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. This paper explores how smartwatch technology, including timing notifications, can support children with ADHD during distance learning due to COVID-19. We implemented a 6-week pilot study of a Digital Health Intervention (DHI) with ten families. The DHI included a smartwatch and a smartphone. Google calendars were synchronized across devices to guide children through daily schedules. After the sixth week, we conducted parent interviews to understand the use of smartwatches and the impact on children's functioning, and we collected physiological data directly from the smartwatch. Our results demonstrated that children successfully adopted the use of the smartwatch, and parents believed the intervention was helpful, especially in supporting the development of organizational skills in their children. Overall, we illustrate how even simple DHIs, such as using smartwatches to promote daily organization and task completion, have the potential to support children and families, particularly during periods of distance learning. We include practical suggestions to help professionals teach children with ADHD to use smartwatches to improve organization and task completion, especially as it applies to supporting remote instruction.

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. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the "ocean" of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool ( http://mobiosd-hub.com/starpep/ ), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.

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). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.

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. In order to have an efficient classifier, a set of robust features that can capture what differentiates an AMP from another that is not, has to be selected. However, the number of candidate descriptors is large (in the order of thousands) to allow for an exhaustive search of all possible combinations. Therefore, efficient and effective feature selection techniques are required. In this work, we propose an efficient wrapper technique to solve the feature selection problem for AMPs identification. The method is based on a Genetic Algorithm that uses a variable-length chromosome for representing the selected features and uses an objective function that considers the Mathew Correlation Coefficient and the number of selected features. Computational experiments show that the proposed method can produce competitive results regarding sensitivity, specificity, and MCC. Furthermore, the best classification results are achieved by using only 39 out of 272 molecular descriptors.

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. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites. RESULTS: After collecting web pages from a large variety of bioactive peptide databases, we organized the web content into an integrated graph database (starPepDB) that holds a total of 71 310 nodes and 348 505 relationships. In this graph structure, there are 45 120 nodes representing peptides, and the rest of the nodes are connected to peptides for describing metadata. Additionally, to facilitate a better understanding of the integrated data, a software tool (starPep toolbox) has been developed for supporting visual network analysis in a user-friendly way; providing several functionalities such as peptide retrieval and filtering, network construction and visualization, interactive exploration and exporting data options. AVAILABILITY AND IMPLEMENTATION: Both starPepDB and starPep toolbox are freely available at http://mobiosd-hub.com/starpep/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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

Californiconus californicus, previously named Conus californicus, 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 Mycobacterium tuberculosis (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 3.52⁻0.22 µM, similar concentrations to drugs used in clinics. The peptide was purified from the venom using reverse phase high-performance liquid chromatography (RP-HPLC), a partial sequence was constructed by Edman degradation, completed by RACE and confirmed with venom gland transcriptome. The 32-mer peptide containing eight cysteine residues was named O1_cal29b, according to the current nomenclature for this type of molecule. Moreover, transcriptomic analysis of O-superfamily toxins present in the venom gland of the snail allowed us to assign several signal peptides to O2 and O3 superfamilies not described before in C. californicus, with new conotoxins frameworks.

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. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored. RESULTS: We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides. CONCLUSIONS: The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes.

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. We observed that both physicochemical and centrality descriptors of residues effectively relate protein structure and protein function, and that physicochemical descriptors better describe critical residues. We also show that critical residues are better classified when residue criticality is considered as a binary attribute (i.e., residues are considered critical or not critical). Using this binary annotation for critical residues 8 models rendered accurate and non-overlapping classification of critical residues, confirming the multi-factorial character of the structure-function relationship of proteins.

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. The relative contribution of ethnic background, neighbourhood socioeconomic status (SES), age, and gender was assessed. Findings revealed that ethnic background, neighborhood SES, and age differentially predicted children's pre-literacy skills. The implications of these results for understanding the role played by these demographic and socio-cultural variables in alphabetic literacy acquisition are discussed. The second purpose of this study was to add to the growing literature on the nature of reading challenges in children who are learning to read a transparent orthography-Spanish. Cross-linguistic research between different subtypes of readers will add to understand the impact of language characteristics in reading acquisition. Finally, the present study suggested that early assessment of pre-literacy skills can be a highly effective way to determine the instructional needs of students who are at risk for reading failure before formal reading instruction begins.

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. The relative contribution of ethnic background, neighbourhood socioeconomic status (SES), age, and gender was assessed. Findings revealed that ethnic background, neighborhood SES, and age differentially predicted children’s pre-literacy skills. The implications of these results for understanding the role played by these demographic and socio cultural variables in alphabetic literacy acquisition are discussed. The second purpose of this study was to add to the growing literature on the nature of reading challenges in children who are learning to read a transparent orthography-Spanish. Cross-linguistic research between different subtypes of readers will add to understand the impact of language characteristics in reading acquisition. Finally, the present study suggested that early assessment of pre-literacy skills can be a highly effective way to determine the instructional needs of students who are at risk for reading failure before formal reading instruction begins (AU)

El objetivo principal del presente estudio fue examinar los predictores tempranos de la lectura (ej.: conocimiento sobre el material impreso, comprensión oral, vocabulario receptivo, denominación rápida de objetos y letras y conciencia fonológica), en una muestra de niños de educación infantil (edad media = 5 años y 9 meses), de los cuales 77 eran madrileños, pertenecientes a la cultura mayoritaria, 48 inmigrantes latinos y 30 madrileños de etnia gitana. La contribución relativa a la adquisición lectora del grupo étnico, estatus socioeconómico, edad y género fue evaluada. Los hallazgos revelan que el grupo étnico, estatus socioeconómico y la edad predicen de modo diferente la habilidad prelectora de los niños. Las implicaciones de estos resultados son discutidas. El segundo objetivo de este estudio fue añadir nuevos datos a la creciente literatura sobre los retos que afrontan los niños que aprenden a leer en una ortografía transparente, como es el caso del español. La investigación entre lenguas y diferentes subtipos de lectores hará que comprendamos mejor el impacto que las características de una lengua tiene en la adquisición lectora. Por último, el presente estudio sugiere que el diagnóstico temprano de las habilidades prelectoras, antes de que la instrucción formal de la lectura comience, puede ser muy efectivo para determinar las necesidades de estudiantes que se encuentran en situación de riesgo de padecer dificultades en la lectura (AU)

Prescripción de antibióticos en el Policlínico Mártires de Calabazar / Antibiotics prescription in Mártires de Calabazar Polyclinic

Se realizó un estudio observacional retrospectivo con el diseño de un estudio de utilización de medicamentos del tipo prescripción-indicación, en el Policlínico Mártires de Calabazar con los objetivos de evaluar la adecuación de la prescripción de antimicrobianos a la política terapéutica establecida en APS, identificar los diagnósticos que con mayor frecuencia motivaban su prescripción, así como determinar los antimicrobianos prescriptos más frecuentemente. Los antimicrobianos prescriptos con mayor frecuencia fueron: la tetraciclina, el cotrimoxazol, la penicilina G y la eritromicina; y los diagnósticos que motivaron su prescripción fueron las faringoamigdalitis agudas, seguidas de las infecciones bucodentales y del aparato urinario. La adecuación global del tratamiento fue de un 66 por ciento(AU)

Prescripción de antibióticos en el Policlínico Mártires de Calabazar / Antibiotics prescription in Mártires de Calabazar Polyclinic

Se realizó un estudio observacional retrospectivo con el diseño de un estudio de utilización de medicamentos del tipo prescripción-indicación, en el Policlínico Mártires de Calabazar con los objetivos de evaluar la adecuación de la prescripción de antimicrobianos a la política terapéutica establecida en APS, identificar los diagnósticos que con mayor frecuencia motivaban su prescripción, así como determinar los antimicrobianos prescriptos más frecuentemente. Los antimicrobianos prescriptos con mayor frecuencia fueron: la tetraciclina, el cotrimoxazol, la penicilina G y la eritromicina; y los diagnósticos que motivaron su prescripción fueron las faringoamigdalitis agudas, seguidas de las infecciones bucodentales y del aparato urinario. La adecuación global del tratamiento fue de un 66 por ciento.