Study of meta-analysis strategies for network inference using information-theoretic approaches

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches focused on individual datasets, which typically suffer from some experimental bias and a small number of samples. To date, there are mainly two strategies for the problem of interest: the first one (”data merging”) merges all datasets together and then infers a GRN whereas the other (”networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. In this paper, we evaluate the performances of various metaanalysis approaches mentioned above with a systematic set of experiments based on in silico benchmarks. Furthermore, we present a new meta-analysis approach for inferring GRNs from multiple studies. Our proposed approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix.Peer ReviewedPostprint (author's final draft

minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

On the Impact of Entropy Estimation on Transcriptional Regulatory Network Inference Based on Mutual Information

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes) network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Phosphorus recovery from a pilot-scale grate furnace: influencing factors beyond wet chemical leaching conditions

Phosphorus is a non-renewable resource going to exhaustion in the future. Sewage sludge ash is a promising secondary raw material due to its high phosphorus content. In this work, the distribution of 19 elements in bottom and cyclone ashes from pilot-scale grate furnace have been monitored to determine the suitability for the phosphorus acid extraction. Moreover, the influence of some parameters beyond wet chemical leaching conditions were investigated. Experimental results showed that bottom ash presented lower contamination in comparison to cyclone ash and low co-dissolution of heavy metals (especially Cr, Pb and Ni), while high phosphorus extraction efficiencies (76-86%) were achieved. High Al content in the bottom ash (9.4%) negatively affected the phosphorus extraction efficiency as well as loss on ignition, while the particle size reduction was necessary for ensuring a suitable contact surface. The typology of precipitating agents did not strongly affect the phosphorus precipitation, while pH was the key parameter. At pH 3.5-5, phosphorus precipitation efficiencies higher than 90% were achieved, with a mean phosphorus content in the recovered material equal to 16-17%, comparable to commercial fertilizers. Instead, the co-precipitation of Fe and Al had a detrimental effect on the recovered material, indicating the need for additional treatments

Environmental Impact of Surgical Masks Consumption in Italy Due to COVID-19 Pandemic

The COVID-19 pandemic suddenly changed the lifestyle of billions of people. Face masks became indispensable to protect from the contagion providing a significant environmental impact. The aim of this work is to propose possible solutions to decrease masks’ impact on the environment. For this reason, different masks (surgical and fabric) were considered, and the CO2 emissions associated with the mask materials production were calculated. Carbon Footprint (CF) for each material composing the masks was evaluated through the database Ces Selector 2019. The software Qgis (version 2.18.20) allows us to elaborate the CO2 emissions maps for each Italian region. Finally, for surgical masks, which are often imported from abroad, the CF related to transport was considered. It results that fabric masks are a sustainable solution to prevent contagion. The total CO2 emission associated with the use of fabric masks from the beginning of the pandemic (March 2020) to December 2021 resulted in about 7 kton compared to 350 kton for surgical masks

Chemical Analysis of Air Particulate Matter Trapped by a Porous Material, Synthesized from Silica Fume and Sodium Alginate

This work shows the ability of a new porous material (SUNSPACE), obtained by industrial by-products, to sequestrate air particulate matter (PM). This activity allows introducing the azure chemistry approach, devoted to better link new remediation strategies and sustainability. In particular, SUNSPACE is synthesized from silica fume and sodium alginate; it can be shaped in a porous solid, and it looks promising for environmental application as nanoparticle sequestration. Studies to evaluate the sequestration capability of SUNSPACE are performed in different environments, with and without anthropogenic sources of PM. Solid SUNSPACE disc samples are used as passive samplers and exposed for one and two months, in vertical and horizontal positions, indoor, and outdoor. Total reflection X-ray fluorescence technique is employed to perform elemental chemical analysis of the entrapped PM. Two sample preparation strategies to evaluate the composition of PM are considered: sample sonication in Milli-Q water and total sample mineralization by microwave acid digestion. These two options are proposed to analyse different PM fractions: in particular, sonication allows removing the coarse PM, entrapped on external material surface pores; on the contrary, digestion can offer information on fine and ultrafine PM, trapped in internal pores. Results confirm the ability of the porous material to sequestrate air PM and the differences in the sample preparation, supported by elemental analysis, and show the difference in the coarse and fine air particulate matter composition. In summary, the new material results as very promising for applications requiring nanoparticle sequestration