Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic Approach

Matchmaking arises when supply and demand meet in an electronic marketplace, or when agents search for a web service to perform some task, or even when recruiting agencies match curricula and job profiles. In such open environments, the objective of a matchmaking process is to discover best available offers to a given request. We address the problem of matchmaking from a knowledge representation perspective, with a formalization based on Description Logics. We devise Concept Abduction and Concept Contraction as non-monotonic inferences in Description Logics suitable for modeling matchmaking in a logical framework, and prove some related complexity results. We also present reasonable algorithms for semantic matchmaking based on the devised inferences, and prove that they obey to some commonsense properties. Finally, we report on the implementation of the proposed matchmaking framework, which has been used both as a mediator in e-marketplaces and for semantic web services discovery

The Human SLC25A33 and SLC25A36 Genes of Solute Carrier Family 25 Encode Two Mitochondrial Pyrimidine Nucleotide Transporters

The human genome encodes 53 members of the solute carrier family 25 (SLC25), also called the mitochondrial carrier family, many of which have been shown to transport inorganic anions, amino acids, carboxylates, nucleotides, and coenzymes across the inner mitochondrial membrane, thereby connecting cytosolic and matrix functions. Here two members of this family, SLC25A33 and SLC25A36, have been thoroughly characterized biochemically. These proteins were overexpressed in bacteria and reconstituted in phospholipid vesicles. Their transport properties and kinetic parameters demonstrate that SLC25A33 transports uracil, thymine, and cytosine (deoxy)nucleoside di- and triphosphates by an antiport mechanism and SLC25A36 cytosine and uracil (deoxy)nucleoside mono-, di-, and triphosphates by uniport and antiport. Both carriers also transported guanine but not adenine (deoxy)nucleotides. Transport catalyzed by both carriers was saturable and inhibited by mercurial compounds and other inhibitors of mitochondrial carriers to various degrees. In confirmation of their identity (i) SLC25A33 and SLC25A36 were found to be targeted to mitochondria and (ii) the phenotypes of Saccharomyces cerevisiae cells lacking RIM2, the gene encoding the well characterized yeast mitochondrial pyrimidine nucleotide carrier, were overcome by expressing SLC25A33 or SLC25A36 in these cells. The main physiological role of SLC25A33 and SLC25A36 is to import/export pyrimidine nucleotides into and from mitochondria, i.e. to accomplish transport steps essential for mitochondrial DNA and RNA synthesis and breakdown

Predicting Human Emotions using EEG-based Brain computer Interface and Interpretable Machine Learning

EEG-based brain-computer interface (BCI) devices have proved to be powerful tools for predicting human emotions. Although Deep learning (DL) techniques have been extensively used to build emotion recognition architectures using EEG-based BCI, they lack interpretability. We propose a prototype of an EEG-based emotion recognition system that can detect the user's emotional state using a deep learning model embedded into an interpretable framework to analyze the decisions of the model and the contributions of the features. The proposed model achieves high performance while showing relevant information on the impact of frequency and spatial features used to predict the emotional states

A yeast-based screening unravels potential therapeutic molecules for mitochondrial diseases associated with dominant ant1 mutations

Mitochondrial diseases result from inherited or spontaneous mutations in mitochondrial or nuclear DNA, leading to an impairment of the oxidative phosphorylation responsible for the synthesis of ATP. To date, there are no effective pharmacological therapies for these pathologies. We performed a yeast-based screening to search for therapeutic drugs to be used for treating mito-chondrial diseases associated with dominant mutations in the nuclear ANT1 gene, which encodes for the mitochondrial ADP/ATP carrier. Dominant ANT1 mutations are involved in several degen-erative mitochondrial pathologies characterized by the presence of multiple deletions or depletion of mitochondrial DNA in tissues of affected patients. Thanks to the presence in yeast of the AAC2 gene, orthologue of human ANT1, a yeast mutant strain carrying the M114P substitution equivalent to adPEO-associated L98P mutation was created. Five molecules were identified for their ability to suppress the defective respiratory growth phenotype of the haploid aac2M114P . Furthermore, these molecules rescued the mtDNA mutability in the heteroallelic AAC2/aac2M114P strain, which mimics the human heterozygous condition of adPEO patients. The drugs were effective in reducing mtDNA instability also in the heteroallelic strain carrying the R96H mutation equivalent to the more severe de novo dominant missense mutation R80H, suggesting a general therapeutic effect on diseases associated with dominant ANT1 mutations

Rating Prediction with Contextual Conditional Preferences

Exploiting contextual information is considered a good solution to improve the quality of recommendations, aiming at suggesting more relevant items for a specific context. On the other hand, recommender systems research still strive for solving the cold-start problem, namely where not enough information about users and their ratings is available. In this paper we propose a new rating prediction algorithm to face the cold-start system scenario, based on user interests model called contextual conditional preferences. We present results obtained with three publicly available data sets in comparison with several state-of-the-art baselines. We show that usage of contextual conditional preferences improves the prediction accuracy, even when all users have provided a few feedbacks, and hence small amount of data is available

Adherence and Constancy in LIME-RS Explanations for Recommendation

Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence. In particular, post-hoc approaches have proved to be the most easily applicable ones to increasingly complex recommendation models, which are then treated as black boxes. The most recent literature has shown that for post-hoc explanations based on local surrogate models, there are problems related to the robustness of the approach itself. This consideration becomes even more relevant in human-related tasks like recommendation. The explanation also has the arduous task of enhancing increasingly relevant aspects of user experience such as transparency or trustworthiness. This paper aims to show how the characteristics of a classical post-hoc model based on surrogates is strongly model-dependent and does not prove to be accountable for the explanations generatedThe authors acknowledge partial support of PID2019-108965GB-I00, PONARS01_00876BIO-D,CasadelleTecnologie mergenti della Cittàdi Matera, PONARS01_00821FLET4.0, PIAServiziLocali2.0,H2020Passapartout-Grantn. 101016956, PIAERP4.0,andIPZS-PRJ4_IA_NORMATIV

A description logic based approach for matching user profiles

Several applications require the matching of user profiles, e.g., job recruitment or dating systems. In this paper we present a logical framework for specifying user profiles that allows profile description to be incomplete in the parts that are unavailable or are considered irrelevant by the user. We present an algorithm for matching demands and supplies of profiles, taking into account incompleteness of profiles and incompatibility between demand and supply. We specialize our framework to dating services; however, the same techniques can be directly applied to several other contexts

Dependence of antibody gene diversification on uracil excision

Activation-induced deaminase (AID) catalyses deamination of deoxycytidine to deoxyuridine within immunoglobulin loci, triggering pathways of antibody diversification that are largely dependent on uracil-DNA glycosylase (uracil-N-glycolase [UNG]). Surprisingly efficient class switch recombination is restored to ung−/− B cells through retroviral delivery of active-site mutants of UNG, stimulating discussion about the need for UNG's uracil-excision activity. In this study, however, we find that even with the overexpression achieved through retroviral delivery, switching is only mediated by UNG mutants that retain detectable excision activity, with this switching being especially dependent on MSH2. In contrast to their potentiation of switching, low-activity UNGs are relatively ineffective in restoring transversion mutations at C:G pairs during hypermutation, or in restoring gene conversion in stably transfected DT40 cells. The results indicate that UNG does, indeed, act through uracil excision, but suggest that, in the presence of MSH2, efficient switch recombination requires base excision at only a small proportion of the AID-generated uracils in the S region. Interestingly, enforced expression of thymine-DNA glycosylase (which can excise U from U:G mispairs) does not (unlike enforced UNG or SMUG1 expression) potentiate efficient switching, which is consistent with a need either for specific recruitment of the uracil-excision enzyme or for it to be active on single-stranded DNA

Semantic search in RealFoodTrade

We present RealFoodTrade (RFT), a system that allows farmers and fisher- men to sell their products directly to the end-buyer. RFT mak es use of Linked Data sets, together with a domain ontology designed by expert s, to perform semantic search over products on sale. RFT employs geo-locat ion technology on mobile devices to match demand and supply according to the l ocation. We sketch the semantic search techniques in RFT and illustrat e a prototype tailored to the fishing industry

Current State-of-the-Art of AI Methods Applied to MRI

Di Noia, C., Grist, J. T., Riemer, F., Lyasheva, M., Fabozzi, M., Castelli, M., Lodi, R., Tonon, C., Rundo, L., & Zaccagna, F. (2022). Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics, 12(9), 1-16. [2125]. https://doi.org/10.3390/diagnostics12092125Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.publishersversionpublishe