87 research outputs found
How virus persistence can initiate the tumorigenesis process
No full textHuman oncogenic viruses are defined as necessary but not sufficient to initiate cancer. Experimental evidence suggests that the oncogenic potential of a virus is effective in cells that have already accumulated a number of genetic mutations leading to cell cycle deregulation. Current models for viral driven oncogenesis cannot explain why tumor development in carriers of tumorigenic viruses is a very rare event, occurring decades after virus infection. Considering that viruses are mutagenic agents per se and human oncogenic viruses additionally establish latent and persistent infections, we attempt here to provide a general mechanism of tumor initiation both for RNA and DNA viruses, suggesting viruses could be both necessary and sufficient in triggering human tumorigenesis initiation. Upon reviewing emerging evidence on the ability of viruses to induce DNA damage while subverting the DNA damage response and inducing epigenetic disturbance in the infected cell, we hypothesize a general, albeit inefficient hit and rest mechanism by which viruses may produce a limited reservoir of cells harboring permanent damage that would be initiated when the virus first hits the cell, before latency is established. Cells surviving virus generated damage would consequently become more sensitive to further damage mediated by the otherwise insufficient transforming activity of virus products expressed in latency, or upon episodic reactivations (viral persistence). Cells with a combination of genetic and epigenetic damage leading to a cancerous phenotype would emerge very rarely, as the probability of such an occurrence would be dependent on severity and frequency of consecutive hit and rest cycles due to viral reinfections and reactivations
Protagonisti del secondo trecento bolognese
No full textViene illustrata la situazione della pittura bolognese negli anni successivi alla morte di Vitale degli Equi. Oltre a Simone di Filippo e a Jacopo Avanzi, si prendono in esame Andrea de' Bartoli e Jacopo Avanzi
Ionic Liquid Synthesis of Catalysts for Direct CO2 Hydrogenation to shortchain hydrocarbons
No full textThe direct conversion of carbon dioxide into lower olefins (C2-C4) is a highly desirable process as a sustainable production route1,2. These lower olefins, such as ethylene, propylene, and butenes, are crucial components in the chemical industry and for Liquefied Petroleum Gas (LPG). The reaction proceeds via two main consecutive reactions: Reverse Water Gas Shift (RWGS) to produce CO followed by the further conversion of CO to hydrocarbons via the Fischer−Tropsch reaction3. Recent studies 45highlight the cost-effectiveness and satisfactory performance of Fe-based catalysts in both reaction steps, while exploring bimetallic catalysts, particularly Ru and Fe combinations, to enhance olefin selectivity6., with precise MNP synthesis as a crucial factor for performance control.The study introduces a novel approach for synthesizing iron-ruthenium bimetallic catalysts that utilizes ionic liquids as solvents7, ensuring precise and uniform distribution of active metal phases. Advanced characterizations and extensive tests reveal that this method surpasses traditional colloid-based techniques, resulting in superior selectivity for target hydrocarbons
GPU-acceleration of Navier-Stokes solvers for compressible wall-bounded flows: the case of URANOS
No full textThe present paper describes the performance of URANOS, a high-fidelity Direct and Large- Eddy Simulation Navier-Stokes solver specifically developed for wall-bounded compressible flows. The code combines cutting-edge numerical methods peculiarly developed for high-speed turbulent flow simulations and is tailored to modern high-performance computing systems due to MPI parallelization combined with multi-GPUs communication access. In particular, OpenACC directives are implemented for GPU enabling offloading computational loads onto accelerators cards, making URANOS an easily maintained solver as well as guaranteeing extreme flexibility and portability. The solver validation is detailed for a broad range of Mach numbers, from low-speed to compressible cases. In particular, velocity statistics and Reynolds stress components for canonical channel flow and turbulent boundary layer configurations obtained with URANOS well agree with high-quality DNS data. Computational performance and scaling properties are tested on several multi-GPU-equipped clusters. Thus, with URANOS, the scientific community can take advantage of a GPU-accelerated solver in dealing with fluid modeling for aerodynamics applications. The source code is available under a BSD license at the following link: https://gitlab.com/fralusa/uranos_gpu
Investigating the dynamics of bulk snow density in dry and wet conditions using a one-dimensional model
No full textThe snowpack is a complicated multiphase mixture with mechanical, hydraulic, and thermal properties highly variable during the year in response to climatic forcings. Bulk density is a macroscopic property of the snowpack used, together with snow depth, to quantify the water stored. In seasonal snowpacks, the bulk density is characterized by a strongly non-linear behaviour due to the occurrence of both dry and wet conditions. In the literature, bulk snow density estimates are obtained principally with multiple regressions, and snowpack models have put the attention principally on the snow depth and snow water equivalent. Here a one-dimensional model for the temporal dynamics of the snowpack, with particular attention to the bulk snow density, has been proposed, accounting for both dry and wet conditions. The model represents the snowpack as a two-constituent mixture: a dry part including ice structure, and air; and a wet part constituted by liquid water. It describes the dynamics of three variables: the depth and density of the dry part and the depth of liquid water. The model has been calibrated and validated against hourly data registered at three SNOTEL stations, western US, with mean values of the Nash–Sutcliffe coefficient ?0.73–0.97 in the validation period.Geoscience & EngineeringCivil Engineering and Geoscience
A random forest approach to quality-checking automatic snow-depth sensor measurements
No full text<jats:p>Abstract. State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low – especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a random forest classifier to automatize the quality assurance and quality control (QA/QC) procedure of near-surface snow-depth measurements collected through ultrasonic sensors, with particular reference to the differentiation of snow cover from grass or bare-ground data and to the detection of random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (northwestern Italian Alps) and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, it being the most suited to describe the performances of a model in the case of a multiclass imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %) yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests the robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow data, enhancing their use in snow models.
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IONIC LIQUIDS-BASED INNOVATIVE SYNTHESIS OF Fe-Ru BIMETALLIC CATALYSTS FOR CO2 HYDROGENATION: A SUSTAINABLE APPROACH TOWARDS NET-ZERO FUEL PRODUCTION
No full textThe use of a bifunctional catalysts capable of facilitating both the Reverse Water Gas Shift (RWGS) and Fischer−Tropsch reactions is crucial for efficiently converting CO2 into lower olefins (C2-C4), which is essential for sustainable chemical production and LPG. Recent research highlights the cost-effectiveness and efficacy of Fe-based catalysts, especially Fe/Ru bimetallic catalysts, in improving olefin selectivity
Water-balance response to climate variability, a small-to-large scale Italian dataset
No full textUnderstanding how deficit of precipitation impacts the hydrological cycle is of growing interest and is essential for water resource management. It has been recently observed that the relationship between precipitation and runoff during droughts is subjected to a shift in the sense that the predicted runoff is much less than the one expected due to the deficit in precipitation. Unraveling why this occurs requires an accurate knowledge of all the components of the water balance equation. However, large-scale and consistent samples of precipitation, runoff, evapotranspiration, ET and change in storage have always been challenging to collect. Here, we hypothesized that blending ground-based and remote-sensing data products could fill this gap. We present a countrywide dataset of catchment-scale water balance, covering the last 10 water years in Italy. Italy shows a broad variety of climatic and topographic features and faced several droughts over recent years. We use ground-based daily runoff data, interpolated precipitation maps, and a remote-sensed daily evapotranspiration dataset from the LSASAF ET product. The ET dataset is additionally compared with flux towers data across the country, obtaining root mean square errors on the order of 30 mm/month. Lastly, changes in storage are estimated to close the water balance. More than 100 catchments - including the major Italian basins - are selected, according to data availability and reliability. These catchments cover a wide range of size, morphologic and climatic characteristics.
This dataset is a strategic source of information to analyze catchment-scale runoff, ET and storage response to climatic variability across climates and landscapes
Novel synthesis approaches for CO2 Hydrogenation catalysts using Ionic Liquids
No full textThe conversion of carbon dioxide into lower olefins (C2-C4) represents a highly desirable process for establishing a sustainable production pathway. These lower olefins, including ethylene, propylene, and butenes, play pivotal roles in the chemical industry and the production of Liquefied Petroleum Gas (LPG). The reaction unfolds through two consecutive primary processes: Reverse Water Gas Shift (RWGS), generating CO, followed by the subsequent transformation of CO into hydrocarbons through the Fischer−Tropsch reaction. Recent research has underscored the cost-effectiveness and satisfactory performance of Febased catalysts in both reaction steps, with an exploration of bimetallic catalysts, particularly combinations of Ru and Fe, aimed at enhancing olefin selectivity. Precise synthesis of multinanoparticle (MNP) becomes a critical factor for performance control in this context. The study introduces an innovative approach to synthesize iron-ruthenium bimetallic catalysts, utilizing ionic liquids as solvents. This method ensures the precise and uniform distribution of active metal phases. Advanced characterizations and extensive tests reveal that this technique outperforms traditional colloid-based methods, resulting in superior selectivity for the desired hydrocarbons
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