2100 AI: Reflections on the mechanisation of scientific discovery

The pace of research is nowadays extremely intensive, with datasets and publications being published at an unprecedented rate. In this context data science, artificial intelligence, machine learning and big data analytics are providing researchers with new automatic techniques which not only help them to manage this flow of information but are also able to identify automatically interesting patterns and insights in this vast sea of information. However, the emergence of mechanised scientific discovery is likely to dramatically change the way we do science, thus introducing and amplifying serious societal implications on the role of researchers themselves, which need to be analysed thoroughly

Functional assessment of cancer therapy questionnaire for breast cancer (FACT-B+4): Italian version validation

BACKGROUND: Improvements in breast cancer diagnosis and treatment led to an increased incidence of survivors' rate. The healthcare system has to face new problems related not only to the treatment of the disease, but also to the management of the quality of life after the diagnosis. The aim of this study was to validate the Italian version of the Functional Assessment of Cancer Therapy - Breast (FACT-B+4) questionnaire and to evaluate its reliability. METHODS: The questionnaire was administered twice, with an interval of three days between each administration, to a cohort of women of the Breast Surgical Unit, PoliclincoUmberto I. Cronbach's alpha was used as a measure of the internal consistency of the Italian version. RESULTS: The Italian version of the tool was administered to 55 subjects. The Cronbach's alpha for most scores registered values >0.7, both at baseline and at the follow-up analysis, therefore the subscale showed good internal consistency. CONCLUSIONS: The Italian version of FACT-B+4 demonstrated acceptable reliability properties in the Breast Unit patients. The use of this questionnaire seemed to be effective and in line with the results derived from the English and Spanishversions. Internal consistency and validity had similar performance results

Mediterranean diet adherence and synergy with acute myocardial infarction and its determinants. a multicenter case-control study in Italy.

Cardiovascular diseases are the leading causes of mortality and morbidity in Western countries. The possible synergistic effect of poor adherence to a Mediterranean diet (MD) and other risk factors for acute myocardial infarction (AMI) such as hypertension, cholesterol, ever smoker, BMI> 25, diabetes, has not been deeply studied. Design Case-control study. Methods Patients with first AMI and controls from four tertiary referral Italian centers were screened for enrolment. Dietary information was collected through a questionnaire and a MD adherence score was calculated. Physical activity and smoking habits were also registered. The Synergy Index was calculated according to Rothman. Results 127 cases and 173 controls were enrolled. The analysis was conducted using a dichotomous variable for the MD score with values 7 representing good adherence. Multivariate analysis showed the following variables associated to AMI: ever smoker (OR = 2.08), diabetes (OR = 1.42), hypertension (OR = 2.08), hypercholesterolemia (OR = 2.47), BMI> 25 (OR = 1.99), while a protective effect emerged both in subjects scoring > 7 on the MD score(OR = 0.55) and in subjects resident of Southern Italy (OR = 0.38). A synergistic effect does exist between poor adherence to the MD and the following risk factors: hypertension, hypercholesterolemia, BMI >25, diabetes and being a resident in central and northern Italy. Conclusion Synergy between heart disease risk factors and MD underlines the need to enlarge the list of known modifiable cardiovascular risk factors to include and promote adherence to Mediterranean dietary habits

Compact Modeling and Mitigation of Parasitics in Crosspoint Accelerators of Neural Networks

In-memory computing (IMC) can accelerate data-intensive tasks, such as matrix-vector multiplication (MVM) or artificial neural networks (ANNs) inference, by means of the crosspoint memory array, allowing to reduce time and energy consumption. IMC accuracy, however, is affected by nonidealities, such as variability of the conductive weights or IR drop along wires due to parasitic resistances, whose impact steeply increases with the increase of array size. This work proposes a compact model to assess the impact of nonidealities for various circuital implementations, together with architectural schemes for their mitigation based on replicated arrays. The proposed mitigation techniques allow to restore the ANN accuracy from 72.7% to 94.9%, close to the software accuracy of 96.9%, in view of an increased area and energy consumption

Association between work related stress and health related quality of life: the impact of socio-demographic variables. A cross sectional study in a region of central Italy

The aim of this work is investigate relationship between health-related quality of life and work-related stress and the impact of gender, education level, and age on this relationship. A cross-sectional study was conducted among workers of various setting in Rome and Frosinone. Work-related stress was measured with a demand-control questionnaire and health-related functioning by SF (short form)-12 health survey. There were 611 participants. Men reported high mental composite summary (MCS) and physical composite summary (PCS). In multivariate analysis age, gender (p < 0.001) and job demand (0.045) predicted low PCS. Low MCS predicted poor PCS. Job demand and educational level resulted negatively associated with MCS. In an analysis stratified for age, gender, and educational level, gender and age resulted effect modifier for MCS, gender and education level for PCS. In women increase of decision latitude predict (p = 0.001) an increase in MCS; a low job demand predict high MCS in male (p ≤ 0.001). In younger workers, a lower level of job demand predicted high MCS (<0.001). For PCS, gender and education level resulted effect modifier. In women, high decision latitude predicted higher PCS (p = 0.001) and lower level of job demand results in higher PCS (p ≤ 0.001). Higher educational level resulted predictor of low PCS. Management of risk about work-related stress should consider socio-demographic factors

Are there effective interventions to increase physical activity in children and young people? An umbrella review

Background: Obesity and physical inactivity among children and young people are public health concerns. While numerous interventions to promote physical activity are available, little is known about the most effective ones. This study aimed to summarize the existing evidence on interventions that aim to increase physical activity. Methods: A systematic review of reviews was conducted. Systematic reviews and meta-analyses published from January 2010 until November 2017 were identified through PubMed, Scopus and the Cochrane Library. Two reviewers independently assessed titles and abstracts, performed data extraction and quality assessment. Outcomes as level of physical activity and body mass index were collected in order to assess the efficacy of interventions. Results: A total 30 studies examining physical activity interventions met the inclusion criteria, 15 systematic reviews and 15 meta-analyses. Most studies (N = 20) were implemented in the school setting, three were developed in preschool and childcare settings, two in the family context, five in the community setting and one miscellaneous context. Results showed that eight meta-analyses obtained a small increase in physical activity level, out of which five were conducted in the school, two in the family and one in the community setting. Most promising programs had the following characteristics: included physical activity in the school curriculum, were long-term interventions, involved teachers and had the support of families. Conclusion: The majority of interventions to promote physical activity in children and young people were implemented in the school setting and were multicomponent. Further research is needed to investigate nonschool programs

In-memory computing with emerging memory devices: Status and outlook

Supporting data for "In-memory computing with emerging memory devices: status and outlook", submitted to APL Machine Learning