Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage.

Handwriting learning delays should be addressed early to prevent their exacerbation and long-lasting consequences on whole children's lives. Ideally, proper training should start even before learning how to write. This work presents a novel method to disclose potential handwriting problems, from a pre-literacy stage, based on drawings instead of words production analysis. Two hundred forty-one kindergartners drew on a tablet, and we computed features known to be distinctive of poor handwriting from symbols drawings. We verified that abnormal features patterns reflected abnormal drawings, and found correspondence in experts' evaluation of the potential risk of developing a learning delay in the graphical sphere. A machine learning model was able to discriminate with 0.75 sensitivity and 0.76 specificity children at risk. Finally, we explained why children were considered at risk by the algorithms to inform teachers on the specific weaknesses that need training. Thanks to this system, early intervention to train specific learning delays will be finally possible.

How Data Analytics and Big Data Can Help Scientists in Managing COVID-19 Diffusion: Modeling Study to Predict the COVID-19 Diffusion in Italy and the Lombardy Region.

BACKGROUND: COVID-19 is the most widely discussed topic worldwide in 2020, and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. OBJECTIVE: In this paper, a data analytics study on the diffusion of COVID-19 in Italy and the Lombardy Region is developed to define a predictive model tailored to forecast the evolution of the diffusion over time. METHODS: Starting with all available official data collected worldwide about the diffusion of COVID-19, we defined a predictive model at the beginning of March 2020 for the Italian country. RESULTS: This paper aims at showing how this predictive model was able to forecast the behavior of the COVID-19 diffusion and how it predicted the total number of positive cases in Italy over time. The predictive model forecasted, for the Italian country, the end of the COVID-19 first wave by the beginning of June. CONCLUSIONS: This paper shows that big data and data analytics can help medical experts and epidemiologists in promptly designing accurate and generalized models to predict the different COVID-19 evolutionary phases in other countries and regions, and for second and third possible epidemic waves.

A web-enabled database of human gene expression controlled annotations for gene list functional evaluation.

Modern biomolecular high-throughput technologies can produce experimental results made of lists of hundreds of candidate interesting genes in the condition under study. These lists need to be biologically interpreted to achieve a better knowledge of the patho-physiological phenomena involved in the studied conditions. To reach this goal, several functional, structural, and phenotypic annotations are available within heterogeneous and widely distributed databanks. Among them, gene expression information is a useful resource to better understand gene functions. We previously developed GFINDer, a Web server that aggregates genomic annotations sparsely available in numerous databanks accessible via the Internet and allows performing statistical analysis of functional and phenotypic annotations of gene lists. To take full advantage of gene expression information provided by eVOC ontologies, we imported them in the GFINDer system. For this purpose and to keep updated such information in the GFINDer database when new releases of them are available, we designed and implemented specific parsing and updating procedures. Moreover, we developed new GFINDer modules that allow annotating human nucleotide sequences with the imported eVOC controlled information on their expression features in order to explore and statistically analyze them.