Catalogue Number
BN-O1177
Analysis Method
Specification
98%(HPLC)
Storage
2-8°C
Molecular Weight
132.16
Appearance
Botanical Source
Structure Type
Category
SMILES
C1=CC2=C(C=CN2)C=C1N
Synonyms
1H-indol-5-ylamine/5-aminoindazole/5-Indolamine/Indole,5-amino/Indol-5-ylamine/1H-Indol-5-amine/5-Aminoindole/5-amino-indole/5-amino-1H-indole/indole-5-ylamine
IUPAC Name
1H-indol-5-amine
Density
1.3±0.1 g/cm3
Solubility
Flash Point
195.0±7.6 °C
Boiling Point
354.0±15.0 °C at 760 mmHg
Melting Point
131-133 °C (dec.)(lit.)
InChl
InChl Key
ZCBIFHNDZBSCEP-UHFFFAOYSA-N
WGK Germany
RID/ADR
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#:5192-03-0) 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.
3182727
Valine dehydrogenase was purified to homogeneity from the crude extracts of Streptomyces aureofaciens. The molecular weight of the native enzyme was 116,000 by equilibrium ultracentrifugation and 118,000 by size exclusion high-performance liquid chromatography. The enzyme was composed of four subunits with molecular weights of 29,000. The isoelectric point was 5.1. The enzyme required NAD+ as a cofactor, which could not be replaced by NADP+. Sulfhydryl reagents inhibited the enzyme activity. The pH optimum was 10.7 for oxidative deamination of L-valine and 9.0 for reductive amination of alpha-ketoisovalerate. The Michaelis constants were 2.5 mM for L-valine and 0.10 mM for NAD+. For reductive amination the Km values were 1.25 mM for alpha-ketoisovalerate, 0.023 mM for NADH, and 18.2 mM for NH4Cl.
Isolation and characterization of valine dehydrogenase from Streptomyces aureofaciens.
I Vancurova, A Vancura, J Volc, J Neuzil, M Flieger, G Basarova, and V Bĕhal
1988 Nov;
32164533
Background
Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases motivates the development of interpretative and visual analysis methods, such as classification trees, which not only provide predictive models for diagnosing patients, but can also help to discover new clinical insights.
Results
In this paper, we analyzed healthy and chronic (diabetic, hypertensive) patients associated with the University Hospital of Fuenlabrada in Spain. Each patient was classified into a single health status according to clinical risk groups (CRGs). The CRGs characterize a patient through features such as age, gender, diagnosis codes, and drug codes. Based on these features and the CRGs, we have designed classification trees to determine the most discriminative decision features among different health statuses. In particular, we propose to make use of statistical data visualizations to guide the selection of features in each node when constructing a tree. We created several classification trees to distinguish among patients with different health statuses. We analyzed their performance in terms of classification accuracy, and drew clinical conclusions regarding the decision features considered in each tree. As expected, healthy patients and patients with a single chronic condition were better classified than patients with comorbidities. The constructed classification trees also show that the use of antipsychotics and the diagnosis of chronic airway obstruction are relevant for classifying patients with more than one chronic condition, in conjunction with the usual DM and/or EH diagnoses.
Conclusions
We propose a methodology for constructing classification trees in a visually guided manner. The approach allows clinicians to progressively select the decision features at each of the tree nodes. The process is guided by exploratory data analysis visualizations, which may provide new insights and unexpected clinical information.
Chronic health status, Diabetes, Hypertension, Multivariate visualization, Classification trees, Diagnoses, Drugs
Visually guided classification trees for analyzing chronic patients
Cristina Soguero-Ruiz,corresponding author#1 Inmaculada Mora-Jimenez,#1 Miguel A. Mohedano-Munoz,2 Manuel Rubio-Sanchez,#2 Pablo de Miguel-Bohoyo,3 and Alberto Sanchez#2,4
2020;
28706347
In this work, we demonstrate that a preferential Ga-for-Zn cation exchange is responsible for the increase in photoluminescence that is observed when gallium oleate is added to InZnP alloy QDs. By exposing InZnP QDs with varying Zn/In ratios to gallium oleate and monitoring their optical properties, composition, and size, we conclude that Ga3+ preferentially replaces Zn2+, leading to the formation of InZnP/InGaP core/graded-shell QDs. This cation exchange reaction results in a large increase of the QD photoluminescence, but only for InZnP QDs with Zn/In ≥ 0.5. For InP QDs that do not contain zinc, Ga is most likely incorporated only on the quantum dot surface, and a PL enhancement is not observed. After further growth of a GaP shell and a lattice-matched ZnSeS outer shell, the cation-exchanged InZnP/InGaP QDs continue to exhibit superior PL QY (over 70%) and stability under long-term illumination (840 h, 5 weeks) compared to InZnP cores with the same shells. These results provide important mechanistic insights into recent improvements in InP-based QDs for luminescent applications.
Ga for Zn Cation Exchange Allows for Highly Luminescent and Photostable InZnP-Based Quantum Dots
Francesca Pietra,†# Nicholas Kirkwood,†# Luca De Trizio,‡ Anne W. Hoekstra,† Lennart Kleibergen,† Nicolas Renaud,† Rolf Koole,∥ Patrick Baesjou,∥⊥ Liberato Manna,‡§ and Arjan J. Houtepen*†
2017 Jun 27
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