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Artistic pieces can be studied from several perspectives, one example being their reception among readers over time. In the present work, we approach this interesting topic from the standpoint of literary works, particularly assessing the task of predicting whether a book will become a best seller. Unlike previous approaches, we focused on the full content of books and considered visualization and classification tasks. We employed visualization for the preliminary exploration of the data structure and properties, involving SemAxis and linear discriminant analyses. To obtain quantitative and more objective results, we employed various classifiers. Such approaches were used along with a dataset containing (i) books published from 1895 to 1923 and consecrated as best sellers by the Publishers Weekly Bestseller Lists and (ii) literary works published in the same period but not being mentioned in that list. Our comparison of methods revealed that the best-achieved result—combining a bag-of-words representation with a logistic regression classifier—led to an average accuracy of 0.75 both for the leave-one-out and 10-fold cross-validations. Such an outcome enhances the difficulty in predicting the success of books with high accuracy, even using the full content of the texts. Nevertheless, our findings provide insights into the factors leading to the relative success of a literary work. [ABSTRACT FROM AUTHOR]

Epidermal growth factor receptor EGFR inhibitors are widely used as first line therapy for the treatment of non-small-cell lung cancer (NSCLC) in patients harboring EGFR mutation. However, the acquisition of a second-site mutation (T790 M) limited the efficacy and developed resistance. Therefore, discovery and development of specific drug target for this mutation is of urgent needs. In our study we used the ChemDiv diversity database for receptor-based virtual screening to secure EGFR-TK inhibitors chemotherapeutics. We identified four compounds that bind to the ATP-binding region of the EGFR-TK using AutoDock 4.0 and AutoDock Vina1.1.2 and post-docking investigations. The ligand showed hydrophobic interactions to the hydrophobic region of the binding site and engaged in hydrogen bonding with Met793. The ligands also explored π–cation interactions between the π-system of the ligand–phenyl ring and the positive amino group of Lys745. Molecular mechanics Poisson–Boltzmann surface area MM/PBSA per-residue energy decomposition analyses revealed that Val726, Leu792, Met793, Gly796, Cys797, Leu798, and Thr844 contributed the most to the binding energy. Biological evaluation of the retrieved hit compounds showed suppressing activity against EGFR auto phosphorylation and selective apoptosis-induced effects toward lung cancer cells harboring the EGFR L858R/T790M double mutation. Our work anticipated into novel and specific EGFR-TKIs and identified new compounds with therapeutic potential against lung cancer. [ABSTRACT FROM AUTHOR]

Harlow Gale is often depicted as the first experimentalist in advertising thought. This positioning elides influences which impacted upon his thinking. In this article, we outline Gale's involvement with psychical research and its implications for advertising. These narratives are situated within a genealogy of subliminal processes across advertising and marketing theory from the late nineteenth century through to social cognition studies today. Gale's connection with psychical research, in conjunction with early practitioner reflections on the unconscious, formed the enabling conditions for his major contributions to advertising. Psychical scholarship spotlighted the centrality of the "multiplex self" to human functioning. While psychical framings scaffolded Gale's empirical, conceptual, and theoretical work, it also limited greater engagement with his insights. However, subsequent modifications of advertising theory and practice are underwritten by levels of continuity and discontinuity that facilitate the identification of psychically indebted bodies of thought from Gale's time to the present day. Taken together, these analytic associations provide a substantive reorientation of historical and contemporary accounts of advertising theory and practice. [ABSTRACT FROM AUTHOR]

Digitisation has become an essential part of archival and library strategies to enhance access to collections. As the digital content is increasing due to large-scale digitisation projects, it is expected that providing digital access to the analogue collections will eventually reduce the number of archival records accessed in the reading room. In this paper, we investigate this issue using two approaches: system dynamics and agent-based modelling. We first analyse real data in order to identify the dynamic hypothesis of the model. Then, a sensitivity analysis is conducted on two baseline models to identify scenarios that match the real dataset. Although the two approaches suceed to simulate the number of requests in the reading room, the experimental results show that a better fit is obtained in the agent-based model when not only the number of records that have been accessed and digitised is taken into account, but also the number of times that such records have been accessed before digitisation. The proposed model can be used to explore the impact of different digitisation strategies on the decrease in access requests in the archival and library reading rooms. [ABSTRACT FROM AUTHOR]

This document is a list of references for a research paper on various topics related to mental health, artificial intelligence, and data analysis. The references cover studies on loneliness and mental health during the COVID-19 pandemic, risk factors for suicidal thoughts and behaviors, medical big data, machine learning techniques, and the use of artificial intelligence in mental health research. The document also includes resources on the validation and accuracy of diagnoses, classification of psychiatric disorders, and the challenges and opportunities of data sharing in mental health research. [Extracted from the article]