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  • Brand : BIOFRON

  • Catalogue Number : BN-O1074

  • Specification : 90%(HPLC)

  • CAS number : 66304-01-6

  • Formula : C7H4O3S2

  • Molecular Weight : 200.23

  • Volume : 5mg

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Catalogue Number


Analysis Method





Molecular Weight



Botanical Source

Structure Type





3H-1,2-Benzodithiol-3-one, 1,1-dioxide/1,1-dioxo-1λ6,2-benzodithiol-3-one/3H-1,2-Benzodithiol-3-one 1,1-dioxide/BEAUCAGE REAGENT/1,2-benzodithiol-3-one-1,1-dioxide



1.7±0.1 g/cm3


Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.

Flash Point

204.6±24.0 °C

Boiling Point

414.6±28.0 °C at 760 mmHg

Melting Point

103-107 °C(lit.)


InChl Key

WGK Germany


HS Code Reference

Personal Projective Equipment

Correct Usage

For Reference Standard and R&D, Not for Human Use Directly.

Meta Tag

provides coniferyl ferulate(CAS#:66304-01-6) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

No Technical Documents Available For This Product.




Asymmetric division of neural stem cells is a fundamental strategy to balance their self-renewal and differentiation. It is long thought that microtubules are not essential for cell polarity in asymmetrically dividing Drosophila melanogaster neuroblasts (NBs; neural stem cells). Here, we show that Drosophila ADP ribosylation factor like-2 (Arl2) and Msps, a known microtubule-binding protein, control cell polarity and spindle orientation of NBs. Upon arl2 RNA intereference, Arl2-GDP expression, or arl2 deletions, microtubule abnormalities and asymmetric division defects were observed. Conversely, overactivation of Arl2 leads to microtubule overgrowth and depletion of NBs. Arl2 regulates microtubule growth and asymmetric division through localizing Msps to the centrosomes in NBs. Moreover, Arl2 regulates dynein function and in turn centrosomal localization of D-TACC and Msps. Arl2 physically associates with tubulin cofactors C, D, and E. Arl2 functions together with tubulin-binding cofactor D to control microtubule growth, Msps localization, and NB self-renewal. Therefore, Arl2- and Msps-dependent microtubule growth is a new paradigm regulating asymmetric division of neural stem cells.


Arl2- and Msps-dependent microtubule growth governs asymmetric division


Keng Chen,1,2 Chwee Tat Koe,1,2 Zhanyuan Benny Xing,3 Xiaolin Tian,4 Fabrizio Rossi,5 Cheng Wang,1 Quan Tang,6,7 Wenhui Zong,6,7 Wan Jin Hong,8 Reshma Taneja,2,9 Fengwei Yu,1,2,6,7 Cayetano Gonzalez,5,10 Chunlai Wu,4 Sharyn Endow,1,3 and Hongyan Wangcorresponding author1,2,9

Publish date

2016 Mar 14




Douglas-fir (Pseudotsuga menziesii), one of the most economically and ecologically important tree species in the world, also has one of the largest tree breeding programs. Although the coastal and interior varieties of Douglas-fir (vars. menziesii and glauca) are native to North America, the coastal variety is also widely planted for timber production in Europe, New Zealand, Australia, and Chile. Our main goal was to develop a SNP resource large enough to facilitate genomic selection in Douglas-fir breeding programs. To accomplish this, we developed a 454-based reference transcriptome for coastal Douglas-fir, annotated and evaluated the quality of the reference, identified putative SNPs, and then validated a sample of those SNPs using the Illumina Infinium genotyping platform.

We assembled a reference transcriptome consisting of 25,002 isogroups (unique gene models) and 102,623 singletons from 2.76 million 454 and Sanger cDNA sequences from coastal Douglas-fir. We identified 278,979 unique SNPs by mapping the 454 and Sanger sequences to the reference, and by mapping four datasets of Illumina cDNA sequences from multiple seed sources, genotypes, and tissues. The Illumina datasets represented coastal Douglas-fir (64.00 and 13.41 million reads), interior Douglas-fir (80.45 million reads), and a Yakima population similar to interior Douglas-fir (8.99 million reads). We assayed 8067 SNPs on 260 trees using an Illumina Infinium SNP genotyping array. Of these SNPs, 5847 (72.5%) were called successfully and were polymorphic.

Based on our validation efficiency, our SNP database may contain as many as ~200,000 true SNPs, and as many as ~69,000 SNPs that could be genotyped at ~20,000 gene loci using an Infinium II array—more SNPs than are needed to use genomic selection in tree breeding programs. Ultimately, these genomic resources will enhance Douglas-fir breeding and allow us to better understand landscape-scale patterns of genetic variation and potential responses to climate change.


A SNP resource for Douglas-fir: de novo transcriptome assembly and SNP detection and validation


Glenn T Howe,corresponding author1 Jianbin Yu,1,2 Brian Knaus,3 Richard Cronn,3 Scott Kolpak,1 Peter Dolan,4 W Walter Lorenz,5 and Jeffrey FD Dean5

Publish date





Antidrug antibody (ADA) responses impact drug safety, potency, and efficacy. It is generally assumed that ADA responses are associated with human leukocyte antigen (HLA) class II-restricted CD4+ T-cell reactivity. Although this review does not address ADA responses per se, the analysis presented here is relevant to the topic, because measuring or predicting CD4+ T-cell reactivity is a common strategy to address ADA and immunogenicity concerns. Because human CD4+ T-cell reactivity relies on the recognition of peptides bound to HLA class II, prediction, or measurement of the capacity of different peptides to bind or be natural ligands of HLA class II is used as a predictor of CD4+ T-cell reactivity and ADA development. Thus, three different interconnected variables are commonly utilized in predicting T-cell reactivity: major histocompatibility complex (MHC) binding, capacity to be generated as natural HLA ligands, and T-cell immunogenicity. To provide the scientific community with guidance in the relative merit of different approaches, it is necessary to clearly define what outcomes are being considered. Thus, the accuracy of HLA binding predictions varies as a function of what the outcome predicted is, whether it is binding itself, natural processing, or T-cell immunogenicity. Furthermore, it is necessary that the accuracy of prediction is based on rigorous benchmarking, grounded by fair, objective, transparent, and experimental criteria. In this review, we provide our perspective on how different variables and methodologies predict each of the various outcomes and point out knowledge gaps and areas to be addressed by further experimental work.


anti drug antibodies (ADA), CD4 T cell, MHC-prediction, prediction benchmarking, immunogenicity


Major Histocompatibility Complex Binding, Eluted Ligands, and Immunogenicity: Benchmark Testing and Predictions


Sinu Paul,1 Alba Grifoni,1 Bjoern Peters,1,2 and Alessandro Sette1,2,*

Publish date


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