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Colutehydroquinone

$628

  • Brand : BIOFRON

  • Catalogue Number : BD-P0337

  • Specification : 98.0%(HPLC)

  • CAS number : 181311-16-0

  • Formula : C18H20O6

  • Molecular Weight : 332.352

  • PUBCHEM ID : 101997792

  • Volume : 25mg

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Catalogue Number

BD-P0337

Analysis Method

HPLC,NMR,MS

Specification

98.0%(HPLC)

Storage

2-8°C

Molecular Weight

332.352

Appearance

Powder

Botanical Source

Structure Type

Flavonoids

Category

SMILES

COC1=CC2=C(CC(CO2)C3=CC(=C(C(=C3O)OC)OC)O)C=C1

Synonyms

2,3-dimethoxy-5-[(3R)-7-methoxy-3,4-dihydro-2H-chromen-3-yl]benzene-1,4-diol

IUPAC Name

2,3-dimethoxy-5-[(3R)-7-methoxy-3,4-dihydro-2H-chromen-3-yl]benzene-1,4-diol

Applications

Density

Solubility

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

Flash Point

Boiling Point

Melting Point

InChl

InChI=1S/C18H20O6/c1-21-12-5-4-10-6-11(9-24-15(10)7-12)13-8-14(19)17(22-2)18(23-3)16(13)20/h4-5,7-8,11,19-20H,6,9H2,1-3H3/t11-/m0/s1

InChl Key

RRVWIPPRKMQDAO-NSHDSACASA-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#:181311-16-0) 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

30800378

Abstract

Animal cognitive abilities are frequently quantified in strictly controlled settings, with laboratory-reared subjects. Results from these studies have merit for clarifying proximate mechanisms of performance and the potential upper limits of certain cognitive abilities. Researchers often assume that performance on laboratory-based assessments accurately represents the abilities of wild conspecifics, but this is infrequently tested. In this experiment, we quantified the performance of wild and captive corvid subjects on an extractive foraging task. We found that performance was not equivalent, and wild subjects were faster at problem-solving to extract the food reward. By contrast, there was no difference in the time it took for captive and wild solvers to repeat the behaviour to get additional food rewards (learning speed). Our findings differ from the few other studies that have statistically compared wild and captive performance on assessments of problem-solving and learning. This indicates that without explicitly testing it, we cannot assume that captive animal performance on experimental tasks can be generalized to the species as a whole. To better understand the causes and consequences of a variety of animal cognitive abilities, we should measure performance in the social and physical environment in which the ability in question evolved.

KEYWORDS

animal cognition, animal captivity, ecological validity, corvids

Title

Captive jays exhibit reduced problem-solving performance compared to wild conspecifics

Author

Kelsey B. McCune,1 Piotr Jablonski,2,3 Sang-im Lee,2,4 and Renee R. Ha1

Publish date

2019 Jan

PMID

29736139

Abstract

Three new species of the genus Plato from caves in the states of Para and Minas Gerais, Brazil, are described. P. novalima sp. n., from Minas Gerais, is the first record of the genus in the southeastern region of Brazil. P. ferriferus sp. n. and P. striatus sp. n., from Carajas, Para, north of Brazil, are also described. The former is an extremely abundant species, whereas the latter has only one known male specimen. Cuacuba gen. n. is proposed and represented by two new species, C. mariana sp. n. (type species) and C. morrodopilar sp. n., both from the state of Minas Gerais. Morphology of genitalia in Cuacuba gen. n. is similar to other Theridiosomatidae genera and is herein discussed. None of the proposed species presents troglomorphic adaptations. They are widespread, abundant inside caves in different and large karst areas, and each genus prefers different lithologies.

KEYWORDS

biospeleology, Neotropical region, taxonomy

Title

Three new species of the spider genus Plato and the new genus Cuacuba from caves of the states of Para and Minas Gerais, Brazil (Araneae, Theridiosomatidae)

Author

Pedro H. Prete,1 Igor Cizauskas,1 and Antonio D. Brescovit1

Publish date

2018;

PMID

24936414

Abstract

A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis — a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH). Certain imaging features based upon early computerized tomography (CT), in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH). The sample was split into training (n = 106) and test sets (n = 10), repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM), along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort). Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC), of the SVM (0.744) compared favourably with that of prognostic scores (original and adapted versions: 0.626-0.720; p < 0.01). The SVM also identified 9 out of 16 SICHs, as opposed to 1-5 using prognostic scores, assuming a 10% SICH frequency (p < 0.001). In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods.

KEYWORDS

Stroke, Thrombolysis, Prediction, Machine learning, Imaging

Title

Prediction of stroke thrombolysis outcome using CT brain machine learning

Author

Paul Bentley,⁎ Jeban Ganesalingam, Anoma Lalani Carlton Jones, Kate Mahady, Sarah Epton, Paul Rinne, Pankaj Sharma, Omid Halse, Amrish Mehta, and Daniel Rueckert

Publish date

2014;