Exploring Constrained Type-2 fuzzy sets

Fuzzy logic has been widely used to model human reasoning thanks to its inherent capability of handling uncertainty. In particular, the introduction of Type-2 fuzzy sets added the possibility of expressing uncertainty even on the definition of the membership functions. Type-2 sets, however, don’t pose any restrictions on the continuity or convexity of their embedded sets while these properties may be desirable in certain contexts. To overcome this problem, Constrained Type-2 fuzzy sets have been proposed. In this paper, we focus on Interval Constrained Type-2 sets to see how their unique structure can be exploited to build a new inference process. This will set some ground work for future developments, such as the design of a new defuzzification process for Constrained Type-2 fuzzy systems

Novel techniques for modelling uncertain human reasoning in explainable artificial intelligence

In recent years, there has been a growing need for intelligent systems that not only are able to provide reliable predictions but can also produce explanations for their outputs. The demand for increased explainability has led to the emergence of explainable artificial intelligence (XAI) as a specific research field. In this context, fuzzy logic systems represent a promising tool thanks to their inherently interpretable structure. The use of a rule-base and linguistic terms, in fact, have allowed researchers to design models with a transparent decision process, from which it is possible to extract human-understandable explanations. The use of interval type-2 fuzzy logic in the XAI field, however, is limited: the improved performances of interval type-2 fuzzy systems and their ability to handle a higher degree of uncertainty comes at the cost of increased complexity that makes the semantic mapping between the input and outputs harder to understand intuitively. The presence of type-reduction, in some contexts fail to preserve the semantic value of the fuzzy sets and rules involved in the decision process. By semantic value, we specifically refer to the capacity of interpreting the output of the fuzzy system in respect to the pre-defined and thus understood linguistic variables used for the antecedents and consequents of the system. An attempt at increasing the explainability of interval type-2 fuzzy logic was first established by Garibaldi and Guadarrama in 2011, with the introduction of constrained type-2 fuzzy sets. However, extensive work needs to be carried out to develop the algorithms necessary for their practical use in fuzzy systems. The aim of this thesis is to extend the initial work on constrained interval type-2 fuzzy sets to develop a framework that preserves the semantic value throughout the modelling and decision process. Achieving this goal would allow the creation of a new class of fuzzy systems that show additional interpretable properties, and could further encourage the use of interval type-2 fuzzy logic in XAI. After the formal definition of the required components and theorems, different approaches are explored to develop inference algorithms that preserve the semantic value of the sets during the input-output mapping, while keeping reasonable run-times on modern computer hardware. The novel frameworks are then tested in a series of practical applications from the real world, in order to assess both their prediction performances and show the quality of the explanations these models can generate. Finally, the original definitions of constrained intervals type-2 fuzzy sets are refined to produce a novel approach which combines uncertain data and represents them using intuitive constrained interval type-2 fuzzy sets. Overall, as a result of the work presented here, it is now possible to design constrained interval type-2 fuzzy systems that preserve the enhanced semantic value provided by constrained interval-type-2 fuzzy sets throughout the inference, type-reduction and defuzzification stages. This characteristic is then used to improve the semantic interpretability of the system outputs, making constrained interval type-2 fuzzy systems a valuable alternative to interval type-2 fuzzy systems in XAI. The research presented here has resulted in three journal articles, two of which have already been published in IEEE Transactions on Fuzzy Systems, and four papers presented at the FUZZ-IEEE international conference between 2018 and 2020

Eradication of Candida albicans persister cell biofilm by the membranotropic peptide gH625

Biofilm formation poses an important clinical trouble due to resistance to antimicrobial agents; therefore, there is an urgent demand for new antibiofilm strategies that focus on the use of alternative compounds also in combination with conventional drugs. Drug-tolerant persisters are present in Candida albicans biofilms and are detected following treatment with high doses of amphotericin B. In this study, persisters were found in biofilms treated with amphotericin B of two clinical isolate strains, and were capable to form a new biofilm in situ. We investigated the possibility of eradicating persister-derived biofilms from these two Candida albicans strains, using the peptide gH625 analogue (gH625-M). Confocal microscopy studies allowed us to characterize the persister-derived biofilm and understand the mechanism of interaction of gH625-M with the biofilm. These findings confirm that persisters may be responsible for Candida biofilm survival, and prove that gH625-M was very effective in eradicating persister-derived biofilms both alone and in combination with conventional antifungals, mainly strengthening the antibiofilm activity of fluconazole and 5-flucytosine. Our strategy advances our insights into the development of effective antibiofilm therapeutic approaches

Constrained interval type-2 fuzzy sets

In many contexts, type-2 fuzzy sets are obtained from a type-1 fuzzy set to which we wish to add uncertainty. However, in the current type-2 representation there is no restriction on the shape of the footprint of uncertainty and the embedded sets that can be considered acceptable. This leads, usually, to the loss of the semantic relationship between the type-2 fuzzy set and the concept it models. As a consequence, the interpretability of some of the embedded sets and the explainability of the uncertainty measures obtained from them can decrease. To overcome these issues, constrained type-2 fuzzy sets have been proposed. However, no formal definitions for some of their key components (e.g. acceptable embedded sets) and constrained operations have been given. The goal of this paper is to provide some theoretical underpinning for the definition of constrained type-2 sets, their inferencing and defuzzification method. To conclude, the constrained inference framework is presented, applied to two real world cases and briefly compared to the standard interval type-2 inference and defuzzification method

Paradoxical effects of chemotherapy on tumor relapse and metastasis promotion.

Several lines of compelling pre-clinical evidence identify chemotherapy as a potentially double-edged sword: therapeutic efficacy on the primary tumor may in fact be counterbalanced by the induction of tumor/host reactive responses supportive for survival and dissemination of cancer cell subpopulations. This paradoxical effect of chemotherapy can affect different districts such as the primary tumor, the circulation and distant organs by simultaneously shaping properties and composition of tumor and stromal cells. At the primary tumor site, chemotherapy has been reported to promote selection of chemoresistant and disseminating tumor cells endowed with properties of cancer stem cells (CSCs) through activation of autocrine and paracrine self-renewing/survival pathways promoted jointly by therapy-selected tumor and stromal cells. Resistant CSCs represent seeds for tumor relapse and increased infiltration by immune cells, together with enhanced vascular permeability induced by chemotherapy, facilitates tumor cells intravasation, the first step of the metastatic cascade. As a consequence of primary tumor/metastasis re-shaping induced by chemotherapy, circulating tumor cells (CTCs) detected during therapy can display a shift towards a more mesenchymal and stem-like phenotype, conductive to increased ability to survive in the circulation and seed distant organs. At the metastatic site, host responses to therapy activate inflammatory pathways that ultimately facilitate tumor cells extravasation and metastatic colonization. Finally, cooperation of immune cells and endothelial cells at perivascular niches favors the extravasation of tumor cells endowed with high potential for metastasis initiation and protects them from chemotherapy. This review highlights the paradoxical pro-metastatic effects of chemotherapy linking reactive responses to treatment to tumor relapse and metastasis formation through primary tumor remodeling and generation of a favorable pro-metastatic niche

Novel techniques for modelling uncertain human reasoning in explainable artificial intelligence

In recent years, there has been a growing need for intelligent systems that not only are able to provide reliable predictions but can also produce explanations for their outputs. The demand for increased explainability has led to the emergence of explainable artificial intelligence (XAI) as a specific research field. In this context, fuzzy logic systems represent a promising tool thanks to their inherently interpretable structure. The use of a rule-base and linguistic terms, in fact, have allowed researchers to design models with a transparent decision process, from which it is possible to extract human-understandable explanations. The use of interval type-2 fuzzy logic in the XAI field, however, is limited: the improved performances of interval type-2 fuzzy systems and their ability to handle a higher degree of uncertainty comes at the cost of increased complexity that makes the semantic mapping between the input and outputs harder to understand intuitively. The presence of type-reduction, in some contexts fail to preserve the semantic value of the fuzzy sets and rules involved in the decision process. By semantic value, we specifically refer to the capacity of interpreting the output of the fuzzy system in respect to the pre-defined and thus understood linguistic variables used for the antecedents and consequents of the system. An attempt at increasing the explainability of interval type-2 fuzzy logic was first established by Garibaldi and Guadarrama in 2011, with the introduction of constrained type-2 fuzzy sets. However, extensive work needs to be carried out to develop the algorithms necessary for their practical use in fuzzy systems. The aim of this thesis is to extend the initial work on constrained interval type-2 fuzzy sets to develop a framework that preserves the semantic value throughout the modelling and decision process. Achieving this goal would allow the creation of a new class of fuzzy systems that show additional interpretable properties, and could further encourage the use of interval type-2 fuzzy logic in XAI. After the formal definition of the required components and theorems, different approaches are explored to develop inference algorithms that preserve the semantic value of the sets during the input-output mapping, while keeping reasonable run-times on modern computer hardware. The novel frameworks are then tested in a series of practical applications from the real world, in order to assess both their prediction performances and show the quality of the explanations these models can generate. Finally, the original definitions of constrained intervals type-2 fuzzy sets are refined to produce a novel approach which combines uncertain data and represents them using intuitive constrained interval type-2 fuzzy sets. Overall, as a result of the work presented here, it is now possible to design constrained interval type-2 fuzzy systems that preserve the enhanced semantic value provided by constrained interval-type-2 fuzzy sets throughout the inference, type-reduction and defuzzification stages. This characteristic is then used to improve the semantic interpretability of the system outputs, making constrained interval type-2 fuzzy systems a valuable alternative to interval type-2 fuzzy systems in XAI. The research presented here has resulted in three journal articles, two of which have already been published in IEEE Transactions on Fuzzy Systems, and four papers presented at the FUZZ-IEEE international conference between 2018 and 2020

Management challenges of deep infiltrating endometriosis.

Deep infiltrating endometriosis (DIE) is considered the most aggressive form among the three phenotypes that constitute endometriosis. It can affect the whole pelvis, subverting the anatomy and functionality of vital organs, with an important negative impact on the patient’s quality of life. The diagnosis of DIE is based on clinical and physical examination, instrumental examination, and, if surgery is needed, the identification and biopsy of lesions. The choice of the best therapeutic approach for women with DIE is often challenging. Therapeutic options include medical and surgical treatment, and the decision should be dictated by the patient’s medical history, disease stage, symptoms severity, and personal choice. Medical therapy can control the symptoms and stop the development of pathology, keeping in mind the side effects derived from a long-term treatment and the risk of recurrence once suspended. Surgical treatment should be proposed only when it is strictly necessary (failed hormone therapy, contraindications to hormone treatment, severity of symptoms, infertility), preferring, whenever possible, a conservative approach performed by a multidisciplinary team. All therapeutic possibilities have to be explained by the physicians in order to help the patients to make the right choice and minimize the impact of the disease on their lives

Genetic Characterization of Endometriosis Patients: Review of the Literature and a Prospective Cohort Study on a Mediterranean Population

The pathogenesis of endometriosis is unknown, but some evidence supports a genetic predisposition. The purpose of this study was to evaluate the recent literature on the genetic characterization of women affected by endometriosis and to evaluate the influence of polymorphisms of the wingless‐type mammalian mouse tumour virus integration site family member 4 (WNT4), vezatin (VEZT), and follicle stimulating hormone beta polypeptide (FSHB) genes, already known to be involved in molecular mechanisms associated with the proliferation and development of endometriotic lesions in the Sardinian population. Materials and Methods: In order to provide a comprehensive and systematic tool for those approaching the genetics of endometriosis, the most cited review, observational, cohort and case‐control studies that have evaluated the genetics of endometriosis in the last 20 years were collected. Moreover, 72 women were recruited for a molecular biology analysis of whole‐blood samples—41 patients affected by symptomatic endometriosis and 31 controls. The molecular typing of three single nucleotide polymorphisms (SNPs) was evaluated in patients and controls: rs7521902, rs10859871 and rs11031006, mapped respectively in the WNT4, VEZT and FSHB genes. In this work, the frequency of alleles, genotypes and haplotypes of these SNPs in Sardinian women is described. Results: From the initial search, a total of 73 articles were chosen. An analysis of the literature showed that in endometriosis pathogenesis, the contribution of genetics has been well supported by many studies. The frequency of genotypes observed in the groups of the study population of 72 women was globally coherent with the law of the Hardy–Weinberg equilibrium. For the SNP rs11031006 (FSHB), the endometriosis group did not show an increase in genotypic or allelic frequency due to this polymorphism compared to the control group (p = 0.9999, odds ratio (OR) = 0.000, 95% confidence interval (CI), 0.000–15.000 and p = 0.731, OR = 1639, 95% CI, 0.39–683, respectively, for the heterozygous genotype and the polymorphic minor allele). For the SNP rs10859871 (VEZT), we found a significant difference in the frequency of the homozygous genotype in the control group compared to the affected women (p = 0.0111, OR = 0.0602, 95% CI, 0.005–0.501). For the SNP rs7521902 (WNT4), no increase in genotypic or allelic frequency between the two groups was shown (p = 0.3088, OR = 0.4133, 95% CI, 0.10–1.8 and p = 0.3297, OR = 2257, 95% CI, 0.55–914, respectively, for the heterozygous genotype and the polymorphic minor allele). Conclusion: An analysis of recent publications on the genetics of endometriosis showed a discrepancy in the results obtained in different populations. In the Sardinian population, the results obtained do not show a significant association between the investigated variants of the genes and a greater risk of developing endometriosis, although several other studies in the literature have shown the opposite. Anyway, the data underline the importance of evaluating genetic variants in different populations. In fact, in different ethnic groups, it is possible that specific risk alleles could act differently in the pathogenesis of the disease