We Offer Worldwide Shipping
Login Wishlist

5-Methoxycanthin-6-one

$928

  • Brand : BIOFRON

  • Catalogue Number : BN-O0936

  • Specification : 98%(HPLC)

  • CAS number : 15071-56-4

  • Formula : C15H10N2O2

  • Molecular Weight : 250.25

  • PUBCHEM ID : 259218

  • Volume : 5mg

Available on backorder

Quantity
Checkout Bulk Order?

Catalogue Number

BN-O0936

Analysis Method

HPLC,NMR,MS

Specification

98%(HPLC)

Storage

-20℃

Molecular Weight

250.25

Appearance

Yellow powder

Botanical Source

This product is isolated and purified from the barks of Ailanthus altissima

Structure Type

Category

Standards;Natural Pytochemical;API

SMILES

COC1=CC2=NC=CC3=C2N(C1=O)C4=CC=CC=C34

Synonyms

5-methoxy-canthin-6-one/5-Methoxy-6-canthinon/5-Methoxycanthinone/Canthin-6-one,5-methoxy/5-Methoxy-canthin-6-on/5-Methoxycanthinone

IUPAC Name

3-methoxy-1,6-diazatetracyclo[7.6.1.05,16.010,15]hexadeca-3,5(16),6,8,10,12,14-heptaen-2-one

Density

1.4g/cm3

Solubility

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

Flash Point

194.2ºC

Boiling Point

397.4ºC at 760 mmHg

Melting Point

InChl

InChl Key

TXEFUSAHPIYZHD-UHFFFAOYSA-N

WGK Germany

RID/ADR

HS Code Reference

2933990000

Personal Projective Equipment

Correct Usage

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

Meta Tag

provides coniferyl ferulate(CAS#:15071-56-4) 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.

PMID

9755205

Abstract

One of the most frequent outcomes of interspecific hybridizations in Drosophila is hybrid male sterility. Genetic dissection of this reproductive barrier has revealed that the number of responsible factors is very high and that these factors are frequently engaged in complex epistatic interactions. Traditionally, research strategies have been based on contrasting introgressions of chromosome segments that produce male sterility with those that allow fertility. Few studies have investigated the phenotypes associated with the boundary between fertility and sterility. In this study, we cointrogressed three different X chromosome segments from Drosophila mauritiana into D. simulans. Hybrid males with these three segments are usually fertile, by conventional fertility assays. However, their spermatogenesis shows a significant slowdown, most manifest at lower temperatures. Each of the three introgressed segments retards the arrival of sperm to the seminal vesicles. Other small disturbances in spermatogenesis are evident, which altogether lead to an overall reduction in the amount of motile sperm in their seminal vesicles. These results suggest that a delay in the timing of spermatogenesis, which might be brought about by the cumulative action of many different factors of minor segment, may be the primary cause of hybrid male sterility.

Title

Hidden effects of X chromosome introgressions on spermatogenesis in Drosophila simulans x D. mauritiana hybrids unveiled by interactions among minor genetic factors.

Author

X R Maside, J P Barral, and H F Naveira

Publish date

1998 Oct

PMID

29652856

Abstract

Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.

KEYWORDS

microcystin, anatoxin, cylindrospermopsin, temperature, direct effects, indirect effects, spatial distribution, European Multi Lake Survey

Title

Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins

Author

Evanthia Mantzouki,1,* Miquel Lurling,2,3 Jutta Fastner,4 Lisette de Senerpont Domis,2,3 Elżbieta Wilk-Woźniak,5 Judita Koreivienė,6 Laura Seelen,2,3 Sven Teurlincx,3 Yvon Verstijnen,2 Wojciech Krztoń,5 Edward Walusiak,5 Jūratė Karosienė,6 Jūratė Kasperovicienė,6 Ksenija Savadova,6 Irma Vitonytė,6 Carmen Cillero-Castro,7 Agnieszka Budzyńska,8 Ryszard Goldyn,8 Anna Kozak,8 Joanna Rosińska,8 Elżbieta Szelag-Wasielewska,8 Piotr Domek,8 Natalia Jakubowska-Krepska,8 Kinga Kwasizur,9 Beata Messyasz,9 Aleksandra Pełechata,9 Mariusz Pełechaty,9 Mikolaj Kokocinski,9 Ana Garcia-Murcia,10 Monserrat Real,10 Elvira Romans,10 Jordi Noguero-Ribes,10 David ParreNo Duque,10 Elisabeth Fernandez-Moran,10 Nusret Karakaya,11 Kerstin Haggqvist,12 Nilsun Demir,13 Meryem Beklioglu,14 Nur Filiz,14 Eti E. Levi,14 Ugur Iskin,14 Gizem Bezirci,14 ulku Nihan TavSanoglu,14 Koray ozhan,15 Spyros Gkelis,16 Manthos Panou,16 ozden Fakioglu,17 Christos Avagianos,18 Triantafyllos Kaloudis,18 Kemal celik,19 Mete Yilmaz,20 Rafael Marce,21 Nuria Catalan,21,22 Andrea G. Bravo,22 Moritz Buck,22 William Colom-Montero,23 Kristiina Mustonen,23 Don Pierson,23 Yang Yang,23 Pedro M. Raposeiro,24 Vitor Goncalves,24 Maria G. Antoniou,25 Nikoletta Tsiarta,25 Valerie McCarthy,26 Victor C. Perello,26 Tõnu Feldmann,27 Alo Laas,27 Kristel Panksep,27 Lea Tuvikene,27 Ilona Gagala,28 Joana Mankiewicz-Boczek,28 Meral Apaydın Yagcı,29 Sakir cınar,29 Kadir capkın,29 Abdulkadir Yagcı,29 Mehmet Cesur,29 Fuat Bilgin,29 Cafer Bulut,29 Rahmi Uysal,29 Ulrike Obertegger,30 Adriano Boscaini,30 Giovanna Flaim,30 Nico Salmaso,30 Leonardo Cerasino,30 Jessica Richardson,31 Petra M. Visser,32 Jolanda M. H. Verspagen,32 Tunay Karan,33 Elif Neyran Soylu,34 Faruk MaraSlıoglu,35 Agnieszka Napiorkowska-Krzebietke,36 Agnieszka Ochocka,37 Agnieszka Pasztaleniec,37 Ana M. Antão-Geraldes,38 Vitor Vasconcelos,39 João Morais,39 Micaela Vale,39 Latife Koker,40 Reyhan Akcaalan,40 Meric Albay,40 Dubravka Špoljarić Maronić,41 Filip Stević,41 Tanja Žuna Pfeiffer,41 Jeremy Fonvielle,42 Dietmar Straile,43 Karl-Otto Rothhaupt,43 Lars-Anders Hansson,44 Pablo Urrutia-Cordero,22,44 Luděk Blaha,45 Rodan Geriš,46 Marketa Frankova,47 Mehmet Ali Turan Kocer,48 Mehmet Tahir Alp,49 Spela Remec-Rekar,50 Tina Elersek,51 Theodoros Triantis,52 Sevasti-Kiriaki Zervou,52 Anastasia Hiskia,52 Sigrid Haande,53 Birger Skjelbred,53 Beata Madrecka,54 Hana Nemova,55 Iveta Drastichova,55 Lucia Chomova,55 Christine Edwards,56 Tugba Ongun Sevindik,57 Hatice Tunca,57 Burcin onem,57 Boris Aleksovski,58 Svetislav Krstić,58 Itana Bokan Vucelić,59 Lidia Nawrocka,60 Pauliina Salmi,61 Danielle Machado-Vieira,62 Alinne Gurjão de Oliveira,62 Jordi Delgado-Martin,63 David Garcia,63 Jose Luis Cereijo,63 Joan Gomà,64 Mari Carmen Trapote,64 Teresa Vegas-Vilarrúbia,64 Biel Obrador,64 Magdalena Grabowska,65 Maciej Karpowicz,65 Damian Chmura,66 Barbara Úbeda,67 Jose angel Galvez,67 Arda ozen,68 Kirsten Seestern Christoffersen,69 Trine Perlt Warming,69 Justyna Kobos,70 Hanna Mazur-Marzec,70 Carmen Perez-Martinez,71 Eloisa Ramos-Rodriguez,71 Lauri Arvola,72 Pablo Alcaraz-Parraga,73 Magdalena Toporowska,74 Barbara Pawlik-Skowronska,74 Michał Niedźwiecki,74 Wojciech Pęczuła,74 Manel Leira,75 Armand Hernandez,76 Enrique Moreno-Ostos,77 Jose Maria Blanco,77 Valeriano Rodriguez,77 Jorge Juan Montes-Perez,77 Roberto L. Palomino,77 Estela Rodriguez-Perez,77 Rafael Carballeira,78 Antonio Camacho,79 Antonio Picazo,79 Carlos Rochera,79 Anna C. Santamans,79 Carmen Ferriol,79 Susana Romo,80 Juan Miguel Soria,80 Julita Dunalska,81 Justyna Sieńska,81 Daniel Szymański,81 Marek Kruk,82 Iwona Kostrzewska-Szlakowska,83 Iwona Jasser,84 Petar Žutinić,85 Marija Gligora Udovic,85 Anđelka Plenković-Moraj,85 Magdalena Frak,86 Agnieszka Bańkowska-Sobczak,87 Michał Wasilewicz,87 Korhan ozkan,88 Valentini Maliaka,2,89,90 Kersti Kangro,27,91 Hans-Peter Grossart,42,92 Hans W. Paerl,93 Cayelan C. Carey,94 and Bas W. Ibelings1

Publish date

2018 Apr;

PMID

21338505

Abstract

Background
As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible alternative.

Results
Here we used a genetic algorithm to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes, allowing to clearly situate the canonical code in the fitness landscape. This novel proposal of the use of evolutionary computing provides a new perspective in the open debate between the use of the statistical approach, which postulates that the genetic code conserves amino acid properties far better than expected from a random code, and the engineering approach, which tends to indicate that the canonical genetic code is still far from optimal. We used two models of hypothetical codes: one that reflects the known examples of codon reassignment and the model most used in the two approaches which reflects the current genetic code translation table. Although the standard code is far from a possible optimum considering both models, when the more realistic model of the codon reassignments was used, the evolutionary algorithm had more difficulty to overcome the efficiency of the canonical genetic code.

Conclusions
Simulated evolution clearly reveals that the canonical genetic code is far from optimal regarding its optimization. Nevertheless, the efficiency of the canonical code increases when mistranslations are taken into account with the two models, as indicated by the fact that the best possible codes show the patterns of the standard genetic code. Our results are in accordance with the postulates of the engineering approach and indicate that the main arguments of the statistical approach are not enough to its assertion of the extreme efficiency of the canonical genetic code.

Title

Simulated evolution applied to study the genetic code optimality using a model of codon reassignments

Author

Jose Santoscorresponding author1 and angel Monteagudo1

Publish date

2011;


Description :

Empty ...