Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.

Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompasses three image collections wherein rodent neuronal cell nuclei and cytoplasm are stained with diverse markers to highlight their anatomical or functional characteristics. Specifically, we release 1874 high-resolution images alongside 750 corresponding ground-truth annotations for several learning tasks, including semantic segmentation, object detection and counting.

Study on the effects of the restrictive measures for containment of the COVID-19 pandemic on the reproduction number in Italian regions.

Since November 6th, 2020, Italian regions have been classified according to four levels, corresponding to specific risk scenarios, for which specific restrictive measures have been foreseen. By analyzing the time evolution of the reproduction number , we estimate how much different restrictive measures affect , and we quantify the combined effect of the diffusion of virus variants and the beginning of the vaccination campaign upon the trend. We also compute the time delay between implementation of restrictive measures and the resulting effects.

Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet.

Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subject to fatigue errors and suffers from arbitrariness due to the operator's interpretation of the borderline cases. We propose a Deep Learning approach that exploits a fully-convolutional network in a binary segmentation fashion to localize the objects of interest.

Looking for pathways related to COVID-19: confirmation of pathogenic mechanisms by SARS-CoV-2-host interactome.

In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, but the mechanisms of interaction between host and SARS-CoV-2, determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein-protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2.

A study on the possible merits of using symptomatic cases to trace the development of the COVID-19 pandemic.

In a recent work, we introduced a novel method to compute the effective reproduction number and we applied it to describe the development of the COVID-19 outbreak in Italy. The study is based on the number of daily positive swabs as reported by the Italian Dipartimento di Protezione Civile. Recently, the Italian Istituto Superiore di Sanità made available the data relative of the symptomatic cases, where the reporting date is the date of beginning of symptoms instead of the date of the reporting of the positive swab.

A Statistical Analysis of Death Rates in Italy for the Years 2015-2020 and a Comparison with the Casualties Reported from the COVID-19 Pandemic.

We analyze the data about casualties in Italy in the period 1 January 2015 to 30 September 2020 released by the Italian National Institute of Statistics (ISTAT). The aim of this article was the description of a statistically robust methodology to extract quantitative values for the seasonal excesses of deaths featured by the data, accompanying them with correct estimates of the relative uncertainties. We will describe the advantages of the method adopted with respect to others listed in literature.

A simplified estimate of the effective reproduction number using its relation with the doubling time and application to Italian COVID-19 data.

A simplified method to compute , the effective reproduction number, is presented. The method relates the value of to the estimation of the doubling time performed with a local exponential fit. The condition corresponds to a growth rate equal to zero or equivalently an infinite doubling time.

Hibernation and Radioprotection: Gene Expression in the Liver and Testicle of Rats Irradiated under Synthetic Torpor.

Hibernation has been proposed as a tool for human space travel. In recent years, a procedure to induce a metabolic state known as "synthetic torpor" in non-hibernating mammals was successfully developed. Synthetic torpor may not only be an efficient method to spare resources and reduce psychological problems in long-term exploratory-class missions, but may also represent a countermeasure against cosmic rays.

Hibernation for space travel: Impact on radioprotection.

Hibernation is a state of reduced metabolic activity used by some animals to survive in harsh environmental conditions. The idea of exploiting hibernation for space exploration has been proposed many years ago, but in recent years it is becoming more realistic, thanks to the introduction of specific methods to induce hibernation-like conditions (synthetic torpor) in non-hibernating animals. In addition to the expected advantages in long-term exploratory-class missions in terms of resource consumptions, aging, and psychology, hibernation may provide protection from cosmic radiation damage to the crew.