Metal (Pb, Cd, and Zn) Binding to Diverse Organic Matter Samples and Implications for Speciation Modeling

This study evaluated the influence of dissolved organic matter (DOM) properties on the speciation of Pb, Zn and Cd. Six DOM samples were categorized into autochthonous and allochthonous source based on their absorbance and fluorescence properties. The concentration of free metal ions (CM2+) measured by titration using Absence of Gradient and Nernstian Equilibrium Stripping (AGNES) was compared with that predicted by Windermere Humic Aqueous Model (WHAM). At the same binding condition (pH, dissolved organic carbon, ionic strength, and total metal concentration) the allochthonous DOM showed a higher level of Pb binding than the autochthonous DOM (84- to 504-fold CPb2+ variation). This dependency, however, was less pronounced for Zn (12- to 74-fold CZn2+ variation) and least for Cd (2- to 14-fold CCd2+ variation). The WHAM performance was affected by source variation through the active DOM fraction (F). The commonly used F = 1.3 provided reliable CPb2+ for allochthonous DOMs and acceptable CCd2+ for all DOM, but significantly underpredicted CPb2+ and CZn2+ for autochthonous DOM. Adjusting F improved CM2+ predictions, but the optimum F values were metal-specific (e.g., 0.03 - 1.9 for Pb), as showed by linear correlations with specific optical indexes. The results indicate a potential to improve WHAM by incorporating rapid measurement of DOM optical properties for site-specific F.This work was financially supported by the Canada Research Chairs program (C.G.), and the Natural Sciences and Engineering Research Council of Canada (C.G. and D.S.S.). The authors also thank Dr. Chad Cuss for the help with data analysis. J.G., J.P., and E.C. gratefully acknowledge support for this research from the Spanish Ministry of Economy, Industry and Competitiveness MINECO (project nos. CTM2013-48967 and CTM2016-78798). The three anonymous reviewers are also acknowledged for their constructive comments that helped improve the manuscript

Phoenix : deep speech based automatic speech recognition system for Italian language

LAUREA MAGISTRALEQuesta tesi mirava a sviluppare un sistema di trascrizione automatica (ASR), per la lingua italiana. Una parte fondamentale dello sviluppo del sistema ASR `e quella di raccogliere un ampio ed eterogeneo corpus di registrazioni audio, con le relative trascrizioni, in modo che il sistema possa costruire una rappresentazione generale della lingua. Allo stato attuale, non esistono ASR open source, basati su corpus e vocabolari di grandi dimensioni. Da qui la necessit‘a di sviluppare un nuovo strumento. Deep Speech `e un sistema di riconoscimento vocale all’avanguardia che utilizza tecniche di deep learning end-to-end. Questa architettura `e diversa dai sistemi vocali tradizionali. I sistemi tradizionali hanno prestazioni scadenti in ambienti rumorosi. Al contrario, Deep Speech `e in grado di modellare il rumore di fondo, il riverbero o i cambiamenti degli altoparlanti. Inoltre, l’addestramento non richiede di fornire un lessico di fonemi, come avviene per gli approcci tradizionali. Deep Speech si basa sulle reti neurali ricorrenti (RNN), ed ‘e ottimizzato per sfruttare le GPU. Il nostro sistema, chiamato Phoenix, raggiunge un WER del 13.8% WER e supera il precedente prototipo, basato sul toolkit Kaldi. I risultati mostrano che Phoenix ha una buona accuratezza e conferma come l’approccio basato su reti neural sia superiore a quello tradizionale.This thesis aimed at developing a system that can transcribe all the Italian audio that containing human speech into text. A basic part of ASR system development is to collect large and heterogeneous corpus of audio and their transcription, so that the system can build a general representation of the language. In view of the fact that a few existing systems tailored for Italian are developed on a small corpus, so it is necessary to build a new system specifically for Italian and train existing solutions on a larger corpus. Deep Speech is a state-of-art speech recognition system that using end-toend deep learning. This architecture is different from traditional speech systems. Traditional systems have a bad performance in noisy environments. On the contrary, Deep Speech can model background noise, reverberation or speaker changes, without manually designed components, and can directly learn the functions with robustness to such effects. Furthermore, training does not require providing a lexicon of phonemes, as is the case with traditional approaches. The key method of Deep Speech is to use the optimized RNN training system that uses GPUs and a set of novel data synthesis techniques, which can effectively obtain a large number of diverse data for training. Our system, called Phoenix, reaches a WER of 13.8% and exceeds the previous prototype, based on the Kaldi toolkit. The result shows that Phoenix has good accuracy and confirms that the neural netwrok based approach is better than the traditional one

Correlation and entanglement of two-component Bose-Einstein condensates in a double well

We consider a novel system of two-component atomic Bose-Einstein condensate in a double-well potential. Based on the well-known two-mode approximation, we demonstrate that there are obvious avoided level-crossings when both interspecies and intraspecies interactions of two species are increased. The quantum dynamics of the system exhibits revised oscillating behaviors compared with a single component condensate. We also examine the entanglement of two species. Our numerical calculations show the onset of entanglement can be signed as a violation of Cauchy-Schwarz inequality of second-order cross correlation function. Consequently, we use Von Neumann entropy to quantity the degree of entanglement

MRI signal intensity differentiation of brainstem encephalitis induced by Enterovirus 71: a classification approach for acute and convalescence stages

Additional file 3. After tick the segment region, Medflovan show the segmentation region of lesion

Research on the Construction of Innovation and Entrepreneurship Gold Course in Universities under the Background of Guangdong-Hong Kong-Macao Greater Bay Area

Innovation and entrepreneurship education is regarded as the third passport by the United Nations, and the cultivation of innovative and entrepreneurship talents has been paid more and more attention by the Chinese government. The core of innovative entrepreneurship talents training lies in the construction of “golden course” of innovative entrepreneurship. In order to create the “golden course” , we should focus on the integration of “innovation consciousness, ability building and professional disciplines”.Participatory teaching methods should be adopted in classroom teaching, and activities courses such as entrepreneurship project declaration should be embedded. Practical assessment and team evaluation should be strengthened, and the ability of teachers inside and outside school should be improved. Developing Micro courses with Internet + technology is also required

Influence of phthalates on glucose homeostasis and atherosclerosis in hyperlipidemic mice

BACKGROUND: Phthalates are widely used as plasticizer and are considered as a typical endocrine-disrupting chemical. Epidemiological studies have associated serum or urinary phthalate metabolites with the prevalence of type 2 diabetes or related phenotypes. However, direct evidence supporting a causal role for exposure to phthalates in type 2 diabetes is lacking. METHODS: To determine the potential influence of phthalates on glucose homeostasis and atherosclerosis, female apolipoprotein E-deficient (Apoe(−/−)) mice were started at 6 weeks of age on a Western diet together with or without Bis-(2-ethylhexyl) phthalate. Phthalate was administered in drinking water at a daily dosage of 100 mg/kg. We examined glucose and insulin tolerance, plasma glucose and triglyceride levels, body weight, and atherosclerotic lesions in the aortic root. RESULTS: Two weeks after treatment, phthalate-exposed mice had significantly higher fasting blood glucose level (97.9 ± 2.1 vs. 84.3 ± 5.3 mg/dl, P = 0.034) and exhibited a trend of increased glucose intolerance compared to control mice. Insulin tolerance test on non-fasted mice 3 weeks after treatment revealed that phthalate had little influence on insulin sensitivity though phthalate-treated mice had a higher glucose concentration (159.2 ± 6.0 vs. 145.2 ± 3.6 mg/dl; P = 0.086). On the Western diet, Apoe(−/−) mice showed a time-dependent rise in fasting plasma glucose and triglyceride levels. However, no significant differences were observed between phthalate-treated and control mice in either phenotype after 4, 8, and 12 weeks of phthalate exposure. Neither body weight nor atherosclerotic lesions of Apoe(−/−) mice was affected. CONCLUSION: This study indicates that exposure to phthalates gives rise to a brief interference of glucose homeostasis but has little impact on the development of type 2 diabetes and atherosclerosis in Apoe(−/−) mice

Genetic variation in eight Chinese cattle breeds based on the analysis of microsatellite markers

Genetic variability and genetic relationships were investigated among eight Chinese cattle breeds using 12 microsatellite markers. Three hundred and fifty-two alleles were detected and the average number of alleles per locus ranged from 8.33 ± 1.67 in the Jiaxian breed to 21.33 ± 5.60 in the Qinchuan breed with a mean value of 13.91. The total number of alleles per microsatellite ranged from 21 (INRA005, HEL1) to 40 (HEL13), with a mean of 29.33 per locus. The fixation indices at the 12 loci in the eight breeds were very low with a mean of 0.006. A principal components analysis and the construction of a neighborjoining tree showed that these eight Chinese cattle breeds cluster into three groups i.e. the Yanbian andChineseHolstein, theNanyang and Jiaxian, and the four remaining breeds.This clustering agrees with the origin and geographical distributions of these Chinese breeds