Multiplatform Approach Research Articles

Metabolite profiling and identification of novel umami compounds in the chaya leaves of two species using multiplatform metabolomics

Chaya (Cnidoscolus chayamansa and C. aconitifolius) is a fast-growing medicinal plant, and its leaves exhibit a strong umami taste. Here metabolite variation and umami-related compounds in the leaves of two chaya species were determined using a multiplatform untargeted-metabolomics approach, electronic tongue, and in silico screening. Metabolite profiles varied between the leaves of the two species and among leaf maturation stages. Young leaves exhibited the highest umami taste intensity, followed by mature and old leaves. Partial least square regression and computational molecular docking analyses revealed five potent umami substances (quinic acid, trigonelline, alanyl–tyrosine, leucyl–glycyl–proline, and leucyl–aspartyl–glutamine) and three known umami compounds (l-glutamic acid, pyroglutamic acid, and 5′-adenosine monophosphate). The five substances were validated as novel umami compounds using electronic tongue assay; leucyl–glycyl–proline exhibited synergism with monosodium glutamate, thereby enhancing the umami taste. Thus, substances contributing to the taste of chaya leaves were successfully identified.

Design and validation of a 63K genome-wide SNP-genotyping platform for caribou/reindeer (Rangifer tarandus)

BackgroundDevelopment of large single nucleotide polymorphism (SNP) arrays can make genomic data promptly available for conservation problematic. Medium and high-density panels can be designed with sufficient coverage to offer a genome-wide perspective and the generated genotypes can be used to assess different genetic metrics related to population structure, relatedness, or inbreeding. SNP genotyping could also permit sexing samples with unknown associated metadata as it is often the case when using non-invasive sampling methods favored for endangered species. Genome sequencing of wild species provides the necessary information to design such SNP arrays. We report here the development of a SNP-array for endangered Rangifer tarandus using a multi-platform sequencing approach from animals found in diverse populations representing the entire circumpolar distribution of the species.ResultsFrom a very large comprehensive catalog of SNPs detected over the entire sample set (N = 894), a total of 63,336 SNPs were selected. SNP selection accounted for SNPs evenly distributed across the entire genome (~ every 50Kb) with known minor alleles across populations world-wide. In addition, a subset of SNPs was selected to represent rare and local alleles found in Eastern Canada which could be used for ecotype and population assignments - information urgently needed for conservation planning. In addition, heterozygosity from SNPs located in the X-chromosome and genotyping call-rate of SNPs located into the SRY gene of the Y-chromosome yielded an accurate and robust sexing assessment. All SNPs were validated using a high-throughput SNP-genotyping chip.ConclusionThis design is now integrated into the first genome-wide commercially available genotyping platform for Rangifer tarandus. This platform would pave the way to future genomic investigation of populations for this endangered species, including estimation of genetic diversity parameters, population assignments, as well as animal sexing from genetic SNP data for non-invasive samples.

Multi-platform study of the extreme bloom of the barrel jellyfish Rhizostoma pulmo (Cnidaria: Scyphozoa) in the northernmost gulf of the Mediterranean Sea (Gulf of Trieste) in April 2021

Abstract. On 7 April 2021, an exceptional bloom of the scyphomedusa Rhizostoma pulmo was observed in the Gulf of Trieste (Italy). Blooms of this species in the northern Adriatic Sea have been reported since the late 1800s: the density of jellyfish observed in 2021 reached more than 10 specimens per square metre. We analyse the bloom from a multi-platform approach using observations and model data at different timescales. We attempt to explain the intensity of the bloom as a consequence of thermohaline and hydrodynamical conditions in the gulf. Meteo-oceanographic conditions that may have contributed to the exceptional aggregation of jellyfish observed along the northernmost coast of the Adriatic Sea are discussed in detail. Specifically, our results indicate that this bloom was enabled by (1) the presence of a high number of jellyfish in the gulf, probably linked to the anomalously warm sea conditions in spring 2020 and winter 2021, which may have favoured a longer reproductive period and enhanced survival of adult R. pulmo, respectively; and (2) strong wind events, such as the bora wind for the Gulf of Trieste, which enhanced upwelling and mixing processes in the gulf, bringing the jellyfish from the deeper waters to the surface and clustering them along the coast.

Enhancing the accuracy of surgical wound excision following burns trauma via application of Rapid Evaporative IonisationMass Spectrometry (REIMS)

BackgroundSurgical wound excision is a necessary procedure for burn patients that require the removal of eschar. The extent of excision is currently guided by clinical judgement, with excessinto healthy tissue potentially leading to excessive scar, or inadequate debridement increasing risk of infection. Thus, an objective real-time measure to facilitate accurate excision could support clinical judgement and improve this surgical procedure. This study was designed to investigate the potential use of Rapid evaporative ionisation mass spectrometry (REIMS) as a tool to support data-driven objective tissue excision. MethodsData were acquired using a multi-platform approach that consisted of both Rapid Evaporative Ionisation Mass Spectrometry (REIMS) performed on intact skin, and comprehensive liquid chromatography-mass spectrometry (LC-MS/MS) lipidomics performed on homogenised skin tissue extracts. Data were analysed using principal components analysis (PCA) and multivariate orthogonal projections to latent squares discriminant analysis (OPLS-DA) and logistic regression to determine the predictability of the models. ResultsPCA and OPLS-DA models of the REIMS and LC-MS/MS lipidomics data reported separation of excised and healthy tissue. Molecular fingerprints generated from REIMS analysis of healthy skin tissue revealed a high degree of heterogeneity, however, intra-individual variance was smaller than inter-individual variance. Both platforms indicated high levels of skin classification accuracy. In addition, OPLS-DA of the LC-MS/MS lipidomic data revealed significant differences in specific lipid classes between healthy control and excised skin samples; including lower free fatty acids (FFA), monoacylglycerols (MAG), lysophosphatidylglycerol (LPG) and lysophosphatidylethanolamines (LPE) in excised tissue and higher lactosylceramides (LCER) and cholesterol esters (CE) compared to healthy control tissue. ConclusionsHaving established the heterogeneity in the biochemical composition of healthy skin using REIMS and LC-MS/MS, our data show that REIMS has the potential to distinguish between excied and healthy skin tissue samples. This pilot study suggests that REIMS may be an effective tool to support accurate tissue excision during burn surgery.

Cross-platform Tech University Courses Teaching in a Heterogeneous Data Environment

After analyzing the pros and cons of existing online education platforms, we find that the disaggregation between multiple supplementary education platforms has many inconveniences for technical course teaching. We propose and design a multiway collaboration model in a heterogeneous data environment to make the multi-platform teaching approach more convenient. This model helps to address the limitations of the multi-platform teaching and learning approach.

Nuevos formatos del vídeo electoral en redes. La estrategia multiplataforma de los partidos españoles en las elecciones de 2019

Tras décadas de influencia televisiva, la imagen y el lenguaje audiovisual se han convertido en elementos centrales de la comunicación política. La irrupción de las redes sociales ha dinamizado todavía más el uso estratégico del vídeo, al permitir una difusión autónoma de contenidos y una aproximación más cercana a los ciudadanos. Nuestro objetivo es analizar la gestión del vídeo en redes como herramienta electoral desde un enfoque multiplataforma novedoso. A partir de un modelo propio de análisis de contenido, se revisa formato y nivel de espectacularización de los vídeos, y se comparan estrategias partidistas. La muestra la componen 2566 vídeos de campaña publicados en las cuentas de Twitter, Instagram, Facebook y YouTube de los cinco partidos estatales más votados en las elecciones generales de noviembre de 2019: PSOE, PP, VOX, Podemos y Ciudadanos. Los resultados muestran la aparición de nuevos formatos como el spot digital y una difusión estratégica diferenciada por plataformas. La espectacularización se manifiesta, sobre todo, en la apuesta por la personalización sobre el líder, al que se le asignan atributos de carácter profesional.

Genome sequence of the aurodox-producing bacterium Streptomyces goldiniensis ATCC 21386.

We report the genome sequence of Streptomyces goldiniensis ATCC 21386, a strain which produces the anti-bacterial and anti-virulence polyketide, aurodox. The genome of S. goldiniensis ATCC 21386 was sequenced using a multiplatform hybrid approach, revealing a linear genome of ~10 Mbp with a G+C content of 71%. The genome sequence revealed 36 putative biosynthetic gene clusters (BGCs), including a large region of 271 Kbp that was rich in biosynthetic capability. The genome sequence is deposited in DDBJ/EMBL/GenBank with the accession number PRJNA602141.

Atomic data and opacity calculations in La v–x ions for the investigation of kilonova emission spectra

ABSTRACT A new set of radiative parameters for spectral lines in La v–x ions is reported in this paper. These data were determined through the use of a multiplatform approach involving three independent theoretical methods, i.e. the relativistic Hartree–Fock method including core-polarization corrections (HFR + CPOL), the multiconfiguration Dirac–Hartree–Fock (MCDHF) method, and the particle-hole configuration-interaction (PH-CI) method implemented in the AMBiT program. Based on cross-comparisons between the results obtained with these three methods, and from comparisons with the few previously published experimental and theoretical data, the most complete and reliable set of wavelengths, transition probabilities, and oscillator strengths was then used to determine the necessary opacities for the analysis of the spectra emitted in the early phases of kilonovae following neutron star mergers, i.e. for typical conditions corresponding to temperatures $T \gt 20\,000 $ K, a density ρ = 10−10 g cm−3, and a time after the merger t = 0.1 d.

Multiplatform Metabolomics Studies of Human Cancers With NMR and Mass Spectrometry Imaging.

The status of metabolomics as a scientific branch has evolved from proof-of-concept to applications in science, particularly in medical research. To comprehensively evaluate disease metabolomics, multiplatform approaches of NMR combining with mass spectrometry (MS) have been investigated and reported. This mixed-methods approach allows for the exploitation of each individual technique’s unique advantages to maximize results. In this article, we present our findings from combined NMR and MS imaging (MSI) analysis of human lung and prostate cancers. We further provide critical discussions of the current status of NMR and MS combined human prostate and lung cancer metabolomics studies to emphasize the enhanced metabolomics ability of the multiplatform approach.

Australian regional journalists’ role perceptions at a time of upheaval

Almost 200 journalism outlets closed, decreased their service, ended print editions, or merged with other newsrooms between January 2019 and Februrary 2021, accelerated by the COVID-19 crisis. Regional news outlets were among the hardest hit, intensifying calls for effective government policy and industry interventions to bolster the sector. Although there is a well-established tradition of local and regional journalistic role performance scholarship, few studies in an Australian context have taken a multiplatform approach, investigating the perceptions of TV, radio, online news, and print journalists. To help address this gap, this paper draws on survey data of 307 regional Australian journalists and 31 in-depth interviews. Our analysis reveals differences in the way regional reporters understand and narrate their roles, highlighting important distinctions between media platforms that need to be considered in contemporary debates about the sustainability of Australian regional journalism.

Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

Capturing and identifying field-based agricultural activities, such as the start, duration and end of irrigation, together with crop sowing/germination, growing period and time of harvest, offer informative metrics that can assist in precision agricultural activities in addition to broader water and food security monitoring efforts. While optically based band-ratios, such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), have been used as descriptors for monitoring crop dynamics, data are not always available due to the influence of clouds and other atmospheric effects on optical sensors. Satellite-based microwave systems, such as the synthetic aperture radar (SAR), offer an all-weather advantage in monitoring soil and crop conditions. In this paper, we leverage the relative strengths of both optical- and microwave-based approaches by combining high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. A microwave backscatter model was used to analyze the responses of simulated backscatters to soil moisture, NDVI and NDWI (both are correlated with vegetation water content and can be regarded as vegetation descriptors), allowing an empirical relationship between these two platforms. A correlation analysis was also performed using Sentinel-1 SAR and Sentinel-2 optical data over crops of maize, alfalfa, carrot and Rhodes grass in Al Kharj farm of Saudi Arabia to identify an appropriate SAR-based vegetation descriptor. The results illustrate the relationship between SAR and both NDVI and NDWI and demonstrated the relationship between the cross-polarization ratio (VH/VV) and the two optical indices. We explore the capacity of this multi-platform and multi-sensor approach to inform on the spatio-temporal dynamics of a range of agricultural activities, which can be used to facilitate field-based management decisions.

An Outdoor Aging Study to Investigate the Release of Per- And Polyfluoroalkyl Substances (PFAS) from Functional Textiles.

The emission of per- and polyfluoroalkyl substances (PFAS) from functional textiles was investigated via an outdoor weathering experiment in Sydney, Australia. Polyamide (PA) textile fabrics treated with different water-repellent, side-chain fluorinated polymers (SFPs) were exposed on a rooftop to multiple natural stressors, including direct sunlight, precipitation, wind, and heat for 6-months. After weathering, additional stress was applied to the fabrics through abrasion and washing. Textile characterization using a multiplatform analytical approach revealed loss of both PFAS-containing textile fragments (e.g., microfibers) as well as formation and loss of low molecular weight PFAS, both of which occurred throughout weathering. These changes were accompanied by a loss of color and water repellency of the textile. The potential formation of perfluoroalkyl acids (PFAAs) from mobile residuals was quantified by oxidative conversion of extracts from unweathered textiles. Each SFP-textile finish emitted a distinct PFAA pattern following weathering, and in some cases the concentrations exceeded regulatory limits for textiles. In addition to transformation of residual low molecular weight PFAA-precursors, release of polymeric PFAS from degradation and loss of textile fibers/particles contributed to overall PFAS emissions during weathering.

Skotomorphogenesis exploits threonine to promote hypocotyl elongation.

Mobilisation of seed storage reserves is important for seedling establishment in Arabidopsis. In this process, sucrose is synthesised from triacylglycerol via core metabolic processes. Mutants with defects in triacylglycerol-to-sucrose conversion display short etiolated seedlings. We found that whereas sucrose content in the indole-3-butyric acid response 10 (ibr10) mutant was significantly reduced, hypocotyl elongation in the dark was unaffected, questioning the role of IBR10 in this process. To dissect the metabolic complexity behind cell elongation, a quantitative-based phenotypic analysis combined with a multi-platform metabolomics approach was applied. We revealed that triacylglycerol and diacylglycerol breakdown were disrupted in ibr10, resulting in low sugar content and poor photosynthetic ability. Importantly, batch-learning self-organised map clustering revealed that threonine level was correlated with hypocotyl length. Consistently, exogenous threonine supply stimulated hypocotyl elongation, indicating that sucrose levels are not always correlated with etiolated seedling length, suggesting the contribution of amino acids in this process.

Declarars: A Multi-Platform Approach to Herd Declaration by Rural Producers in the Context of Agriculture 4.0

Declarars: A Multi-Platform Approach to Herd Declaration by Rural Producers in the Context of Agriculture 4.0

Editorial of Special Issue “Detecting Geospace Perturbations Caused by Earth”

A systematic multiparametric and multiplatform approach to detect and study geo-space perturbations attributed to preparation processes related to natural hazards is fundamental in order to obtain useful insights on a series of complex dynamic phenomena of the Earth system, namely, earthquakes, volcanic and Saharan dust events, as well as geomagnetic disturbances [...]

Changes in Circulating Metabolites during Weight Loss and Weight Loss Maintenance in Relation to Cardiometabolic Risk.

(1) Background: There is a substantial lack of knowledge of the biochemical mechanisms by which weight loss and weight regain exert their beneficial and adverse effects, respectively, on cardiometabolic outcomes. We examined associations between changes in circulating metabolites and changes in cardiometabolic risk factors during diet-induced weight loss and weight loss maintenance. (2) Methods: This prospective analysis of data from the Satiety Innovation (SATIN) study involved adults living with overweight and obesity (mean age=47.5). One hundred sixty-two subjects achieving ≥8% weight loss during an initial 8-week low-calorie diet (LCD) were included in a 12-week weight loss maintenance period. Circulating metabolites (m=123) were profiled using a targeted multiplatform approach. Data were analyzed using multivariate linear regression models. (3) Results: Decreases in the concentrations of several phosphatidylcholines (PCs), sphingomyelins (SMs), and valine were consistently associated with decreases in total (TChol) and low-density lipoprotein cholesterol (LDL-C) levels during the LCD. Increases in PCs and SMs were significantly associated with increases in TChol and LDL-C during the weight loss maintenance period. Decreases and increases in PCs during LCD and maintenance period, respectively, were associated with decreases in the levels of triglycerides. (4) Conclusions: The results of this study suggest that decreases in circulating PCs and SMs during weight loss and the subsequent weight loss maintenance period may decrease the cardiovascular risk through impacting TChol and LDL-C.

Dissecting fine-flavor cocoa bean fermentation through metabolomics analysis to break down the current metabolic paradigm

Cocoa fermentation plays a crucial role in producing flavor and bioactive compounds of high demand for food and nutraceutical industries. Such fermentations are frequently described as a succession of three main groups of microorganisms (i.e., yeast, lactic acid, and acetic acid bacteria), each producing a relevant metabolite (i.e., ethanol, lactic acid, and acetic acid). Nevertheless, this view of fermentation overlooks two critical observations: the role of minor groups of microorganisms to produce valuable compounds and the influence of environmental factors (other than oxygen availability) on their biosynthesis. Dissecting the metabolome during spontaneous cocoa fermentation is a current challenge for the rational design of controlled fermentations. This study evaluates variations in the metabolic fingerprint during spontaneous fermentation of fine flavor cocoa through a multiplatform metabolomics approach. Our data suggested the presence of two phases of differential metabolic activity that correlate with the observed variations on temperature over fermentations: an exothermic and an isothermic phase. We observed a continuous increase in temperature from day 0 to day 4 of fermentation and a significant variation in flavonoids and peptides between phases. While the second phase, from day four on, was characterized for lower metabolic activity, concomitant with small upward and downward fluctuations in temperature. Our work is the first to reveal two phases of metabolic activity concomitant with two temperature phases during spontaneous cocoa fermentation. Here, we proposed a new paradigm of cocoa fermentation that considers the changes in the global metabolic activity over fermentation, thus changing the current paradigm based only on three main groups of microorganism and their primary metabolic products.

Multi-platform LiDAR approach for detecting coarse woody debris in a landscape with varied ground cover

ABSTRACT Coarse woody debris (CWD), or fallen logs, is known to be an essential habitat element for many organisms. CWD also supports ecosystem functioning through soil formation, nutrient cycling, and carbon storage. For these reasons, accurate assessments of CWD across landscapes are of interest to many ecologists and landscape managers, but traditional field-based measurements can be time-consuming and sampling strategies may not be representative of entire landscapes. Light detection and ranging (LiDAR) technologies may be able to provide a more rapid assessment of the number and volume of CWD across wide areas. However, most research using LiDAR for forest and woodland inventory assessment has focused on standing wood. Detection accuracy of CWD with LiDAR can be impacted by the point density of LiDAR data, ground layer vegetation, and sensor positioning relative to other vegetation or landscape structural features. We used a high-resolution terrestrial laser scanner (TLS), an unoccupied aerial vehicle (UAV) laser scanner (ULS), and a combination of data from both sensors (i.e. fused data, FLS) to estimate CWD in a grassy woodland ecosystem. The study area comprised plots with different types and amounts of vegetation cover and different types of CWD, both naturally occurring and introduced including dispersed, clumped, or a mixture of both types. This enabled a more detailed exploration of model performance across sensor types, vegetation types, and ground cover biomass. A random forest (RF) classification algorithm and noise removing operations on raster imagery were used to classify CWD. Completeness and correctness accuracy with the developed method were highly variable depending on the data and ground vegetation cover and ranged between 20% and 86%, and 12% and 96%, respectively, in comparison with field data. The LiDAR-derived digital surface model (DSM), surface roughness, and topographic position index were important variables for CWD detection. We found that the detection accuracy of CWD varied with the vegetation type, amount of ground vegetation cover, and LiDAR data. Ground cover density had a strong negative impact on accuracy, particularly for TLS and FLS data.

Latent heat flux variability and response to drought stress of black poplar: A multi-platform multi-sensor remote and proximal sensing approach to relieve the data scarcity bottleneck

High-throughput mapping of latent heat flux (λET) is critical to efforts to optimize water resources management and to accelerate forest tree breeding for improved drought tolerance. Ideally, investigation of the energy response at the tree level may promote tailored irrigation strategies and, thus, maximize crop biomass productivity. However, data availability is limited and planning experimental campaigns in the field can be highly operationally complex. To this end, a multi-platform multi-sensor observational approach is herein developed to dissect the λET signature of a black poplar (Populus nigra) breeding population (“POP6”) at the canopy level. POP6 comprised more than 4600 trees representing 503 replicated genotypes, whose parents were derived from contrasting environmental conditions. Trees were trialed in two adjacent plots where different irrigation treatments (moderate drought [mDr] and well-watered [WW]) were applied. Data collected from satellite and unmanned aerial vehicles (UAVs) remote sensing as well as from ground-based proximal sensors were integrated at consistent spatial aggregation and combined to compute the surface energy balance of the trees through a modified Priestley-Taylor method. Here, we demonstrated that λET response was significantly different between WW and mDr trees, whereby genotypes in mDr conditions exhibited larger standard deviations. Importantly, genotypes classified as drought tolerant based on the stress susceptibility index (SSI) presented λET values significantly higher than the rest of the population. This study confirmed that water limitation in mDr settings led to reduced soil moisture in the tree root zone and, thus, to lower λET. These results pave the way to breeding poplar and other bioenergy crops with this underexploited trait for higher λET. Most notably, the illustrated work demonstrates a multi-platform multi-sensor data fusion approach to tackle the global challenge of monitoring landscape-scale ecosystem processes at fine resolution.

Genetic and phenotypic attributes of splenic marginal zone lymphoma

AbstractSplenic marginal zone B-cell lymphoma (SMZL) is a heterogeneous clinico-biological entity. The clinical course is variable, multiple genes are mutated with no unifying mechanism, and essential regulatory pathways and surrounding microenvironments are diverse. We sought to clarify the heterogeneity of SMZL by resolving different subgroups and their underlying genomic abnormalities, pathway signatures, and microenvironment compositions to uncover biomarkers and therapeutic vulnerabilities. We studied 303 SMZL spleen samples collected through the IELSG46 multicenter international study (NCT02945319) by using a multiplatform approach. We carried out genetic and phenotypic analyses, defined self-organized signatures, validated the findings in independent primary tumor metadata and determined correlations with outcome data. We identified 2 prominent genetic clusters in SMZL, termed NNK (58% of cases, harboring NF-κB, NOTCH, and KLF2 modules) and DMT (32% of cases, with DNA-damage response, MAPK, and TLR modules). Genetic aberrations in multiple genes as well as cytogenetic and immunogenetic features distinguished NNK- from DMT-SMZLs. These genetic clusters not only have distinct underpinning biology, as judged by differences in gene-expression signatures, but also different outcomes, with inferior survival in NNK-SMZLs. Digital cytometry and in situ profiling segregated 2 basic types of SMZL immune microenvironments termed immune-suppressive SMZL (50% of cases, associated with inflammatory cells and immune checkpoint activation) and immune-silent SMZL (50% of cases, associated with an immune-excluded phenotype) with distinct mutational and clinical connotations. In summary, we propose a nosology of SMZL that can implement its classification and also aid in the development of rationally targeted treatments.