Understanding the structure-emulsification relationship of gum ghatti – a review of recent advances

This paper is based on a series of physical and chemical investigations to understand the structure-function properties of gum ghatti. After elucidating the detailed molecular structure of two gum ghatti fractions, the structure of its component glycoprotein was investigated whereby, the protein sequence and hydrophobicity were identified, followed by the conformational analysis of the gum and its fractions. Many techniques were used for the elucidation of the fine structures, which included methylation analysis-GC-MS, Maldi-TOF MS and 2D NMR spectroscopy, homonuclear ¹H/¹H correlations spectroscopy (COSY, TOCSY), heteronuclear ¹³C/¹H multiple-quantum coherence spectroscopy (HMQC) and heteronuclear multiple bond correlation (HMBC). Conformational properties were studied using a modelling system (Insight II) to relate the hydrophobicity of the protein moieties with the complex structures of the carbohydrates. These studies now provide an explanation for the excellent emulsification properties of gum ghatti in oil-in-water emulsions, which enable its application in the food, cosmetic and/or pharmaceutical industries

Structural characterization of an α-1, 6-linked galactomannan from natural Cordyceps 2 sinensis

An α-1, 6-linked galactomannan was isolated and purified from natural Cordyceps sinensis. The fine structure analysis of this polysaccharide was elucidated based on partial acid hydrolysis, monosaccharide composition, methylation and 1D/2D nuclear magnetic resonance (NMR) spectroscopy. Monosaccharide composition analysis revealed that this polysaccharide was mainly composed of galactose (68.65%), glucose (6.65%) and mannose (24.02%). However, after partial acid hydrolysis the percentages of galactose, glucose and mannose were changed to 3.96%, 13.82% and 82.22%, respectively. The molecular weight of this polysaccharide was 7207. Methylation and NMR analysis revealed that this galactomannan had a highly branched structure, mainly consisted of a mannan skeleton and galactofuranosyl chains. The structure of galactofuranosyl part was formed by alternating (1 → 5)-lined β-Galf and (1 → 6)-liked β-Galf or a single (1 → 6)-liked β-Galf, attaching to the O-2 and O-4 of the mannose chain, and terminated at β-T-Galf. The mannan core was revealed by analyzing the partial acid hydrolysate of the galactomannan and the structure was composed of (1 → 6)-linked α-Manp backbone, with substituted at C-2 by short chains of 2-substituted Manp or Galf branches

Cordyceps Sinensis: anti-fibrotic and inflammatory effects of a cultured polysaccharide extract

It has been suggested that the traditional Chinese herbal preparation Cordyceps Sinensis (CS) may have a beneficial effect in renal disease. To satisfy increasing demand, CS derivatives have been produced by aseptic mycelia cultivation. We have demonstrated antifibrotic activity of cultured CS previously. The aim of this study was to examine bioactivity of a polysaccharide isolated from cultured CS with a complicated monosaccharide composition, mainly consisting of Gal, Glc and Man. This polysaccharide antagonised the effect of TGF-b1 in stimulating the expression of collagen in the HK2 renal cell line. This was associated with down regulation of the TGF-b receptor Alk5. In addition the polysaccharide antagonised IL-1b stimulated sICAM-1 dependent adherence of monocytes to a monolayer of HK2 cell. This was associated with increased expression of the primary receptor for hyaluronan CD44, and was abrogated by removal of the cell surface hyaluronan pericellular coat. In summary we describe both anti-fibrotic and anti-inflammatory activity in a polysaccharide isolated from cultured CS

Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites

Peer reviewedPublisher PD

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae

Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The TP309 cultivar is susceptible to infection by Xoo strain PXO99. A transgenic variety, TP309_Xa21, expresses the pattern recognition receptor Xa21, and is resistant. PXO99△raxST, a strain lacking the raxST gene, is able to overcome Xa21-mediated immunity. We used a single extraction solvent to demonstrate comprehensive metabolomics and transcriptomics profiling under sample limited conditions, and analyze the molecular responses of two rice lines challenged with either PXO99 or PXO99△raxST. LC–TOF raw data file filtering resulted in better within group reproducibility of replicate samples for statistical analyses. Accurate mass match compound identification with molecular formula generation (MFG) ranking of 355 masses was achieved with the METLIN database. GC–TOF analysis yielded an additional 441 compounds after BinBase database processing, of which 154 were structurally identified by retention index/MS library matching. Multivariate statistics revealed that the susceptible and resistant genotypes possess distinct profiles. Although few mRNA and metabolite differences were detected in PXO99 challenged TP309 compared to mock, many differential changes occurred in the Xa21-mediated response to PXO99 and PXO99△raxST. Acetophenone, xanthophylls, fatty acids, alkaloids, glutathione, carbohydrate and lipid biosynthetic pathways were affected. Significant transcriptional induction of several pathogenesis related genes in Xa21 challenged strains, as well as differential changes to GAD, PAL, ICL1 and Glutathione-S-transferase transcripts indicated limited correlation with metabolite changes under single time point global profiling conditions

Genetics of rheumatoid arthritis contributes to biology and drug discovery

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological datasets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here, we performed a genome-wide association study (GWAS) meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single nucleotide polymorphisms (SNPs). We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 1012–4. We devised an in-silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci (cis-eQTL)6, and pathway analyses7–9 – as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations and knock-out mouse phenotypes – to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks