Digital health intervention for children with ADHD to improve mental health intervention, patient experiences, and outcomes: a study protocol.

Background: Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent childhood psychiatric condition with profound public health, personal, and family consequences. ADHD requires comprehensive treatment; however, lack of communication and integration across multiple points of care is a substantial barrier to progress. Given the chronic and pervasive challenges associated with ADHD, innovative approaches are crucial.

Parents' perspectives on a smartwatch intervention for children with ADHD: Rapid deployment and feasibility evaluation of a pilot intervention to support distance learning during COVID-19.

Distance learning in response to the COVID-19 pandemic presented tremendous challenges for many families. Parents were expected to support children's learning, often while also working from home. Students with Attention Deficit Hyperactivity Disorder (ADHD) are at particularly high risk for setbacks due to difficulties with organization and increased risk of not participating in scheduled online learning.

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.

The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases.

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques.

Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension).

An automatic representation of peptides for effective antimicrobial activity classification.

Antimicrobial peptides (AMPs) are a promising alternative to small-molecules-based antibiotics. These peptides are part of most living organisms' innate defense system. In order to computationally identify new AMPs within the peptides these organisms produce, an automatic AMP/non-AMP classifier is required.

Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis.

Motivation: Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors.

The Diversified O-Superfamily in Presents a Conotoxin with Antimycobacterial Activity.

, previously named , has always been considered a unique species within cone snails, because of its molecular, toxicological and morphological singularities; including the wide range of its diet, since it is capable of preying indifferently on fish, snails, octopus, shrimps, and worms. We report here a new cysteine pattern conotoxin assigned to the O1-superfamily capable of inhibiting the growth of (Mtb). The conotoxin was tested on a pathogen reference strain (H37Rv) and multidrug-resistant strains, having an inhibition effect on growth with a minimal inhibitory concentration (MIC) range of 3.

Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach.

Background: Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model's performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed.

Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure.

Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship, yet two important limitations have precluded a proper analysis of the structure-function relationship of proteins: (i) the lack of a formal definition of what critical residues are and (ii) the lack of a systematic evaluation of methods and protein structure features. To address this problem, here we introduce an index to quantify the protein-function criticality of a residue based on experimental data and a strategy aimed to optimize both, descriptors of protein structure (physicochemical and centrality descriptors) and machine learning algorithms, to minimize the error in the classification of critical residues.

Early predictors of reading in three groups of native Spanish speakers: Spaniards, Gypsies, and Latin Americans.

The main purpose of the study reported here was to examine the early linguistic predictors of reading (e.g., Knowledge About Print, Listening Comprehension, Receptive Vocabulary, Rapid Naming of Objects and Letters, and Phonological Awareness), for a sample of 77 Spaniards, 48 Latinos, and 30 Gypsies kindergartens (mean age = 5 years 9 months) living in Spain.