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- ItemNon Thermal Plasma Sources of Production of Active Species for Biomedical Uses: Analyses, Optimization and Prospect(London : IntechOpen, 2011) Yousfi, M.; Merbahi, N.; Sarrette, P. J.; Eichwald, O.; Ricard, A.; Gardou, J.P.; Ducasse, O.; Benhenni, M.; Fazel-Rezai, Reza[no abstract available]
- ItemThe systems biology format converter(London : BioMed Central, 2016) Rodriguez, Nicolas; Pettit, Jean-Baptiste; Dalle Pezze, Piero; Li, Lu; Henry, Arnaud; van Iersel, Martijn P.; Jalowicki, Gael; Kutmon, Martina; Natarajan, Kedar N.; Tolnay, David; Stefan, Melanie I.; Evelo, Chris T.; Le Novère, NicolasBackground: Interoperability between formats is a recurring problem in systems biology research. Many tools have been developed to convert computational models from one format to another. However, they have been developed independently, resulting in redundancy of efforts and lack of synergy. Results: Here we present the System Biology Format Converter (SBFC), which provide a generic framework to potentially convert any format into another. The framework currently includes several converters translating between the following formats: SBML, BioPAX, SBGN-ML, Matlab, Octave, XPP, GPML, Dot, MDL and APM. This software is written in Java and can be used as a standalone executable or web service. Conclusions: The SBFC framework is an evolving software project. Existing converters can be used and improved, and new converters can be easily added, making SBFC useful to both modellers and developers. The source code and documentation of the framework are freely available from the project web site.
- ItemNew Paradigm for a targeted cancer therapeutic approach: A short review on potential synergy of gold nanoparticles and Cold Atmospheric Plasma(Basel : MDPI, 2017) Aryal, Sajesan; Bisht, GunjanApplication of Gold nanoparticles and Cold Atmospheric plasma as a targeted therapeutic adjunct has been widely investigated separately in cancer therapy. Gold nanoparticles, with their biocompatibility, lower cytotoxicity and superior efficacy, are becoming substantially more significant in modern cancer therapy. Likewise, cold atmospheric plasma, with rich reactive species including reactive oxygen species (ROS) and reactive nitrogen species (RNS), is being explored to selectively target and kill cancer cells, making them a promising anticancer agent. Recent scientific studies have shown that there is a potential synergy between these two aspects. Induction of apoptosis/necrosis due to oxidative stress may be a probable mechanism of their cytotoxic effect. The synergetic effect of the two therapeutic approaches could be tantamount to maximized targeted efficacy on the treatment of diseases like cancer.
- ItemSciPy 1.0: fundamental algorithms for scientific computing in Python(London [u.a.] : Nature Publishing Group, 2020) Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt; Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, Pearu; Weckesser, Warren; Bright, Jonathan; van der Walt, Stéfan J.; Brett, Matthew; Wilson, Joshua; Millman, K. Jarrod; Mayorov, Nikolay; Nelson, Andrew R. J.; Jones, Eric; Kern, Robert; Larson, Eric; Carey, C J; Polat, İlhan; Feng, Yu; Moore, Eric W.; VanderPlas, Jake; Laxalde, Denis; Perktold, Josef; Cimrman, Robert; Henriksen, Ian; Quintero, E. A.; Harris, Charles R.; Archibald, Anne M.; Ribeiro, Antônio H.; Pedregosa, Fabian; van Mulbregt, Paul; Vijaykumar, Aditya; Bardelli, Alessandro Pietro; Rothberg, Alex; Hilboll, Andreas; Kloeckner, Andreas; Scopatz, Anthony; Lee, Antony; Rokem, Ariel; Woods, C. Nathan; Fulton, Chad; Masson, Charles; Häggström, Christian; Fitzgerald, Clark; Nicholson, David A.; Hagen, David R.; Pasechnik, Dmitrii V.; Olivetti, Emanuele; Martin, Eric; Wieser, Eric; Silva, Fabrice; Lenders, Felix; Wilhelm, Florian; Young, G.; Price, Gavin A.; Ingold, Gert-Ludwig; Allen, Gregory E.; Lee, Gregory R.; Audren, Hervé; Probst, Irvin; Dietrich, Jörg P.; Silterra, Jacob; Webber, James T; Slavič, Janko; Nothman, Joel; Buchner, Johannes; Kulick, Johannes; Schönberger, Johannes L.; de Miranda Cardoso, José Vinícius; Reimer, Joscha; Harrington, Joseph; Rodríguez, Juan Luis Cano; Nunez-Iglesias, Juan; Kuczynski, Justin; Tritz, Kevin; Thoma, Martin; Newville, Matthew; Kümmerer, Matthias; Bolingbroke, Maximilian; Tartre, Michael; Pak, Mikhail; Smith, Nathaniel J.; Nowaczyk, Nikolai; Shebanov, Nikolay; Pavlyk, Oleksandr; Brodtkorb, Per A.; Lee, Perry; McGibbon, Robert T.; Feldbauer, Roman; Lewis, Sam; Tygier, Sam; Sievert, Scott; Vigna, Sebastiano; Peterson, Stefan; More, Surhud; Pudlik, Tadeusz; Oshima, Takuya; Pingel, Thomas J.; Robitaille, Thomas P.; Spura, Thomas; Jones, Thouis R.; Cera, Tim; Leslie, Tim; Zito, Tiziano; Krauss, Tom; Upadhyay, Utkarsh; Halchenko, Yaroslav O.; Vázquez-Baeza, YoshikiSciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
- ItemCorrecting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling(San Francisco, California, US : PLOS, 2023) Mayerhöfer, Thomas G.; Noda, Isao; Pahlow, Susanne; Heintzmann, Rainer; Popp, JürgenRecently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.