Trend Analysis of Air Quality Index in Catania from 2010 to 2014

Abstract Information on air quality in urban areas represents an important objective to raise awareness and participation of citizens towards those measures aimed at containing and reducing vehicular traffic. For several years at the international level, evaluation procedures have been adopted by indices. One of the first synthetic indices, adopted by the United States Environmental Protection Agency (US-EPA), was the Pollution Standard Index (PSI). In 1999, the EPA replaced the PSI index with Air Quality Index (AQI), which includes two new sub-indices, the ozone at ground level and fine particulate. Despite the European Decisions 97/101/EC and 2001/752/EC, have established an exchange of information from networks and individual stations measuring ambient air pollution in Member States, the use of a single index has not yet been defined that allows you to compare different realities. This heterogeneity emerges in Italy as well, where only a few Environmental Protection Agencies disclose indexes to inform citizens. In this article, the Air Quality Index (AQI) currently used by the United States Environmental Protection Agency has been applied to the metropolitan city of Catania, in order to analyze the level of pollution daily from 2010 to 2014. Through the use of the AQI it was possible to synthesize in a single daily value, concentrations of major pollutants in urban areas (NO2, O3, CO, SO2, PM10) for the entire period. For the calculation procedure of the AQI, the data concentrations were provided by Municipal Ecology and Environment Office. The data relates to three monitoring stations, whose locations have not changed over the years. This also made it possible to evaluate the change in frequency of AQI agglomerations where the monitoring units have been positioned. The value obtained by the AQI for each station has been ranked in six levels of pollution; each level has been associated with a particular coloring allowing this information to be more intuitive. Lastly, it was possible to reach the air quality assessment in urban environment from the frequency variations of each level derived from the year 2010 until 2014

Cascade feed forward neural network-based model for air pollutants evaluation of single monitoring stations in urban areas

In this paper, air pollutants concentrations for N O2 , N O, N Ox and P M 10 in a single monitoring station are predicted using the data coming from other different monitoring stations located nearby. A cascade feed forward neural network based modeling is proposed. The main aim is to provide a methodology leading to the introduction of virtual monitoring station points consistent with the actual stations located in the city of Catania in Italy.

Experimental Analysis of a Plume Dispersion Around Obstacles

Abstract Nowadays, transport and deposition of aerosol particles (PM 2.5 , PM 10 , TSP) caused by industrial plants, environmental applications and transports, are of great concern to public health. Despite the establishment by the European Union of emission standards (European directive 2008/50/CE e.g) to control the limits of particulates in the air, the emissions by industrial plants are still not accurately monitored. In particular, the interaction between plume dispersion and obstacles, such as buildings, is not currently well studied. A lot of theoretical researches were carried out in this field with a lack of experimental data comparison. This paper focuses on a laboratory work made to better explain the interaction of a continuous plume released from a point source and various obstacles. First of all a vertical pipe was reproduced, a continuous aerosol emitter was characterized in terms of a specified and controlled mass flow and the ratio between smoke emission and the total suspended particulates thanks to use of the certified gravimetric calculation of PM 10 . The experimental campaigns were conducted by means of a wind tunnel all the data collected were validated. The characterization of plume was made by the use of several sensors and calculation of velocity in several points of the field. Moreover, the plume dispersion was studied also by using digital image analysis. It was then investigated downwind the influence of obstacles of various shapes and distances from source in terms of aerosol concentration in several points

Theoretical and Experimental Study of Gaussian Plume Model in Small Scale System

Abstract Atmospheric dispersion pollution modelling is of great and actual concern in the scientific international community. Many dispersion models have been developed and used to estimate the downwind ambient concentration of air pollutants from sources such as industrial plants, vehicular traffic or accidental chemical release. Among them, Gaussian model is perhaps the most commonly used model type. It is often used to predict the dispersion of air pollution plumes originated from ground-level or elevated sources. In this research an experimental campaign was carried out in the wind tunnel of the Industrial Engineering Department of University of Catania. It was tested an emission plume of particulate matters and the concentrations of PM 10 were evaluated in several points downwind beyond the emitter. Both the wind velocity and PM 10 mass flow were varied in order to test the differences in terms of PM10 concentrations in the sampling points. A Gaussian plume mathematical model was developed according the boundaries conditions of the experimental campaign. The results of the model were compared with experimental ones in order to identify the limits and the advantages of this model in such a small scale system

Tracking muscle wasting and disease activity in facioscapulohumeral muscular dystrophy by qualitative longitudinal imaging

Background: Facioscapulohumeral muscular dystrophy (FSHD) is one of the most frequent late-onset muscular dystrophies, characterized by progressive fatty replacement and degeneration involving single muscles in an asynchronous manner. With clinical trials at the horizon in this disease, the knowledge of its natural history is of paramount importance to understand the impact of new therapies. The aim of this study was to assess disease progression in FSHD using qualitative muscle magnetic resonance imaging, with a focus on the evolution of hyperintense lesions identified on short-tau inversion recovery (STIR+) sequences, hypothesized to be markers of active muscle injury. Methods: One hundred genetically confirmed consecutive FSHD patients underwent lower limb muscle magnetic resonance imaging at baseline and after 365 \ub1 60 days in this prospective longitudinal study. T1 weighted (T1w) and STIR sequences were used to assess fatty replacement using a semiquantitative visual score and muscle oedema. The baseline and follow-up scans of each patient were also evaluated by unblinded direct comparison to detect the changes not captured by the scoring system. Results: Forty-nine patients showed progression on T1w sequences after 1 year, and 30 patients showed at least one new STIR+ lesion. Increased fat deposition at follow-up was observed in 13.9% STIR+ and in only 0.21% STIR- muscles at baseline (P < 0.001). Overall, 89.9% of the muscles that showed increased fatty replacement were STIR+ at baseline and 7.8% were STIR+ at 12 months. A higher number of STIR+ muscles at baseline was associated with radiological worsening (odds ratio 1.17, 95% confidence interval 1.06\u20131.30, P = 0.003). Conclusions: Our study confirms that STIR+ lesions represent prognostic biomarkers in FSHD and contributes to delineate its radiological natural history, providing useful information for clinical trial design. Given the peculiar muscle-by-muscle involvement in FSHD, MRI represents an invaluable tool to explore the modalities and rate of disease progression

Re-evaluation of the role of Indian germplasm as center of melon diversification based on genotyping-by-sequencing analysis

[EN] BackgroundThe importance of Indian germplasm as origin and primary center of diversity of cultivated melon is widely accepted. Genetic diversity of several collections from Indian has been studied previously, although an integrated analysis of these collections in a global diversity perspective has not been possible. In this study, a sample of Indian collections together with a selection of world-wide cultivars to analyze the genetic diversity structure based on Genotype by Sequence data.ResultsA set of 6158 informative Single Nucleotide Polymorphism (SNP) in 175 melon accessions was generated. Melon germplasm could be classified into six major groups, in concordance with horticultural groups. Indian group was in the center of the diversity plot, with the highest genetic diversity. No strict genetic differentiation between wild and cultivated accessions was appreciated in this group. Genomic regions likely involved in the process of diversification were also found. Interestingly, some SNPs differentiating inodorus and cantalupensis groups are linked to Quantitiative Trait Loci involved in ripening behavior (a major characteristic that differentiate those groups). Linkage disequilibrium was found to be low (17kb), with more rapid decay in euchromatic (8kb) than heterochromatic (30kb) regions, demonstrating that recombination events do occur within heterochromatn, although at lower frequency than in euchromatin. Concomitantly, haplotype blocks were relatively small (59kb). Some of those haplotype blocks were found fixed in different melon groups, being therefore candidate regions that are involved in the diversification of melon cultivars.ConclusionsThe results support the hypothesis that India is the primary center of diversity of melon, Occidental and Far-East cultivars have been developed by divergent selection. Indian germplasm is genetically distinct from African germplasm, supporting independent domestication events. The current set of traditional Indian accessions may be considered as a population rather than a standard collection of fixed landraces with high intercrossing between cultivated and wild melons.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO)-FEDER grant AGL2015-64625-C2-R to AJM (project conception, experiments, data acquisition and analysis, manuscript writing, publication costs), AGL2017-85563-C2-1-R and the PROMETEO/2017/078 grant funded by Generalitat Valenciana (Conselleria d'Educacio, Investigacio, Cultura i Esport) to BP (project conception, provide samples and manuscript drafting). AD was supported by a Jae-Doc contract from CSIC (experiments and manuscript drafting).Gonzalo, M.; Díaz Bermúdez, A.; Dhillon, NPS.; Reddy, UK.; Picó Sirvent, MB.; Monforte Gilabert, AJ. (2019). Re-evaluation of the role of Indian germplasm as center of melon diversification based on genotyping-by-sequencing analysis. BMC Genomics. 20:1-13. https://doi.org/10.1186/s12864-019-5784-0S11320Munger HM, Robinson RW. Nomenclature of Cucumis melo L. Cucurbit Genet Coop Rep. 1991;14:43–4.Pitrat M, Hanelt P, Hammer K. Some comments on infraspecific classification of cultivars of melon. Proc Cucurbitaceae. 2000;2000(510):29–36.Pitrat M. Melon genetic resources: phenotypic diversity and horticultural taxonomy. In: Grumet R, Katzir N, editors. Genetics and genomics of the Cucurbitaceae. New York: J Garcia-Mas Springer; 2017. p. 25–60.Stepansky A, Kovalski I, Perl-Treves R. Intraspecific classification of melons (Cucumis melo L.) in view of their phenotypic and molecular variation. Plant Syst Evol. 1999;217(3–4):313–32.Monforte AJ, Garcia-Mas J, Arus P, Minch E. Genetic variability in melon based on microsatellite variation. Plant Breed. 2003;122(2):153–7.Esteras C, Formisano G, Roig C, Diaz A, Blanca J, Garcia-Mas J, Gomez-Guillamon ML, Lopez-Sese AI, Lazaro A, Monforte AJ, et al. SNP genotyping in melons: genetic variation, population structure, and linkage disequilibrium. Theor Appl Genet. 2013;126(5):1285–303.Sebastian P, Schaefer H, Telford IRH, Renner SS. Cucumber (Cucumis sativus) and melon (C. melo) have numerous wild relatives in Asia and Australia, and the sister species of melon is from Australia. Proc Natl Acad Sci U S A. 2010;107(32):14269–73.Newton C. Growing, gathering and offering: predynastic plant economy at Adaïma (upper Egypt). In: R. Cappers Groningen Institute of Archaeology, editor. Fields of change: progress in African archaeobotany; 2007. p. 139–55.Paris HS. Overview of the origins and history of the five major cucurbit crops: issues for ancient DNA analysis of archaeological specimens. Veg Hist Archaeobotany. 2016;25(4):405–14.Sabato D, Esteras C, Oscar G, Peña-Chocarro L, Leida C, Ucchesu M, Usai A, Bacchetta G, Pico B. Molecular and morphological characterisation of the oldest Cucumis melo L. seeds found in the Western Mediterranean Basin. Archaeol. Anthropol. Sci. 2017;11(3):789–810.Serres-Giardi L, Dogimont C. How microsatellite diversity helps to understand the domestication history of melon. In: Cucurbitaceae 2012: proceedings of the Xth Eucarpia meeting on genetics and breeding of Cucurbitaceae; 2012. p. 254–63.Endl J, Achigan-Dako EG, Pandey AK, Monforte AJ, Pico B, Schaefer H. Repeated domestication of melon (Cucumis melo) in Africa and Asia and a new close relative from India. Am J Bot. 2018;105(10):1662–71.Dhillon NPS, Ranjana R, Singh K, Eduardo I, Monforte AJ, Pitrat M, Dhillon NK, Singh PP. Diversity among landraces of Indian snapmelon (Cucumis melo var. momordica). Genet Resour Crop Evol. 2007;54(6):1267–83.Dhillon NPS, Singh J, Fergany M, Monforte AJ, Sureja K. Phenotypic and molecular diversity among landraces of snapmelon (Cucumis melo var. momordica) adapted to the hot and humid tropics of eastern India. Plant Genet Resour. 2009;7(3):291–300.Fergany M, Kaur B, Monforte AJ, Pitrat M, Rys C, Lecoq H, Dhillon NPS, Dhaliwal SS. Variation in melon (Cucumis melo) landraces adapted to the humid tropics of southern India. Genet Resour Crop Evol. 2011;58(2):225–43.Roy A, Bal SS, Fergany M, Kaur S, Singh H, Malik AA, Singh J, Monforte AJ, Dhillon NPS. Wild melon diversity in India (Punjab state). Genet Resour Crop Evol. 2012;59(5):755–67.Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6(5):e19379.Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet. 2011;12(7):499–510.Nimmakayala P, Tomason YR, Abburi VL, Alvarado A, Saminathan T, Vajja VG, Salazar G, Panicker GK, Levi A, Wechter WP, et al. Genome-wide differentiation of various melon horticultural groups for use in GWAS for fruit firmness and construction of a high resolution genetic map. Front Plant Sci. 2016;7:1437.Gur A, Tzuri G, Meir A, Sa'ar U, Portnoy V, Katzir N, Schaffer AA, Li L, Burger J, Tadmor Y. Genome-wide linkage-disequilibrium mapping to the candidate gene level in melon (Cucumis melo). Sci Rep. 2017;7:9770.Pavan S, Marcotrigiano AR, Ciani E, Mazzeo R, Zonno V, Ruggieri V, Lotti C, Ricciardi L. Genotyping-by-sequencing of a melon (Cucumis melo L.) germplasm collection from a secondary center of diversity highlights patterns of genetic variation and genomic features of different gene pools. BMC Genomics. 2017;18:59.Deleu W, Esteras C, Roig C, González-To M, Fernández-Silva I, Gonzalez-Ibeas D, Blanca J, Aranda MA, Arús P, Nuez F, Monforte AJ, Picó M, Garcia-Mas J. A set of EST-SNPs for map saturation and cultivar identification in melon. BMC Plant Biology. 2009;9(1):90.Leida C, Moser C, Esteras C, Sulpice R, Lunn JE, de Langen F, Monforte AJ, Pico B. Variability of candidate genes, genetic structure and association with sugar accumulation and climacteric behavior in a broad germplasm collection of melon (Cucumis melo L.). BMC Genet. 2015;16:28.Qi J, Liu X, Shen D, Miao H, Xie B, Li X, Zeng P, Wang S, Shang Y, Gu X, et al. A genomic variation map provides insights into the genetic basis of cucumber domestication and diversity. Nat Genet. 2013;45(12):1510–U1149.Wang Y-L, Gao L-Y, Yang S-Y, Xu Y-B, Zhu H-Y, Yang L-M, Li Q, Hu J-B, Sun S-R, Ma C-S. Molecular diversity and population structure of oriental thin-skinned melons, Cucumis melo subsp agrestis, revealed by a set of core SSR markers. Sci Hortic. 2018;229:59–64.Zhang Y, Fan X, Aierken Y, Ma X, Yi H, Wu M. Genetic diversity of melon landraces (Cucumis melo L.) in the Xinjiang Uygur autonomous region on the basis of simple sequence repeat markers. Genet Resour Crop Evol. 2017;64(5):1023–35.Omari S, Kamenir Y, Benichou JIC, Pariente S, Sela H, Perl-Treves R. Landraces of snake melon, an ancient middle eastern crop, reveal extensive morphological and DNA diversity for potential genetic improvement. BMC Genet. 2018;19:34.Tomason Y, Nimmakayala P, Levi A, Reddy UK. Map-based molecular diversity, linkage disequilibrium and association mapping of fruit traits in melon. Mol Breed. 2013;31(4):829–41.Vegas J, Garcia-Mas J, Monforte AJ. Interaction between QTLs induces an advance in ethylene biosynthesis during melon fruit ripening. Theor Appl Genet. 2013;126(6):1531–44.Rios P, Argyris J, Vegas J, Leida C, Kenigswald M, Tzuri G, Troadec C, Bendahmane A, Katzir N, Pico B, et al. ETHQV6.3 is involved in melon climacteric fruit ripening and is encoded by a NAC domain transcription factor. Plant J. 2017;91(4):671–83.Moreno E, Obando JM, Dos-Santos N, Fernandez-Trujillo JP, Monforte AJ, Garcia-Mas J. Candidate genes and QTLs for fruit ripening and softening in melon. Theor Appl Genet. 2008;116(4):589–602.Argyris JM, Ruiz-Herrera A, Madriz-Masis P, Sanseverino W, Morata J, Pujol M, Ramos-Onsins SE, Garcia-Mas J. Use of targeted SNP selection for an improved anchoring of the melon (Cucumis melo L.) scaffold genome assembly. BMC Genomics. 2015;16:4.Comadran J, Ramsay L, MacKenzie K, Hayes P, Close TJ, Muehlbauer G, Stein N, Waugh R. Patterns of polymorphism and linkage disequilibrium in cultivated barley. Theor Appl Genet. 2011;122(3):523–31.Vos PG, Paulo MJ, Voorrips RE, Visser RGF, van Eck HJ, van Eeuwijk FA. Evaluation of LD decay and various LD-decay estimators in simulated and SNP-array data of tetraploid potato. Theor Appl Genet. 2017;130(1):123–35.Ruggieri V, Francese G, Sacco A, D'Alessandro A, Rigano MM, Parisi M, Milone M, Cardi T, Mennella G, Barone A. An association mapping approach to identify favourable alleles for tomato fruit quality breeding. BMC Plant Biol. 2014;14:337.Bauchet G, Grenier S, Samson N, Bonnet J, Grivet L, Causse M. Use of modern tomato breeding germplasm for deciphering the genetic control of agronomical traits by genome wide association study. Theor Appl Genet. 2017;130(5):875–89.Dhillon NPS, Monforte AJ, Pitrat M, Pandey S, Singh PK, et al. Melon landraces of India: contributions and importance. Plant Breeding Rev. 2012;35:85–150.Garcia-Mas J, Benjak A, Sanseverino W, Bourgeois M, Mir G, Gonzalez VM, Henaff E, Camara F, Cozzuto L, Lowy E, et al. The genome of melon (Cucumis melo L.). Proc Natl Acad Sci U S A. 2012;109(29):11872–7.Yoo J, Lee Y, Kim Y, Rha SY, Kim Y. SNP analyzer 2.0: a web-based integrated workbench for linkage disequilibrium analysis and association analysis. BMC Bioinf. 2008;9:290.Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633–5.Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8):2611–20.Excoffier L, Laval G, Schneider S. Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinforma. 2005;1:47–50.Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population-structure. Evolution. 1984;38(6):1358–70.Peltier J. LOESS utility for excel. Peltier Tech Blog. http://peltiertech.com/WordPress/loess-smoothing-in-excel . 2009

Chronic kidney disease in patients with normal eGFR at baseline: results from EuroSIDA

info:eu-repo/semantics/nonPublishe

Reasons why HIV-positive women do not want to have a child: the questionnaire-based DIDI study

Given that the majority of HIV‐positive women are of reproductive age, it is necessary to understand the interaction between HIV and family planning, especially as antiretroviral medications allow to live longer, healthier lives. Aim of this analysis form the DIDI study was to assess prevalence of motherhood desire in current years and to identify variables associated pregnancy decision‐making in HIV‐infected women. DIDI is an Italian, 16‐center, questionnaire‐based survey performed in 585 HIV‐positive women between Nov. 2010 and Feb. 2011. The items covered in the self‐administered questionnaire included: socio‐demographic characteristics, sexual and gynecological health, motherhood desire, strategies adopted to become pregnant, reasons for not wanting a child, partnership, HIV disclosure, physical and mental health, ART adherence, drug use. For the present analysis only women aged<45 years and engaged in a partnership were included. Absence of motherhood desire was defined by a negative answer at the question whether the women at present would like to have a child. 178 women were included: mean age 39 (IQR, 33–42), HIV transmission heterosexual 75%, IVDU 11%, heterosexual/IVDU 2.5%, not known 7.5%; mean CD4 and HIV‐RNA were 552/mmc (+252) and 3.85 c/ml (+4.7), respectively. Absence of motherhood desire was found in 61% of women; 50% of women declared that HIV negatively affected motherhood desire, and 22% declared a decrease in desire after start of ART. The probability of vertical transmission was estimated higher than 50% by 19% of women, even when adopting all preventive measures. Not wanting a child was associated with: fear of vertical transmission (p<0.001), fear of not being able to raise the child (p<0.001), decline in motherhood desire after HIV (p=0.007), unstable partnership (p=0.02). At multivariable analysis, variables found to be significantly associated with negative pregnancy decision‐making were: fear of vertical transmission (AOR 3.75; 95%CI 1.18–11.89), economic restrictions (AOR 0.28; 95% CI 0.10–0.76 In conclusion, absent motherhood desire in HIV‐positive women with child‐bearing potential is frequent and essential information on vertical HIV transmission is lacking. HIV‐positive women of childbearing age may benefit from counseling interventions sensitive to factors that influence infected women's pregnancy decisions

Proportion and factors associated with recent HIV infection in a cohort of patients seen for care in Italy over 1996-2014: Data from the ICONA Foundation Study cohort.

In Italy the prevalence of recent HIV infection (RHI) isn't currently monitored. Early diagnosis is crucial to allow introduction of antiretroviral therapy (cART) in the recent phase of infection. We aimed to estimate the proportion and the determinants of RHI among patients enrolled in the ICONA cohort; we explored differences in the median time from HIV diagnosis to cART initiation and in the viro-immunological response between RHI and Less Recent HIV infections (NRHI). We included antiretroviral-naïve HIV-positive patients enrolled in the cohort with documented dates of HIV-negative and positive antibodies tests, grouped in RHI (estimated date of seroconversion within 12 months of enrolment) and NRHI. Proportion of RHI and the trend of this proportion by calendar period (1996-2014) were investigated (Chi-square test). Logistic regression analysis was employed to identify factors associated with RHI. The time from seroconversion to cART initiation was compared in RHI and NRHI overall and after stratification by calendar period (survival analysis). We finally explored the time from starting cART to HIV-RNA <50 copies/mL and to CD4+ gain ≥200 cells/mmc by Cox regression. HIV seroconversion could be estimated for 2608/12,616 patients: 981/2608 (37.6%) were RHI. Proportion of RHI increased in recent calendar periods and was associated with younger age, baseline higher HIV-RNA and CD4+ count. There wasn't difference in the 2-year estimates of cART start between RHI and NRHI, regardless of calendar period. Rates and hazards of virological response were similar in RHI versus NRHI. RHI showed a 1.5-fold higher probability of CD4+ gain, also following adjustment for calendar period and cART regimen, and for age, HCV and smoking; the difference in probability was however attenuated after further controlling for baseline HIV-RNA and CD4+ T-cells. The increased proportion of RHI over time suggests that in recent years in Italy HIV infections are more likely to be detected earlier than before. The similar rates of cART introduction and viro-immunological response in RHI and NRHI probably reflect the efficacy of the modern cART regimens. An improvement of the prevention services is warranted to allow an early cART access, also in the perspective of therapy as prevention