siRNA desiging
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siRNAs play a critical role in new therapies. in these slides, we will learn how to design a siRNA. if you have any question, you can e-mail me: reza.rakhshi21@gmail.com
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如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
The binding of cosmological structures by massless topological defects
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
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
Micronuclei test.M.sc.zoology.fisheries.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Sharlene Leurig - Enabling Onsite Water Use with Net Zero Water
Sharlene Leurig - Enabling Onsite Water Use with Net Zero WaterTexas Alliance of Groundwater Districts
Presented at June 6-7 Texas Alliance of Groundwater Districts Business MeetingThe debris of the ‘last major merger’ is dynamically young
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
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ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
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The binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defects
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Deep Software Variability and Frictionless Reproducibility
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Sharlene Leurig - Enabling Onsite Water Use with Net Zero Water
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The debris of the ‘last major merger’ is dynamically young
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
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siRNA desiging
- 1. siRNA Design Reza Rakhshi Master student of Med Biotechnology
- 2. Find CDS Sequence: 1. Go NCBI 2. Choose Nucleotide Database 3. Write your Gene name 4. Active mRNA and Ref seq 5. Extract CDS Reza Rakhshi, Master student of Medical Biotechnology
- 3. Find CDS Sequence-step2,3: Reza Rakhshi, Master student of Medical Biotechnology 2 3
- 4. Find CDS Sequence-step4: Reza Rakhshi, Master student of Medical Biotechnology
- 5. Find CDS Sequence-step5: Reza Rakhshi, Master student of Medical Biotechnology FASTA
- 6. Create siRNA-invitrogen Database: Reza Rakhshi, Master student of Medical Biotechnology
- 7. Create siRNA- appoint parameter: Reza Rakhshi, Master student of Medical Biotechnology Write Accession OR number or paste FASTA seq.
- 8. Create siRNA- appoint parameter: Reza Rakhshi, Master student of Medical Biotechnology
- 9. Create siRNA- Results: Reza Rakhshi, Master student of Medical Biotechnology 1. Good seq has AT at 𝟑′ and CG at 𝟓′ 2. Good seq must not repeated seq Seem good items
- 10. Analyse seq: – First of all we must get antisense seq from sense seq. – Use Gene Runner App for this work – Then use Oligo analyser App. Reza Rakhshi, Master student of Medical Biotechnology Antisense seq Sense seq
- 11. Analyse seq: Reza Rakhshi, Master student of Medical Biotechnology Paste Antisense seq and analye frequent options
- 12. Analyse seq: Reza Rakhshi, Master student of Medical Biotechnology Temperature and structure is normal
- 13. Analyse seq- immunogenic seq: – Use Gene Runner App. – Press Crtl + F and find all of immunogenic seq available below: 1. GTCCTTCAA 2. TGTGT 3. TGT 4. TCA 5. UGGC 6. GT-rich sequences 7. AT-rich sequences 8. T-rich sequences Reza Rakhshi, Master student of Medical Biotechnology
- 14. Reza Rakhshi, Master student of Medical Biotechnology
- 15. mRNA accessibility: – Need RNA Fold app. 1. Paste (whole mRNA) FASTA on box 2. Press PROCEED 3. Find siRNA in whole mRNA Reza Rakhshi, Master student of Medical Biotechnology
- 16. Reza Rakhshi, Master student of Medical Biotechnology 1 2
- 17. Analyse RNA Fold: Reza Rakhshi, Master student of Medical Biotechnology
- 18. Find siRNA in whole genome – We need Gene Runner App. 1. Paste whole mRNA 2. Press Ctrl + F and find siRNa seq (sense seq) 3. Find your siRNA in RNA Fold pic Reza Rakhshi, Master student of Medical Biotechnology
- 19. Reza Rakhshi, Master student of Medical Biotechnology 1 2 3 siRNA position
- 20. Reza Rakhshi, Master student of Medical Biotechnology Start nt (1197) finish nt (1221) Better than this sequence exist in loop because your siRNA can stick easily to it.
- 21. Reza Rakhshi, Master student of Medical Biotechnology Thanks for your attention