actinomycetes bervanakis
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The document summarizes the results of DNA and metabolic profiling of several actinomycete environmental isolates. Key findings include: 1) DNA sequencing identified the isolates as belonging to genera including Streptomyces, Micromonospora, and Verrucosispora. 2) Fermentation experiments identified optimal growth media and times for each isolate, with some producing anti-microbial compounds active against test pathogens. 3) Extracts of fermentation broths and mycelia were screened via bioassays, TLC, and UV spectroscopy, revealing different metabolic profiles between isolates.
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When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Randomised Optimisation Algorithms in DAPHNE
Slides from talk:
Aleš Zamuda: Randomised Optimisation Algorithms in DAPHNE .
Austrian-Slovenian HPC Meeting 2024 – ASHPC24, Seeblickhotel Grundlsee in Austria, 10–13 June 2024
https://ashpc.eu/
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Particle-In-Cell (PIC) algorithm,
Plasma expansion in the dipole magnetic field.
What is greenhouse gasses and how many gasses are there to affect the Earth.
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
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Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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In this presentation include the prototype drug SAR on thus or with their examples .
Syllabus of Second Year B. Pharmacy
2019 PATTERN.
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actinomycetes bervanakis
- 1. FASTA Database Similarity Search Table 1. DNA similarity search of sequenced amplified PCR products, amplified from Cerylid Environmental Isolates. Cerylid Environmental Isolates Actinomycete Genera 350 PKS I gene 1113 PKS I gene 3023 PKS II gene 1488 PKS II gene Streptomyces 19 (64-76%)* 18 (70-80%) 33 (60-80%) 38 (68-79%) Micromonospora 3 (69-71%) 4 (73-77%) 0 0 Actinomadura 0 0 2 (65-70%) 2 (72-75%) Amycolatopsis 1 (69%) 1 (76%) 0 0 Saccharopolyspora 2 (66-69%) 2 (74%) 1 (66%) 1 (74%) Kibdelsporangium 0 0 1 (68%) 1 (73%) * Percentage Identity with corresponding gene 16SrDNA Sequencing Classification of Cerylid Environmental Isolates A30000350 = Genus Streptomyces A00001113 = Genus Streptomyces A30003023 = Genus Verrucosispora A00001488 = Genus Modestobacter
- 2. Evaluation of Production Media Bioassays Phase II : Overview Chemical Expression Studies Large Scale Production of Metabolites Identification of Metabolite (Cerylid Biosciences ) Partial Chemical Characterisation
- 3. Phase II: Expression of Secondary Metabolites Agar YME (Yeast-Malt Extract) Liquid Fermentations Solid State Fermentations (in progress) Chemical Characterisation (in progress), - TLC, HPLC, UV Spectroscopy
- 4. Screening for Metabolite Production in Submerged Fermentations Inoculation of Seed Medium IM22 Inoculation of Production Media Metabolite Extractions 2 - 10 days Well Type Bioassays 3days Thin Layer Chromatography Active Extracts Bioautography UV Scans Extraction Studies
- 5. C.albicans M.luteus S.aureus Bioassays of Actinomycete A A A1 A2 C1 C2 A3 B1 B2 B3 C1 C2 EtAc Control A1 A2 A3 B1 B2 B3 C3 A = Supernatant untreated #350 A1 = Supernatant Extracted 2x #350 A2 = Mycelial Extract #350 A3 = Organic Extract #350 B1 = Organic Extract #1113 B2 = Supernatant Extracted 2x #1113 B3 = Mycelial Extract #1113 C1 = Supernatant Extracted 2x 2381 C2 = Mycelial Extract 2381 C3 = Organic Extract 2381
- 6. Fermentation of Actinomycetes Environmental Isolate # Supernatant Mycelial Extract Optimal Fermentation Medium Optimal Ferment. Time (hrs) SA ML CA S.A ML CA A30000350 23 26 10 23 26.5 14.5 Dextrin 240 A00001113 25 29 18.5 28 30 21 153 240 A00002381 24 27 28 30 32 31 Dextrin 240 A00003675 N/D N/D 10.5 N/D N/D 21.5 153m 168 A10000347 N/D 22.5 N/D N/D 25 N/D IM25 240 A00002702 N/D 21.5 N/D N/D 19.5 N/D IM25 240 * N/D = no zones of inhibition detected Table 2: Summary of fermentations, indicating zones of inhibition (mm) observed from environmental actinomycete isolates supernatants and mycelial extracts.