Temporal trends of polycyclic aromatic hydrocarbons (PAHs) in a dated sediment core of a high atitude mountain lake: Chungara Lake-Northern Chile (18° S)

In this study levels, fuxes and temporal trend of PAHs are reported in a sediment core from Chungará Lake (18° S) in Northern Chile. The sediments were obtained by Kayac corer and freeze dried. PAHs were extracted in SOXHLET system and measured by HPLC with diode array detector and fuorescence detection. Sedimentary record chronology was determined using210Pb isotopes and organic carbon (%) was also measured in sediments. Concentrations (ng g-1 d.w.) of PAHs were low ranging from ∼1 to 50. PAHs fngerprint was dominated by 3-ring (21%) and 4-ring. Organic carbon (%) ranged from ∼17 to 24 (21±3) and no statistical signifcant correlation (p<0.05) was detected between OC (%) and PAHs along the sediment core. PAHs fuxes (μg m-2yr-1) fuctuated from ∼0.3 (cm 1) to 35 (cm 5) in 1978. LPAHs/HPAHs ratios (0.04 to 3) indicate petrogenic and pyrolytic origin of PAHs. This results contributes with new information of PAHs deposition at high altitudinal lake in Southern Hemisphere

SEM Remote Control with a 3D Option

Abstract Remote control of a scientific instrument is a topic gaining more and more attention between instrument users and operators. The project presented in this article reports results obtained from two distinct research efforts. The main outcome from the first research was the realization of an application to remote-control a Scanning Electron Microscope (SEM), while the main outcome from the second research was the implementation of a procedure to reconstruct 3D surfaces

Investigating parameter transferability across models and events for a Semiarid Mediterranean Catchment

Physically based distributed hydrologic models (DHMs) simulate watershed processes by applying physical equations with a variety of simplifying assumptions and discretization approaches. These equations depend on parameters that, in most cases, can be measured and, theoretically, transferred across different types of DHMs. The aim of this study is to test the potential of parameter transferability in a real catchment for two contrasting periods among three DHMs of varying complexity. The case study chosen is a small Mediterranean catchment where the TIN-based Real-time Integrated Basin Simulator (tRIBS) model was previously calibrated and tested. The same datasets and parameters are used here to apply two other DHMs-the TOPographic Kinematic Approximation and Integration model (TOPKAPI) and CATchment HYdrology (CATHY) models. Model performance was measured against observed discharge at the basin outlet for a one-year period (1930) corresponding to average wetness conditions for the region, and for a much drier two-year period (1931-1932). The three DHMs performed comparably for the 1930 period but showed more significant differences (the CATHY model in particular for the dry period. In order to improve the performance of CATHY for this latter period, an hypothesis of soil crusting was introduced, assigning a lower saturated hydraulic conductivity to the top soil layer. It is concluded that, while the physical basis for the three models allowed transfer of parameters in a broad sense, transferability can break down when simulation conditions are greatly altered

Confirming the function of a Final Bronze Age wine processing site in the Nuraghe Genna Maria in Villanovaforru (South Sardinia)

The stone artefact in the hut γ of the NuragheGenna Maria, object of this study, is part of a compound still unpublished today and dated to the Nuragic period. It was found during a 1991 excavation, revealing a situation unchanged since the collapse occurred between the 10th and 9th century B.C., thus preserving the situation at the time of the collapse to this day. The presence of tartaric acid - the marker considered to determinate the presence of wines or products deriving from grapes - has been determined using HPLC-DAD and UHPLC-HQOMS. So the findings under examination, together with the overall evaluation of the archaeological aspects examined, suggests to positively consider the stone artifact as a "laccus" (the latin word for wine presses, still used in the Sardinian language today ) for grape crushing. The internal slope of the floor of the "laccus" allowed the extraction of juice with rapid separation of juice from berry skins. The presence in Sardinia of a large number of "stone wine presses" ("palmenti" in Italian) such as that of the Nuraghe Genna Maria studied in this article, brings a contribution to their dating and confirm the existence of an oenological industry on the island in the Archaic period (9th-10th century B.C.)

Random walks and search in time-varying networks

The random walk process underlies the description of a large number of real world phenomena. Here we provide the study of random walk processes in time varying networks in the regime of time-scale mixing; i.e. when the network connectivity pattern and the random walk process dynamics are unfolding on the same time scale. We consider a model for time varying networks created from the activity potential of the nodes, and derive solutions of the asymptotic behavior of random walks and the mean first passage time in undirected and directed networks. Our findings show striking differences with respect to the well known results obtained in quenched and annealed networks, emphasizing the effects of dynamical connectivity patterns in the definition of proper strategies for search, retrieval and diffusion processes in time-varying network

Exposure to Endocrine Disruptors and Nuclear Receptors Gene Expression in Infertile and Fertile Men from Italian Areas with Different Environmental Features

Internal levels of selected endocrine disruptors (EDs) (i.e., perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), di-2-ethylhexyl-phthalate (DEHP), mono-(2-ethylhexyl)-phthalate (MEHP), and bisphenol A (BPA)) were analyzed in blood/serum of infertile and fertile men from metropolitan, urban and rural Italian areas. PFOS and PFOA levels were also evaluated in seminal plasma. In peripheral blood mononuclear cells (PBMCs) of same subjects, gene expression levels of a panel of nuclear receptors (NRs), namely estrogen receptor α (ERα) estrogen receptor β (ERβ), androgen receptor (AR), aryl hydrocarbon receptor (AhR), peroxisome proliferator-activated receptor γ (PPARγ) and pregnane X receptor (PXR) were also assessed. Infertile men from the metropolitan area had significantly higher levels of BPA and gene expression of all NRs, except PPARγ, compared to subjects from other areas. Subjects from urban areas had significantly higher levels of MEHP, whereas subjects from rural area had higher levels of PFOA in both blood and seminal plasma. Interestingly, ERα, ERβ, AR, PXR and AhR expression is directly correlated with BPA and inversely correlated with PFOA serum levels. Our study indicates the relevance of the living environment when investigating the exposure to specific EDs. Moreover, the NRs panel in PBMCs demonstrated to be a potential biomarker of effect to assess the EDs impact on reproductive health

Light field image compression

Light field imaging based on a single-tier camera equipped with a micro-lens array has currently risen up as a practical and prospective approach for future visual applications and services. However, successfully deploying actual light field imaging applications and services will require identifying adequate coding solutions to efficiently handle the massive amount of data involved in these systems. In this context, this chapter presents some of the most recent light field image coding solutions that have been investigated. After a brief review of the current state of the art in image coding formats for light field photography, an experimental study of the rate-distortion performance for different coding formats and architectures is presented. Then, aiming at enabling faster deployment of light field applications and services in the consumer market, a scalable light field coding solution that provides backward compatibility with legacy display devices (e.g., 2D, 3D stereo, and 3D multiview) is also presented. Furthermore, a light field coding scheme based on a sparse set of microimages and the associated blockwise disparity is also presented. This coding scheme is scalable with three layers such that the rendering can be performed with the sparse micro-image set, the reconstructed light field image, and the decoded light field image.info:eu-repo/semantics/acceptedVersio

Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Introduction: Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals. Methods: This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A. Results: The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk. Discussion: The present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration

Modeling teams performance using deep representational learning on graphs

AbstractMost human activities require collaborations within and across formal or informal teams. Our understanding of how the collaborative efforts spent by teams relate to their performance is still a matter of debate. Teamwork results in a highly interconnected ecosystem of potentially overlapping components where tasks are performed in interaction with team members and across other teams. To tackle this problem, we propose a graph neural network model to predict a team’s performance while identifying the drivers determining such outcome. In particular, the model is based on three architectural channels: topological, centrality, and contextual, which capture different factors potentially shaping teams’ success. We endow the model with two attention mechanisms to boost model performance and allow interpretability. A first mechanism allows pinpointing key members inside the team. A second mechanism allows us to quantify the contributions of the three driver effects in determining the outcome performance. We test model performance on various domains, outperforming most classical and neural baselines. Moreover, we include synthetic datasets designed to validate how the model disentangles the intended properties on which our model vastly outperforms baselines.</jats:p