AN INTEGRATED APPROACH FOR POLLUTION MONITORING: SMART ACQUIREMENT AND SMART INFORMATION

Air quality is a factor of primary importance for the quality of life. The increase of the pollutants percentage in the air can cause serious problems to the human and environmental health. For this reason it is essential to monitor its values to prevent the consequences of an excessive concentration, to reduce the pollution production or to avoid the contact with major pollutant concentration through the available tools. Some recently developed tools for the monitoring and sharing of the data in an effective system permit to manage the information in a smart way, in order to improve the knowledge of the problem and, consequently, to take preventing measures in favour of the urban air quality and human health. In this paper, the authors describe an innovative solution that implements geomatics sensors (GNSS) and pollutant measurement sensors to develop a low cost sensor for the acquisition of pollutants dynamic data using a mobile platform based on bicycles. The acquired data can be analysed to evaluate the local distribution of pollutant density and shared through web platforms that use standard protocols for an effective smart use

Application of a multiscale approach for modeling the rheology of complex fluids in industrial mixing equipment

Many industrial sectors, like the personal care one, make wide use of mixing processes that involve complex fluids. However, modeling the rheology of these fluids is still challenging due to their non-Newtonian behavior, which depends also on the local composition. Computational tools such as dissipative particle dynamics (DPD) have been already used to calculate the equilibrium properties of these systems. Moreover, different works have been focused on the calculation of transport properties from these mesoscale DPD simulations. Multiscale approaches have been proposed to couple rheological information from DPD with computational fluid dynamics (CFD) simulations. The CFD technique reproduces the macroscale piece of equipment, implementing a rheology model built using the Gaussian process regression, a mathematical tool related to machine learning. In this work, such a framework is tested on an industrial process, to assess its performance on a realistic application. The investigated system is a solution at a high concentration of sodium lauryl ether sulfate in water under laminar fluid dynamics regime. The results show that the mixture correctly exhibits a shear-thinning behavior and presents viscosity values in good agreement with rheology experiments. While the feasibility of the coupling approach is shown, further studies on DPD are needed to improve the accuracy and the predictability of the methodology

Optimisation of a one-step PCR assay for the diagnosis of Flavescence doreerelated phytoplasmas in field-grown grapevines and vector populations

Field-infected grapevines and natural populations of Scaphoideus titanus have been analysed to detect group V phytoplasmas associated with flavescence doree in northwestern Italy using nested PCR. A first amplification driven by universal ribosomal primers R16SF2/R2 was followed by a second round assisted by R16(V)F1/R1 primers and subsequent RFLP analysis. To optimize the test, nested PCRs were compared with direct amplification assisted by the group V-specific fAY/rEY primer pair, directed towards other ribosomal sequences. In nested and direct PCRs, respectively, DNAs from 71 and 57 out of 96 grapevines (i.e. 73.9 and 59.3 %) and 51 and 50 out of 108 insects (i.e. 47.2 and 46.3 %) reacted positively. Although it was not possible to determine the subgroup of the phytoplasmas after fAY/rEY amplification, these primers could be used successfully in mass screening of plant material and insect populations. They could detect, in single-step amplification, the phytoplasmas in 80 and 98 % of the plant and insect samples, respectively, that were already indexed as positive using nested PCR. This strongly reduced the number of samples requiring the nested approach, with beneficial effects on costs, labour and risks of the analysis.

Development of a virtual methodology based on physical and data-driven models to optimize engine calibration

Virtual engine calibration exploiting fully-physical plant models is the most promising solution for the reduction of time and cost of the traditional calibration process based on experimental testing. However, accuracy issues on the estimation of pollutant emissions are still unresolved. In this context, the paper shows how a virtual test rig can be built by combining a fully-physical engine model, featuring predictive combustion and NOx sub-models, with data-driven soot and particle number models. To this aim, a dedicated experimental campaign was carried out on a 1.6 liter EU6 diesel engine. A limited subset of the measured data was used to calibrate the predictive combustion and NOx sub-models. The measured data were also used to develop data-driven models to estimate soot and particulate emissions in terms of Filter Smoke Number (FSN) and Particle Number (PN), respectively. Inputs from engine calibration parameters (e.g., fuel injection timing and pressure) and combustion-related quantities computed by the physical model (e.g., combustion duration), were then merged. In this way, thanks to the combination of the two different datasets, the accuracy of the abovementioned models was improved by 20% for the FSN and 25% for the PN. The coupled physical and data-driven model was then used to optimize the engine calibration (fuel injection, air management) exploiting the Non-dominated Sorting genetic algorithm. The calibration obtained with the virtual methodology was then adopted on the engine test bench. A BSFC improvement of 10 g/kWh and a combustion reduction of 3.0 dB in comparison with the starting calibration was achieved

Effects of miRNA-15 and miRNA-16 expression replacement in chronic lymphocytic leukemia : implication for therapy

This work was supported by: Associazione Italiana Ricerca sul Cancro (AIRC) Grant 5 x mille n.9980, (to M.F., F.M. A. N., P.T. and M.N.) ; AIRC I.G. n. 14326 (to M.F.), n.10136 and 16722 (A.N.), n.15426 (to F.F.). AIRC and Fondazione CaRiCal co-financed Multi Unit Regional Grant 2014 n.16695 (to F.M.). Italian Ministry of Health 5x1000 funds (to S.Z. and F.F). A.G R. was supported by Associazione Italiana contro le Leucemie-Linfomi-Mielomi (AIL) Cosenza - Fondazione Amelia Scorza (FAS). S.M. C.M., M.C., L.E., S.B. were supported by AIRC.Peer reviewedPostprin

Feeding Pre-weaned Calves With Waste Milk Containing Antibiotic Residues Is Related to a Higher Incidence of Diarrhea and Alterations in the Fecal Microbiota

The cows receiving antibiotics for intra-mammary infection (IMI) produce milk that cannot be marketed. This is considered waste milk (WM), and a convenient option for farmers is using it as calf food. However, adding to the risk of selecting resistant bacteria, residual antibiotics might interfere with the gut microbiome development and influence gastrointestinal health. We assessed the longitudinal effect of unpasteurized WM containing residual cefalexin on calf intestinal health and fecal microbiota in an 8-week trial. After 3 days of colostrum, six calves received WM and six calves received bulk tank milk (BM) for 2 weeks. For the following 6 weeks, all 12 calves received milk substitute and starter feed. Every week for the first 2 weeks and every 2 weeks for the remaining 6 weeks, we subjected all calves to clinical examination and collected rectal swabs for investigating the fecal microbiota composition. Most WM calves had diarrhea episodes in the first 2 weeks of the trial (5/6 WM and 1/6 BM), and their body weight was significantly lower than that of BM calves. Based on 16S rRNA gene analysis, WM calves had a lower fecal microbiota alpha diversity than that in BM calves, with the lowest p-value at Wk4 (p < 0.02), 2 weeks after exposure to WM. The fecal microbiota beta diversity of the two calf groups was also significantly different at Wk4 (p < 0.05). Numerous significant differences were present in the fecal microbiota taxonomy of WM and BM calves in terms of relative normalized operational taxonomic unit (OTU) levels, affecting five phyla, seven classes, eight orders, 19 families, and 47 genera. At the end of the trial, when 6 weeks had passed since exposure to WM, the phyla Bacteroidetes, Firmicutes, and Saccharibacteria were lower, while Chlamydiae were higher in WM calves. Notably, WM calves showed a decrease in beneficial taxa such as Faecalibacterium, with a concomitant increase in potential pathogens such as Campylobacter, Pseudomonas, and Chlamydophila spp. In conclusion, feeding pre-weaned calves with unpasteurized WM containing antibiotics is related to a higher incidence of neonatal diarrhea and leads to significant changes in the fecal microbiota composition, further discouraging this practice in spite of its short-term economic advantages

Meta-analysis of trials comparing anastrozole and tamoxifen for adjuvant treatment of postmenopausal women with early breast cancer

<p>Abstract</p> <p>Objective</p> <p>It was aimed to review the literature and make a meta-analysis of the trials on both upfront, switching, and sequencing anastrozole in the adjuvant treatment of early breast cancer.</p> <p>Methods</p> <p>The PubMed, ClinicalTrials.gov and Cochrane databases were systematically reviewed for randomized-controlled trials comparing anastrozole with tamoxifen in the adjuvant treatment of early breast cancer.</p> <p>Results</p> <p>The combined hazard rate of 4 trials for event-free survival (EFS) was 0.77 (95%CI: 0.70–0.85) (<it>P </it>< 0.0001) for patients treated with anastrozole compared with tamoxifen. In the second analysis in which only ITA, ABCSG 8, and ARNO 95 trials were included and ATAC (upfront trial) was excluded, combined hazard rate for EFS was 0.64 (95%CI: 0.52–0.79) (<it>P </it>< 0.0001). In the third analysis including hazard rate for recurrence-free survival (excluding non-disease related deaths) of estrogen receptor-positive patients for ATAC trial and hazard rate for EFS of all patients for the rest of the trials, combined hazard rate was 0.73 (95%CI: 0.65–0.81) (<it>P </it>< 0.0001).</p> <p>Conclusion</p> <p>Anastrozole appears to have superior efficacy than tamoxifen in the adjuvant hormonal treatment of early breast cancer. Until further clinical evidence comes up, aromatase inhibitors should be the initial hormonal therapy in postmenopausal early breast cancer patients and switching should only be considered for patients who are currently receiving tamoxifen.</p