Academic literature on the topic 'Differential calculus – Data processing – United States'

High failure rates in the Differential and Integral Calculus courses and the subsequent dropout caused by them are a reason for concern in many universities around the world, both in developing countries such as Brazil and developed ones such as the United States of America. So, it is not surprising that there is lots of interest in researching about this context, and possible ways of action. This is the focus of this article, to study about the origins of this situation, analyzing the causes of the students’ difficulties and presenting Collaborative Learning as an effective way to change this situation. The article is based on data from several universities that have shown interest in this method. The results demonstrate the effectiveness of this practice, both in academic and psychological performance of the students, thus showing the need for a greater incentive of this technique, as well as an intense study of the way it should be done.

We present an automatic and unsupervised tool for the systematic generation of Sentinel-1 (S1) differential synthetic aperture radar interferometry (DInSAR) coseismic products. In particular, the tool first retrieves the location, depth, and magnitude of every seismic event from interoperable online earthquake catalogs (e.g., the United States Geological Survey (USGS) and the Italian National Institute of Geophysics and Volcanology (INGV) and then, for significant (with respect to a set of selected thresholds) earthquakes, it automatically triggers the downloading of S1 data and their interferometric processing over the area affected by the earthquake. The automatic system we developed has also been implemented within a Cloud-Computing (CC) environment, specifically the Amazon Web Services, with the aim of creating a global database of DInSAR S1 coseismic products, which consist of displacement maps and the associated wrapped interferograms and spatial coherences. This information will progressively be made freely available through the European Plate Observing System (EPOS) Research Infrastructure, thus providing the scientific community with a large catalog of DInSAR data that can be helpful for investigating the dynamics of surface deformation in the seismic zones around the Earth. The developed tool can also support national and local authorities during seismic crises by quickly providing information on the surface deformation induced by earthquakes.

The school-to-prison pipeline (STPP) describes the corridor between the education system and the justice system which is increasingly filled with children and youth who have mental health challenges. Approximately 22% of children (under 18 years old) in the general U.S. population have psychiatric disorders, as compared to approximately 70% of justice-involved children (Cocozza & Shufelt, 2006; Teplin et al., 2002). This article uses the differential behavior hypothesis and the differential selection/processing hypothesis to critically examine the intersection of the mental health, education, and juvenile justice systems and the overrepresentation of mental illness for justice-involved youth in the United States. Early identification, assessment, barriers to care and health disparities, school discipline, and the criminalization of children and youth with mental illness are explored with global implications. Recommendations and promising practices are offered including: improved data and service provider collaborations, community-based services and systems of care, diversion and decarceration, juvenile mental health courts, and juvenile crisis intervention teams.

Maize products are very significant for domestic consumption as well as industrial uses both locally and globally. For there to truly appreciate the spread of maize production in Africa, the geospatial mapping and subsequent comparison of the value chain for Nigeria and Rwanda were necessitated hence the purpose of this study. Farm mapping geospatial techniques and remotely sensed data were used for both Nigeria and Rwanda in this study. GIMMS Global Agricultural Monitoring data from United States Department of Agriculture (USDA) were adopted for Nigeria and Rwanda. The crop calendars of both countries were examined which thereafter reviewed a marked distinction among them. The results of the agroecological zones for the two countries showed a significant variation in their distribution and types, which in turn affect both the planting and harvesting of maize; storage, marketing, processing, and policy framework for maize products value chain in Nigeria and Rwanda. Mapping of the two countries was carried out and the normalized differential vegetation index (NDVI) and the policy associated with maize value chains were checked and reported.

SUMMARY Despite their importance as a fundamental constraint on Earth properties, regional-scale measurements of body-wave seismic attenuation are scarce. This is partially a result of the difficulty in producing robust estimates of attenuation. In this paper, we focus on measuring differential attenuation on records of teleseismic P waves. We examine a unique data set of five records of the North Korean nuclear test of 2017 measured at five broad-band seismic stations deployed within a few metres of each other but using different installation procedures. Given their extreme proximity, we expect zero differential intrinsic attenuation between the different records. However, we find that different attenuation measurement methods and implementation parameters in fact produce significant apparent differential attenuation (Δt*). Frequency-domain methods yield a wide range of Δt* estimates between stations, depending on measurement bandwidth and nuances of signal processing. This measurement instability increases for longer time windows. Time domain methods are largely insensitive to the frequency band being considered but are sensitive to the time window that is chosen. We determine that signal-generated noise can affect measurements in both the frequency and time domain. In some cases, the range of results amounts to a significant fraction of the range of differential attenuation across the conterminous United States as determined by a recent study. We suggest some approaches to manage the inherent instability in these measurements and recommend best practices to confidently estimate body wave attenuation.

Our studies have indicated that the relative concentration of Se or Hg to As in urine and blood positively correlates with percentage of inorganic arsenic (% Inorg-As) and percentage of monomethlyarsonic acid [% MMA (V)]. We also found a negative correlation with percentage of dimethylarsinic acid [% DMA (V)] and the ratio of % DMA (V) to % MMA (V). In another study, we found that a group of proteins were significantly over expressed and conversely other groups were under-expressed in tissues in Na-As (III) treated hamsters. Introduction.Inorganic arsenic (Inorg-As) in drinking water.One of the largest public health problems at present is the drinking of water containing levels of Inorg-As that are known to be carcinogenic. At least 200 million people globally are at risk of dying because of arsenic (As) in their drinking water1-3. The chronic ingestion of Inorg-As can results in skin cancer, bladder cancer, lung cancer, and cancer of other organs1-3. The maximum contamination level (MCL) of U.S. drinking water for arsenic is 10 ug/L. The arsenic related public health problem in the U.S. is not at present anywhere near that of India4, Bangladesh4, and other countries5. Metabolism and toxicity of Inorg-As and arsenic species.Inorg-As is metabolized in the body by alternating reduction of pentavalent arsenic to trivalent form by enzymes and addition of a methyl group from S-adenosylmethionine6, 7; it is excreted mainly in urine as DMA (V)8. Inorganic arsenate [Inorg-As (V)]is biotransformed to Inorg-As (III), MMA (V), MMA (III), DMA (V), and DMA (III)6(Fig. 1). Therefore, the study of the toxicology of Inorg-As (V) involves at least these six chemical forms of arsenic. Studies reported the presence of 3+ oxidation state arsenic biotransformants [MMA (III) and DMA (III)] in human urine9and in animal tissues10. The MMA (III) and DMA (III) are more toxic than other arsenicals11, 12. In particular MMA (III) is highly toxic11, 12. In increased % MMA in urine has been recognized in arsenic toxicity13. In addition, people with a small % MMA in urine show less retention of arsenic14. Thus, the higher prevalence of toxic effects with increased % MMA in urine could be attributed to the presence of toxic MMA (III) in the tissue. Previous studies also indicated that males are more susceptible to the As related skin effects than females13, 15. A study in the U.S population reported that females excreted a lower % Inorg-As as well as % MMA, and a higher % DMA than did males16. Abbreviation: SAM, S-adenosyl-L-methionine; SAHC, S-adenosyl-L-homocysteine. Differences in susceptibility to arsenic toxicity might be manifested by differences in arsenic metabolism among people. Several factors (for examples, genetic factors, sex, duration and dosage of exposure, nutritional and dietary factors, etc.) could be influence for biotransformation of Inorg-As,6, 17 and other unknown factors may also be involved. The interaction between As, Se, and Hg.The toxicity of one metal or metalloid can be dramatically modulated by the interaction with other toxic and essential elements18. Arsenic and Hg are toxic elements, and Se is required to maintain good health19. But Se is also toxic at high levels20. Recent reports point out the increased risk of squamous cell carcinoma and non-melanoma skin cancer in those treated with 200 ug/day of selenium (Nutritional Prevention of Cancer Trial in the United States)21. However, it is well known that As and Se as well as Se and Hg act as antagonists22. It was also reported that Inorg-As (III) influenced the interaction between selenite and methyl mercury23. A possible molecular link between As, Se, and Hg has been proposed by Korbas et al. (2008)24. The identifying complexes between the interaction of As and Se, Se and Hg as well as As, Se, and Hg in blood of rabbit are shown in Table 1. Influence of Se and Hg on the metabolism of Inorg-As.The studies have reported that Se supplementation decreased the As-induced toxicity25, 26. The concentrations of urinary Se expressed as ug/L were negatively correlated with urinary % Inorg-As and positively correlated with % DMA27. The study did not address the urinary creatinine adjustment27. Other researchers suggested that Se and Hg decreased As methylation28-31(Table 2). They also suggested that the synthesis of DMA from MMA might be more susceptible to inhibition by Se (IV)29 as well as by Hg (II)30,31 compared to the production of MMA from Inorg-As (III). The inhibitory effects of Se and Hg were concentration dependent28-31. The literature suggests that reduced methylation capacity with increased % MMA (V), decreased % DMA (V), or decreased ratios of % DMA to % MMA in urine is positively associated with various lesions32. Lesions include skin cancer and bladder cancer32. The results were obtained from inorganic arsenic exposed subjects32. Our concern involves the combination of low arsenic (As) and high selenium (Se) ingestion. This can inhibit methylation of arsenic to take it to a toxic level in the tissue. Dietary sources of Se and Hg.Global selenium (Se) source are vegetables in the diet. In the United States, meat and bread are the common source. Selenium deficiency in the US is rare. The US Food and Drug Administration (FDA) has found toxic levels of Se in dietary supplements, up to 200 times greater than the amount stated on the label33. The samples contained up to 40,800 ug Se per recommended serving. For the general population, the most important pathway of exposure to mercury (Hg) is ingestion of methyl mercury in foods. Fish (including tuna, a food commonly eaten by children), other seafood, and marine mammals contain the highest concentrations. The FDA has set a maximum permissible level of 1 ppm of methyl mercury in the seafood34. The people also exposed mercury via amalgams35. Proteomic study of Inorg-As (III) injury.Proteomics is a powerful tool developed to enhance the study of complex biological system36. This technique has been extensively employed to investigate the proteome response of cells to drugs and other diseases37, 38. A proteome analysis of the Na-As (III) response in cultured lung cells found in vitro oxidative stress-induced apoptosis39. However, to our knowledge, no in vivo proteomic study of Inorg-As (III) has yet been conducted to improve our understanding of the cellular proteome response to Inorg-As (III) except our preliminary study 40. Preliminary Studies: Results and DiscussionThe existing data (Fig. 1) from our laboratory and others show the complex nature of Inorg-As metabolism. For many years, the major way to study, arsenic (As) metabolism was to measure InorgAs (V), Inorg-As (III), MMA (V), and DMA (V) in urine of people chronically exposed to As in their drinking water. Our investigations demonstrated for the first time that MMA (III) and DMA (III) are found in human urine9. Also we have identified MMA (III) and DMA (III) in the tissues of mice and hamsters exposed to sodium arsenate [Na-As (V)]10, 41. Influence of Se as well as Hg on the As methyltransferase.We have reported that Se (IV) as well as mercuric chloride (HgCl2) inhibited As (III) methyltransferase and MMA (III) methyltransferase in rabbit liver cytosol. Mercuric chloride was found to be a more potent inhibitor of MMA (III) methyltransferase than As (III) methyltransferase30. These results suggested that Se and Hg decreased arsenic methylation. The inhibitory effects of Se and Hg were concentration dependent30. Influence of Se and Hg in urine and blood on the percentage of urinary As metabolites.Our human studies indicated that the ratios of the concentrations of Se or Hg to As in urine and blood were positively correlated with % Inorg-As and % MMA (V). But it negatively correlated with % DMA (V) and the ratios of % DMA (V) to % MMA (V) in urine of both males and females (unpublished data) (Table 3). These results confirmed that the inhibitory effects of Se as well as Hg for the methylation of Inorg-As in humans were concentration dependent. We also found that the concentrations of Se and Hg were negatively correlated with % Inorg-As and % MMA (V). Conversely it correlated positively with % DMA (V) and the ratios of % DMA (V) to % MMA (V) in urine of both sexes (unpublished data). These correlations were not statistically significant when urinary concentrations of Se and Hg were adjusted for urinary creatinine (Table 3). Interactions of As, Se, Hg and its relationship with methylation of arsenic are summarized in Figure 2. Sex difference distribution of arsenic species in urine.Our results indicate that females have more methylation capacity of arsenic as compared to males. In our human studies (n= 191) in Mexico, we found that females (n= 98) had lower % MMA (p<0.001) and higher % DMA (p=0.006) when compared to males (n= 93) (Fig. 3). The means ratio of % MMA (V) to % Inorg-As and % DMA (V) to %MMA (V) were also lower (p<0.05) and higher (p<0.001), respectively in females compared to males. The protein expression profiles in the tissues of hamsters exposed to Na-As (III).In our preliminary studies40, hamsters were exposed to Na-As (III) (173 pg/ml as As) in their drinking water for 6 days and control hamsters were given only the water used to make the solutions for the experimental animals. After DIGE (Two-dimensional differential in gel electrophoresis) and analysis by the DeCyder software, several protein spots were found to be over-expressed (red spot) and several were under expressed (green spot) as compared to control (Figs. 4a-c). Three proteins (one was over-expressed and two were under-expressed) of each tissue (liver and urinary bladder) were identified by LC-MS/MS (liquid chromatography-tandem mass spectrometry).DIGE in combination with LC-MS/MS is a powerful tool that may help cancer investigators to understand the molecular mechanisms of cancer progression due to Inorg-As. Propose a new researchThese results suggested that selenium (Se) as well as mercury (Hg) may influence the methylation of Inorg-As and this influence could be dependent on the concentration of Se, Hg and/or the sex of the animal. Our study also suggested that the identification and functional assignment of the expressed proteins in the tissues of Inorg-As (III) exposed animals will be useful for understanding and helping to formulate a theory dealing with the molecular events of arsenic toxicity and carcinogenicity.Therefore, it would be very useful if we could do a research study with combination of Inorg-arsenic, Se, and Hg. The new research protocol could be the following:For metabolic processing, hamsters provide a good animal model. For carcinogenesis, mouse model is well accepted. The aims of this project are: 1) To map the differential distributions of arsenic (As) metabolites/species in relation to selenium (Se) and mercury (Hg) levels in male and female hamsters and 2) To chart the protein expression profile and identify the defense proteins in mice and hamsters after As injury. Experimental hamsters (male or female) will include four groups. The first group will be treated with Na arseniteNa-As(III), the second group with Na-As (III) and Na-selenite (Na-Se (IV)], the third group with Na As (III) and methyl mercuric chloride (MeHgCl), and the final group with Na-As (III), Na-Se (IV), and MeHgci at different levels. Urine and tissue will be collected at different time periods and measured for As species using high performance liquid chromatography/inductively coupled plasma-mass spectrometry (HPLC/ICP-MS). For proteomics, mice (male and female) and hamsters (male and female) will be exposed to Na-As (III)at different levels in tap water, and control mice and hamsters will be given only the tap water. Tissue will be harvested at different time periods. TWO dimensional differential in gel electrophoresis (2D-DIGE) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) will be employed to identify the expressed protein. In summary, we intend to extend our findings to: 1) Differential distribution of As metabolites in kidney, liver, lung, and urinary bladder of male and female hamsters exposed to Na-As (III), and combined with Na-As (III) and Na-Se (IV) and/or MeHgCl at different levels and different time periods, 2) Show the correlation of As species distribution in the tissue and urine for both male and female hamsters treated with and without Na-Se (IV) and/or MeHgCl, and 3) Show protein expression profile and identify the defense proteins in the tissues (liver, lung, and urinary bladder epithelium) in mice after arsenic injury. The significance of this study: The results of which have the following significances: (A) Since Inorg-As is a human carcinogen, understanding how its metabolism is influenced by environmental factors may help understand its toxicity and carcinogenicity, (B) The interactions between arsenic (As), selenium (Se), and mercury (Hg) are of practical significance because populations in various parts of the world are simultaneously exposed to Inorg-As & Se and/or MeHg, (C) These interactions may inhibit the biotransformation of Inorg-As (III) which could increase the amount and toxicity of Inorg-As (III) and MMA (III) in the tissues, (D) Determination of arsenic species profile in the tissues after ingestion of Inorg-As (III), Se (IV), and/or MeHg+ will help understand the tissue specific influence of Se and Hg on Inorg-As (III) metabolism, (E) Correlation of arsenic species between tissue and urine might help to understand the tissue burden of arsenic species when researchers just know the distribution of arsenic species in urine, (F) The identification of the defense proteins (over-expressed and under-expressed) in the tissues of the mouse may lead to understanding the mechanisms of inorganic arsenic injury in human. The Superfund Basic Research Program NIEHS Grant Number ES 04940 from the National Institute of Environmental Health Sciences supported this work. Additional support for the mass spectrometry analyses was provided by grants from NIWHS ES 06694, NCI CA 023074 and the BIO5 Institute of the University of Arizona. Acknowledge:The Authorwantsto dedicate this paper to the memory of Dr. H. VaskenAposhian and Dr. Mary M. Aposhian who collected urine and bloodsamples from Mexican population. The work was done under Prof. H. V. Aposhian sole supervision and with his great contribution. References NRC (National Research Council). Arsenic in Drinking Water. Update to the 1999 Arsenic in Drinking Water Report. National Academy Press, Washington, DC. 2001. Gomez-Caminero, A.; Howe, P.; Hughes, M.; Kenyon, ; Lewis, D. R.; Moore, J.; Mg, J.; Aitio, A.; Becking, G. Environmental Health Criteria 224. Arsenic and Arsenic Compounds (Second Edition). International Programme on Chemical Safety, World Health Organization. 2001. Chen, C. J.; Chen, C. W.; Wu, M.; Kuo, T. L. Cancer potential in liver, lung, bladder, and kidney due to ingested inorganic arsenic in drinking water. Br. J. Cancer. 1992, 66, 888-892. Chakraborti, D.; Rahman, M.; Paul, K.; Chowdhury, U. K.; Sengupta, M. K.; Lodh, D.; Chanda, C. R.; Saha, K. C.; Mukherjee, S. C. Arsenic calamity in the Indian subcontinent. What lessons have been learned? 2002, 58, 3-22. Nordstrom, D. K. Worldwide occurrences of arsenic in ground water. Scienc 2002, 296, 2143-2145. Aposhian, H. V.; Aposhian, M. M. Arsenic toxicology: five question Chem. Res. Toxicol. 2006, 19, 1-15. Aposhian, H. V. Enzymatic methylation of arsenic species and other new approaches to arsenic toxicity. An Rev. Pharmacol. Toxicol. 1997, 37, 397-419. Vahter, M. Variation in human metabolism of arsenic. In: Abernathy, C. O.; Calderon, R. L.; Chappell, W. R., (eds) Arsenic exposure and Health effect Elsevier Science, New York, 1999, pp 267-279. Aposhian, H. V., Gurzau, E. , Le, X. C., Gurzau, A., Healy, S. M., Lu, X., Ma, M., Yip, L., Zakharyan, R. A., Maiorino, R. M., Dart, R. C., Tircus, M. G., Gonzalez-Ramirez, D., Morgan, D. L., Avram, D., Aposhian, M. M. (2000). Occurrence of monomethylarsonous acid in urine of humans exposed to inorganic arsenic. Chem. Res. Toxicol. 13, 693-697. ; U. K.; Zakharyan, R. A.; Hernandez, A.; Avram, M.D.; Kopplin, M. J.; Aposhian, H. V. Glutathione-S-transferase-omega [MMA (V) reductase] knockout mice: Enzyme and arsenic species concentrations in tissues after arsenate administration. Toxicol. Appl. Pharmacol. 2006, 216, 446-457. Styblo, M.; Del Razo, L. M.; Vega, L.; Germolec, D. R.; LeCluyse, E. L.; Hamilton, G. A.; Reed, W.; Wang, C.; Cullen, W. R.; Thomas, D.J. Comparative toxicity of trivalent and pentavalent inorganic and methylated arsenicals in rat and human cells. A Toxicol., 2000, 74, 289-299. Petrick, J. S.; Jagadish, B.; Mash, E. A.; Aposhian, H. V. Monomethylarsonous acid (MMAIII) and arsenite: LD50 in hamsters and in vitro inhibition of pyruvate dehydrogenase. Ch Res. Toxicol. 2001, 14, 651-656. Lindberg, A. L.; Rahman, M.; Persson, L. A.; Vahter, M. The risk of arsenic induced skin lesions in Bangladeshi men and women is affected by arsenic metabolism and the age at first exposure. Appl. Pharmacol. 2008, 230, 9-16. Vahter, M. Mechanisms of arsenic biotransformation. Toxicolog 2002, 181-182, 211-217. Chen, Y. C.; Guo, Y. L.; Su, H. J.; Hsueh, Y. M.; Smith, T. J.; Ryan, L. M.; Lee, M. S.; Chao, S. C.; Lee, J. Y.; Christiani, D. C. Arsenic methylation and skin cancer risk in southwestern Taiwan. Occup. Environ. Med. 2003, 45, 241-248. Steinmaus, C.; Carrigan, K.; Kalman, D.; Atallah, R.; Yuan, Y.; Smith, A.H. Dietary intake and arsenic methylation in a U.S. population. Health Perspect. 2005, 113, 1153-1159. Tseng, C. H. A review on environmental factors regulating arsenic methylation in humans. Appl. Pharmacol. 2009, 235, 338-350. Goyer, R. A. Factors influencing metal toxicity. In: Goyer, R. A.; Klaassen, C. D.; Waalkes, M. P. (eds) Metal toxicolog Academic Press, San Diego, 1995, pp 31-45. Wilber, C. G. Toxicology of selenium. Toxicol. 1980, 17, 171-230. Skerfving, S. Interaction between selenium and methylmercury. Environ. Health Persp 1978, 25, 57-65. Duffield-Lillico, A. J.; Slate, E. H.; Reid, M. E.; Turnbull, B. W.; Wilkins, P. A.; Combs, G. F.; Kim Park, Jr. H.; Gross, E. G.; Graham, G. F.; Stratton, M. S.; Marshall, J. R.; Clark, L. C. Selenium supplementation and secondary prevention of nonmelanoma skin cancer in a randomized trial. Natl. Cancer Inst. 2003, 95, 1477-1481. Gailer, J. Arsenic-selenium and mercury-selenium bonds in biology. Chem. Rev. 2007, 251, 234-254. Alexander, J. The influence of arsenite on the interaction between selenite and methyl mercury. Dev. Toxicol. Environ. Sci. 1980, 8, 585-590. Korbas, M.; Percy, J.; Gailer, J.; George, G. N. A possible molecular link between the toxicological effects of arsenic, selenium and methyl mercury: methyl mercury (II) selenobis (S glutathionyl) arsenic (III). J. Biol. Inorg. Chem. 2008, 13, 461-470. Yang, ; Wang, W.; Hou, S.; Peterson, P. J.; Williams, W. P. Effect of selenium supplementation on arsenism: an intervention trial in Inner Mongolia. Environ. Geochem. Health. 2002, 24, 359-374. Verret, W. J.; Chen, Y.; Ahmed, A.; Islam, T.; Parvez, F.; Kibriya, M. G.; Graziano, J. H.; Ahsan, H. Effects of vitamin E and selenium on arsenic-induced skin lesions. Occup. Environ. Med. 2005, 47, 1026-1035. Hsueh, Y. M.; Ko, Y. F.; Huang, Y. K.; Chen, H. W.; Chiou, H. Y.; Huang, Y. L.; Yang, M. ; Chen, C. J. Determinants of inorganic arsenic methylation capability among residents of the Lanyang Basin, Taiwan: arsenic and selenium exposure and alcohol consumption. Toxicol. Lett. 2003, 137, 49-63. Kenyon, E. M.; Hughes, M. K.; Levander, 0. Influence of dietary selenium on the disposition of arsenate in the female B6C3F1 mouse. J. Toxicol. Environ. Health. 1997, 51, 279-299. Styblo, M.; Thomas, D, J. Selenium modifies the metabolism and toxicity of arsenic in primary rat hepatocytes. Toxicol Appl. Pharmacol. 2001, 172, 52-61. Zakharyan, R.; Wu, Y.; Bogdan, G. M.; Aposhian, H. V. Enzymatic methylation of arsenic compounds: assay, partial purification, and properties of arsenite methyltransferase and monomethylarsonic acid methyltransferase of rabbit liver. Res. Toxicol.1995, 8, 1029-1038. Styblo, M.; Delnomdedieu, M.; Thomas, D. J. Mono- and dimethylation of arsenic in rat liver cytosol in vitro. -Biol. Interact. 1996, 99, 147-164. Tseng C. H. Arsenic methylation, urinary arsenic metabolites and human diseases: current perspective. J. Environ. Sci. Health Part C. 2007, 25, 1-22. FDA (The US Food and Drug administration). (2008). Hazardous levels of selenium in samples of "Total Body Formula" and "Total Body Mega Formula”. FDA Ne 2008. ATSDR (Agency for Toxic Substances and Disease Registry). Toxicological profile for mercury (CAS # 7439-97-6). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. 1999. Dye, B. A.; Schober, S. E.; Dillon, C. F.; Jones, R. L.; Fryar, C.; McDowell, M.; Sinks, T. H. Urinary mercury concentrations associated with dental restorations in adults women aged 16-49 years: United States, 1999-2000. O Environ. Med. 2005, 62, 368-375. Lau, A. T.; He, Q. Y.; Chiu, J. F. Proteomic technology and its biomedical applications. A Biophys. Sin. 2003, 35, 965-975. Jungblut, P. R.; Zimny-Arndt, U.; Zeindl-Eberhart, E.; Stulik, J.; Koupilova, K.; Pleissner, K. P.; Otto, A.; Muller, E. C.; Sokolowska-Kohler, W.; Grabher, G.; Stoffler, G. Proteomics in human disease: cancer, heart and infectious diseases. Electrophoresis. 1999, 20, 2100-2110. Hanash, S. M.; Madoz-Gurpide, J.; Misek, D. E. Identification of novel targets for cancer therapy using expression proteomics. L 2002, 16, 478-485. Lau, A. T.; He, Q. Y.; Chiu, J. F. A proteome analysis of the arsenite response in cultured lung cells: evidence for in vitro oxidative stress-induced apoptosis. J. 2004, 382, 641-650. Chowdhury, U. K.; Aposhian, H. V. Protein expression in the livers and urinary bladders of hamsters exposed to sodium arsenite. A N. Y. Acad. Sci. 2008, 1140, 325-334. Sampayo-Reyes, A.; Zakharyan, R. A.; Healy, S. M.; Aposhian, A. V. Monomethylarsonic acid reductase and monomethylarsonou acid in hamster tissue. Chem. Res. Toxicol. 2000, 13, 1181-1186.

AbstractThe stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supply by the public health agencies in countries as Brazil, United States and India is investigated. We perform a numerical analysis using the stochastic differential equation in Itô’s calculus for the estimating of novel cases daily, as well as analytical calculations solving the correspondent Fokker–Planck equation for the probability density distribution of novel cases, P(N(t), t). Our results display that the model based in the Itô’s diffusion fits well to the results due to uncertainty in the official data and to the number of tests realized in populations of each country.

Better prediction tools for future solar and wind power are crucial to reducing the requirement for controlling energy associated with the conventional power facilities. For optimal power grid integrating of highly variable wind power output, a strong forecast is extremely crucial. In this part, we concentration on wind power for the near run projections and conduct a wind unification study in the western United States using data from the National Research Conducted by the university (NREL). Our approach derives functional connections directly from data, unlike physical systems that rely on exceedingly difficult differential calculus. By recasting the prediction problem as a regression problem, we investigate several regression methodologies such as regression models, knearest strangers, and regression algorithms. In our testing, we look at projections for specific machines along with power from the wind parks, proving that a classification algorithm for predicting short-term electricity generation is feasible.

AbstractThis study investigates the role of exposure to English on discourse-reference processing by native Japanese speakers. Shoji et al. (2016a, The repeated name penalty, the overt pronoun penalty, and topic in Japanese. Journal of Psycholinguistic Research. http://link.springer.com/article/10.1007 %2Fs10936-016-9424-4) found that Japanese-English bilinguals residing in the United States show a Repeated Name Penalty (RNP; Gordon et al. 1993. Pronouns, names, and the centering of attention in discourse. Cognitive Science 17. 311–347) and an Overt Pronoun Penalty (OPP; Gelormini-Lezama and Almor 2011, Repeated names, overt pronouns, and null pronouns in Spanish. Language Cognitive Processes 26. 437–454) in Japanese with both topic (wa-marked) subject anaphors and non-topic (ga-marked) subject anaphors, indicating that the different morphological markings on anaphors do not alter these effects. In contrast, more recent data collected from L1-immersed Japanese speakers residing in Japan (Shoji et al. 2016b, The repeated name penalty and the overt pronoun penalty in Japanese. Unpublished manuscript) show that these speakers do not show a RNP or an OPP for topic-marked anaphors. Here we report a reanalysis of Shoji et al.’s (2016a) results showing that these effects are moderated by participants’ Age of Arrival (AOA; i. e. the age at which participants first arrived to the place where their second language is regularly spoken). Participants with an early AOA showed differential processing patterns for topic-marked anaphors and non-topic anaphors, while participants with late AOA did not. We propose as an explanation that early AOA bilinguals represent different languages separately, while late AOA bilinguals tend to rely on a single unified language system.

This paper studies price bargaining when both parties have left-digit bias when processing numbers. The empirical analysis focuses on the auto finance market in the United States, using a large data set of 35 million auto loans. Incorporating left-digit bias in bargaining is motivated by several intriguing observations. The scheduled monthly payments of auto loans bunch at both $9- and $0-ending digits, especially over $100 marks. In addition, $9-ending loans carry a higher interest rate, and $0-ending loans have a lower interest rate. We develop a Nash bargaining model that allows for left-digit bias from both consumers and finance managers of auto dealers. Results suggest that both parties are subject to this basic human bias: the perceived difference between $9- and the next $0-ending payments is larger than $1, especially between $99- and $00-ending payments. The proposed model can explain the phenomena of payments bunching and differential interest rates for loans with different ending digits. We use counterfactuals to show a nuanced impact of left-digit bias, which can both increase and decrease the payments. Overall, bias from both sides leads to a $33 increase in average payment per loan compared with a benchmark case with no bias. This paper was accepted by Matthew Shum, marketing.

Defence date: 19 January 1998
Examining board: Prof. Giuseppe Bertola, EUI ; Prof. James Bradford DeLong, Harvard University ; Prof. Stephen Machin, University College London ; Prof. Robert Waldmann, EUI and IGIER, Milan, Supervisor
PDF of thesis uploaded from the Library digitised archive of EUI PhD theses completed between 2013 and 2017