The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases. The service requires the previous knowledge of at least another gene responsible for the disease and the linkage area, or else of two disease associated genetic intervals.

Glioblastoma multiforme (GBM) is,the most common and lethal primary brain tumor in adults. We combined neuroimaging and DNA microarray analysis to create a multidimensional map of gene-expression patterns in GBM that provided clinically relevant insights into tumor biology. Tumor contrast enhancement and mass effect predicted activation of specific hypoxia and proliferation gene-expression programs, respectively.

Biotechnology is an area of great innovations that promises to have deep impact on everyday life thanks to profound changes in biology, medicine, and health care. This article will span from the description of the biochemical principles of molecular biology to the definition of the physics that supports the technology and to the devices and algorithms necessary to observe molecular events in a controlled, portable, and highly parallel manner.

The importance of circuits and systems for high-throughput biological data acquisition in biomedical research are discussed. High-throughput biological data acquisition and processing technologies have shifted the focus of biological research from the the traditional experimental science to that of information science. Powerful computation and communication means can be applied to a very large amount of apparently incoherent data coming from biomedical research.

Background: Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine - the scientific approach to medicine in tight relation with basic science, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).

The study of complex hereditary diseases is a very challenging area of research. The expanding set of in silico approaches offers a flourishing ground for the acceleration of meaningful findings in this area by exploitation of rich and diverse sources of omic data. These approaches are cheap, flexible, extensible, often complementary and can continuously integrate new information and tests to improve the selection of genes responsible for hereditary diseases.

Background: Sequencing technologies applied to mammals' microbiomes have revolutionized our understanding of health and disease. Hence, to assess diseases' progression as well as therapies longterm effects, the impact of maladies and drugs on the gut-intestinal (GI) microbiome has to be evaluated.

In March 2019, the PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyper-spectral satellite was launched by the Italian Space Agency (ASI), and it is currently operational on a global basis. The mission includes the hyperspectral imager PRISMA working in the 400-2500 nm spectral range with 237 bands and a panchromatic (PAN) camera (400-750 nm). This paper presents an evaluation of the PRISMA top-of-atmosphere (TOA) L1 products using different in situ measurements acquired over a fragmented rural area in Southern Italy (Pignola) between October 2019 and July 2021.

We describe preliminary results from a multiobjective graph matching algorithm, in the coarsening step of an aggregation-based Algebraic MultiGrid (AMG) preconditioner, for solving large and sparse linear systems of equations on highend parallel computers. We have two objectives. First, we wish to improve the convergence behavior of the AMG method when applied to highly anisotropic problems. Second, we wish to extend the parallel package PSCToolkit to exploit multi-threaded parallelism at the node level on multi-core processors.