The isolation and structural elucidation of voacristine hydroxyindolenine

Isolation and characterization of voacristine hydroxyindolenin

arrostii roots

A new acylated and triterpenoidal saponin, named GS1, was isolated from the roots of Gypsophila arrostii Guss. On the basis of acid hydrolysis, comprehensive spectroscopic analyses and comparison with spectral data of known compounds, its structure was established as 3-O--D-xylopyranosyl-(12)-[-D-xylopyranosyl-(13)]--D-glucopyranosyl-{21-O-[(E)-3,4,5trimethoxycinnamoyl]}21-hydroxygypsogenin 28-O--D-glucopyranosyl-(12)- [-D-arabinopyranosyl-(13)]--D-xylopyranosyl-(13]--L-rhamnopyranosyl ester. This article deals with the isolation and structural elucidation of new acylated and oleanane-type saponin

Optical Spectra of p-Doped PEDOT Nano-Aggregates Provide Insight into the Material Disorder

Highly doped Poly(3,4-ethylenedioxythiophene) or PEDOT is a conductive polymer with a wide range of applications in energy conversion due to its ease of processing, optical properties and high conductivity. The latter is influenced by processing conditions, including formulation, annealing, and solvent treatment of the polymer, which also affects the polymer arrangement. Here we show that the analysis of the optical spectra of PEDOT domains reveals the nature and magnitude of the structural disorder in the material. In particular, the optical spectra of objects on individual domains can be used for the elucidation of the molecular disorder in oligomer arrangement which is a key factor affecting the conductivity

Structure elucidation of polyheptazine imide by electron diffraction — a templated 2D carbon nitride networkw

Structure elucidation of a condensed carbon IV) nitride with a stoichiometry close to C3N4 by electron diffraction reveals a two-dimensional planar heptazine-based network containing isolated melamine molecules in the trigonal voids

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included

Strain prioritization and genome mining for enediyne natural products

The enediyne family of natural products has had a profound impact on modern chemistry, biology, and medicine, and yet only 11 enediynes have been structurally characterized to date. Here we report a genome survey of 3,400 actinomycetes, identifying 81 strains that harbor genes encoding the enediyne polyketide synthase cassettes that could be grouped into 28 distinct clades based on phylogenetic analysis. Genome sequencing of 31 representative strains confirmed that each clade harbors a distinct enediyne biosynthetic gene cluster. A genome neighborhood network allows prediction of new structural features and biosynthetic insights that could be exploited for enediyne discovery. We confirmed one clade as new C-1027 producers, with a significantly higher C-1027 titer than the original producer, and discovered a new family of enediyne natural products, the tiancimycins (TNMs), that exhibit potent cytotoxicity against a broad spectrum of cancer cell lines. Our results demonstrate the feasibility of rapid discovery of new enediynes from a large strain collection. IMPORTANCE Recent advances in microbial genomics clearly revealed that the biosynthetic potential of soil actinomycetes to produce enediynes is underappreciated. A great challenge is to develop innovative methods to discover new enediynes and produce them in sufficient quantities for chemical, biological, and clinical investigations. This work demonstrated the feasibility of rapid discovery of new enediynes from a large strain collection. The new C-1027 producers, with a significantly higher C-1027 titer than the original producer, will impact the practical supply of this important drug lead. The TNMs, with their extremely potent cytotoxicity against various cancer cells and their rapid and complete cancer cell killing characteristics, in comparison with the payloads used in FDA-approved antibody-drug conjugates (ADCs), are poised to be exploited as payload candidates for the next generation of anticancer ADCs. Follow-up studies on the other identified hits promise the discovery of new enediynes, radically expanding the chemical space for the enediyne family

Modal Considerations on Information Processing and Computation in the Nervous System

We can characterize computationalism very generally as a complex thesis with two main parts: the thesis that the brain (or the nervous system) is a computational system and the thesis that neural computation explains cognition. As Piccinini and Bahar (2012) point out, over the last six decades, computationalism has been the mainstream theory of cognition. Nevertheless, there is still substantial debate about which type of computation explains cognition, and computationalism itself still remains controversial. My aim in this paper is to make two main contributions to the debate about the first subthesis of computationalism, i.e. that the brain is a computational system. First, I want to offer an accurate elucidation of the notion relevant for understanding computationalism (the notion of computation) and clarify the relation between computation and information as well as the relations between both computation and information processing and the nervous system. Second, I want to argue against a peculiar form of computationalism: the thesis that neural processes are constitutively computational in some sense; that neural processes cannot be realized by a system that is not in some sense computational. I will call this thesis "modal computationalism". In particular, I want to argue that neural processing can be realized by a system that is not a sui generis computer (i. e., a computing system that is neither digital nor analog) and by a system that is not a generic computer (a computer in the most general sense: one that includes digital, analog, and any other kind of computation). Actual neural processing is presumed to be computational in these two senses (Piccinini and Bahar 2012). I will argue that, even if this is true, neural processing can be realized by a computing system that is not of the same kind as those that perform actual neural processing and even by a system that is not computational at all.Fil: Wajnerman Paz, Abel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentin