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. Here we show the preliminary results from an experiment in rats exposed to ionizing radiation in normothermic conditions or synthetic torpor. Animals were irradiated with 3 Gy X-rays and organs were collected 4 h after exposure. Histological analysis of liver and testicle showed a reduced toxicity in animals irradiated in torpor compared to controls irradiated at normal temperature and metabolic activity. The expression of ataxia telangiectasia mutated (ATM) in the liver was significantly downregulated in the group of animal in synthetic torpor. In the testicle, more genes involved in the DNA damage signaling were downregulated during synthetic torpor. These data show for the first time that synthetic torpor is a radioprotector in non-hibernators, similarly to natural torpor in hibernating animals. Synthetic torpor can be an effective strategy to protect humans during long term space exploration of the solar system.

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. The contribution is two-fold. First, thanks to the variety of annotations and their accessible formats, we anticipate our work will facilitate methodological advancements in computer vision approaches for segmentation, detection, feature extraction, unsupervised and self-supervised learning, transfer learning, and related areas. Second, by enabling extensive exploration and benchmarking, we hope Fluorescent Neuronal Cells v2 will catalyze breakthroughs in fluorescence microscopy analysis and promote cutting-edge discoveries in life sciences.

Study on the effects of the restrictive measures for containment of the COVID-19 pandemic on the reproduction number R t 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 R t , we estimate how much different restrictive measures affect R t , and we quantify the combined effect of the diffusion of virus variants and the beginning of the vaccination campaign upon the R t trend. We also compute the time delay between implementation of restrictive measures and the resulting effects. Three different models to describe the effects of restrictive measures are discussed and the results are cross-checked with two different algorithms for the computation of R t .

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

A simplified method to compute R t , the effective reproduction number, is presented. The method relates the value of R t to the estimation of the doubling time performed with a local exponential fit. The condition R t = 1 corresponds to a growth rate equal to zero or equivalently an infinite doubling time. Different assumptions on the probability distribution of the generation time are considered. A simple analytical solution is presented in case the generation time follows a gamma distribution.

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 R t 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. In this paper, we will discuss merits and drawbacks of this data, quantitatively comparing the quality of the pandemic indicators computed with the two samples.

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. Counts are then retrieved as the number of detected items. Specifically, we introduce a Unet-like architecture, cell ResUnet (c-ResUnet), and compare its performance against 3 similar architectures. In addition, we evaluate through ablation studies the impact of two design choices, (i) artifacts oversampling and (ii) weight maps that penalize the errors on cells boundaries increasingly with overcrowding. In summary, the c-ResUnet outperforms the competitors with respect to both detection and counting metrics (respectively, [Formula: see text] score = 0.81 and MAE = 3.09). Also, the introduction of weight maps contribute to enhance performances, especially in presence of clumping cells, artifacts and confounding biological structures. Posterior qualitative assessment by domain experts corroborates previous results, suggesting human-level performance inasmuch even erroneous predictions seem to fall within the limits of operator interpretation. Finally, we release the pre-trained model and the annotated dataset to foster research in this and related fields.

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. The data exhibit a clear sinusoidal behavior, whose fit allows for a robust subtraction of the baseline trend of casualties in Italy, with a surplus of mortality in correspondence to the flu epidemics in winter and to the hottest periods in summer. The overall quality of the fit to the data turns out to be very good, an indication of the validity of the chosen model. We discuss the trend of casualties in Italy by different classes of ages and for the different genders. We finally compare the data-subtracted casualties, as reported by ISTAT, with those reported by the Italian Department for Civil Protection (DPC) relative to the deaths directly attributed to the Coronavirus Disease 2019 caused by the SARS-CoV-2 virus (COVID-19), and we point out the differences in the two samples, collected under different assumptions.

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. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific clinical outcomes. We identified possible host responses induced by specific proteins of SARS-CoV-2, underlining the important role of specific viral accessory proteins in pathogenic phenotypes of severe COVID-19 patients.

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. Data from over half century ago in animal models suggest indeed that radiation effects are reduced during hibernation. We will review the mechanisms of increased radioprotection in hibernation, and discuss possible impact on human space exploration.