over-Union and the Dice Coefficient metrics. The outcomes obtained for the liver automatic segmentation are quite good (IOU = 0.82; DC = 0.35); the outcomes obtained for the tumor automatic segmentation (IOU = 0.35; DC = 0.46) are, instead, affected by some limitations: it can be state that the algorithm is almost always able to detect the location of the tumor, but it tends to underestimate its dimensions. The purpose is to achieve the CT images of the HCC tumors, necessary for features extraction. The 14 Haralick features calculated from the 3D-GLCM, the 120 Radiomic features and the patients' clinical information are collected to build a dataset of 153 features. Now, the goal is to build a model able to discriminate, based on the features given, the tumors that will undergo MVI and those that will not. This task can be seen as a classification problem: each tumor needs to be classified either as “MVI positive” or “MVI negative”. Techniques for features selection are implemented to identify the most descriptive features for the problem at hand and then, a set of classification models are trained and compared. Among all, the models with the best performances (around 80-84% ± 8-15%) result to be the XGBoost Classifier, the SDG Classifier and the Logist Regression models (without penalization and with Lasso, Ridge or Elastic Net penalization).

Optimal pose selection for the calibration of an overconstrained Cable-Driven Parallel Robot

In this project an optimal pose selection method for the calibration of an overconstrained Cable-Driven Parallel robot is presented. This manipulator belongs to a subcategory of parallel robots, where the classic rigid "legs" are replaced by cables. Cables are flexible elements that bring advantages and disadvantages to the robot modeling. For this reason, there are many open research issues, and the calibration of geometric parameters is one of them. The identification of the geometry of a robot, in particular, is usually called Kinematic Calibration. Many methods have been proposed in the past years for the solution of the latter problem. Although these methods are based on calibration using different kinematic models, when the robot’s geometry becomes more complex, their robustness and reliability decrease. This fact makes the selection of the calibration poses more complicated. The position and the orientation of the endeffector in the workspace become important in terms of selection. Thus, in general, it is necessary to evaluate the robustness of the chosen calibration method, by means, for example, of a parameter such as the observability index. In fact, it is known from the theory, that the maximization of the above mentioned index identifies the best choice of calibration poses, and consequently, using this pose set may improve the calibration process. The objective of this thesis is to analyze optimization algorithms which aim to calculate an optimal choice of poses both in quantitative and qualitative terms. Quantitatively, because it is of fundamental importance to understand how many poses are needed. Not necessarily a greater number of poses leads to a better result. Qualitatively, because it is useful to understand if the selected combination of poses actually gives additional information in the process of the identification of the parameters.

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.

Optimized methods to digital survey and 3D modelling of cultural heritage assets

Currently making digital 3D models and replicas of the cultural heritage assets play an important role in the preservation and having a high detail source for future research and intervention. In this dissertation, it is tried to assess different methods for digital surveying and making 3D replicas of cultural heritage assets in different scales of size. The methodologies vary in devices, software, workflow, and the amount of skill that is required. The three phases of the 3D modelling process are data acquisition, modelling, and model presentation. Each of these sections is divided into sub-sections and there are several approaches, methods, devices, and software that may be employed, furthermore, the selection process should be based on the operation's goal, available facilities, the scale and properties of the object or structure to be modeled, as well as the operators' expertise and experience. The most key point to remember is that the 3D modelling operation should be properly accurate, precise, and reliable; therefore, there are so many instructions and pieces of advice on how to perform 3D modelling effectively. It is an attempt to compare and evaluate the various ways of each phase in order to explain and demonstrate their differences, benefits, and drawbacks in order to serve as a simple guide for new and/or inexperienced users.

Evaluation Of Different Treatment Methods Against Denture Stomatitis: A Randomized Clinical Study.

The aim of this clinical study was to determine the efficacy of Uncaria tomentosa (cat's claw) against denture stomatitis (DS). Fifty patients with DS were randomly assigned into 3 groups to receive 2% miconazole, placebo, or 2% U tomentosa gel. DS level was recorded immediately, after 1 week of treatment, and 1 week after treatment. The clinical effectiveness of each treatment was measured using Newton's criteria. Mycologic samples from palatal mucosa and prosthesis were obtained to determinate colony forming units per milliliter (CFU/mL) and fungal identification at each evaluation period. Candida species were identified with HiCrome Candida and API 20C AUX biochemical test. DS severity decreased in all groups (P < .05). A significant reduction in number of CFU/mL after 1 week (P < .05) was observed for all groups and remained after 14 days (P > .05). C albicans was the most prevalent microorganism before treatment, followed by C tropicalis, C glabrata, and C krusei, regardless of the group and time evaluated. U tomentosa gel had the same effect as 2% miconazole gel. U tomentosa gel is an effective topical adjuvant treatment for denture stomatitis.

Estimated Disability-adjusted Life Years Averted By Long-term Provision Of Long Acting Contraceptive Methods In A Brazilian Clinic.

What is the contribution of the provision, at no cost for users, of long acting reversible contraceptive methods (LARC; copper intrauterine device [IUD], the levonorgestrel-releasing intrauterine system [LNG-IUS], contraceptive implants and depot-medroxyprogesterone [DMPA] injection) towards the disability-adjusted life years (DALY) averted through a Brazilian university-based clinic established over 30 years ago. Over the last 10 years of evaluation, provision of LARC methods and DMPA by the clinic are estimated to have contributed to DALY averted by between 37 and 60 maternal deaths, 315-424 child mortalities, 634-853 combined maternal morbidity and mortality and child mortality, and 1056-1412 unsafe abortions averted. LARC methods are associated with a high contraceptive effectiveness when compared with contraceptive methods which need frequent attention; perhaps because LARC methods are independent of individual or couple compliance. However, in general previous studies have evaluated contraceptive methods during clinical studies over a short period of time, or not more than 10 years. Furthermore, information regarding the estimation of the DALY averted is scarce. We reviewed 50 004 medical charts from women who consulted for the first time looking for a contraceptive method over the period from 2 January 1980 through 31 December 2012. Women who consulted at the Department of Obstetrics and Gynaecology, University of Campinas, Brazil were new users and users switching contraceptive, including the copper IUD (n = 13 826), the LNG-IUS (n = 1525), implants (n = 277) and DMPA (n = 9387). Estimation of the DALY averted included maternal morbidity and mortality, child mortality and unsafe abortions averted. We obtained 29 416 contraceptive segments of use including 25 009 contraceptive segments of use from 20 821 new users or switchers to any LARC method or DMPA with at least 1 year of follow-up. The mean (± SD) age of the women at first consultation ranged from 25.3 ± 5.7 (range 12-47) years in the 1980s, to 31.9 ± 7.4 (range 16-50) years in 2010-2011. The most common contraceptive chosen at the first consultation was copper IUD (48.3, 74.5 and 64.7% in the 1980s, 1990s and 2000s, respectively). For an evaluation over 20 years, the cumulative pregnancy rates (SEM) were 0.4 (0.2), 2.8 (2.1), 4.0 (0.4) and 1.3 (0.4) for the LNG-IUS, the implants, copper IUD and DMPA, respectively and cumulative continuation rates (SEM) were 15.1 (3.7), 3.9 (1.4), 14.1 (0.6) and 7.3 (1.7) for the LNG-IUS, implants, copper IUD and DMPA, respectively (P < 0.001). Over the last 10 years of evaluation, the estimation of the contribution of the clinic through the provision of LARC methods and DMPA to DALY averted was 37-60 maternal deaths; between 315 and 424 child mortalities; combined maternal morbidity and mortality and child mortality of between 634 and 853, and 1056-1412 unsafe abortions averted. The main limitations are the number of women who never returned to the clinic (overall 14% among the four methods under evaluation); consequently the pregnancy rate could be different. Other limitations include the analysis of two kinds of copper IUD and two kinds of contraceptive implants as the same IUD or implant, and the low number of users of implants. In addition, the DALY calculation relies on a number of estimates, which may vary in different parts of the world. LARC methods and DMPA are highly effective and women who were well-counselled used these methods for a long time. The benefit of averting maternal morbidity and mortality, child mortality, and unsafe abortions is an example to health policy makers to implement more family planning programmes and to offer contraceptive methods, mainly LARC and DMPA, at no cost or at affordable cost for the underprivileged population. This study received partial financial support from the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant # 2012/12810-4 and from the National Research Council (CNPq), grant #573747/2008-3. B.F.B., M.P.G., and V.M.C. were fellows from the scientific initiation programme from FAPESP. Since the year 2001, all the TCu380A IUD were donated by Injeflex, São Paulo, Brazil, and from the year 2006 all the LNG-IUS were donated by the International Contraceptive Access Foundation (ICA), Turku, Finland. Both donations are as unrestricted grants. The authors declare that there are no conflicts of interest associated with this study.

Evaluation Of The Effect Of Different Methods Of Microabrasion And Polishing On Surface Roughness Of Dental Enamel.

The microabrasion technique of enamel consists of selectively abrading the discolored areas or causing superficial structural changes in a selective way. In microabrasion technique, abrasive products associated with acids are used, and the evaluation of enamel roughness after this treatment, as well as surface polishing, is necessary. This in-vitro study evaluated the enamel roughness after microabrasion, followed by different polishing techniques. Roughness analyses were performed before microabrasion (L1), after microabrasion (L2), and after polishing (L3).Thus, 60 bovine incisive teeth divided into two groups were selected (n=30): G1- 37% phosphoric acid (37%) (Dentsply) and pumice; G2- hydrochloric acid (6.6%) associated with silicon carbide (Opalustre - Ultradent). Thereafter, the groups were divided into three sub-groups (n=10), according to the system of polishing: A - Fine and superfine granulation aluminum oxide discs (SofLex 3M); B - Diamond Paste (FGM) associated with felt discs (FGM); C - Silicone tips (Enhance - Dentsply). A PROC MIXED procedure was applied after data exploratory analysis, as well as the Tukey-Kramer test (5%). No statistical differences were found between G1 and G2 groups. L2 differed statistically from L1 and showed superior amounts of roughness. Differences in the amounts of post-polishing roughness for specific groups (1A, 2B, and 1C) arose, which demonstrated less roughness in L3 and differed statistically from L2 in the polishing system. All products increased enamel roughness, and the effectiveness of the polishing systems was dependent upon the abrasive used.

Effects Of Sterilization Methods On The Physical, Chemical, And Biological Properties Of Silk Fibroin Membranes.

Silk fibroin has been widely explored for many biomedical applications, due to its biocompatibility and biodegradability. Sterilization is a fundamental step in biomaterials processing and it must not jeopardize the functionality of medical devices. The aim of this study was to analyze the influence of different sterilization methods in the physical, chemical, and biological characteristics of dense and porous silk fibroin membranes. Silk fibroin membranes were treated by several procedures: immersion in 70% ethanol solution, ultraviolet radiation, autoclave, ethylene oxide, and gamma radiation, and were analyzed by scanning electron microscopy, Fourier-transformed infrared spectroscopy (FTIR), X-ray diffraction, tensile strength and in vitro cytotoxicity to Chinese hamster ovary cells. The results indicated that the sterilization methods did not cause perceivable morphological changes in the membranes and the membranes were not toxic to cells. The sterilization methods that used organic solvent or an increased humidity and/or temperature (70% ethanol, autoclave, and ethylene oxide) increased the silk II content in the membranes: the dense membranes became more brittle, while the porous membranes showed increased strength at break. Membranes that underwent sterilization by UV and gamma radiation presented properties similar to the nonsterilized membranes, mainly for tensile strength and FTIR results.

Selection And Validation Of Reference Genes For Functional Studies In The Calliphoridae Family.

The genera Cochliomyia and Chrysomya contain both obligate and saprophagous flies, which allows the comparison of different feeding habits between closely related species. Among the different strategies for comparing these habits is the use of qPCR to investigate the expression levels of candidate genes involved in feeding behavior. To ensure an accurate measure of the levels of gene expression, it is necessary to normalize the amount of the target gene with the amount of a reference gene having a stable expression across the compared species. Since there is no universal gene that can be used as a reference in functional studies, candidate genes for qPCR data normalization were selected and validated in three Calliphoridae (Diptera) species, Cochliomyia hominivorax Coquerel, Cochliomyia macellaria Fabricius, and Chrysomya albiceps Wiedemann . The expression stability of six genes ( Actin, Gapdh, Rp49, Rps17, α -tubulin, and GstD1) was evaluated among species within the same life stage and between life stages within each species. The expression levels of Actin, Gapdh, and Rp49 were the most stable among the selected genes. These genes can be used as reliable reference genes for functional studies in Calliphoridae using similar experimental settings.

Classification Of Frankfurters By Ft-raman Spectroscopy And Chemometric Methods.

Frankfurters are widely consumed all over the world, and the production requires a wide range of meat and non-meat ingredients. Due to these characteristics, frankfurters are products that can be easily adulterated with lower value meats, and the presence of undeclared species. Adulterations are often still difficult to detect, due the fact that the adulterant components are usually very similar to the authentic product. In this work, FT-Raman spectroscopy was employed as a rapid technique for assessing the quality of frankfurters. Based on information provided by the Raman spectra, a multivariate classification model was developed to identify the frankfurter type. The aim was to study three types of frankfurters (chicken, turkey and mixed meat) according to their Raman spectra, based on the fatty vibrational bands. Classification model was built using partial least square discriminant analysis (PLS-DA) and the performance model was evaluated in terms of sensitivity, specificity, accuracy, efficiency and Matthews's correlation coefficient. The PLS-DA models give sensitivity and specificity values on the test set in the ranges of 88%-100%, showing good performance of the classification models. The work shows the Raman spectroscopy with chemometric tools can be used as an analytical tool in quality control of frankfurters.

Mechanical And Photoelastic Analysis Of Four Different Fixation Methods For Mandibular Body Fractures.

The aim of the present study was to compare four methods of fixation in mandibular body fractures. Mechanical and photoelastic tests were performed using polyurethane and photoelastic resin mandibles, respectively. The study groups contained the following: (I), two miniplates of 2.0 mm; (II) one 2.0 mm plate and an Erich arch bar; (III) one 2.4 mm plate and an Erich arch bar, and (IV) one 2.0 mm plate and one 2.4 mm plate. The differences between the mean values were analyzed using Tukey's test, the Mann-Whitney test and the Bonferroni correction. Group II recorded the lowest resistance, followed by groups I, IV and III. The photoelastic test confirmed the increase of tension in group II. The 2.4 mm system board in linear mandibular body fractures provided more resistance and the use of only one 2.0 mm plate in the central area of the mandible created higher tension.

Delivery Of A Urology Online Course Using Moodle Versus Didactic Lectures Methods.

To subjectively and objectively compare an accessible interactive electronic library using Moodle with lectures for urology teaching of medical students. Forty consecutive fourth-year medical students and one urology teacher were exposed to two teaching methods (4 weeks each) in the form of problem-based learning: - lectures and - student-centered group discussion based on Moodle (modular object-oriented dynamic learning environment) full time online delivered (24/7) with video surgeries, electronic urology cases and additional basic principles of the disease process. All 40 students completed the study. While 30% were moderately dissatisfied with their current knowledge base, online learning course delivery using Moodle was considered superior to the lectures by 86% of the students. The study found the following observations: (1) the increment in learning grades ranged from 7.0 to 9.7 for students in the online Moodle course compared to 4.0-9.6 to didactic lectures; (2) the self-reported student involvement in the online course was characterized as large by over 60%; (3) the teacher-student interaction was described as very frequent (50%) and moderately frequent (50%); and (4) more inquiries and requisitions by students as well as peer assisting were observed from the students using the Moodle platform. The Moodle platform is feasible and effective, enthusing medical students to learn, improving immersion in the urology clinical rotation and encouraging the spontaneous peer assisted learning. Future studies should expand objective evaluations of knowledge acquisition and retention.