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1. Experimental and numerical analysis of the effective parameters on desalinated water flow in a stepped solar still.

Author

Khalili, M., Mostafavi, S. A., Karimi, B., and Ghaderi, M.

Subjects
SALINE water conversion, NUMERICAL analysis, SOLAR radiation, SOLAR stills, WATER use, SALINE waters, RESEARCH personnel
Abstract

Freshwater is an essential resource due to climate change, population growth, and groundwater contamination. The seas and oceans contain a lot of valuable saltwater. One of the economic ways to convert this expensive product from salt water is to use a stepped solar still. The low efficiency of the majority of solar stills has intrigued researchers to find ways to improve their performance. In this paper, using a neural network based on experimental data, the optimal conditions to reach maximum water production have been identified. The flow rate of desalinated water, the impact of sunlight radiation, the cover tilt angle regarding the horizon, the sodium metasilicate addition to the glass cover, the distance between the glass cover and the water surface, and finally the flow rate of the cooling stream over the glass cover were examined. The results have shown that to achieve more water at higher solar radiation levels, it is necessary to increase the cover tilt angle. Also, in the same conditions, using a low amount of sodium metasilicate compared to not using it increases efficiency by 34% on average. Also, decreasing the distance between the glass cover and the water surface increases the efficiency of this equipment. It was also observed that in low solar radiation, a low cooling flow rate is needed, and in high solar radiation, a high cooling flow rate is needed for more efficiency. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Particulate material 2.5 change in the city using EURO4 urban buses technology case study: Arak City, Iran.

Author

Mostafavi, S. A., Dadsetan, A., and Safikhani, H.

Subjects
INTELLIGENT transportation systems, CITY traffic, BUSES, TRAFFIC congestion, BUS transportation, AIR pollution, UNITS of time
Abstract

In this paper, the effect of utilizing urban buses with either outdated or modern environmental technologies on the air pollution of Arak city is modeled. Arak is an industrial city with population and traffic congestion; hence, such modeling is of paramount significance. Here, the roads with heavy traffic are modeled and the PM2.5 pollutants emitted by light and heavy vehicles in the rush hours over the entire 365 days of the year were contemplated. Three different cases of only light vehicles, 2 light vehicles and EURO3 urban buses, and 3 light vehicles and EURO4 urban buses were considered. All required data including synoptic and upper air meteorological data, topography data and mobile source data are utilized for modeling. For modeling vehicle-caused emission, all traffic data including type of the vehicle, number of vehicles and quality of traffic were used. Results showed that urban busses are highly influential on the PM2.5 pollutants in Arak city. In the presence of EURO3 and EURO4 urban buses, the concentration exceeds 5 and 10 times the standard level, respectively. Moreover, with the replacement of EURO3 urban buses with their EURO4 rivals, the number of polluted days over the period of a year would decrease from 26 to 10 days. [ABSTRACT FROM AUTHOR]

Published
2022
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4. Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Author

Czamara, D. (Darina), Eraslan, G. (Goekcen), Page, C. M. (Christian M.), Lahti, J. (Jari), Lahti-Pulkkinen, M. (Marius), Hamalainen, E. (Esa), Kajantie, E. (Eero), Laivuori, H. (Hannele), Villa, P. M. (Pia M.), Reynolds, R. M. (Rebecca M.), Nystad, W. (Wenche), Haberg, S. E. (Siri E.), London, S. J. (Stephanie J.), O'Donnell, K. J. (Kieran J.), Garg, E. (Elika), Meaney, M. J. (Michael J.), Entringer, S. (Sonja), Wadhwa, P. D. (Pathik D.), Buss, C. (Claudia), Jones, M. J. (Meaghan J.), Lin, D. T. (David T. S.), MacIsaac, J. L. (Julie L.), Kobor, M. S. (Michael S.), Koen, N. (Nastassja), Zar, H. J. (Heather J.), Koenen, K. C. (Karestan C.), Dalvie, S. (Shareefa), Stein, D. J. (Dan J.), Kondofersky, I. (Ivan), Mueller, N. S. (Nikola S.), Theis, F. J. (Fabian J.), Raikkonen, K. (Katri), Binder, E. B. (Elisabeth B.), Wray, N. R. (Naomi R.), Ripke, S. (Stephan), Mattheisen, M. (Manuel), Trzaskowski, M. (Maciej), Byrne, E. M. (Enda M.), Abdellaoui, A. (Abdel), Adams, M. J. (Mark J.), Agerbo, E. (Esben), Air, T. M. (Tracy M.), Andlauer, T. F. (Till F. M.), Bacanu, S.-A. (Silviu-Alin), Baekvad-Hansen, M. (Marie), Beekman, A. T. (Aartjan T. F.), Bigdeli, T. B. (Tim B.), Blackwood, D. H. (Douglas H. R.), Bryois, J. (Julien), Buttenschon, H. N. (Henriette N.), Bybjerg-Grauholm, J. (Jonas), Cai, N. (Na), Castelao, E. (Enrique), Christensen, J. H. (Jane Hvarregaard), Clarke, T.-K. (Toni-Kim), Coleman, J. R. (Jonathan R., I), Colodro-Conde, L. (Lucia), Couvy-Duchesne, B. (Baptiste), Craddock, N. (Nick), Crawford, G. E. (Gregory E.), Davies, G. (Gail), Deary, I. J. (Ian J.), Degenhardt, F. (Franziska), Derks, E. M. (Eske M.), Direk, N. (Nese), Dolan, C. V. (Conor, V), Dunn, E. C. (Erin C.), Eley, T. C. (Thalia C.), Escott-Price, V. (Valentina), Kiadeh, F. F. (Farnush Farhadi Hassan), Finucane, H. K. (Hilary K.), Forstner, A. J. (Andreas J.), Frank, J. (Josef), Gaspar, H. A. (Helena A.), Gill, M. (Michael), Goes, F. S. (Fernando S.), Gordon, S. D. (Scott D.), Grove, J. (Jakob), Hall, L. S. (Lynsey S.), Hansen, C. S. (Christine Soholm), Hansen, T. F. (Thomas F.), Herms, S. (Stefan), Hickie, I. B. (Ian B.), Hoffmann, P. (Per), Homuth, G. (Georg), Horn, C. (Carsten), Hottenga, J.-J. (Jouke-Jan), Hougaard, D. M. (David M.), Ising, M. (Marcus), Jansen, R. (Rick), Jorgenson, E. (Eric), Knowles, J. A. (James A.), Kohane, I. S. (Isaac S.), Kraft, J. (Julia), Kretzschmar, W. W. (Warren W.), Krogh, J. (Jesper), Kutalik, Z. (Zoltan), Li, Y. (Yihan), Lind, P. A. (Penelope A.), MacIntyre, D. J. (Donald J.), MacKinnon, D. F. (Dean F.), Maier, R. M. (Robert M.), Maier, W. (Wolfgang), Marchini, J. (Jonathan), Mbarek, H. (Hamdi), McGrath, P. (Patrick), McGuffin, P. (Peter), Medland, S. E. (Sarah E.), Mehta, D. (Divya), Middeldorp, C. M. (Christel M.), Mihailov, E. (Evelin), Milaneschi, Y. (Yuri), Milani, L. (Lili), Mondimore, F. M. (Francis M.), Montgomery, G. W. (Grant W.), Mostafavi, S. (Sara), Mullins, N. (Niamh), Nauck, M. (Matthias), Ng, B. (Bernard), Nivard, M. G. (Michel G.), Nyholt, D. R. (Dale R.), O'Reilly, P. F. (Paul F.), Oskarsson, H. (Hogni), Owen, M. J. (Michael J.), Painter, J. N. (Jodie N.), Pedersen, C. B. (Carsten Bocker), Pedersen, M. G. (Marianne Girtz), Peterson, R. E. (Roseann E.), Pettersson, E. (Erik), Peyrot, W. J. (Wouter J.), Pistis, G. (Giorgio), Posthuma, D. (Danielle), Quiroz, J. A. (Jorge A.), Qvist, P. (Per), Rice, J. P. (John P.), Riley, B. P. (Brien P.), Rivera, M. (Margarita), Mirza, S. S. (Saira Saeed), Schoevers, R. (Robert), Schulte, E. C. (Eva C.), Shen, L. (Ling), Shi, J. (Jianxin), Shyn, S. I. (Stanley, I), Sigurdsson, E. (Engilbert), Sinnamon, G. C. (Grant C. B.), Smit, J. H. (Johannes H.), Smith, D. J. (Daniel J.), Stefansson, H. (Hreinn), Steinberg, S. (Stacy), Streit, F. (Fabian), Strohmaier, J. (Jana), Tansey, K. E. (Katherine E.), Teismann, H. (Henning), Teumer, A. (Alexander), Thompson, W. (Wesley), Thomson, P. A. (Pippa A.), Thorgeirsson, T. E. (Thorgeir E.), Traylor, M. (Matthew), Treutlein, J. (Jens), Trubetskoy, V. (Vassily), Uitterlinden, A. G. (Andre G.), Umbricht, D. (Daniel), Van der Auwera, S. (Sandra), van Hemert, A. M. (Albert M.), Viktorin, A. (Alexander), Visscher, P. M. (Peter M.), Wang, Y. (Yunpeng), Webb, B. T. (Bradley T.), Weinsheimer, S. M. (Shantel Marie), Wellmann, J. (Juergen), Willemsen, G. (Gonneke), Witt, S. H. (Stephanie H.), Wu, Y. (Yang), Xi, H. S. (Hualin S.), Yang, J. (Jian), Zhang, F. (Futao), Arolt, V. (Volker), Baune, B. T. (Bernhard T.), Berger, K. (Klaus), Boomsma, D. I. (Dorret, I), Cichon, S. (Sven), Dannlowski, U. (Udo), de Geus, E. J. (E. J. C.), DePaulo, J. R. (J. Raymond), Domenici, E. (Enrico), Domschke, K. (Katharina), Esko, T. (Tonu), Grabe, H. J. (Hans J.), Hamilton, S. P. (Steven P.), Hayward, C. (Caroline), Heath, A. C. (Andrew C.), Kendler, K. S. (Kenneth S.), Kloiber, S. (Stefan), Lewis, G. (Glyn), Li, Q. S. (Qingqin S.), Lucae, S. (Susanne), Madden, P. A. (Pamela A. F.), Magnusson, P. K. (Patrik K.), Martin, N. G. (Nicholas G.), McIntosh, A. M. (Andrew M.), Metspalu, A. (Andres), Mors, O. (Ole), Mortensen, P. B. (Preben Bo), Mueller-Myhsok, B. (Bertram), Nordentoft, M. (Merete), Noethen, M. M. (Markus M.), O'Donovan, M. C. (Michael C.), Paciga, S. A. (Sara A.), Pedersen, N. L. (Nancy L.), Penninx, B. W. (Brenda W. J. H.), Perlis, R. H. (Roy H.), Porteous, D. J. (David J.), Potash, J. B. (James B.), Preisig, M. (Martin), Rietschel, M. (Marcella), Schaefer, C. (Catherine), Schulze, T. G. (Thomas G.), Smoller, J. W. (Jordan W.), Stefansson, K. (Kari), Tiemeier, H. (Henning), Uher, R. (Rudolf), Voelzke, H. (Henry), Weissman, M. M. (Myrna M.), Werge, T. (Thomas), Lewis, C. M. (Cathryn M.), Levinson, D. F. (Douglas F.), Breen, G. (Gerome), Borglum, A. D. (Anders D.), Sullivan, P. F. (Patrick F.), Czamara, D. (Darina), Eraslan, G. (Goekcen), Page, C. M. (Christian M.), Lahti, J. (Jari), Lahti-Pulkkinen, M. (Marius), Hamalainen, E. (Esa), Kajantie, E. (Eero), Laivuori, H. (Hannele), Villa, P. M. (Pia M.), Reynolds, R. M. (Rebecca M.), Nystad, W. (Wenche), Haberg, S. E. (Siri E.), London, S. J. (Stephanie J.), O'Donnell, K. J. (Kieran J.), Garg, E. (Elika), Meaney, M. J. (Michael J.), Entringer, S. (Sonja), Wadhwa, P. D. (Pathik D.), Buss, C. (Claudia), Jones, M. J. (Meaghan J.), Lin, D. T. (David T. S.), MacIsaac, J. L. (Julie L.), Kobor, M. S. (Michael S.), Koen, N. (Nastassja), Zar, H. J. (Heather J.), Koenen, K. C. (Karestan C.), Dalvie, S. (Shareefa), Stein, D. J. (Dan J.), Kondofersky, I. (Ivan), Mueller, N. S. (Nikola S.), Theis, F. J. (Fabian J.), Raikkonen, K. (Katri), Binder, E. B. (Elisabeth B.), Wray, N. R. (Naomi R.), Ripke, S. (Stephan), Mattheisen, M. (Manuel), Trzaskowski, M. (Maciej), Byrne, E. M. (Enda M.), Abdellaoui, A. (Abdel), Adams, M. J. (Mark J.), Agerbo, E. (Esben), Air, T. M. (Tracy M.), Andlauer, T. F. (Till F. M.), Bacanu, S.-A. (Silviu-Alin), Baekvad-Hansen, M. (Marie), Beekman, A. T. (Aartjan T. F.), Bigdeli, T. B. (Tim B.), Blackwood, D. H. (Douglas H. R.), Bryois, J. (Julien), Buttenschon, H. N. (Henriette N.), Bybjerg-Grauholm, J. (Jonas), Cai, N. (Na), Castelao, E. (Enrique), Christensen, J. H. (Jane Hvarregaard), Clarke, T.-K. (Toni-Kim), Coleman, J. R. (Jonathan R., I), Colodro-Conde, L. (Lucia), Couvy-Duchesne, B. (Baptiste), Craddock, N. (Nick), Crawford, G. E. (Gregory E.), Davies, G. (Gail), Deary, I. J. (Ian J.), Degenhardt, F. (Franziska), Derks, E. M. (Eske M.), Direk, N. (Nese), Dolan, C. V. (Conor, V), Dunn, E. C. (Erin C.), Eley, T. C. (Thalia C.), Escott-Price, V. (Valentina), Kiadeh, F. F. (Farnush Farhadi Hassan), Finucane, H. K. (Hilary K.), Forstner, A. J. (Andreas J.), Frank, J. (Josef), Gaspar, H. A. (Helena A.), Gill, M. (Michael), Goes, F. S. (Fernando S.), Gordon, S. D. (Scott D.), Grove, J. (Jakob), Hall, L. S. (Lynsey S.), Hansen, C. S. (Christine Soholm), Hansen, T. F. (Thomas F.), Herms, S. (Stefan), Hickie, I. B. (Ian B.), Hoffmann, P. (Per), Homuth, G. (Georg), Horn, C. (Carsten), Hottenga, J.-J. (Jouke-Jan), Hougaard, D. M. (David M.), Ising, M. (Marcus), Jansen, R. (Rick), Jorgenson, E. (Eric), Knowles, J. A. (James A.), Kohane, I. S. (Isaac S.), Kraft, J. (Julia), Kretzschmar, W. W. (Warren W.), Krogh, J. (Jesper), Kutalik, Z. (Zoltan), Li, Y. (Yihan), Lind, P. A. (Penelope A.), MacIntyre, D. J. (Donald J.), MacKinnon, D. F. (Dean F.), Maier, R. M. (Robert M.), Maier, W. (Wolfgang), Marchini, J. (Jonathan), Mbarek, H. (Hamdi), McGrath, P. (Patrick), McGuffin, P. (Peter), Medland, S. E. (Sarah E.), Mehta, D. (Divya), Middeldorp, C. M. (Christel M.), Mihailov, E. (Evelin), Milaneschi, Y. (Yuri), Milani, L. (Lili), Mondimore, F. M. (Francis M.), Montgomery, G. W. (Grant W.), Mostafavi, S. (Sara), Mullins, N. (Niamh), Nauck, M. (Matthias), Ng, B. (Bernard), Nivard, M. G. (Michel G.), Nyholt, D. R. (Dale R.), O'Reilly, P. F. (Paul F.), Oskarsson, H. (Hogni), Owen, M. J. (Michael J.), Painter, J. N. (Jodie N.), Pedersen, C. B. (Carsten Bocker), Pedersen, M. G. (Marianne Girtz), Peterson, R. E. (Roseann E.), Pettersson, E. (Erik), Peyrot, W. J. (Wouter J.), Pistis, G. (Giorgio), Posthuma, D. (Danielle), Quiroz, J. A. (Jorge A.), Qvist, P. (Per), Rice, J. P. (John P.), Riley, B. P. (Brien P.), Rivera, M. (Margarita), Mirza, S. S. (Saira Saeed), Schoevers, R. (Robert), Schulte, E. C. (Eva C.), Shen, L. (Ling), Shi, J. (Jianxin), Shyn, S. I. (Stanley, I), Sigurdsson, E. (Engilbert), Sinnamon, G. C. (Grant C. B.), Smit, J. H. (Johannes H.), Smith, D. J. (Daniel J.), Stefansson, H. (Hreinn), Steinberg, S. (Stacy), Streit, F. (Fabian), Strohmaier, J. (Jana), Tansey, K. E. (Katherine E.), Teismann, H. (Henning), Teumer, A. (Alexander), Thompson, W. (Wesley), Thomson, P. A. (Pippa A.), Thorgeirsson, T. E. (Thorgeir E.), Traylor, M. (Matthew), Treutlein, J. (Jens), Trubetskoy, V. (Vassily), Uitterlinden, A. G. (Andre G.), Umbricht, D. (Daniel), Van der Auwera, S. (Sandra), van Hemert, A. M. (Albert M.), Viktorin, A. (Alexander), Visscher, P. M. (Peter M.), Wang, Y. (Yunpeng), Webb, B. T. (Bradley T.), Weinsheimer, S. M. (Shantel Marie), Wellmann, J. (Juergen), Willemsen, G. (Gonneke), Witt, S. H. (Stephanie H.), Wu, Y. (Yang), Xi, H. S. (Hualin S.), Yang, J. (Jian), Zhang, F. (Futao), Arolt, V. (Volker), Baune, B. T. (Bernhard T.), Berger, K. (Klaus), Boomsma, D. I. (Dorret, I), Cichon, S. (Sven), Dannlowski, U. (Udo), de Geus, E. J. (E. J. C.), DePaulo, J. R. (J. Raymond), Domenici, E. (Enrico), Domschke, K. (Katharina), Esko, T. (Tonu), Grabe, H. J. (Hans J.), Hamilton, S. P. (Steven P.), Hayward, C. (Caroline), Heath, A. C. (Andrew C.), Kendler, K. S. (Kenneth S.), Kloiber, S. (Stefan), Lewis, G. (Glyn), Li, Q. S. (Qingqin S.), Lucae, S. (Susanne), Madden, P. A. (Pamela A. F.), Magnusson, P. K. (Patrik K.), Martin, N. G. (Nicholas G.), McIntosh, A. M. (Andrew M.), Metspalu, A. (Andres), Mors, O. (Ole), Mortensen, P. B. (Preben Bo), Mueller-Myhsok, B. (Bertram), Nordentoft, M. (Merete), Noethen, M. M. (Markus M.), O'Donovan, M. C. (Michael C.), Paciga, S. A. (Sara A.), Pedersen, N. L. (Nancy L.), Penninx, B. W. (Brenda W. J. H.), Perlis, R. H. (Roy H.), Porteous, D. J. (David J.), Potash, J. B. (James B.), Preisig, M. (Martin), Rietschel, M. (Marcella), Schaefer, C. (Catherine), Schulze, T. G. (Thomas G.), Smoller, J. W. (Jordan W.), Stefansson, K. (Kari), Tiemeier, H. (Henning), Uher, R. (Rudolf), Voelzke, H. (Henry), Weissman, M. M. (Myrna M.), Werge, T. (Thomas), Lewis, C. M. (Cathryn M.), Levinson, D. F. (Douglas F.), Breen, G. (Gerome), Borglum, A. D. (Anders D.), and Sullivan, P. F. (Patrick F.)

Abstract

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk.

Published
2019

7. Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing.

Author

Mostafavi, S, Battle, A, Zhu, X, Potash, J B, Weissman, M M, Shi, J, Beckman, K, Haudenschild, C, McCormick, C, Mei, R, Gameroff, M J, Gindes, H, Adams, P, Goes, F S, Mondimore, F M, MacKinnon, D F, Notes, L, Schweizer, B, Furman, D, and Montgomery, S B

Subjects
*TYPE I interferons, *GENE expression, *MENTAL depression genetics, *RNA sequencing, *PATHOLOGICAL psychology, *GENETICS of bipolar disorder, *GENETICS of schizophrenia
Abstract

A study of genome-wide gene expression in major depressive disorder (MDD) was undertaken in a large population-based sample to determine whether altered expression levels of genes and pathways could provide insights into biological mechanisms that are relevant to this disorder. Gene expression studies have the potential to detect changes that may be because of differences in common or rare genomic sequence variation, environmental factors or their interaction. We recruited a European ancestry sample of 463 individuals with recurrent MDD and 459 controls, obtained self-report and semi-structured interview data about psychiatric and medical history and other environmental variables, sequenced RNA from whole blood and genotyped a genome-wide panel of common single-nucleotide polymorphisms. We used analytical methods to identify MDD-related genes and pathways using all of these sources of information. In analyses of association between MDD and expression levels of 13 857 single autosomal genes, accounting for multiple technical, physiological and environmental covariates, a significant excess of low P-values was observed, but there was no significant single-gene association after genome-wide correction. Pathway-based analyses of expression data detected significant association of MDD with increased expression of genes in the interferon α/β signaling pathway. This finding could not be explained by potentially confounding diseases and medications (including antidepressants) or by computationally estimated proportions of white blood cell types. Although cause-effect relationships cannot be determined from these data, the results support the hypothesis that altered immune signaling has a role in the pathogenesis, manifestation, and/or the persistence and progression of MDD. [ABSTRACT FROM AUTHOR]

Published
2014
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9. Biotransformation of methyl tert-butyl ether by human cytochrome P450 2A6.

Author

Shamsipur, Mojtaba, Miran Beigi, Ali, Teymouri, Mohammad, Poursaberi, Tahereh, Mostafavi, S., Soleimani, Parviz, Chitsazian, Fereshteh, and Tash, Shahram

Subjects
BIOTRANSFORMATION (Metabolism), CYTOCHROME P-450, GAS chromatography/Mass spectrometry (GC-MS), FORMALDEHYDE, TERT-Butyl halides, PUBLIC health
Abstract

Methyl tert-butyl ether (MTBE) is widely used as gasoline oxygenate and octane number enhancer for more complete combustion in order to reduce the air pollution caused by motor vehicle exhaust. The possible adverse effects of MTBE on human health are of major public concern. However, information on the metabolism of MTBE in human tissues is scarce. The present study demonstrates that human cytochrome P450 2A6 is able to metabolize MTBE to tert-butyl alcohol (TBA), a major circulating metabolite and marker for exposure to MTBE. As CYP2A6 is known to be constitutively expressed in human livers, we infer that it may play a significant role in metabolism of gasoline ethers in liver tissue. [ABSTRACT FROM AUTHOR]

Published
2012
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10. Solvent optimization on Taxol extraction from Taxus baccata L., using HPLC and LC-MS.

Author

Sadeghi-aliabadi, H., Asghari, G., Mostafavi, S. A., and Esmaeili, A.

Subjects
SOLVENTS, PACLITAXEL, YEW, HIGH performance liquid chromatography, ANTINEOPLASTIC agents, PLANT extracts, ACETONE
Abstract

Background and the purpose of the study: Taxol, a natural antitumor agent, was first isolated from the extract of the bark of Taxus brevifolia Nutt., which is potentially a limited source for Taxol. In the search of an alternative source, optimum and cost benefit extracting solvents, various solvents with different percentage were utilized to extract Taxol from needles of Taxus baccata. Methods: One g of the dried needles of Taxus baccata, collected from Torkaman and Noor cities of Iran, was extracted with pure ethanol or acetone and 50% and 20% of ethanol or acetone in water. Solvents were evaporated to dryness and the residues were dissolved in 5 ml of methanol and filtered. To one ml of the filtrate was added 50 µl of cinamyl acetate as the internal standard and 20 µl of the resulting solution was subjected to the HPLC to determine the extraction efficiencies of tested solvents. Five µl of filtrate was also subjected to the LC-MS using water/acetonitrile (10/90) as mobile phase and applying positive electrospray ionization (ESI) to identify the authenticity of Taxol. Results: Results of this study indicated that Taxol extraction efficiency was enhanced as the percentage of ethanol or acetone was increased. HPLC analysis showed that Taxol could be quantified by UV detection using standard curve. The standard curve covering the concentration ranges of 7.8 - 500 µg/ml was linear (r²= 0.9992) and CV% ranged from 0.52 to 15.36. LC-MS analysis using ESI in positive-ion mode confirmed the authenticity of Taxol (m/z 854; M+H), as well as some adduct ions such as M+Na (m/z 876), M+K (m/z 892) and M+CH3CN+H2O (m/z 913). Conclusions: The results suggest that 100% acetone is the best solvent for the extraction of Taxol from Taxus baccata needles. [ABSTRACT FROM AUTHOR]

Published
2009

11. Deep learning decodes the principles of differential gene expression.

Author

Tasaki S, Gaiteri C, Mostafavi S, and Wang Y

Abstract

Identifying the molecular mechanisms that control differential gene expression (DE) is a major goal of basic and disease biology. We develop a systems biology model to predict DE, and mine the biological basis of the factors that influence predicted gene expression, in order to understand how it may be generated. This model, called DEcode , utilizes deep learning to predict DE based on genome-wide binding sites on RNAs and promoters. Ranking predictive factors from the DEcode indicates that clinically relevant expression changes between thousands of individuals can be predicted mainly through the joint action of post-transcriptional RNA-binding factors. We also show the broad potential applications of DEcode to generate biological insights, by predicting DE between tissues, differential transcript-usage, and drivers of aging throughout the human lifespan, of gene coexpression relationships on a genome-wide scale, and of frequently DE genes across diverse conditions. Researchers can freely utilize DEcode to identify influential molecular mechanisms for any human expression data - www.differentialexpression.org.

Published
2020
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