Catalogue Number
BN-O1484
Analysis Method
HPLC,NMR,MS
Specification
98%(HPLC)
Storage
-20℃
Molecular Weight
286.3
Appearance
Powder
Botanical Source
This product is isolated and purified from the herbs of Scilla scilloides
Structure Type
Flavonoids
Category
Standards;Natural Pytochemical;API
SMILES
C1C(C(=O)C2=C(C=C(C=C2O1)O)O)CC3=CC=C(C=C3)O
Synonyms
4'-demethyl-5-O-methyl-dihydroeucomin/iristectorigenin-A/Iristectorigenin B/4'-demethyl-3,9-dihydropunctatin/4H-1-Benzopyran-4-one, 2,3-dihydro-5,7-dihydroxy-3-[(4-hydroxyphenyl)methyl]-/5,7-Dihydroxy-3-(4-hydroxybenzyl)-2,3-dihydro-4H-chromen-4-one/4,4'-demethyl-3,9-dihydropuctatin/5,7,4'-Trihydroxy-6,3'-dimethoxyisoflavone
IUPAC Name
5,7-dihydroxy-3-[(4-hydroxyphenyl)methyl]-2,3-dihydrochromen-4-one
Density
1.5±0.1 g/cm3
Solubility
Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.
Flash Point
220.0±20.6 °C
Boiling Point
574.8±39.0 °C at 760 mmHg
Melting Point
InChl
InChl Key
FIASLUPJXGTCKM-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#:107585-77-3) 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.
31534300
Establishing balanced nutrient requirements for maize (Zea mays L.) in the Northern Nigerian Savanna is paramount to develop site-specific fertilizer recommendations to increase maize yield, profits of farmers and avoid negative environmental impacts of fertilizer use. The model QUEFTS (QUantitative Evaluation of Fertility of Tropical Soils) was used to estimate balanced nitrogen (N), phosphorus (P) and potassium (K) requirements for maize production in the Northern Nigerian Savanna. Data from on-farm nutrient omission trials conducted in 2015 and 2016 rainy seasons in two agro-ecological zones in the Northern Nigerian Savanna (i.e. Northern Guinea Savanna “NGS” and Sudan Savanna “SS”) were used to parameterize and validate the QUEFTS model. The relations between indigenous soil N, P, and K supply and soil properties were not well described with the QUEFTS default equations and consequently new and better fitting equations were derived. The parameters of maximum accumulation (a) and dilution (d) in kg grain per kg nutrient for the QUEFTS model obtained were respectively 35 and 79 for N, 200 and 527 for P and 25 and 117 for K in the NGS zone; 32 and 79 for N, 164 and 528 for P and 24 and 136 for K in the SS zone; and 35 and 79 for N, 199 and 528 for P and 24 and 124 for K when the data of the two zones were combined. There was a close agreement between observed and parameterized QUEFTS predicted yields in each of the agro-ecological zone (R2 = 0.69 for the NGS and 0.75 for the SS). Although with a slight reduction in the prediction power, a good fit between the observed and model predicted grain yield was also detected when the data for the two agro-ecological zones were combined (R2 = 0.67). Therefore, across the two agro-ecological zones, the model predicted a linear relationship between grain yield and above-ground nutrient uptake until yield reached about 50 to 60% of the yield potential. When the yield target reached 60% of the potential yield (i.e. 6.0 t ha−1), the model showed above-ground balanced nutrient uptake of 20.7, 3.4 and 27.1 kg N, P, and K, respectively, per one tonne of maize grain. These results suggest an average NPK ratio in the plant dry matter of about 6.1:1:7.9. We concluded that the QUEFTS model can be widely used for balanced nutrient requirement estimations and development of site-specific fertilizer recommendations for maize intensification in the Northern Nigerian Savanna.
Site-specific fertilizer recommendations, Indigenous nutrient supply, Soil fertility variability, QUEFTS model, Zea mays L.
Balanced nutrient requirements for maize in the Northern Nigerian Savanna: Parameterization and validation of QUEFTS model
Bello M. Shehu, Bassam A. Lawan, Jibrin M. Jibrin, Alpha Y. Kamara, Ibrahim B. Mohammed, Jairos Rurinda, Shamie Zingore, Peter Craufurd, Bernard Vanlauwe, Adam M. Adam, Roel Merckx
2019 Sep 1
26425035
The regional incidence rates of out-of-hospital cardiac arrest (OHCA) were traditionally calculated with the residential population as the denominator. The aim of this study was to estimate the true incidence rate of OHCA and to investigate characteristics of regions with overestimated and underestimated OHCA incidence rates. We used the national OHCA database from 2006 to 2010. The nighttime residential and daytime transient populations were investigated from the 2010 Census. The daytime population was calculated by adding the daytime influx of population to, and subtracting the daytime outflow from, the nighttime residential population. Conventional age-standardized incidence rates (CASRs) and daytime corrected age-standardized incidence rates (DASRs) for OHCA per 100,000 person-years were calculated in each county. A total of 97,291 OHCAs were eligible. The age-standardized incidence rates of OHCAs per 100,000 person-years were 34.6 (95% CI: 34.3-35.0) in the daytime and 24.8 (95% CI: 24.5-25.1) in the nighttime among males, and 14.9 (95% CI: 14.7-15.1) in the daytime, and 10.4 (95% CI: 10.2-10.6) in the nighttime among females. The difference between the CASR and DASR ranged from 35.4 to -11.6 in males and from 6.1 to -1.0 in females. Through the Bland-Altman plot analysis, we found the difference between the CASR and DASR increased as the average CASR and DASR increased as well as with the larger daytime transient population. The conventional incidence rate was overestimated in counties with many OHCA cases and in metropolitan cities with large daytime population influx and nighttime outflow, while it was underestimated in residential counties around metropolitan cities.
Out-of-Hospital Cardiac Arrest, Incidence, Epidemiology
Presumed Regional Incidence Rate of Out-of-Hospital Cardiac Arrest in Korea
Young Sun Ro, Seung-sik Hwang, Sang Do Shin, Daikwon Han, Sungchan Kang, Kyoung Jun Song, Sung-il Cho
2015 Oct;
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