When did coronavirus arrive in Europe?

The first cluster of coronavirus cases in Europe was officially detected on 21st February 2020 in Northern Italy, even if recent evidence showed sporadic first cases in Europe since the end of 2019. In this study, we have tested the presence of coronavirus in Italy and, even more importantly, we have assessed whether the virus had already spread sooner than 21st February. We use a counterfactual approach and certified daily data on the number of deaths (deaths from any cause, not only related to coronavirus) at the municipality level. Our estimates confirm that coronavirus began spreading in Northern Italy in mid-January

Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review

This literature review rigorously examines the growing scientific interest in computational methods for Tissue Engineering and Regenerative Medicine biofabrication, a leading-edge area in biomedical innovation, emphasizing the need for accurate, multi-stage, and multi-component biofabrication process models. The paper presents a comprehensive bibliometric and contextual analysis, followed by a literature review, to shed light on the vast potential of computational methods in this domain. It reveals that most existing methods focus on single biofabrication process stages and components, and there is a significant gap in approaches that utilize accurate models encompassing both biological and technological aspects. This analysis underscores the indispensable role of these methods in understanding and effectively manipulating complex biological systems and the necessity for developing computational methods that span multiple stages and components. The review concludes that such comprehensive computational methods are essential for developing innovative and efficient Tissue Engineering and Regenerative Medicine biofabrication solutions, driving forward advancements in this dynamic and evolving field

Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies.

The mammalian cortex contains a great variety of neuronal cells. In particular, GABAergic interneurons, which play a major role in neuronal circuit function, exhibit an extraordinary diversity of cell types. In this regard, single-cell RNA-seq analysis is crucial to study cellular heterogeneity. To identify and analyze rare cell types, it is necessary to reliably label cells through known markers. In this way, all the related studies are dependent on the quality of the employed marker genes. Therefore, in this work, we investigate how a set of chosen inhibitory interneurons markers perform. The gene set consists of both immunohistochemistry-derived genes and single-cell RNA-seq taxonomy ones. We employed various human and mouse datasets of the brain cortex, consequently processed with the Monocle3 pipeline. We defined metrics based on the relations between unsupervised cluster results and the marker expression. Specifically, we calculated the specificity, the fraction of cells expressing, and some metrics derived from decision tree analysis like entropy gain and impurity reduction. The results highlighted the strong reliability of some markers but also the low quality of others. More interestingly, though, a correlation emerges between the general performances of the genes set and the experimental quality of the datasets. Therefore, the proposed method allows evaluating the quality of a dataset in relation to its reliability regarding the inhibitory interneurons cellular heterogeneity study

Accounting for attitudes and perceptions influencing users' willingness to purchase Electric Vehicles through a Hybrid Choice Modeling approach based on Analytic Hierarchy Process

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Approaches for Modelling User’s Acceptance of Innovative Transportation Technologies and Systems

The gradual penetration of new transport modes and/or new technologies (advanced information systems, automotive technologies, etc.) requires effective theoretical paradigms able to interpret and model transportation system users’ propensity to purchase and use them. Along with the traditional approaches mainly based on random utility theory, it is a common opinion that numerous nonquantitative variables (such as psychological factors, attitudes, perceptions, etc.) may affect users’ behaviors. Different traditional approaches and more advanced ones (e.g. hybrid choice model (HCM) with latent variables, theory of planned behaviour, regret theory, prospect theory, etc.) may be identified and properly applied in the literature. In particular, the chapter will focus on the hybrid choice modeling with latent variables, aiming to incorporate users’ perceptions, attitudes and concerns in order to model the user’s propensity to use and the willingness to buy a new technology. The methodology overview and the results of the application at real data are discussed

High-resolution sample size enrichment of single-cell multi-modal low-throughput Patch-seq datasets

Single-cell multimodal technologies are becoming the hot topic of single-cell heterogeneity and function studies, promising to unravel the hidden relationship and functionalities of different aspects of the cells. Among the plethora of single-cell technologies, interesting is the patch-seq technology, which simultaneously performs Patch clamp measures and scRNA-seq on the same cells. However, given the experimental limitations of throughput of Patch clamp, the scRNA-seq analysis is challenging because it requires more samples to investigate cellular heterogeneity. Usually, the solution is associating the cells with the cell types in an existing scRNA-seq dataset. However, doing so loses part of the single cell resolution of the multimodal technique. Therefore, this work proposes a procedure leveraging the Seurat Integration process to find from a reference dataset t he most similar cells to the ones from the patch-seq. The similarity is how much gene expression profiles are identical, and to evaluate that, this work defines various etrics based on R and Index. In this way, one obtains a selection of suitable Reference cells to enrich the number of cells on which to perform multimodal investigation

The Gray Zone

The Gray Zone ∗ Federico Crudu† University of Siena and CRENoS Roberta Di Stefano ‡ Sapienza University of Rome Giovanni Mellace§ University of Southern Denmark Silvia Tiezzi¶ University of Siena March 2022 Abstract On March 23, 2020, in response to the COVID-19 pandemic, Italy declared a nation- wide lockdown. A month earlier, on February 23, the Italian government ordered its military police to seal the borders and declared a Red Zone around 10 municipalities of the province of Lodi and in Vo’ Euganeo, a small town in Padua province. On the same day, Confindustria Bergamo, the province’s industrial association, posted a video on social media against having a lockdown in the area of Bergamo and was supported by key business leaders and local administrators. Despite having a similar infection rate to the Red Zone municipalities, the government decided not to extend the Red Zone to the municipalities of Bergamo province with high infection rates. Bergamo later became one of the deadliest outbreaks of the first wave of the virus in the Western world. What would have happened had the Red Zone been extended to that area? We use the Synthetic Control Method to estimate the causal effect of (not) declaring a Red Zone in the Bergamo area on daily excess mortality. We find that about two-thirds of the reported deaths could have been avoided had the Italian government declared the area a Red Zone

GRAIGH: Gene Regulation accessibility integrating GeneHancer database

Single-cell assays for transposase-accessible chromatin sequencing data represent a potent tool for exploring the epigenetic heterogeneity within cell populations. Despite their power, understanding the chromatin accessibility landscape poses challenges. This study introduces Gene Regulation Accessibility Integrating GeneHancer (GRAIGH), a novel approach to interpreting genome accessibility by integrating information from the GeneHancer database, detailing genome-wide enhancer-to-gene associations. Initially, we outline the methods for integrating GeneHancer with scATAC-seq data. This involves creating a new matrix where GeneHancer element IDs replace traditional accessibility peaks as features. Subsequently, the paper assesses the method’s ability to analyze data and detect cellular heterogeneity. Notably, our findings demonstrate the selective accessibility of GeneHancer elements for distinct cell types, with connected genes serving as precise marker genes. Furthermore, we explore the specificity of GeneHancer element accessibility, highlighting their high selectivity against gene activity. This investigation underscores the potential of Gene Regulation Accessibility Integrating GeneHancer in unraveling the complexities of chromatin accessibility, offering insights into the nuanced relationship between accessibility and cellular heterogeneity

Was there a COVID-19 harvesting effect in Northern Italy?

We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, associated with the COVID-19 pandemic by looking at the excess mortality trends of an area that registered one of the highest death tolls in the world during the first wave, Northern Italy. We do not find any evidence of a sizable COVID-19 harvesting effect, neither in the summer months after the slowdown of the first wave nor at the beginning of the second wave. According to our estimates, only a minor share of the total excess deaths detected in Northern Italian municipalities over the entire period under scrutiny (February - November 2020) can be attributed to an anticipatory role of COVID-19. A slightly higher share is detected for the most severely affected areas (the provinces of Bergamo and Brescia, in particular), but even in these territories, the harvesting effect can only account for less than 20% of excess deaths. Furthermore, the lower mortality rates observed in these areas at the beginning of the second wave may be due to several factors other than a harvesting effect, including behavioral change and some degree of temporary herd immunity. The very limited presence of short-run mortality displacement restates the case for containment policies aimed at minimizing the health impacts of the pandemic

Two Variables Algorithms for Solving the Stochastic Equilibrium Assignment with Variable Demand: Performance Analysis and Effects of Path Choice Models

In this paper a general fixed-point approach dealing with multi-user (stochastic) equilibrium assignment with variable demand is proposed. The main focus is on (i) the implementation and comparison of different algorithm solutions based on successive averages methods calculated on one (arc flows, arc costs) and on two variables (arc flows and path satisfaction; arc costs and demand flows); (ii) the effects of algorithm efficiency on different path choice models and/or travel demand choice models. In terms of the best performing algorithmic solution, the effects of different path choice models, such as Multinomial Logit model, C-Logit model and Multinomial Probit model were implemented, and algorithmic efficiency was investigated w.r.t. a real network