Capillary electrophoresis-mass spectrometry characterisation of secondary metabolites from the antihyperglycaemic plant Genista tenera

Genista tenera is endemic to the Portuguese island of Madeira, where an infusion of the aerial parts of the plant is used in folk medicine as an antidiabetic agent. Consequently the medicinal properties of the secondary metabolites of this plant have been the subject of an ongoing study. A recently reported LC-MS method using a 100 min separation allowed identification of five flavonoid components in an extract of the aerial parts of this plant. In order to obtain additional information on the range and complexity of the plant’s secondary metabolite components a CE-MS method has been developed and applied for the analysis of an extract of G. tenera. Twenty-six different components are distinguished in an analysis time of only 10 min. Results demonstrate that CE-MS/MS rapidly generates data complementary to those obtainable by LC-MS/MS and is particularly suited to the analysis of plant metabolites where concentration is not limiting.BBSRC, University of York, Treaty of Windsor Anglo-Portuguese joint research programme, Thermo Electron, Analytical Chemistry Trust Fund, Royal Society of Chemistry Analytical Division, Engineering and Physical Sciences Research Council (EPSRC)info:eu-repo/semantics/publishedVersio

The InterPro protein families database: the classification resource after 15 years

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 201

Theorising terminology development: Frames from language acquisition and the philosophy of science

The manner in which our conceptualisation and practice of terminology development can be informed by processes of knowledge change in child language development and a paradigm shift in disciplines, has been relatively underexplored. As a result, insights into what appears to be fundamental processes of knowledge change have not been employed to reflect on terminology development, its dynamics, requirements and relationship to related fields. In this article, frames of knowledge change in child language development and the philosophy of science are used to examine terminology development as knowledge growth that is signalled lexico-semantically through a range of transformations: addition, deletion, redefinition and reorganisation. The analysis is shown to have implications for work procedures, expertise types, critique, and for the relationships between terminology development and translating

Heavy element production in a compact object merger observed by JWST

The mergers of binary compact objects such as neutron stars and black holes are of central interest to several areas of astrophysics, including as the progenitors of gamma-ray bursts (GRBs)1, sources of high-frequency gravitational waves (GW)2 and likely production sites for heavy element nucleosynthesis via rapid neutron capture (the r-process)3. Here we present observations of the exceptionally bright gamma-ray burst GRB 230307A. We show that GRB 230307A belongs to the class of long-duration gamma-ray bursts associated with compact object mergers4–6, and contains a kilonova similar to AT2017gfo, associated with the gravitational-wave merger GW1708177–12. We obtained James Webb Space Telescope mid-infrared (mid-IR) imaging and spectroscopy 29 and 61 days after the burst. The spectroscopy shows an emission line at 2.15 microns which we interpret as tellurium (atomic mass A=130), and a very red source, emitting most of its light in the mid-IR due to the production of lanthanides. These observations demonstrate that nucleosynthesis in GRBs can create r-process elements across a broad atomic mass range and play a central role in heavy element nucleosynthesis across the Universe

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine