Preface and keynote’s talk of the Workshop on Social Interaction-based Recommendation (SIR 2018)

This paper summarises all the topics discussed by the invited talk Prof. Gabriella Pasi, during the first edition of the SIR: Workshop on Social Interaction-based Recommendation-The hosted by the 27th International Conference on Information and Knowledge Management (CIKM 2018) - October 22 2018, Turin (Italy)

Valvulopatía mitral congénita en el adulto: a propósito de dos casos

Congenital mitral valve disease represents a very small percentage of the total number of congenital heart diseases. In addition, the population that reaches adulthood with this valvulophaty is unusual. We have reported two cases with mitral regurgitation, derived for their quantification with transesophageal echocardiography by the cardiovascular surgery service under the presumed diagnosis of mitral valve prolapse due to myxomatous disease. The fundamental role of the cardiac image and its interpretation allow an accurate diagnosis and guide the surgeon to make decisions.La valvulopatía mitral congénita representa un porcentaje ínfimo del total de las cardiopatías congénitas. Además, la población adulta con dicha malformación es más escasa aún. Se han documentado dos casos con insuficiencia mitral, derivados para su cuantificación con ecocardiografía transesofágica por el servicio de cirugía cardiovascular bajo el presunto diagnóstico de prolapso valvular mitral por enfermedad mixomatosa. El rol fundamental de la imagen cardíaca y su interpretación permiten realizar un diagnóstico certero y orientar al cirujano para la toma de decisiones

Usefulness and limitations of comprehensive characterization of mRNA splicing profiles in the definition of the clinical relevance of BRCA1/2 variants of uncertain significance

Highly penetrant variants of BRCA1/2 genes are involved in hereditary predisposition to breast and ovarian cancer. The detection of pathogenic BRCA variants has a considerable clinical impact, allowing appropriate cancer-risk management. However, a major drawback is represented by the identification of variants of uncertain significance (VUS). Many VUS potentially affect mRNA splicing, making transcript analysis an essential step for the definition of their pathogenicity. Here, we characterize the impact on splicing of ten BRCA1/2 variants. Aberrant splicing patterns were demonstrated for eight variants whose alternative transcripts were fully characterized. Different events were observed, including exon skipping, intron retention, and usage of de novo and cryptic splice sites. Transcripts with premature stop codons or in-frame loss of functionally important residues were generated. Partial/complete splicing effect and quantitative contribution of different isoforms were assessed, leading to variant classification according to Evidence-based Network for the Interpretation of Mutant Alleles (ENIGMA) consortium guidelines. Two variants could be classified as pathogenic and two as likely benign, while due to a partial splicing effect, six variants remained of uncertain significance. The association with an undefined tumor risk justifies caution in recommending aggressive risk-reduction treatments, but prevents the possibility of receiving personalized therapies with potential beneficial effect. This indicates the need for applying additional approaches for the analysis of variants resistant to classification by gene transcript analyses

The health determinants in young children: Testing a new surveillance system in Italy

In recent years, the scientific community has stressed the need to invest in the first 1,000 days of life - the time spanning between conception and the 2nd birthday - because it is during this period that the foundations of health are laid and whose effects will be present throughout the life and may influence the next generation. Taking this into account, in 2013 the National Centre for Disease Prevention and Control (CCM) of the Italian Ministry of Health promoted and financed a project to test a surveillance system of the main determinants of health concerning the child between the conception period and the 2nd years of life which are included in the National Programme “GenitoriPiù”: folic acid before and during pregnancy, abstention from tobacco and alcohol during pregnancy and lactation, breastfeeding, infant sleep position, vaccination attitude, and early reading. The Project, started in January 2014 and ended in August 2016, has piloted the design, testing, and evaluation of the surveillance system with the view to national extension and the repeatability over time. The surveillance system has been designed to collect data through a questionnaire compiled by mothers in vaccination centres, in order to produce indicators which will enable territorial and intertempo-ral comparisons to be made. The project has shown the feasibility of this system, identifying favourable conditions and possible difficulties, and its ability to collect important information on children's health

Leveraging n-gram neural embeddings to improve deep learning DGA detection

Several families of malware are based on the need to establish a connection with a Command and Control (C&C) server. In addition, to avoid detection, these servers "hide" behind domain names that are periodically changed according to a specific Domain Generation Algorithm (DGA). Hence, the malware that has infected a particular host uses the same DGA to make DNS queries in order to establish a connection with the C&C server. The identification of "malicious" domain names used in DNS queries is therefore crucial for their detection. For this purpose, various machine learning techniques have been used, in particular, recently, deep learning techniques have proved especially effective. However, to get good results, these techniques require very large and labelled training datasets. Nevertheless, the construction of such datasets, decidedly with regard to the collection of malicious domain names, is a very difficult and nonscalable task. In this paper, therefore, we explore the possibility of exploiting unsupervised character n-gram embeddings to improve the performance of a Deep Learning DGA classifier. Embeddings are trained using a large dataset of benign names, opening up the possibility of using a small classifier training dataset requiring a small number of malicious names. A series of experiments, which use the same embedding for classifiers trained with datasets of increasing size, are then presented. These experiments show how the embedding is particularly effective for classifiers trained with small datasets having a small number of malicious names

Measurement of stride time by machine learning: sensitivity analysis for the simplification of the experimental protocol

Limited stride-time variability is considered a marker of safe walking. Thus, the measurement of stride time is a meaningful information for gait analysis. The use of machine-learning (ML) techniques has been proven to be useful to this aim, even if the amount of data provided as input influences the computation process. The present study is aiming to analyze the sensitivity of the experimental protocol (number of sensors and signals) on the performance of a stride-time measurement system based on ML interpretation of surface EMG signals (sEMG). To this purpose, sEMG signals from ten leg muscles of 30 volunteers are used to train a single-layer neural network. Five experimental protocols (from five to one sEMG sensors per leg) are comparatively tested. Results show that reducing the sEMG-protocol complexity (less sensors utilized) is decreasing the prediction performances. Based on the test results, this study proposes an experimental protocol composed of two sEMG sensors per leg (over gastrocnemius lateralis and tibialis anterior), as the best compromise between the need of a simplified experimental set-up and the necessity of high performances (F1-score±SD = 99.0±1.2%; mean absolute value, MAE±SD = 17.9±4.3 ms). The use of only two sEMG probes is going to have a great impact on gait analysis, improving patient comfort and reducing clinical costs and time consumption. A possible, further reduction of experimental protocol to a single muscle (gastrocnemius lateralis) is feasible accepting a less efficient prediction of the stride-time

Gait phase classification from surface emg signals using neural networks

Identification and classification of different gait phases is an essential requirement to temporally characterize muscular recruitment during human walking. The present study proposes a Deep-learning methodology for the classification of the two main gait phases (stance and swing), based on the interpretation of surface electromyographic (sEMG) signals alone. Three different Multi Layer Perceptron (MLP) models are tested to this aim. The present approach does not require specific features to be extracted from the signal, differently from previous studies. 12 healthy adult subjects are analyzed during walking over-ground at comfortable speed. sEMG signals from eight leg muscles are selected. Performance of classifiers is tested vs. gold standard, represented by basographic signals measured by means of three foot-switches. A 10-fold evaluation is computed to take into account the possible variability of the results. The direct comparison among the performances of the three different MLP models shows an average high accuracy over the population (around 95%) for all the models, independent from the increasing complexity. Moreover, the accuracy in each single subject does not fall below 92.6% (range of accuracy variability = 92.6–97.2%). This present study suggests that artificial neural networks may be a suitable tool for the automatic classification of gait phases from electromyographic signals, in overall walking tasks

Selective Optical Switching of Interface-coupled Relaxation Dynamics in Carbon Nanotube-Si Heterojunctions

By properly tuning the photon energy of a femtosecond laser pump, we disentangle, in carbon nanotube–Si (CNT/Si) heterojunctions, the fast relaxation dynamics occurring in CNT from the slow repopulation dynamics due to hole charge transfer at the junction. In this way we are able to track the transfer of the photogenerated holes from the Si depletion layer to the CNT layer, under the action of the built-in heterojunction potential. This also clarifies that CNT play an active role in the junction and do not act only as channels for charge collection and transpor

Hybrid minigene assay: An efficient tool to characterize mrna splicing profiles of nf1 variants

Neurofibromatosis type 1 (NF1) is caused by heterozygous loss of function mutations in the NF1 gene. Although patients are diagnosed according to clinical criteria and few genotype-phenotype correlations are known, molecular analysis remains important. NF1 displays allelic heterogeneity, with a high proportion of variants affecting splicing, including deep intronic alleles and changes outside the canonical splice sites, making validation problematic. Next Generation Sequencing (NGS) technologies integrated with multiplex ligation-dependent probe amplification (MLPA) have largely overcome RNA-based techniques but do not detect splicing defects. A rapid minigene-based system was set up to test the effects of NF1 variants on splicing. We investigated 29 intronic and exonic NF1 variants identified in patients during the diagnostic process. The minigene assay showed the coexistence of multiple mechanisms of splicing alterations for seven variants. A leaky effect on splicing was documented in one de novo substitution detected in a sporadic patient with a specific phenotype without neurofibromas. Our splicing assay proved to be a reliable and fast method to validate novel NF1 variants potentially affecting splicing and to detect hypomorphic effects that might have phenotypic consequences, avoiding the requirement of patient\u2019s RNA