463,219 research outputs found
B-H. Han. Hiperculturalidad. Barcelona: Editorial Herder, 2018.
No full textB-H. Han. Hiperculturalidad. Barcelona: Editorial Herder, 2018. USB-H. Han. Hiperculturalidad. Barcelona: Editorial Herder, 2018. E
Allylic Acetals as Acrolein Oxonium Precursors in Tandem C−H Allylation and [3+2] Dipolar Cycloaddition
No full text© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimThe ruthenium(II)-catalyzed C−H functionalization of (hetero)aryl azomethine imines with allylic acetals is described. The initial formation of allylidene(methyl)oxoniums from allylic acetals could trigger C(sp2)−H allylation, and subsequent endo-type [3+2] dipolar cycloaddition of polar azomethine fragments to deliver valuable indenopyrazolopyrazolones. The utility of this method is showcased by the late-stage functionalization of bioactive molecules such as estrone and celecoxib. Combined experimental and computational investigations elucidate a plausible mechanism of this new tandem reaction. Notably, the reductive transformation of synthesized compounds into biologically relevant diazocine frameworks highlights the importance of the developed methodolog
hyemin-han/BayesFactorFMRI: BayesFactorFMRI V1.0.0
No full textBayesFactorFMRI is a tool developed with R and Python to allow neuroimaging researchers to conduct Bayesian second-level analysis of fMRI data and Bayesian meta-analysis of fMRI images with multiprocessing. This tool was developed to expedite computationally intensive Bayesian fMRI analysis through multiprocessing. Its GUI allows researchers who are not experts in computer programming to feasibly perform Bayesian fMRI analysis. BayesFactorFMRI is available via or GitHub for download. It would be widely reused by neuroimaging researchers who intend to analyse their fMRI data with Bayesian analysis with better sensitivity compared with classical analysis while saving time by distributing analysis tasks into multiple processes.
Please refer to and cite these articles when you use BayesFactorFMRI:
Journal of Open Research Software paper. Bayesian multiple comparison correction: Han, H. (in press). Implementation of Bayesian multiple comparison correction in the second-level analysis of fMRI data: With pilot analyses of simulation and real fMRI datasets based on voxelwise inference. Cognitive Neuroscience, 11(3), 157-169. http://bit.ly/2S6Uka2 Bayesian meta-analysis: Han, H., & Park, J. (2019). Bayesian meta-analysis of fMRI image data. Cognitive Neuroscience, 10(2), 66-76. http://bit.ly/2RCbxZ
Enhanced flux pinning and formation of Ba4Y2CuMoOy in top-seeded melt growth processed YBa2Cu3O7-d superconductors with Mo additions
No full textThe effect of Mo addition (0-10 wt%) on the superconductivity of top-seeded melt growth (TSMG) processed YBa2Cu3O7-y (Y123) superconductors was studied. The low level Mo addition (<= 1 wt%) led to a small decrease of the superconducting transition temperature (T-c) and increase of the critical current density (J(c)). The J(c) improvement induced by the low level Mo additions appeared as a peak effect at the intermediated magnetic fields and peak position shift to the lower magnetic fields with increasing Mo content. The enhanced flux pinning caused by Mo additions seems to be attributed to the partial Cu substitution by Mo, YBa2(Cu1-xMox)(3)O7-d. The high level Mo additions (2-10 wt%), however, led to a large J(c) decrease and broad superconducting transition due to the formation of low-T-c phases and the increased volume of the non-superconducting Mo-containing phase. The second particle phase formed by the high level Mo additions was identified as Ba4Y2CuMoOy (Mo4211) by x-ray diffraction (XRD) and scanning electron microscopy energy dispersive x-ray (SEM EDX) analysis
Letter from C. H. Gensler, Havasupai Agency to Carl Hayden
No full textLetter from C. H. Gensler expressing concern on behalf of the Havasupai Tribe regarding the proposed park boundaries
Citations of the author H C Rajpoot
No full textThe list of the articles, research papers, theses, and book chapters globally citing the author H. C. Rajpoot</p
Letter from Carl Hayden to C. H. Gensler
No full textLetter from Carl Hayden to C. H. Gensler informing him of the proposed Grand Canyon National Park bill
Letter from C. H. Gensler, Havasupai Agency to Carl Hayden
No full textLetter from C. H. Gensler to Carl Hayden asking for a meeting in regards to the Havasupai pasture land in light of the national park bill
Boron-mediated directed aromatic C–H hydroxylation
No full textTransition metal-catalysed C–H hydroxylation is one of the most notable advances in synthetic chemistry during the past few decades and it has been widely employed in the preparation of alcohols and phenols. The site-selective hydroxylation of aromatic C–H bonds under mild conditions, especially in the context of substituted (hetero)arenes with diverse functional groups, remains a challenge. Here, we report a general and mild chelation-assisted C–H hydroxylation of (hetero)arenes mediated by boron species without the use of any transition metals. Diverse (hetero)arenes bearing amide directing groups can be utilized for ortho C–H hydroxylation under mild reaction conditions and with broad functional group compatibility. Additionally, this transition metal-free strategy can be extended to synthesize C7 and C4-hydroxylated indoles. By utilizing the present method, the formal synthesis of several phenol intermediates to bioactive molecules is demonstrated
hyemin-han/BayesFMRI: The first release of BayesFMRI
No full textBayesFMRI is a tool developed with R and Python to allow neuroimaging researchers to conduct Bayesian second-level analysis of fMRI data and Bayesian meta-analysis of fMRI images with multiprocessing. This tool was developed to expedite computationally intensive Bayesian fMRI analysis through multiprocessing. Its GUI allows researchers who are not experts in computer programming to feasibly perform Bayesian fMRI analysis. BayesFMRI is available via or GitHub for download. It would be widely reused by neuroimaging researchers who intend to analyse their fMRI data with Bayesian analysis with better sensitivity compared with classical analysis while saving time by distributing analysis tasks into multiple processes.
Please refer to and cite these articles when you use BayesFMRI:
Bayesian multiple comparison correction: Han, H. (in press). Implementation of Bayesian multiple comparison correction in the second-level analysis of fMRI data: With pilot analyses of simulation and real fMRI datasets based on voxelwise inference. Cognitive Neuroscience. http://bit.ly/2S6Uka2
Bayesian meta-analysis: Han, H., & Park, J. (2019). Bayesian meta-analysis of fMRI image data. Cognitive Neuroscience, 10(2), 66-76. http://bit.ly/2RCbxZY
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