Enhancing Explainability in Mobility Data Science through a combination of methods

In the domain of Mobility Data Science, the intricate task of interpreting models trained on trajectory data, and elucidating the spatio-temporal movement of entities, has persistently posed significant challenges. Conventional XAI techniques, although brimming with potential, frequently overlook the distinct structure and nuances inherent within trajectory data. Observing this deficiency, we introduced a comprehensive framework that harmonizes pivotal XAI techniques: LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), Saliency maps, attention mechanisms, direct trajectory visualization, and Permutation Feature Importance (PFI). Unlike conventional strategies that deploy these methods singularly, our unified approach capitalizes on the collective efficacy of these techniques, yielding deeper and more granular insights for models reliant on trajectory data. In crafting this synthesis, we effectively address the multifaceted essence of trajectories, achieving not only amplified interpretability but also a nuanced, contextually rich comprehension of model decisions. To validate and enhance our framework, we undertook a survey to gauge preferences and reception among various user demographics. Our findings underscored a dichotomy: professionals with academic orientations, particularly those in roles like Data Scientist, IT Expert, and ML Engineer, showcased a profound, technical understanding and often exhibited a predilection for amalgamated methods for interpretability. Conversely, end-users or individuals less acquainted with AI and Data Science showcased simpler inclinations, such as bar plots indicating timestep significance or visual depictions pinpointing pivotal segments of a vessel's trajectory

Assessment of Circulating MicroRNAs for the Diagnosis and Disease Activity Evaluation in Patients with Ulcerative Colitis by Using the Nanostring Technology

Background: Clinical decision and patient care management in inflammatory bowel diseases is largely based on the assessment of clinical symptoms, while the biomarkers currently in use poorly reflect the actual disease activity. Therefore, the identification of novel biomarkers will serve an unmet clinical need for IBD screening and patient management. We examined the utility of circulating microRNAs for diagnosis and disease activity monitoring in ulcerative colitis (UC) patients. Methods: Blood serum microRNAs were isolated from UC patients with active and inactive disease and healthy donors. High-throughput microRNA profiling was performed using the Nanostring technology platform. Clinical disease activity was captured by calculating the partial Mayo score. C-reactive protein (CRP) was measured in UC patients as part of their clinical monitoring. The profiles of circulating microRNAs and CRP were correlated with clinical disease indices. Results: We have identified a signature of 12 circulating microRNAs that differentiate UC patients from control subjects. Moreover, six of these microRNAs significantly correlated with UC disease activity. Importantly, a set of four microRNAs (hsa-miR-4454, hsa-miR-223-3p, hsa-miR-23a-3p, and hsa-miR-320e) which correlated with UC disease activity, were found to have higher sensitivity and specificity values than CRP. Conclusions: Circulating microRNAs provide a novel diagnostic and prognostic marker for UC patients. The use of an FDA approved platform could accelerate the application of microRNA screening in a GI clinical setting. When used in combination with current diagnostic and disease activity assessment modalities, microRNAs could improve both IBD screening and care management

XAI for time-series classification leveraging image highlight methods

Although much work has been done on explainability in the computer vision and natural language processing (NLP) fields, there is still much work to be done to explain methods applied to time series as time series by nature can not be understood at first sight. In this paper, we present a Deep Neural Network (DNN) in a teacher-student architecture (distillation model) that offers interpretability in time-series classification tasks. The explainability of our approach is based on transforming the time series to 2D plots and applying image highlight methods (such as LIME and GradCam), making the predictions interpretable. At the same time, the proposed approach offers increased accuracy competing with the baseline model with the trade-off of increasing the training time

XAI for All: Can Large Language Models Simplify Explainable AI?

The field of Explainable Artificial Intelligence (XAI) often focuses on users with a strong technical background, making it challenging for non-experts to understand XAI methods. This paper presents "x-[plAIn]", a new approach to make XAI more accessible to a wider audience through a custom Large Language Model (LLM), developed using ChatGPT Builder. Our goal was to design a model that can generate clear, concise summaries of various XAI methods, tailored for different audiences, including business professionals and academics. The key feature of our model is its ability to adapt explanations to match each audience group's knowledge level and interests. Our approach still offers timely insights, facilitating the decision-making process by the end users. Results from our use-case studies show that our model is effective in providing easy-to-understand, audience-specific explanations, regardless of the XAI method used. This adaptability improves the accessibility of XAI, bridging the gap between complex AI technologies and their practical applications. Our findings indicate a promising direction for LLMs in making advanced AI concepts more accessible to a diverse range of users

Evaluating Digital Tools for Sustainable Agriculture using Causal Inference

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, it lacks tangible quantitative evidence on its benefits to the farmers. Field experiments can derive such evidence, but are often costly and time consuming. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust by enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to increase productivity and secure a sustainable and resilient agriculture against a changing climate. As a case study, we perform an empirical evaluation of a recommendation system for optimal cotton sowing, which was used by a farmers' cooperative during the growing season of 2021. We leverage agricultural knowledge to develop a causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently estimate it using several methods on observational data. The results show that a field sown according to our recommendations enjoyed a significant increase in yield (12% to 17%).Comment: Accepted for publication and spotlight presentation at Tackling Climate Change with Machine Learning: workshop at NeurIPS 202

Securitizing the Environment? A discourse analysis of key United Nations documents on climate change

Over the course of the last few years climate change has been increasingly framed in terms of security, frequently featuring in discussions and publications of various security actors and institutions. This development generated a vigorous debate within academia as to whether a securitization of climate change in global politics has occurred. By drawing upon Copenhagen School’s Securitization Theory, this thesis aspires to further contribute to that debate by investigating to what extent institutions with a far-reaching role in global climate governance, but with no explicit ties to security, also advance a securitization of climate change through their specific discursive constructions of the issue. More specifically, this study is concerned with institutions within the United Nations system, which include bodies and agencies such as the Intergovernmental Panel on Climate Change, the United Nations Environment Programme and the United Nations Framework Convention on Climate Change. In order to address the above question, the study employs the method of Critical Discourse Analysis (CDA) and conducts a discursive analysis of selected influential documents published by the aforementioned bodies and agencies in the period between 2014 and 2019. Furthermore, through the utilization of the CDA three-dimensional analytical model, it scrutinizes the interplay between the diverse discourses of environmental security that feature in the texts’ framings of climate change and discusses the potential policy implications that those articulations encompass

Microbial resistance in health and disease of periodical and peri-implant tissues

The growing phenomenon of antimicrobial bacterial resistance is very important, since the emergence of resistant species to antibiotics compromises the treatment of infections and increases patient morbidity. Antimicrobial resistance is directly correlated with the use of antibiotics, which is widespread in our country. Currently, limited data in the literature refer to antimicrobial resistance in the oral cavity and especially regarding bacteria related to aetiopathogenesis of periodontal and peri-implant diseases. Aim of the present study was to investigate the presence in the oral cavity of bacterial genes encoding for various mechanisms of resistance to antibiotics commonly prescribed for treatment of periodontal and peri-implant disease. The investigated genes included tetM and tetQ encoding for resistance to the tetracyclines, nim encoding for resistance to metronidazole and blaTEM which encodes for b-lactamases, the enzymes inactivating b-lactams. The presence of Staphylococcus aureus and the Methicillin Resistant Staphylococcus aureus (MRSA) was also investigated in the same samples. The subject sample of the present study consisted of three groups with different periodontal conditions (periodontal health, n=50, gingivitis, n=52 and chronic periodontitis, n=52) and two groups (n=20 each) with osseointegrated healthy dental implants or with peri-implantitis. All participants were clinically assessed in the whole dentition regarding pocket depth, gingival recession, clinical attachment level and bleeding on probing. All clinical recordings were performed by a constant force computerized periodontal probe, which ensures accuracy and reproducibility of assesments. Samples were taken from the dorsal area of the tongue, the first molars of the participants, from the deepest pockets of chronic periodontitis patients, healthy osseointegrated and diseased implants. A total of 383 clinical samples were analyzed by Polymerase Chain Reaction (PCR), using primers and conditions previously described in the literature. All experiments were repeated twice. Participants have completed a detailed questionnaire regarding antibiotic use and knowledge and oral hygiene habits. According to results of the study, in the subject sample high percentages of detection of tetM and tetQ irrelevant of periodontal (>75%) or peri-implant (>30%) conditions for the two genes. No differences were observed among groups (Kruskall-Wallis and z-test for proportions with Bonferroni corrections, p71%), suggests the necessity of careful selection of cases to be administered for treatment of periodontal infections. Results of the present study regarding the detection of S. aureus (75%) ή περιεμφυτευματικής (>30%) κατάστασης, χωρίς σημαντικές διαφορές μεταξύ των ομάδων. Στατιστικά σημαντικά υψηλότερη συχνότητα ανίχνευσης για το γονίδιο tetQ διαπιστώθηκε σε δείγματα από τη γλώσσα των ασθενών με χρόνια ουλίτιδα και περιοδοντίτιδα, σε σύγκριση με τα υποουλικά δείγματα, γεγονός που υποδηλώνει την αναγκαιότητα απομάκρυνσης της μικροβιακής πλάκας με την καθημερινή υγιεινή, ώστε να εξαλειφθεί μια σημαντική πηγή αυτών των γονιδίων από τους περιοδοντικούς ασθενείς. Τα ευρήματα που αφορούν το γονίδιο nim, υποδηλώνουν την κλινική αποτελεσματικότητα της μετρονιδαζόλης για την αντιμετώπιση των περιοδοντικών και περιεμφυτευματικών νόσων, εφόσον το συγκεκριμένο γονίδιο δεν ανιχνεύθηκε σε κανένα από τα εξεταζόμενα δείγματα. Όσον αφορά τις β-λακτάμες, οι οποίες σύμφωνα με αντίστοιχες πηγές της Ευρωπαικής Ένωσης είναι η πιο συχνά συνταγογραφούμενη κατηγορία αντιβιοτικών στην Ελλάδα, τα συχνά ποσοστά ανίχνευσης του γονιδίου blaTEM ανεξάρτητα περιοδοντικής (>71%) κατάστασης στους συμμετέχοντες, υποδεικνύουν την ανάγκη προσεκτικής χορήγησής τους για λοιμώξεις των περιοδοντικών ιστών. Τα ευρήματα της μελέτης όσον αφορά την παρουσία του S.aureus που ανιχνεύθηκε σε ποσοστά <18% ανεξαρτήτως ομάδας και του ανθεκτικού στην μεθικιλλίνη στελέχους του (MRSA), δεν ενισχύουν την πιθανή συμμετοχή του S.aureus στην αιτιοπαθογένεια της περιοδοντικής και περιεμφυτευματικής νόσου, ή την πιθανότητα μετάδοσης του ιδιαίτερα παθογόνου στελέχους MRSA στην οδοντιατρική κλινική πράξη