An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma.

Asthma is a disease characterized by chronic airway hyperresponsiveness and inflammation, with signs of variable airflow limitation and impaired lung function leading to respiratory symptoms such as shortness of breath, chest tightness and cough. Eosinophilic asthma is a distinct phenotype that affects more than half of patients diagnosed with severe asthma. It can be effectively treated with monoclonal antibodies targeting specific immunological signaling pathways that fuel the inflammation underlying the disease, particularly Interleukin-5 (IL-5), a cytokine that plays a crucial role in asthma. In this study, we propose a data analysis pipeline aimed at identifying subphenotypes of severe eosinophilic asthma in relation to response to therapy at follow-up, which could have great potential for use in routine clinical practice. Once an optimal partition of patients into subphenotypes has been determined, the labels indicating the group to which each patient has been assigned are used in a novel way. For each input variable in a specialized logistic regression model, a clusterwise effect on response to therapy is determined by an appropriate interaction term between the input variable under consideration and the cluster label. We show that the clusterwise odds ratios can be meaningfully interpreted conditional on the cluster label. In this way, we can define an effect measure for the response variable for each input variable in each of the groups identified by the clustering algorithm, which is not possible in standard logistic regression because the effect of the reference class is aliased with the overall intercept. The interpretability of the model is enforced by promoting sparsity, a goal achieved by learning interactions in a hierarchical manner using a special group-Lasso technique. In addition, valid expressions are provided for computing odds ratios in the unusual parameterization used by the sparsity-promoting algorithm. We show how to apply the proposed data analysis pipeline to the problem of sub-phenotyping asthma patients also in terms of quality of response to therapy with monoclonal antibodies.

GradeAid: a framework for automatic short answers grading in educational contexts-design, implementation and evaluation.

Automatic short answer grading (ASAG), a hot field of natural language understanding, is a research area within learning analytics. ASAG solutions are conceived to offload teachers and instructors, especially those in higher education, where classes with hundreds of students are the norm and the task of grading (short)answers to open-ended questionnaires becomes tougher. Their outcomes are precious both for the very grading and for providing students with "ad hoc" feedback. ASAG proposals have also enabled different intelligent tutoring systems. Over the years, a variety of ASAG solutions have been proposed, still there are a series of gaps in the literature that we fill in this paper. The present work proposes GradeAid, a framework for ASAG. It is based on the joint analysis of lexical and semantic features of the students' answers through state-of-the-art regressors; differently from any other previous work, (i) it copes with non-English datasets, (ii) it has undergone a robust validation and benchmarking phase, and (iii) it has been tested on every dataset publicly available and on a new dataset (now available for researchers). GradeAid obtains performance comparable to the systems presented in the literature (root-mean-squared errors down to 0.25 based on the specific tuple ⟨dataset-question⟩). We argue it represents a strong baseline for further developments in the field.

Consumer perception of attributes of organic food in Italy: A CUB model study.

Organic food, consumers and their buying behaviour are well examined fields of research, although there is a lack of consistent findings on consumers' perception about organic food's quality, in terms of healthiness, safety, and environmental sustainability, and on determinants of perceived quality. This study investigates how consumers perceive the quality of organic food, in terms of environmental sustainability, safety, and healthiness. The study also analyses how and to what extent perceived quality of organic food is influenced by the presence of information related to quality on food products' labels and consumers' socio-demographic profile. A survey has been conducted on a convenience sample of Italian consumers, recruited through a snowball sampling technique. An approach based on a Combination of Uniform and shifted Binomial random variables, named CUB model, is adopted to analyse consumers' perceptions in terms of two latent components, feeling and uncertainty. The CUB model approach is suitable for analyses that involve consumers perception. The results suggest that consumers perceive safety of organic food better than healthiness and environmentally sustainable attributes. Findings also highlight that the presence of specific information on food's label contributes to perceive organic food as healthier, safe, and environmentally sustainable: the more the details on food labels, the higher the consumers' perception. Furthermore, consumers' socio-demographic profile plays a significant role: males and females have a different perception of organic food and younger consumers tend to be more prone to buy and consume organic product.

Beta-catenin and epithelial tumors: a study based on 374 oropharyngeal cancers.

INTRODUCTION: Although altered regulation of the Wnt pathway via beta-catenin is a frequent event in several human cancers, its potential implications in oral/oropharyngeal squamous cell carcinomas (OSCC/OPSCC) are largely unexplored. Work purpose was to define association between beta-catenin expression and clinical-pathological parameters in 374 OSCCs/OP-SCCs by immunohistochemistry (IHC). MATERIALS AND METHODS: Association between IHC detected patterns of protein expression and clinical-pathological parameters was assessed by statistical analysis and survival rates by Kaplan-Meier curves. Beta-catenin expression was also investigated in OSCC cell lines by Real-Time PCR. An additional analysis of the DNA content was performed on 22 representative OSCCs/OPSCCs by DNA-image-cytometric analysis. RESULTS AND DISCUSSION: All carcinomas exhibited significant alterations of beta-catenin expression (P < 0.05). Beta-catenin protein was mainly detected in the cytoplasm of cancerous cells and only focal nuclear positivity was observed. Higher cytoplasmic expression correlated significantly with poor histological differentiation, advanced stage, and worst patient outcome (P < 0.05). By Real-Time PCR significant increase of beta-catenin mRNA was detected in OSCC cell lines and in 45% of surgical specimens. DNA ploidy study demonstrated high levels of aneuploidy in beta-catenin overexpressing carcinomas. CONCLUSIONS: This is the largest study reporting significant association between beta-catenin expression and clinical-pathological factors in patients with OSCCs/OPSCCs.