Observer-oriented approach improves species distribution models from citizen science data

Citizen science platforms are increasingly growing, and, storing a huge amount of data on species locations, they provide researchers with essential information to develop sound strategies for species conservation. However, the lack of information on surveyed sites (i.e., where the observers did not record the target species) and sampling effort (e.g., the number of surveys at a given site, by how many observers, and for how much time) strongly limit the use of citizen science data. Thus, we examined the advantage of using an observer-oriented approach (i.e., considering occurrences of species other than the target species collected by the observers of the target species as pseudo-absences and additional predictors relative to the total number of observations, observers, and days in which locations were collected in a given sampling unit, as proxies of sampling effort) to develop species distribution models. Specifically, we considered 15 mammal species occurring in Italy and compared the predictive accuracy of the ensemble predictions of nine species distribution models carried out considering random pseudo-absences versus observer-oriented approach. Through cross-validations, we found that the observer-oriented approach improved species distribution models, providing a higher predictive accuracy than random pseudo-absences. Our results showed that species distribution modeling developed using pseudo-absences derived citizen science data outperform those carried out using random pseudo-absences and thus improve the capacity of species distribution models to accurately predict the geographic range of species when deriving robust surrogate of sampling effort

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

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

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

Applying the theory of real options to the optimal timing of timber harvests

Se propone el enfoque de opciones reales como herramienta económico-financiera para la toma de decisiones estratégicas en el sector forestal. En términos de instrumentos financieros, consideraremos en particular una opción exótica conocida como barrier option del tipo knock-in. Suponemos que la proyección del precio de venta promedio de los subproductos sigue un proceso estocástico del tipo Geométrico Browniano, mientras que la producción se determina mediante simulación de un turno forestal. La decisión de talar la masa forestal surge de comparar en cada periodo, el valor de flujo de fondos en cada nodo (FFij(t)) de una rejilla binomial con el valor esperado en el próximo año (X t+1 x e−rΔ t) ). En un ejemplo analizado el criterio tradicional del VAN indica que el mayor valor actual se produce en el instante t = 0 (año 10), mientras que el enfoque de opciones reales arroja que el máximo valor de ejercicio se da en el periodo t = 8 (año 18).We present here a real options approach as a tool for strategic decision-making in the forestry sector. We consider, in particular, an exotic option known as a knock-in barrier option. We use this approach to determine the optimal timing of harvests. The optimal time is determined by comparing at each period, the cash flow corresponding to each node (FFij(t)) in a binomial lattice with the expected value in the next year (X t+1 x e−rΔ t). The traditional NPV indicates, in an hypothetical context analyzed in the paper, that the largest present value is obtained at the tenth year of standing. The real options approach championed here, instead, indicates that the highest value is at the eighteenth year of standing.Fil: Milanesi, Gastón. Universidad Nacional del SurFil: Woitschach, Guillermo B. M.. Universidad Nacional del SurFil: Broz, Diego R.. Universidad Nacional del Su

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

Tihonov theory and center manifolds for inhibitory mechanisms in enzyme kinetics

Abstract In this paper we study the chemical reaction of inhibition, determine the appropriate parameter ε for the application of Tihonov's Theorem, compute explicitly the equations of the center manifold of the system and find sufficient conditions to guarantee that in the phase space the curves which relate the behavior of the complexes to the substrates by means of the tQSSA are asymptotically equivalent to the center manifold of the system. Some numerical results are discussed

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