Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets

A Survey on Text Classification Algorithms: From Text to Predictions

In recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the automatic extraction of expressive features. The swift development of these methods has led to a plethora of strategies to encode natural language into machine-interpretable data. The latest language modelling algorithms are used in conjunction with ad hoc preprocessing procedures, of which the description is often omitted in favour of a more detailed explanation of the classification step. This paper offers a concise review of recent text classification models, with emphasis on the flow of data, from raw text to output labels. We highlight the differences between earlier methods and more recent, deep learning-based methods in both their functioning and in how they transform input data. To give a better perspective on the text classification landscape, we provide an overview of datasets for the English language, as well as supplying instructions for the synthesis of two new multilabel datasets, which we found to be particularly scarce in this setting. Finally, we provide an outline of new experimental results and discuss the open research challenges posed by deep learning-based language models

Asbestos Fibers Enhance the TMEM16A Channel Activity in Xenopus Oocytes

Background: The interaction of asbestos fibers with target cell membranes is still poorly investigated. Here, we detected and characterized an enhancement of chloride conductance in Xenopus oocyte cell membranes induced by exposure to crocidolite (Croc) asbestos fibers. Methods: A two-microelectrode voltage clamp technique was used to test the effect of Croc fiber suspensions on outward chloride currents evoked by step membrane depolarization. Calcium imaging experiments were also performed to investigate the variation of 'resting' oocyte [Ca2+]i following asbestos exposure. Results: The increase in chloride current after asbestos treatment, was sensitive to [Ca2+]e, and to specific blockers of TMEM16A Ca2+-activated chloride channels, MONNA and Ani9. Furthermore, asbestos treatment elevated the 'resting' [Ca2+]i likelihood by increasing the cell membrane permeability to Ca2 in favor of a tonic activation of TMEME16A channels. Western blot analysis confirmed that TMEME16A protein was endogenously present in the oocyte cell membrane and absorbed by Croc. Conclusion: the TMEM16A channels endogenously expressed by Xenopus oocytes are targets for asbestos fibers and represent a powerful tool for asbestos-membrane interaction studies. Interestingly, TMEM16A channels are highly expressed in many types of tumors, including some asbestos-related cancers, suggesting them, for the first time, as a possible early target of crocidolite-mediated tumorigenic effects on target cell membranes

Tailored treatments in inborn errors of immunity associated with atopy (IEIs-A) with skin involvement

inborn errors of immunity associated with atopy (IEIs-A) are a group of inherited monogenic disorders that occur with immune dysregulation and frequent skin involvement. several pathways are involved in the pathogenesis of these conditions, including immune system defects, alterations of skin barrier and metabolism perturbations. current technological improvements and the higher accessibility to genetic testing, recently allowed the identification of novel molecular pathways involved in IEIs-A, also informing on potential tailored therapeutic strategies. compared to other systemic therapy for skin diseases, biologics have the less toxic and the best tolerated profile in the setting of immune dysregulation. Here, we review IEIs-A with skin involvement focusing on the tailored therapeutic approach according to their pathogenetic mechanism