Fuzzy spectral clustering methods for textual data

Nowadays, the development of advanced information technologies has determined an increase in the production of textual data. This inevitable growth accentuates the need to advance in the identification of new methods and tools able to efficiently analyse such kind of data. Against this background, unsupervised classification techniques can play a key role in this process since most of this data is not classified. Document clustering, which is used for identifying a partition of clusters in a corpus of documents, has proven to perform efficiently in the analyses of textual documents and it has been extensively applied in different fields, from topic modelling to information retrieval tasks. Recently, spectral clustering methods have gained success in the field of text classification. These methods have gained popularity due to their solid theoretical foundations which do not require any specific assumption on the global structure of the data. However, even though they prove to perform well in text classification problems, little has been done in the field of clustering. Moreover, depending on the type of documents analysed, it might be often the case that textual documents do not contain only information related to a single topic: indeed, there might be an overlap of contents characterizing different knowledge domains. Consequently, documents may contain information that is relevant to different areas of interest to some degree. The first part of this work critically analyses the main clustering algorithms used for text data, involving also the mathematical representation of documents and the pre-processing phase. Then, three novel fuzzy versions of spectral clustering algorithms for text data are introduced. The first one exploits the use of fuzzy K-medoids instead of K-means. The second one derives directly from the first one but is used in combination with Kernel and Set Similarity (KS2M), which takes into account the Jaccard index. Finally, in the third one, in order to enhance the clustering performance, a new similarity measure S∗ is proposed. This last one exploits the inherent sequential nature of text data by means of a weighted combination between the Spectrum string kernel function and a measure of set similarity. The second part of the thesis focuses on spectral bi-clustering algorithms for text mining tasks, which represent an interesting and partially unexplored field of research. In particular, two novel versions of fuzzy spectral bi-clustering algorithms are introduced. The two algorithms differ from each other for the approach followed in the identification of the document and the word partitions. Indeed, the first one follows a simultaneous approach while the second one a sequential approach. This difference leads also to a diversification in the choice of the number of clusters. The adequacy of all the proposed fuzzy (bi-)clustering methods is evaluated by experiments performed on both real and benchmark data sets

Pseudomonas aeruginosa Exploits Lipid A and Muropeptides Modification as a Strategy to Lower Innate Immunity during Cystic Fibrosis Lung Infection

Pseudomonas aeruginosa can establish life-long airways chronic infection in patients with cystic fibrosis (CF) with pathogenic variants distinguished from initially acquired strain. Here, we analysed chemical and biological activity of P. aeruginosa Pathogen-Associated Molecular Patterns (PAMPs) in clonal strains, including mucoid and non-mucoid phenotypes, isolated during a period of up to 7.5 years from a CF patient. Chemical structure by MS spectrometry defined lipopolysaccharide (LPS) lipid A and peptidoglycan (PGN) muropeptides with specific structural modifications temporally associated with CF lung infection. Gene sequence analysis revealed novel mutation in pagL, which supported lipid A changes. Both LPS and PGN had different potencies when activating host innate immunity via binding TLR4 and Nod1. Significantly higher NF-kB activation, IL-8 expression and production were detected in HEK293hTLR4/MD2-CD14 and HEK293hNod1 after stimulation with LPS and PGN respectively, purified from early P. aeruginosa strain as compared to late strains. Similar results were obtained in macrophages-like cells THP-1, epithelial cells of CF origin IB3-1 and their isogenic cells C38, corrected by insertion of cystic fibrosis transmembrane conductance regulator (CFTR). In murine model, altered LPS structure of P. aeruginosa late strains induces lower leukocyte recruitment in bronchoalveolar lavage and MIP-2, KC and IL-1β cytokine levels in lung homogenates when compared with early strain. Histopathological analysis of lung tissue sections confirmed differences between LPS from early and late P. aeruginosa. Finally, in this study for the first time we unveil how P. aeruginosa has evolved the capacity to evade immune system detection, thus promoting survival and establishing favourable conditions for chronic persistence. Our findings provide relevant information with respect to chronic infections in CF

Document clustering

Nowadays, the explosive growth in text data emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large-scale data. Given the vast amount of this kind of unstructured data, the majority of it is not classified, hence unsupervised learning techniques show to be useful in this field. Document clustering has proven to be an efficient tool in organizing textual documents and it has been widely applied in different areas from information retrieval to topic modeling. Before introducing the proposals of document clustering algorithms, the principal steps of the whole process, including the mathematical representation of documents and the preprocessing phase, are discussed. Then, the main clustering algorithms used for text data are critically analyzed, considering prototype-based, graph-based, hierarchical, and model-based approaches

A Fuzzy clustering approach for textual data

Document clustering is a process of partitioning a corpus of documents into distinctive clusters based on the content similarity. Traditional (hard or fuzzy) document clustering algorithms are usually relying on the vector representation of documents based on the bag-of-words (BOW) approach, leading to very high dimensions in the vector representation of the corpus. In recent years, spectral clustering has been extensively applied in the field of text classification with support vector machines (SVMs) in combination with string kernels, but little has been done in the field of fuzzy document clustering with kernel-based methods. This work proposes a novel approach to text clustering, by grouping documents into clusters based on a new version of fuzzy spectral clustering with string kernels

Klotho-FGF23, cardiovascular disease, and vascular calcification: black or white?

Patients affected by Chronic Kidney Disease and Mineral Bone Disorder (CKD-MBD) have a high risk of cardiovascular (CV) mortality that is poorly explained by traditional risk factors. The newest medical treatments for CKD-MBD have been associated with encouraging, but still inconsistent, improvement in CV disease complications and patient survival. A better understanding of the biomarkers and mechanisms of left ventricular hypertrophy (LVH), atherosclerosis, and vascular calcification (VC) may help with diagnosis and treatment of the organ damage that occurs secondary to CKD-MBD, thus improving survival. Recent insights about fibroblast growth factor-23 (FGF23) and its co-receptor, Klotho, have led to marked advancement in interpreting data on vascular aging and CKD-MBD. This review will discuss the current experimental and clinical evidence regarding FGF23 and Klotho, with a particular focus on their roles in LVH, atherosclerosis, and VC

The Key Role of Phosphate on Vascular Calcification

Vascular calcification (VC) is common in dialysis and non-dialysis chronic kidney disease (CKD) patients, even in the early stage of the disease. For this reason, it can be considered a CKD hallmark. VC contributes to cardiovascular disease (CVD) and increased mortality among CKD patients, although it has not been proven. There are more than one type of VC and every form represents a marker of systemic vascular disease and is associated with a higher prevalence of CVD in CKD patients, as shown by several clinical studies. Major risk factors for VC in CKD include: Increasing age, dialysis vintage, hyperphosphatemia (particularly in the setting of intermittent or persistent hypercalcemia), and a positive net calcium and phosphate balance. Excessive oral calcium intake, including calcium-containing phosphate binders, increases the risk for VC. Moreover, it has been demonstrated that there is less VC progression with non-calcium-containing phosphate binders. Unfortunately, until now, a specific therapy to prevent progression or to facilitate regression of VC has been found, beyond careful attention to calcium and phosphate balance

New sensing and radar absorbing laminate combining structural damage detection and electromagnetic wave absorption properties

Within the paradigm of smart mobility, the development of innovative materials aimed at improving resilience against structural failure in lightweight vehicles and electromagnetic interferences (EMI) due to wireless communications in guidance systems is of crucial relevance to improve safety, sustainability, and reliability in both aeronautical and automotive applications. In particular, the integration of intelligent structural health monitoring and electromagnetic (EM) shielding systems with radio frequency absorbing properties into a polymer composite laminate is still a challenge. In this paper, we present an innovative system consisting of a multi-layered thin panel which integrates nanostructured coatings to combine EM disturbance suppression and low-energy impact monitoring ability. Specifically, it is composed of a stack of dielectric and conductive layers constituting the sensing and EM-absorbing laminate (SEAL). The conductive layers are made of a polyurethane paint filled with graphene nanoplatelets (GNPs) at different concentrations to tailor the effective electrical conductivity and the functionality of the material. Basically, the panel includes a piezoresistive grid, obtained by selectively spraying onto mylar a low-conductive paint with 4.5 wt.% of GNPs and an EM-absorbing lossy sheet made of the same polyurethane paint but properly modified with a higher weight fraction (8 wt.%) of graphene. The responses of the grid's strain sensors were analyzed through quasi-static mechanical bending tests, whereas the absorbing properties were evaluated through free-space and waveguide-based measurement techniques in the X, Ku, K, and Ka bands. The experimental results were also validated by numerical simulations