Catalogue Number

BD-D0608

Analysis Method

HPLC,NMR,MS

Specification

HPLC≥95%

Storage

2-8°C

Molecular Weight

330.29

Appearance

White powder

Botanical Source

Belamcanda chinensis （L.）DC.

Structure Type

Flavonoids

Category

Standards;Natural Pytochemical;API

SMILES

COC1=C(C=CC(=C1)C2=COC3=C(C2=O)C(=C(C(=C3)O)OC)O)O

Synonyms

4H-1-Benzopyran-4-one,5,7-dihydroxy-3-(4-hydroxy-3-methoxyphenyl)-6-methoxy/Iristectorigenin B/5,7,4'-Trihydroxy-6,3'-dimethoxyisoflavone/5,7-dihydroxy-3-(4-hydroxy-3-methoxyphenyl)-6-methoxy-chromen-4-one/5,7-Dihydroxy-3-(4-hydroxy-3-methoxyphenyl)-6-methoxy-4H-1-benzopyran-4-one/iristectorigenin-A/Iristectorigenin A

IUPAC Name

5,7-dihydroxy-3-(4-hydroxy-3-methoxyphenyl)-6-methoxychromen-4-one

Density

1.483g/cm3

Solubility

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

Flash Point

232ºC

Boiling Point

619ºC at 760 mmHg

Melting Point

237-238 ºC

InChl

InChI=1S/C17H14O7/c1-22-12-5-8(3-4-10(12)18)9-7-24-13-6-11(19)17(23-2)16(21)14(13)15(9)20/h3-7,18-19,21H,1-2H3

InChl Key

CCRPIWFQMLICCY-UHFFFAOYSA-N

WGK Germany

RID/ADR

HS Code Reference

2914500000

Personal Projective Equipment

Correct Usage

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

Meta Tag

provides coniferyl ferulate(CAS#:39012-01-6) 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.

**PMID**

27966576

**Abstract**

In general, high-dimensional predictive modeling (i.e., y(s, t) = Rx(s, t) + ε) poses several challenges including

(1) Physics-based derivation of parameter matrix R: Traditional regression modeling estimates parameter matrix R based on the readily available data set of [x, y]. However, distributed sensing or imaging of spatiotemporal systems provides only the surface profiles y(s, t) such as BSPMs. It is often difficult to directly measure heart-surface potential mappings x(s, t). As such, inferring x(s, t) needs a better knowledge of parameter matrix R. Fortunately, physical laws define the mechanisms of electrical propagation from the heart to the body surface. This, in turn, enables the derivation of parameter matrix R using physics-based principles (i.e., divergence theorem, Green’s theorem).

(2) Ill-conditioned system: Linear systems involving high-dimensional data y(s, t) and x(s, t) are commonly ill-conditioned. This is partly caused by unobserved x(s, t), and partly due to the fact that parameter matrix R is rank deficient (i.e., rank(R) < min{dim(x),dim(y)}). The condition number of R (i.e., cond(R) = ||R||||R−1||) is also shown to be large in high-dimensional predictive modeling (e.g., inverse ECG problems7,8). Moreover, the derivation of R depends, to a great extent, on deterministic physics-based principles and the numerical analysis of complex geometries but does not account for real-world uncertainties. Such uncertainties may be introduced by simplified physical assumptions, geometric variations, measurement noises and other extraneous factors. As a result, high-dimensional prediction models cannot always match satisfactorily with data from real-world experiments. (3) Spatiotemporal regularization: Ill-conditioned systems make the prediction more sensitive to noise factors (e.g., ε) and approximation errors in parameter matrix R. For example, measurement noises can potentially cause a small change Δy in the observed data y(s, t). Considering the estimation of x changes to x + Δx, we will have the changes in the solution expressed as An external file that holds a picture, illustration, etc. Object name is srep39012-m1.jpg. Because of the large condition number cond(R), the pseudo-inverse solution of An external file that holds a picture, illustration, etc. Object name is srep39012-m2.jpg may be completely different. As such, there is an urgent need to develop new statistical approaches that leverage physics-based principles and observed data to account for uncertainties and tackle the ill-conditioned problems. Although x(s, t) and y(s, t) are spatially distributed and dynamically evolving over time, they have spatial and temporal correlations. Very little has been done to develop new spatial regularization methods that handle approximation errors through spatial correlations of dynamic profiles on the complex geometry (e.g., the heart surface), as well as new temporal regularization methods to increase model robustness to measurement noises and other uncertainty factors. This paper presents a new spatiotemporal regularization model to tackle these research challenges and address ill-condtioned problems in high-dimensional predictive modeling. Our contributions in the present investigation are as follows: (1) High-dimensional systems involve complex geometries, which challenge the derivation of parameter matrix R. We developed realistic models of torso-heart geometries, numerically discretized them with the boundary element method, and then utilized physical laws (i.e., divergence theorem and Green’s theorem) to derive the parameter matrix. (2) As physics-based models are deterministic and do not account for real-world uncertainties, we developed a physical-statistical approach that integrates physics-derived parameter matrix R with a spatiotemporal regularization (STRE) method to build the high-dimensional prediction model. This approach leverages data from actual experiments to improve spatial and temporal regularity of the solutions, thereby making the final prediction closer to reality. (3) The proposed STRE model involves quadratic programming and high-dimensional data, which cannot be solved analytically. Iterative algorithms are commonly used such as the multiplicative update method which, however, requires the nonnegative constraint of x(s, t). As such, they are not generally applicable because the electric field involves both positive and negative potentials. We developed a new method of dipole multiplicative update, which is inspired by the dipole assumption in electrodynamic physics. This new idea overcomes the drawbacks of existing multiplicative update methods, and provides a generalized approach to solve spatiotemporal regularization problems. (4) Few, if any, previous works focused on both spatial and temporal regularizations in inverse and forward ECG problems. We evaluated and validated the proposed STRE model in simulation as well as a real-world case study to map electric potentials from the body to the heart surface. Experimental results show that our method not only effectively tackles the ill-conditioned problems in high-dimensional predictive modeling, but also outperforms those regularization models widely used in current practice (i.e., Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods). This research work provides a new and effective approach to investigate disease-altered electric potentials from the body to the heart surface. The remainder of this paper is organized as follows: Section II introduces the research background. Section III presents our research methodology. Section IV describes the experimental design. This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods. Linear regression is a widely used approach for modeling the relationship between explanatory variables x’s and response variable y by the linear function, y = Rx + ε, in which R is a parameter matrix characterizing the model details. Linear regression has widespread applications in various fields such as engineering, healthcare, economics and social science, for predictive modeling, experimental design, or system optimization. Regression parameters are often estimated based on the static data set of explanatory and response variables. However, rapid advancement of distributed sensing and imaging technology brings the proliferation of high-dimensional spatiotemporal data, i.e., y = y(s, t) and x = x(s, t) in healthcare systems. Traditional regression is not generally applicable for predictive modeling in these complex structured systems. For example, Fig. 1 shows the distribution of electric potentials y(s, t) acquired by the ECG sensor network placed on the body surface, also named body surface potential mapping (BSPM)1,2. Medical scientists call for the estimation of electric potentials x(s, t) on the heart surface from BSPM y(s, t) so as to investigate cardiac pathological activities (e.g., tissue damages in the heart)3,4,5,6. However, spatiotemporally varying data and complex torso-heart geometries defy traditional regression modeling and regularization methods.Experimental results are shown in section V. Section VI concludes this paper.

**Title**

Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem

**Author**

Bing Yao1 and Hui Yanga,1

**Publish date**

2016

**PMID**

15716542

**Title**

Changes in motor cortex excitability during muscle fatigue in amyotrophic lateral sclerosis

**Author**

R Nardone, E Buffone, I Florio, and F Tezzon

**Publish date**

2005 Mar;

**PMID**

29151634

**Abstract**

Novel species of fungi described in this study include those from various countries as follows: Australia: Banksiophoma australiensis (incl. Banksiophoma gen. nov.) on Banksia coccinea, Davidiellomyces australiensis (incl. Davidiellomyces gen. nov.) on Cyperaceae, Didymocyrtis banksiae on Banksia sessilis var. cygnorum, Disculoides calophyllae on Corymbia calophylla, Harknessia banksiae on Banksia sessilis, Harknessia banksiae-repens on Banksia repens, Harknessia banksiigena on Banksia sessilis var. cygnorum, Harknessia communis on Podocarpus sp., Harknessia platyphyllae on Eucalyptus platyphylla, Myrtacremonium eucalypti (incl. Myrtacremonium gen. nov.) on Eucalyptus globulus, Myrtapenidiella balenae on Eucalyptus sp., Myrtapenidiella eucalyptigena on Eucalyptus sp., Myrtapenidiella pleurocarpae on Eucalyptus pleurocarpa, Paraconiothyrium hakeae on Hakea sp., Paraphaeosphaeria xanthorrhoeae on Xanthorrhoea sp., Parateratosphaeria stirlingiae on Stirlingia sp., Perthomyces podocarpi (incl. Perthomyces gen. nov.) on Podocarpus sp., Readeriella ellipsoidea on Eucalyptus sp., Rosellinia australiensis on Banksia grandis, Tiarosporella corymbiae on Corymbia calophylla, Verrucoconiothyrium eucalyptigenum on Eucalyptus sp., Zasmidium commune on Xanthorrhoea sp., and Zasmidium podocarpi on Podocarpus sp. Brazil: Cyathus aurantogriseocarpus on decaying wood, Perenniporia brasiliensis on decayed wood, Perenniporia paraguyanensis on decayed wood, and Pseudocercospora leandrae-fragilis on Leandra fragilis. Chile: Phialocephala cladophialophoroides on human toe nail. Costa Rica: Psathyrella striatoannulata from soil. Czech Republic: Myotisia cremea (incl. Myotisia gen. nov.) on bat droppings. Ecuador: Humidicutis dictiocephala from soil, Hygrocybe macrosiparia from soil, Hygrocybe sangayensis from soil, and Polycephalomyces onorei on stem of Etlingera sp. France: Westerdykella centenaria from soil. Hungary: Tuber magentipunctatum from soil. India: Ganoderma mizoramense on decaying wood, Hodophilus indicus from soil, Keratinophyton turgidum in soil, and Russula arunii on Pterigota alata. Italy: Rhodocybe matesina from soil. Malaysia: Apoharknessia eucalyptorum, Harknessia malayensis, Harknessia pellitae, and Peyronellaea eucalypti on Eucalyptus pellita, Lectera capsici on Capsicum annuum, and Wallrothiella gmelinae on Gmelina arborea. Morocco: Neocordana musigena on Musa sp. New Zealand: Candida rongomai-pounamu on agaric mushroom surface, Candida vespimorsuum on cup fungus surface, Cylindrocladiella vitis on Vitis vinifera, Foliocryphia eucalyptorum on Eucalyptus sp., Ramularia vacciniicola on Vaccinium sp., and Rhodotorula ngohengohe on bird feather surface. Poland: Tolypocladium fumosum on a caterpillar case of unidentified Lepidoptera. Russia: Pholiotina longistipitata among moss. Spain: Coprinopsis pseudomarcescibilis from soil, Eremiomyces innocentii from soil, Gyroporus pseudocyanescens in humus, Inocybe parvicystis in humus, and Penicillium parvofructum from soil. Unknown origin: Paraphoma rhaphiolepidis on Rhaphiolepsis indica. USA: Acidiella americana from wall of a cooling tower, Neodactylaria obpyriformis (incl. Neodactylaria gen. nov.) from human bronchoalveolar lavage, and Saksenaea loutrophoriformis from human eye. Vietnam: Phytophthora mekongensis from Citrus grandis, and Phytophthora prodigiosa from Citrus grandis. Morphological and culture characteristics along with DNA barcodes are provided.

**KEYWORDS**

ITS nrDNA barcodes, LSU, novel fungal species, systematics

**Title**

Fungal Planet description sheets: 558-624

**Author**

P.W. Crous, 1 M.J. Wingfield, 2 T.I. Burgess, 3 G.E.St.J. Hardy, 3 P.A. Barber, 4 P. Alvarado, 5 C.W. Barnes, 6 P.K. Buchanan, 7 M. Heykoop, 8 G. Moreno, 8 R. Thangavel, 9 S. van der Spuy, 10 A. Barili, 11 S. Barrett, 12 S.O. Cacciola, 13 J.F. Cano-Lira, 14 C. Crane, 15 C. Decock, 16 T.B. Gibertoni, 17 J. Guarro, 14 M. Guevara-Suarez, 14 V. Hubka, 18 M. Kolařik, 19 C.R.S. Lira, 17 M.E. OrdoNez, 11 M. Padamsee, 7 L. Ryvarden, 20 A.M. Soares, 17 A.M. Stchigel, 14 D.A. Sutton, 21 A. Vizzini, 22 B.S. Weir, 7 K. Acharya, 23 F. Aloi, 13 I.G. Baseia, 24 R.A. Blanchette, 25 J.J. Bordallo, 26 Z. Bratek, 27 T. Butler, 28 J. Cano-Canals, 29 J.R. Carlavilla, 8 J. Chander, 30 R. Cheewangkoon, 31 R.H.S.F. Cruz, 32 M. da Silva, 33 A.K. Dutta, 23 E. Ercole, 34 V. Escobio, 35 F. Esteve-Raventos, 8 J.A. Flores, 11 J. Gene, 14 J.S. Gois, 24 L. Haines, 28 B.W. Held, 25 M. Horta Jung, 36 K. Hosaka, 37 T. Jung, 36 Ž. Jurjević, 38 V. Kautman, 39 I. Kautmanova, 40 A.A. Kiyashko, 41 M. Kozanek, 42 A. Kubatova, 18 M. Lafourcade, 43 F. La Spada, 13 K.P.D. Latha, 44 H. Madrid, 45 E.F. Malysheva, 41 P. Manimohan, 44 J.L. Manjon, 8 M.P. Martin, 46 M. Mata, 47 Z. Merenyi, 27 A. Morte, 26 I. Nagy, 27 A.-C. Normand, 48 S. Paloi, 23 N. Pattison, 49 J. Pawłowska, 50 O.L. Pereira, 33 M.E. Petterson, 7 B. Picillo, 51 K.N.A. Raj, 44 A. Roberts, 52 A. Rodriguez, 26 F.J. Rodriguez-Campo, 53 M. Romański, 54 M. Ruszkiewicz-Michalska, 55 B. Scanu, 56 L. Schena, 57 M. Semelbauer, 58 R. Sharma, 59 Y.S. Shouche, 59 V. Silva, 60 M. Staniaszek-Kik, 61 J.B. Stielow, 1 C. Tapia, 62 P.W.J. Taylor, 63 M. Toome-Heller, 9 J.M.C. Vabeikhokhei, 64 A.D. van Diepeningen, 1 N. Van Hoa, 65 Van Tri M., 65 N.P. Wiederhold, 21 M. Wrzosek, 50 J. Zothanzama, 64 and J.Z. Groenewald 1

**Publish date**

2017 Jun;

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