The effect of known and unknown confounders on the relationship between air pollution and Covid-19 mortality in Italy: A sensitivity analysis of an ecological study based on the E-value.

Back in December 2019, the novel coronavirus disease 2019 (Covid-19) started rapidly spreading worldwide, especially in Italy that was among the most affected countries. The geographical distribution of air pollution and Covid-19 mortality in Italy suggested atmospheric pollution as a worsening factor of severe Covid-19 health outcomes. The present nationwide ecological study focused on all 107 Italian territorial areas, aiming to assess the potential association between Particulate Matter concentration, less than 2.5 µm in diameter (exposure), and Covid-19 mortality rate (outcome) throughout 2020, by looking at 28 potential confounders. A potential positive association between exposure and outcome was observed when performing a multivariate regression analysis with a Negative Binomial model, suggesting that an increase of 1 µg/m3 in the exposure is associated with an increase of 9.0% (95% CI: 6.5%-11.6%) in the average Covid-19 mortality rate, conditional on all 28 potential confounders. A sensitivity analysis, based on the E-value, shows that a hypothetical unmeasured confounder would have to be associated with both PM2.5 concentration and Covid-19 mortality rate by a rate ratio of at least 1.40-fold each to explain away the exposure-outcome association, conditional on all 28 covariates included in the main analysis model. Moreover, the Observed Covariate E-value (OCE) was reported to provide a contextualization of the E-value on the observed covariates included in the study. The OCE sensitivity analysis shows that a set of unknown confounders similar in size and magnitude to the set of the considered climatic factors could potentially explain away the estimated exposure-outcome association. Consequently, the role of climatic factors in the Covid-19 pandemic is worth of further investigation.

Assessing correlations between short-term exposure to atmospheric pollutants and COVID-19 spread in all Italian territorial areas.

The spread of SARS-CoV-2, the beta coronavirus responsible for the current pneumonia pandemic outbreak, has been speculated to be linked to short-term and long-term atmospheric pollutants exposure. The present work has been aimed at analyzing the atmospheric pollutants concentrations (PM10, PM2.5, NO2) and spatio-temporal distribution of cases and deaths (specifically incidence, mortality and lethality rates) across the whole Italian national territory, down to the level of each individual territorial area, with the goal of checking any potential short-term correlation between these two phenomena. The data analysis has been limited to the first quarter of 2020 to reduce the lockdown-dependent biased effects on the atmospheric pollutant levels as much as possible. The analysis looked at non-linear, monotonic correlations using the Spearman non-parametric correlation index. The statistical significance of the Spearman correlations has also been evaluated. The results of the statistical analysis suggest the hypothesis of a moderate-to-strong correlation between the number of days exceeding the annual regulatory limits of PM10, PM2.5 and NO2 atmospheric pollutants and COVID-19 incidence, mortality and lethality rates for all the 107 territorial areas in Italy. A weak-to-moderate correlation seems to exist when considering the 36 territorial areas in four of the most affected regions (Lombardy, Piedmont, Emilia-Romagna and Veneto). Overall, PM10 and PM2.5 showed a higher non-linear correlation than NO2 with incidence, mortality and lethality rates. As to particulate matters, PM10 profile has been compared with the incidence rate variation that occurred in three of the most affected territorial areas in Northern Italy (i.e., Milan, Brescia, and Bergamo). All areas showed a similar PM10 time trend but a different incidence rate variation, that was less severe in Milan compared with Brescia and Bergamo.

Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure.

BACKGROUND: The present knowledge of protein structures at atomic level derives from some 60,000 molecules. Yet the exponential ever growing set of hypothetical protein sequences comprises some 10 million chains and this makes the problem of protein structure prediction one of the challenging goals of bioinformatics. In this context, the protein representation with contact maps is an intermediate step of fold recognition and constitutes the input of contact map predictors. However contact map representations require fast and reliable methods to reconstruct the specific folding of the protein backbone. METHODS: In this paper, by adopting a GRID technology, our algorithm for 3D reconstruction FT-COMAR is benchmarked on a huge set of non redundant proteins (1716) taking random noise into consideration and this makes our computation the largest ever performed for the task at hand. RESULTS: We can observe the effects of introducing random noise on 3D reconstruction and derive some considerations useful for future implementations. The dimension of the protein set allows also statistical considerations after grouping per SCOP structural classes. CONCLUSIONS: All together our data indicate that the quality of 3D reconstruction is unaffected by deleting up to an average 75% of the real contacts while only few percentage of randomly generated contacts in place of non-contacts are sufficient to hamper 3D reconstruction.

Virtual model of the human brain for neurosurgical simulation.

The aim of this work is to develop a realistic virtual model of the human brain that could be used in a neurosurgical simulation for both educational and preoperative planning purposes. The goal of such a system would be to enhance the practice of surgery students, avoiding the use of animals, cadavers and plastic phantoms. A surgeon, before carrying out the real procedure, will, with this system, be able to rehearse by using a surgical simulator based on detailed virtual reality models of the human brain, reconstructed with real patient's medical images. In order to obtain a realistic and useful simulation we focused our research on the physical modelling of the brain as a deformable body and on the interactions with surgical instruments. The developed prototype is based on the mass-spring-damper model and, in order to obtain deformations similar to the real ones, a three tiered structure has been built. In this way, we have obtained local and realistic deformations using an ad-hoc point distribution in the volume where the contact between the brain surface and a surgical instrument takes place.

A virtual interface for interactions with 3D models of the human body.

The developed system is the first prototype of a virtual interface designed to avoid contact with the computer so that the surgeon is able to visualize 3D models of the patient's organs more effectively during surgical procedure or to use this in the pre-operative planning. The doctor will be able to rotate, to translate and to zoom in on 3D models of the patient's organs simply by moving his finger in free space; in addition, it is possible to choose to visualize all of the organs or only some of them. All of the interactions with the models happen in real-time using the virtual interface which appears as a touch-screen suspended in free space in a position chosen by the user when the application is started up. Finger movements are detected by means of an optical tracking system and are used to simulate touch with the interface and to interact by pressing the buttons present on the virtual screen.

A protein structure prediction service in the ProGenGrid system.

This paper describes a protein tertiary structure prediction service implemented in a Grid Environment. The service has been used for predicting the dicarboxylate carrier (DIC) of Saccharomyces cerevisiae by using the homology modelling approach. The visualization of the predicted model is made possible by using an interactive virtual reality environment based on X3D and Ajax3d technologies.

A grid-enabled protein secondary structure predictor.

We present an integrated Grid system for the prediction of protein secondary structures, based on the frequent automatic update of proteins in the training set. The predictor model is based on a feed-forward multilayer perceptron (MLP) neural network which is trained with the back-propagation algorithm; the design reuses existing legacy software and exploits novel grid components. The predictor takes into account the evolutionary information found in multiple sequence alignment (MSA); the information is obtained running an optimized parallel version of the PSI-BLAST tool, based on the MPI Master-Worker paradigm. The training set contains proteins of known structure. Using Grid technologies and efficient mechanisms for running the tools and extracting the data, the time needed to train the neural network is dramatically reduced, whereas the results are comparable to a set of well-known predictor tools.

A services oriented system for bioinformatics applications on the grid.

This paper describes the evolution of the main services of the ProGenGrid (Proteomics & Genomics Grid) system, a distributed and ubiquitous grid environment ("virtual laboratory"), based on Workflow and supporting the design, execution and monitoring of "in silico" experiments in bioinformatics.ProGenGrid is a Grid-based Problem Solving Environment that allows the composition of data sources and bioinformatics programs wrapped as Web Services (WS). The use of WS provides ease of use and fosters re-use. The resulting workflow of WS is then scheduled on the Grid, leveraging Grid-middleware services. In particular, ProGenGrid offers a modular bag of services and currently is focused on the biological simulation of two important bioinformatics problems: prediction of the secondary structure of proteins, and sequence alignment of proteins. Both services are based on an enhanced data access service.

ProGenGrid: a grid-enabled platform for bioinformatics.

In this paper we describe the ProGenGrid (Proteomics and Genomics Grid) system, developed at the CACT/ISUFI of the University of Lecce which aims at providing a virtual laboratory where e-scientists can simulate biological experiments, composing existing analysis and visualization tools, monitoring their execution, storing the intermediate and final output and finally, if needed, saving the model of the experiment for updating or reproducing it. The tools that we are considering are software components wrapped as Web Services and composed through a workflow. Since bioinformatics applications need to use high performance machines or a high number of workstations to reduce the computational time, we are exploiting a Grid infrastructure for interconnecting wide-spread tools and hardware resources. As an example, we are considering some algorithms and tools needed for drug design, providing them as services, through easy to use interfaces such as the Web and Web service interfaces built using the open source gSOAP Toolkit, whereas as Grid middleware we are using the Globus Toolkit 3.2, exploiting some protocols such as GSI and GridFTP.

Computer-based simulator for catheter insertion training.

Minimally invasive surgery procedures are getting common in surgical practice; however the new interventional procedure requires different skills compared to the conventional surgical techniques. The need for training process is very important in order to successfully and safely execute a surgical procedure. Computer-based simulators, with appropriate tactile feedback device, can be an efficient method for facilitating the education and training process. In addition, virtual reality surgical simulators can reduce costs of education and provide realism with regard to tissues behaviour and real-time interaction. This work take into account the results of the HERMES Project (HEmatology Research virtual MEdical System), conceived and managed by Consorzio CETMA-Research Centre; the aim of this project is to build an integrate system in order to simulate a coronary angioplasty intervention.