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Particulate matter (PM) is considered an atmospheric pollutant that mostly affects human health. The finest fractions of PM (PM2.5 or less) play a major role in causing chronic diseases. The aim of this study was to investigate the genotoxic effects of PM0.5 collected in five Italian towns using different bioassays. The role of chemical composition on the genotoxicity induced was also evaluated. The present study was included in the multicentre MAPEC_LIFE project, which aimed to evaluate the associations between air pollution exposure and early biological effects in Italian children. PM10 samples were collected in 2 seasons (winter and spring) using a high-volume multistage cascade impactor. The results showed that PM0.5 represents a very high proportion of PM10 (range 10–63%). PM0.5 organic extracts were chemically analysed (PAHs, nitro-PAHs) and tested by the comet assay (A549 and BEAS-2B cells), MN test (A549 cells) and Ames test on Salmonella strains (TA100, TA98, TA98NR and YG1021). The highest concentrations of PAHs and nitro-PAHs in PM0.5 were observed in the Torino, Brescia and Pisa samples in winter. The Ames test showed low mutagenic activity. The highest net revertants/m3 were observed in the Torino and Brescia samples (winter), and the mutagenic effect was associated with PM0.5 (p < 0.01), PAH and nitro-PAH (p < 0.05) concentrations. The YG1021 strain showed the highest sensitivity to PM0.5 samples. No genotoxic effect of PM0.5 extracts was observed using A549 cells except for some samples in winter (comet assay), while BEAS-2B cells showed light DNA damage in the Torino, Brescia and Pisa samples in winter, highlighting the higher sensitivity of BEAS-2B cells, which was consistent with the Ames test (p < 0.01). The results obtained showed that it is important to further investigate the finest fractions of PM, which represent a relevant percentage of PM10, taking into account the chemical composition and the biological effects induced. Results highlighted the importance to further investigate the finest fractions of PM, which represent a relevant percentage of PM10, taking into account its chemical composition and the biological effects induced.

Raffaella Devescovi,1 Lorenzo Monasta,2 Alice Mancini,3 Maura Bin,1 Valerio Vellante,1 Marco Carrozzi,1 Costanza Colombi4 1Division of Child Neurology and Psychiatry, 2Clinical Epidemiology and Public Health Research Unit, Institute for Maternal and Child Health – IRCCS “Burlo Garofolo”, Trieste, 3Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy; 4Department of Psychiatry, University of Michigan Health System, Ann Arbor, MI, USA Background: Early diagnosis combined with an early intervention program, such as the Early Start Denver Model (ESDM), can positively influence the early natural history of autism spectrum disorders. This study evaluated the effectiveness of an early ESDM-inspired intervention, in a small group of toddlers, delivered at low intensity by the Italian Public Health System.Methods: Twenty-one toddlers at risk for autism spectrum disorders, aged 20–36months, received 3hours/wk of one-to-one ESDM-inspired intervention by trained therapists, combined with parents’ and teachers’ active engagement in ecological implementation of treatment. The mean duration of treatment was 15months. Cognitive and communication skills, as well as severity of autism symptoms, were assessed by using standardized measures at pre-intervention (Time 0 [T0]; mean age =27months) and post-intervention (Time 1 [T1]; mean age =42months).Results: Children made statistically significant improvements in the language and cognitive domains, as demonstrated by a series of nonparametric Wilcoxon tests for paired data. Regarding severity of autism symptoms, younger age at diagnosis was positively associated with greater improvement at post-assessment.Conclusion: Our results are consistent with the literature that underlines the importance of early diagnosis and early intervention, since prompt diagnosis can reduce the severity of autism symptoms and improve cognitive and language skills in younger children. Particularly in toddlers, it seems that an intervention model based on the ESDM principles, involving the active engagement of parents and nursery school teachers, may be effective even when the individual treatment is delivered at low intensity. Furthermore, our study supports the adaptation and the positive impact of the ESDM entirely sustained by the Italian Public Health System. Keywords: early diagnosis, early intervention, autism spectrum disorder, Early Start Denver Model, Public Health System service

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%., JRC.C.5-Air and Climate

So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F5–F7 lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r2 = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed.

The contribution of natural sources to ambient air particulate matter (PM) concentrations is often not considered; however, it may be significant for certain areas and during specific periods of the year. In the framework of the AIRUSE-LIFE+ project, state-of-the-art methods have been employed for assessing the contribution of major natural sources (African dust, sea salt and forest fires) to PM concentrations, in southern European urban areas. 24h measurements of PM10 and PM2.5 mass and chemical composition were performed over the course of a year in five cities: Porto, Barcelona, Milan, Florence and Athens. Net African dust and sea-salt concentrations were calculated based on the methodologies proposed by the EC (SEC 2011/208). The contribution of uncontrolled forest fires was calculated through receptor modelling. Sensitivity analysis with respect to the calculation of African dust was also performed, in order to identify major parameters affecting the estimated net dust concentrations. African dust contribution to PM concentrations was more pronounced in the eastern Mediterranean, with the mean annual relative contribution to PM10 decreasing from 21% in Athens, to 5% in Florence, and around 2% in Milan, Barcelona and Porto. The respective contribution to PM2. 5 was calculated equal to 14% in Athens and from 1.3 to 2.4% in all other cities. High seasonal variability of contributions was observed, with dust transport events occurring at different periods in the western and eastern Mediterranean basin. Sea salt was mostly related to the coarse mode and also exhibited significant seasonal variability. Sea-salt concentrations were highest in Porto, with average relative contributions equal to 12.3% for PM10. Contributions from uncontrolled forest fires were quantified only for Porto and were low on an annual basis (1.4 and 1.9% to PM10 and PM2. 5, respectively); nevertheless, contributions were greatly increased during events, reaching 20 and 22% of 24h PM10 and PM2. 5 concentrations, respectively. © Author(s) 2017.

Recently, the conjunction of high-throughput marker technologies and new statistical methods has given birth to a new breeding strategy called genomic selection(GS).It refers to methods that use genome-wide dense marker genotyping for the prediction of genetic values with enough accuracy to allow selection based on that prediction alone. It consists of two steps: estimation of SNP effects within a training population (TP), and prediction of genomic estimated breeding values (GEBV) in the candidate population(CP)based on SNP genotypes. We present here GS for water use efficiency in rice, in the framework of a pedigree breeding scheme. The TP was composed of 284 accessions belonging to the temperate japonica and tropical japonica rice groups. The CP was composed of 99 F5-F7 lines derived from 36crosses involving 32 accessions of TP. The two populations were genotyped with an average marker density of 4.8perkb, with minimum minor allele frequency of 2.5.Phenotypic traits considered included flowering time (FL), grain yield (GY) and nitrogen balance index (NI)under conventional irrigation (CI) and aerobicsystem (AS).Phenotypes were modeled using two statistical regression methods: genomic best linear unbiased prediction (GBLUP) and reproducing kernel Hilbert Space (RKHS).The models were tested with three incidence matrixes corresponding to densities of 4.8, 9.5 and 13.8marker per kb, and to linkage disequilibrium (LD)thresholds of r²≤1, r²