Networks from gene expression time series: characterization of correlation patterns

This paper describes characteristic features of networks reconstructed from gene expression time series data. Several null models are considered in order to discriminate between informations embedded in the network that are related to real data, and features that are due to the method used for network reconstruction (time correlation).Comment: 10 pages, 3 BMP figures, 1 Table. To appear in Int. J. Bif. Chaos, July 2007, Volume 17, Issue

Self-assembled nanoparticles as multifunctional drugs for anti-microbial therapies

crosscheck: This document is CrossCheck deposited related_data: Supplementary Information copyright_licence: The Royal Society of Chemistry has an exclusive publication licence for this journal copyright_licence: The accepted version of this article will be made freely available after a 12 month embargo period history: Received 15 January 2014; Accepted 22 May 2014; Accepted Manuscript published 22 May 2014; Advance Article published 4 June 2014; Version of Record published 19 June 201

Study of tributary inflows in Lake Iseo with a rotating physical model

The influence of Coriolis force on the currents of large lakes is well acknowledged; very few contributions, however, investigate this aspect in medium-size lakes where its relevance could be questionable. In order to study the area of influence of the two major tributary rivers in Lake Iseo, a rotating vertically distorted physical model of the northern part of this lake was prepared and used, respecting both Froude and Rossby similarity. The model has a horizontal length scale factor of 8000 and a vertical scale factor of 500 and was used both in homogeneous and in thermally stratified conditions. We explored the pattern of water circulation in front of the entrance mouth for dif- ferent hydrologic scenarios at the beginning of spring and in summer. We neglected the influence of winds. The primary purposes of the model were twofold: i) to increase our level of knowledge of the hydrodynamics of Lake Iseo by verifying the occurrence of dynamical effects related to the Earth’s rotation on the plume of the two tributaries that enter the northern part of the lake and ii) to identify the areas of the lake that can be directly influenced by the tributaries’ waters, in order to provide guidance on water quality monitoring in zones of relevant environmental and touristic value. The results of the physical model confirm the relevant role played by the Coriolis force in the northern part of the lake. Under ordinary flow conditions, the model shows a systematic deflection of the inflowing waters towards the western shore of the lake. The inflow triggers a clockwise gyre within the Lovere bay, to the West of the inflow, and a slow counter-clockwise gyre, to the East of the inflow, that returns water towards the river mouth along the eastern shore. For discharges with higher return period, when only the contribution by Oglio River is relevant, the effect of the Earth’s rotation weakens in the entrance zone and the plume has a more rectilinear pattern, whilst in the far field the current driven by the inflows keeps moving along the western shore. On the basis of these results one could expect that the north-western part of the lake between Castro and Lovere, although not aligned with the tributaries’ axes, is more sensitive to accumulation effects related to river-borne pollution. The results obtained with the physical model are critically compared with data obtained from different sources: the trajectory of a lagrangian drogue; a map of reflectivity data from the lake floor; a map of water turbidity at the intrusion depth. The findings are also confirmed by the results of a 3D numerical model of the lake

A distributed approach for parameter estimation in Systems Biology models

Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology Mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models

A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19

COVID-19 is an ongoing global pandemic of coronavirus disease 2019, which may cause severe acute respiratory syndrome. This disease highlighted the limitations of health systems worldwide regarding managing the pandemic. In particular, the lack of diagnostic tests that can quickly and reliably detect infected patients has contributed to the spread of the virus. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) and antigen tests, which are the main diagnostic tests for COVID-19, showed their limitations during the pandemic. In fact, RT-PCR requires several hours to provide a diagnosis and is not properly accurate, thus generating a high number of false negatives. Unlike RT-PCR, antigen tests provide rapid diagnosis but are less accurate in detecting COVID-19 positive patients. Medical imaging is an alternative diagnostic test for COVID-19. In particular, chest computed tomography allows detecting lung infections related to the disease with high accuracy. However, visual analysis of a chest scan generated by computed tomography is a demanding activity for radiologists, making widespread use of this test unfeasible. Therefore, it is essential to lighten their work with automated tools able to provide accurate diagnosis in a short time. To deal with this challenge, in this work, an approach based on 3D Inception CNNs is proposed. Specifically, 3D Inception-V1 and Inception-V3 models have been built and compared. Then, soft-voting ensemble classifier models have been separately built on these models to boost the performance. As for the individual models, results showed that Inception-V1 outperformed Inception-V3 according to different measures. As for the ensemble classifier models, the outcome of experiments pointed out that the adopted voting strategy boosted the performance of individual models. The best results have been achieved enforcing soft voting on Inception-V1 models

ochratoxin a as possible factor trigging autism and its male prevalence via epigenetic mechanism

The role of dysbiosis causing leaky gut with xenobiotic production and absorption is increasingly demonstrated in autism spectrum disorder (ASD) pathogenesis. Among xenobiotics, we focused on ochratoxin A (one of the major food contaminating mycotoxin), that in vitro and in vivo exerts a male-specific neurotoxicity probably via microRNA modulation of a specific target gene. Among possible targets, we focused on neuroligin4X. Interestingly, this gene carries some single nucleotide polymorphisms (SNPs) already correlated with the disease and with illegitimate microRNA binding sites and, being located on X-chromosome, could explain the male prevalence. In conclusion, we propose a possible gene–environment interaction triggering ASD explaining the epigenetic neurotoxic mechanism activated by ochratoxin A in genetically predisposed children. This mechanism offers a clue for male prevalence of the disease and may have an important impact on prevention and cure of ASD

Hydraulic hazard mapping in alpine dam break prone areas: the Cancano dam case

Dam-break hazard assessment is of great importance in the Italian Alps, where a large number of medium and large reservoirs are present in valleys that are characterized by widespread urbanized zones on alluvial fans and along valley floors. Accordingly, there is the need to identify specific operative approaches in order to quantify hydraulic hazard which in mountain regions inevitably differ from the ones typically used in flat flood-prone areas. These approaches take advantage of: 1) specific numerical algorithms to pre-process the massive topographic information generally needed to describe very irregular bathymetries; 2) an appropriate mathematical model coupled with a robust numerical method which can deal in an effective way with variable geometries like the ones typical of natural alpine rivers; 3) suitable criteria for the hydraulic hazard assessment; 4) representative test cases to verify the accuracy of the overall procedure. This contribution presents some preliminary results obtained in the development of this complex toolkit, showing its application to the test case of the Cancano dam-break, for which the results from a physical model are available. This case was studied in 1943 by De Marchi, who investigated the consequences of the potential collapse of the Cancano dam in Northern Italy as a possible war target during the World War II. Although dated, the resulting report (De Marchi, 1945) is very interesting, since it mixes in a synergistic way theoretical, experimental and numerical considerations. In particular, the laboratory data set concerning the dam-break wave propagation along the valley between the Cancano dam and the village of Cepina provides an useful benchmark for testing the predictive effectiveness of mathematical and numerical models in mountain applications. Here we suggest an overall approach based on the 1D shallow water equations that proved particularly effective for studying dam-break wave propagation in alpine valleys, although this kind of problems is naturally subject to "substantial uncertainties and unavoidable arbitrarinesses" (translation from De Marchi, 1945). The equations are solved by means of a shock-capturing finite volume method involving the Pavia Flux Predictor (PFP) scheme proposed by Braschi and Gallati (1992). The comparison between numerical results and experimental data confirms that the mathematical model adopted is capable of capturing the main engineering aspects of the phenomenon modeled by De Marchi

The roughness of the protein energy landscape results in anomalous diffusion of the polypeptide backbone

Some of this work was supported by grants from the NIH and BBSRC