From Rusty Genetics to Octopussy’s Garden

Alaimo critiques the “rusty” understanding of genetics, gender, and sex in Middlesex, advocating instead for queer ecological futurism

Bedrock and soil geochemistry influence the content of chemical elements in wild edible mushrooms (Morchella group) from South Italy (Sicily)

Chemical elements in the samples of wild edible mushrooms of the Morchellagroup collected from different unpolluted Sicilian sites was analyzed by the ICP-MS (method) to detect the content of their minerals and determine whether soil geology and geochemistry can influence the chemical composition in fungi. Results showed that the mushroom samples mainly contained a high concentration of K and P and a wide variety of minor and trace elements (V, Mo, Pb, Ce, Cs, Zr), including heavy metals. Statistical analysis showed that the mushrooms differed in their content of minor and trace elements based on the geological/geographic site of origin. Comparison with other studies showed differences in the content detected in the Sicilian morels with those collected from other geographical sites. Conversely, dif-ferent fungal species collected from similar geological sites in Sicily showed different patterns of accumulation of the elements confirming that bioconcentration in fungi is species- and site-dependent

Recognition of Trace Element Contamination Using Ficus macrophylla Leaves in Urban Environment

Urban areas are characterized by numerous pollutants emitted by anthropic sources both in the form of solid and gaseous particulates. Biomonitoring is an easy, economical, and accessible approach for the determination of atmospheric pollutants. In this study, we used the leaves of Ficusmacrophylla Desf. ex Pers., collected in the city of Palermo (Italy), to determine major and trace elements. Geogenic elements exhibited the highest concentrations, making up 99% of the weight of the analyzed elements (Ca, K, Mg, P, S, Na, Fe, and Al); they range 21,400 (Ca) to 122 µg g-1 (Al). The remaining elements showed median concentrations in the range 47.5-0.05 µg g-1 in the following order of abundance: Sr > Cu > Mn > Zn > Br > Rb > Ba > Pb > Cr > Sb > As > Mo = Sc. Cluster analysis, with Spearman's coefficient to measure sample similarity, identified five main groups, namely, three clusters related to the geogenic background and marine spray; one cluster linked to elements essential to plants, and a final group attributed to the influence of traffic emissions. Calculated enrichment factors (EF) showed that the enrichments found for P and K were linked to plant metabolism; Na and Mg confirmed the role of sea spray; Cu and Zn underlined the contribution linked to anthropic processes and the role of micronutrients in plants.. As, Cr, and Mo had EF values ranging from 10 and 20, and Sb had EF > 90. From geochemical distribution maps of As, Cr, Mo, and Sb it was observed that metal and metalloid concentrations were higher in urban areas and immediately decreased as one moved away from these areas. Local pollution sources play a great role in trace element concentrations in airborne particulate matter. The present study confirms that Ficusmacrophylla leaves are suitable for screening an urban environment to identify concentrations of inorganic chemicals, since they have high tolerance to pollution

VAT tax gap prediction: a 2-steps Gradient Boosting approach

Tax evasion is the illegal evasion of taxes by individuals, corporations, and trusts. The revenue loss from tax avoidance can undermine the effectiveness and equity of the government policies. A standard measure of tax evasion is the tax gap, that can be estimated as the difference between the total amounts of tax theoretically collectable and the total amounts of tax actually collected in a given period. This paper presents an original contribution to bottom-up approach, based on results from fiscal audits, through the use of Machine Learning. The major disadvantage of bottom-up approaches is represented by selection bias when audited taxpayers are not randomly selected, as in the case of audits performed by the Italian Revenue Agency. Our proposal, based on a 2-steps Gradient Boosting model, produces a robust tax gap estimate and, embeds a solution to correct for the selection bias which do not require any assumptions on the underlying data distribution. The 2-steps Gradient Boosting approach is used to estimate the Italian Value-added tax (VAT) gap on individual firms on the basis of fiscal and administrative data income tax returns gathered from Tax Administration Data Base, for the fiscal year 2011. The proposed method significantly boost the performance in predicting with respect to the classical parametric approaches.Comment: 27 pages, 4 figures, 8 tables Presented at NTTS 2019 conference Under review at another peer-reviewed journa

Oil intensities and oil prices : evidence for Latin America

Crude oil prices have dramatically increased over the past years and are now at a historical maximum in nominal terms and very close to it in real terms. It is difficult to argue, at least for net oil importers, that higher oil prices have a positive impact on welfare. In fact, the negative relationship between oil prices and economic activity has been well documented in the literature. Yet, to the extent that higher oil prices lead to lower oil consumption, it would be possible to argue that not all the effects of a price increase are negative. Climate change concerns have been on the rise in recent years and fossil fuel consumption is generally viewed as one of the main causes behind it. Thus this paper explores whether higher oil prices contribute to lowering oil intensities (that is, oil consumption per unit of gross domestic product). The findings show that following an increase in oil prices, OECD countries tend to reduce oil intensity. However, the same result does not hold for Latin America (and more generally for middle-income countries) where oil intensities appear to be unaffected by oil prices. The paper also explores why this is so.Energy Production and Transportation,Oil Refining&Gas Industry,Markets and Market Access,Energy Demand,Environment and Energy Efficiency

Bayesian Population Size Estimation With a Single Sample

La stima della numerosit`a di una popolazione `e un problema comune a vari ambiti di applicazione. Le procedure di stima sono solitamente basate sul noto metodo cattura-ricattura, il quale comporta elevati costi e disturbo della popolazione. Tali considerazioni hanno stimolato la ricerca di tecniche che permettano di ottenere un stima utilizzando un unico campione. Hettiarachchige (Hettiarachchige, C.K.H.: Inference from single occasion capture experiments using genetic markers. PhD Thesis (2016)) propone un metodo applicabile nel caso in cui la popolazione sia composta di due sole generazioni: un gruppo di unit`a generatrici ed uno di unit`a generate. L’obiettivo del nostro lavoro `e quello di ottenere un’estensione Bayesiana dell’originale modello frequentista. Risultati preliminari evidenziano accuratezza degli stimatori Bayesiani sensibilmente migliore rispetto alle alternative frequentiste.The estimation of the size of a finite population is a problem encountered in a variety of applications. One standard statistical approach relies on markrecapture sampling, which may require high costs and annoyance to the population of interest. These considerations have motivated the search for alternative sampling strategies that allow to estimate the size of a population from a single capture. Hettiarachchige (Hettiarachchige, C.K.H.: Inference from single occasion capture experiments using genetic markers. PhD Thesis (2016)) proposes a method that is viable when the population is made of only two generations: a group of generators and one of generated units. We investigate Bayesian methods alternative to the frequentist estimators used by the original author. Preliminary results give evidence of competing performance of the Bayesian approach, which in some cases sensibly outperforms the frequentist alternatives

Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which has built on top of the work of Tarca et al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their deregulation degree, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril

Computing the everyday: social media as data platforms

We conceive social media platforms as sociotechnical entities that variously shape user platform involvement and participation. Such shaping develops along three fundamental data operations that we subsume under the terms of encoding, aggregation, and computation. Encoding entails the engineering of user platform participation along narrow and standardized activity types (e.g., tagging, liking, sharing, following). This heavily scripted platform participation serves as the basis for the procurement of discrete and calculable data tokens that are possible to aggregate and, subsequently, compute in a variety of ways. We expose these operations by investigating a social media platform for shopping. We contribute to the current debate on social media and digital platforms by describing social media as posttransactional spaces that are predominantly concerned with charting and profiling the online predispositions, habits, and opinions of their user base. Such an orientation sets social media platforms apart from other forms of mediating online interaction. In social media, we claim, platform participation is driven toward an endless online conversation that delivers the data footprint through which a computed sociality is made the source of value creation and monetization

Laminar flow through fractal porous materials: The fractional-order transport equation

The anomalous transport of a viscous fluid across a porous media with power-law scaling of the geometrical features of the pores is dealt with in the paper. It has been shown that, assuming a linear force-flux relation for the motion in a porous solid, then a generalized version of the Hagen-Poiseuille equation has been obtained with the aid of Riemann-Liouville fractional derivative. The order of the derivative is related to the scaling property of the considered media yielding an appropriate mechanical picture for the use of generalized fractional-order relations, as recently used in scientific literature