Ricerc@Sapienza

Introduction: Blood-based biomarkers of pathophysiological brain amyloid β (Aβ) accumulation, particularly for preclinical target and large-scale interventions, are warranted to effectively enrich Alzheimer's disease clinical trials and management. Methods: We investigated whether plasma concentrations of the Aβ1–40/Aβ1–42 ratio, assessed using the single-molecule array (Simoa) immunoassay, may predict brain Aβ positron emission tomography status in a large-scale longitudinal monocentric cohort (N = 276) of older individuals with subjective memory complaints.

We firstly introduce the new Springer series Human Perspectives in Health Sciences and Technology (HPHST), and then we move on to illustrate the topic this volume deals with, namely whether machines will replace scientists in scientific development. We then explain the decision of having this volume to be the first volume of the HPHST series. Finally, we describe the organization of this book and give a brief presentation of each chapter.

This book provides a critical reflection on automated science and addresses the question whether the computational tools we developed in last decades are changing the way we humans do science. More concretely: Can machines replace scientists in crucial aspects of scientific practice? The contributors to this book re-think and refine some of the main concepts by which science is understood, drawing a fascinating picture of the developments we expect over the next decades of human-machine co-evolution.

Diatoms (Bacillariophyta), one of the most abundant and diverse groups of marine phytoplankton, respond rapidly to the supply of new nutrients, often out-competing other phytoplankton. Herein, we integrated analyses of the evolution, distribution, and expression modulation of two gene families involved in diatom nitrogen uptake (DiAMT1 and DiNRT2), in order to infer the main drivers of divergence in a key functional trait of phytoplankton. Our results suggest that major steps in the evolution of the two gene families reflected key events triggering diatom radiation and diversification.

Multi-agent systems recently gained a lot of attention for solving machine learning and data mining problems. Furthermore, their peculiar divide-and-conquer approach is appealing when large datasets have to be analyzed. In this paper, we propose a multi-agent classification system able to tackle large datasets where each agent independently explores a random small portion of the overall dataset, searching for meaningful clusters in proper subspaces where they are well-formed (i.e., compact and populated).

Recent trends in scholarship publications, public debate on newspapers, and digital projects in the humanities seem to privilege the quantitative and computational approach to historical studies, as it is considered more objective, and therefore more correct. But some examples driven from direct experience in the field of digital epigraphy show how important the human component of any digital project still is for a whole and more correct comprehension of the traces of the past.

Computational intelligence and pattern recognition techniques are gaining more and more attention as the main computing tools in bioinformatics applications. This is due to the fact that biology by definition, deals with complex systems and that computational intelligence can be considered as an effective approach when facing the general problem of complex systems modelling. Moreover, most data available on shared databases are represented by sequences and graphs, thus demanding the definition of meaningful dissimilarity measures between patterns, which are often non-metric in nature.

Commentary on: Martino, A, Giuliani, A, Todde, V, Bizzarri, M, Rizzi, A, 2019, “Metabolic Networks Classification
Knowledge Discovery by Information Granulation” Computers in Biology and Chemistry, pp. 107187. DOI: 10.1016/j.
compbiolchem.2019.107187

Internet of Things (IoT) devices are starting to play a predominant role in our everyday life. Application systems like Amazon Echo and Google Home allow IoT devices to answer human requests, or trigger some alarms and perform suitable actions. In this scenario, any data information, related device and human interaction are stored in databases and can be used for future analysis and improve the system functionality.

In this paper we propose a novel evolutive agent-based clustering algorithm where agents act as individuals of an evolving population, each one performing a random walk on a different subset of patterns drawn from the entire dataset. Such agents are orchestrated by means of a customised genetic algorithm and are able to perform simultaneously clustering and feature selection.