Betula mcallisteri sp. nov. (sect. Acuminatae, Betulaceae), a new diploid species overlooked in the wild and in cultivation, and its relation to the widespread B. luminifera

Taxa are traditionally identified using morphological proxies for groups of evolutionarily isolated populations. These proxies are common characters deemed by taxonomists as significant. However, there is no general rule on which character or sets of characters are appropriate to circumscribe taxa, leading to discussions and uncertainty. Birch species are notoriously hard to identify due to strong morphological variability and factors such as hybridization and the existence of several ploidy levels. Here, we present evidence for an evolutionarily isolated line of birches from China that are not distinguishable by traditionally assumed taxon recognition proxies, such as fruit or leaf characters. We have discovered that some wild material in China and some cultivated in the Royal Botanic Gardens Edinburgh, formerly recognized as Betula luminifera, differ from other individuals by having a peeling bark and a lack of cambial fragrance. We use restriction site-associated DNA sequencing and flow cytometry to study the evolutionary status of the unidentified Betula samples to assess the extent of hybridization between the unidentified Betula samples and typical B. luminifera in natural populations. Molecular analyses show the unidentified Betula samples as a distinct lineage and reveal very little genetic admixture between the unidentified samples and B. luminifera. This may also be facilitated by the finding that B. luminifera is tetraploid, while the unidentified samples turned out to be diploid. We therefore conclude that the samples represent a yet unrecognized species, which is here described as Betula mcallisteri.Copyright © 2023 Zhang, Ding, Holstein and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. The attached file is the published version of the article.NHM Repositor

The gut–liver axis in immune remodeling of hepatic cirrhosis

In healthy settings, the gut–liver axis allows host–microbiota communications and mediates immune homeostasis through bidirectional regulation. Meanwhile, in diseases, gut dysbiosis, combined with an impaired intestinal barrier, introduces pathogens and their toxic metabolites into the system, causing massive immune alternations in the liver and other extrahepatic organs. Accumulating evidence suggests that these immune changes are associated with the progression of many liver diseases, especially hepatic cirrhosis. Pathogen-associated molecular patterns that originated from gut microbes directly stimulate hepatocytes and liver immune cells through different pattern recognition receptors, a process further facilitated by damage-associated molecular patterns released from injured hepatocytes. Hepatic stellate cells, along with other immune cells, contribute to this proinflammatory and profibrogenic transformation. Moreover, cirrhosis-associated immune dysfunction, an imbalanced immune status characterized by systemic inflammation and immune deficiency, is linked to gut dysbiosis. Though the systemic inflammation hypothesis starts to link gut dysbiosis to decompensated cirrhosis from a clinical perspective, a clearer demonstration is still needed for the role of the gut–liver–immune axis in cirrhosis progression. This review discusses the different immune states of the gut–liver axis in both healthy and cirrhotic settings and, more importantly, summarizes the current evidence about how microbiota-derived immune remodeling contributes to the progression of hepatic cirrhosis via the gut–liver axis

Preparation, Characterization, and Application of Magnetic Fe-SBA-15 Mesoporous Silica Molecular Sieves

Magnetic Fe-SBA-15 mesoporous silica molecular sieves were prepared, characterized, and used for magnetic separation. Wet impregnation, drying, and calcination steps led to iron inclusion within the mesopores. Iron oxide was reduced to the metal form with hydrogen, and the magnetic Fe-SBA-15 was obtained. Fourier-transform infrared spectroscopy confirmed the preparation process from the oxide to metal forms. The structure of magnetic materials was confirmed by Mössbauer spectra. Powder X-ray diffraction data indicated that the structure of Fe-SBA-15 retained the host SBA-15 structure. Brunauer-Emmett-Teller analysis revealed a decrease in surface area and pore size, indicating Fe-SBA-15 coating on the inner surfaces. Scanning electron micrographs confirmed the decrease in size for modified SBA-15 particles. From scanning electron micrographs, it was found that the size of the modified SBA-15 particles decreased. Transmission electron micrographs also confirmed that modified SBA-15 retained the structure of the parent SBA-15 silica. Fe-SBA-15 exhibited strong magnetic properties, with a magnetization value of 8.8 emu g−1. The iron content in Fe-SBA-15 was determined by atom adsorption spectroscopy. Fe-SBA-15 was successfully used for the magnetic separation of three aromatic compounds in water. Our results suggest wide applicability of Fe-SBA-15 magnetic materials for the rapid and efficient separation of various compounds

Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision

The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, and (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a case study, we show that the Text-to-UMLS process can be greatly improved with weak supervision, without any annotated data from domain experts. Our analysis shows that the overall pipeline processing discharge summaries can surface rare disease cases, which are mostly uncaptured in manual ICD codes of the hospital admissions.Comment: 5 pages, 3 figures, accepted for IEEE EMBC 202

Structure, expression differentiation and evolution of duplicated fiber developmental genes in Gossypium barbadense and G. hirsutum

<p>Abstract</p> <p>Background</p> <p>Both <it>Gossypium hirsutum </it>and <it>G. barbadense </it>probably originated from a common ancestor, but they have very different agronomic and fiber quality characters. Here we selected 17 fiber development-related genes to study their structures, tree topologies, chromosomal location and expression patterns to better understand the interspecific divergence of fiber development genes in the two cultivated tetraploid species.</p> <p>Results</p> <p>The sequence and structure of 70.59% genes were conserved with the same exon length and numbers in different species, while 29.41% genes showed diversity. There were 15 genes showing independent evolution between the A- and D-subgenomes after polyploid formation, while two evolved via different degrees of colonization. Chromosomal location showed that 22 duplicate genes were located in which at least one fiber quality QTL was detected. The molecular evolutionary rates suggested that the D-subgenome of the allotetraploid underwent rapid evolutionary differentiation, and selection had acted at the tetraploid level. Expression profiles at fiber initiation and early elongation showed that the transcripts levels of most genes were higher in Hai7124 than in TM-1. During the primary-secondary transition period, expression of most genes peaked earlier in TM-1 than in Hai7124. Homeolog expression profile showed that A-subgenome, or the combination of A- and D-subgenomes, played critical roles in fiber quality divergence of <it>G. hirsutum </it>and <it>G. barbadense</it>. However, the expression of D-subgenome alone also played an important role.</p> <p>Conclusion</p> <p>Integrating analysis of the structure and expression to fiber development genes, suggests selective breeding for certain desirable fiber qualities played an important role in divergence of <it>G. hirsutum </it>and <it>G. barbadense</it>.</p

Neogene global climate change and East Asian dust sources: Combined rutile geochemistry and zircon U-Pb analysis from the northern Chinese Loess Plateau

Peer reviewe

FLAP: A framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database

Introduction: Linking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set.Methods: In this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s (n = 771,588) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB).Results: We achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled (n = 3,876) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top n candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement.Discussion: In conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task