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Microbial dimethylsulfoniopropionate (DMSP) cycling in the ultraoligotrophic eastern Indian Ocean
No full textDimethylsulfoniopropionate (DMSP) is an important source of dissolved organic matter for the marine food web and its cycling is a key step in ocean-atmosphere fluxes involved in the global sulfur cycle. To date, the abundance and biogeography of the genes encoding bacterial DMSP cycling in the eastern Indian Ocean (EIO) is virtually unknown. Moreover, DMSP measurements from the IO are sparse compared to other major oceans. In May–June 2019, we characterized dissolved DMSP (DMSPd) concentrations and the abundance of representative bacterial DMSP cycling genes along the 110 °E transect line as part of a voyage that contributed to Australia's involvement in the second International Indian Ocean Expedition. During the multidisciplinary voyage, surface water samples were collected from 19 stations spanning temperate to tropical waters of the EIO (39.5 °S to 11.5 °S, 110 °E). Somewhat surprisingly, a trend of greater DMSPd was measured in ultraoligotrophic (<0.02 μmol L−1 of nitrate/nitrite), low latitude waters compared to relatively nutrient-rich high latitudes, which contradicts global DMSPd patterns of high concentrations at high latitudes. Additionally, the average DMSPd concentration in EIO samples (17.2 ± 18.64 nM) was an order of magnitude greater than concentrations previously reported at similar latitudes in the Pacific and Atlantic Oceans, which suggests DMSPd is a readily available food source for microbes in a region that is often considered an ocean desert. The abundances of the bacterial DMSP production gene (dsyB), the DMSP lyase gene (dddP) and phylogenetically diverse DMSP demethylation genes (dmdA subclade A/1, D/all and E/2) were reported for the first time in the EIO region, demonstrating significant shifts in all genes with latitude. The SAR11 dmdA (D/all) gene was the dominant DMSP degradation gene across the transect (3.4 ± 0.94% of bacteria) and was notably positively correlated to DMSPd, demonstrating a tight coupling between the variables across the 30° transect. Our results also showed greater DMSPd and relative abundance of genes encoding both DMSP degradation pathways (dddP, dmdA A/1 and D/all) within a Leeuwin Current meander when compared to adjacent stations outside of the meander, providing evidence that mesoscale perturbations from the Leeuwin Current can greatly influence the EIO sulfur cycle. Overall, our data indicates that reduced sulfur in the form of DMSP is an abundant and readily available food source for some microbial metabolisms within the ultraoligotrophic surface waters of the EIO
Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation
No full textBackground
Appropriate anticoagulant therapy for patients with atrial fibrillation (AF) requires assessment of stroke and bleeding risks. However, risk stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive capacity for patients with AF. Multilabel machine learning (ML) techniques may improve predictive performance and support decision-making for anticoagulant therapy. We compared the performance of multilabel ML models with the currently used risk scores for predicting outcomes in AF patients.
Methods
This was a retrospective cohort study of 9670 patients, mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and had 1-year follow-up. The outcomes were ischemic stroke (167), major bleeding (430) admissions, all-cause death (1912) and event-free survival (7387). Discrimination and calibration of ML models were compared with clinical risk scores by area under the curve (AUC). Risk stratification was assessed using net reclassification index (NRI).
Results
Multilabel gradient boosting classifier chain provided the best AUCs for stroke (0.685 95% CI 0.676, 0.694), major bleeding (0.709 95% CI 0.703, 0.716) and death (0.765 95% CI 0.763, 0.768) compared to multi-layer neural networks and classifier chain using support vector machine. It provided modest performance improvement for stroke compared to AUC of CHA2DS2-VASc (0.652, NRI = 3.2%, p-value = 0.1), but significantly improved major bleeding prediction compared to AUC of HAS-BLED (0.522, NRI = 22.8%, p-value < 0.05). It also achieved greater discriminant power for death compared with AUC of CHA2DS2-VASc (0.606, p-value < 0.05). ML models identified additional risk features such as hemoglobin level, renal function.
Conclusions
Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients
Viral stagings across the globe: Performing identity in the era of COVID-19
No full textThis special issue of the Journal of Intercultural Studies is titled ‘Performing Identity in the Era of COVID-19’, and co-mingles our current critical inquiries into the pandemic meaning of ‘performance’’ with our earlier research in global diasporas. While invoking our previous historical context of ‘the era of COVID-19’, we shift focus from migratory liminality to the many ways that we can re-think the notions of performance, performing, and performativity (and the nonperformative) in the context of the global pandemic..
The complex challenge of antenatal steroid therapy nonresponsiveness
No full textAntenatal steroid therapy is standard care for women at imminent risk of preterm delivery. When deliveries occur within 7 days of treatment, antenatal steroid therapy reduces the risk of neonatal death and improves preterm outcomes by exerting diverse developmental effects on the fetal organs, in particular the preterm lung and cardiovascular system. There is, however, sizable variability in antenatal steroid treatment efficacy, and an important percentage of fetuses exposed to antenatal steroid therapy do not respond sufficiently to derive benefit. Respiratory distress syndrome, for example, is a central metric of clinical trials to assess antenatal steroid outcomes. In the present analysis, we addressed the concept of antenatal steroid nonresponsiveness, and defined a failed or suboptimal response to antenatal steroids as death or a diagnosis of respiratory distress syndrome following treatment. For deliveries at 24 to 35 weeks’ gestation, the number needed to treat to prevent 1 case of respiratory distress syndrome was 19 (95% confidence interval, 14–28). Reflecting gestation-dependent risk, for deliveries at >34 weeks’ gestation the number needed to treat was 55 (95% confidence interval, 30–304), whereas for elective surgical deliveries at term this number was 106 (95% confidence interval, 61–421).
We reviewed data from clinical and animal studies investigating antenatal steroid therapy to highlight the significant incidence of antenatal steroid therapy nonresponsiveness (ie, residual mortality or respiratory distress syndrome after treatment), and the potential mechanisms underpinning this outcome variability. The origins of this variability may be related to both the manner in which the therapy is applied (ie, the treatment regimen itself) and factors specific to the individual (ie, genetic variation, stress, infection). The primary aims of this review were: (1) to emphasize to the obstetrical and neonatal communities the extent of antenatal steroid response variability and its potential impact; (2) to propose approaches by which antenatal steroid therapy may be better applied to improve overall benefit; and (3) to stimulate further research toward the empirical optimization of this important antenatal therapy
The 3366 chickpea genomes for research and breeding
No full textGenome sequences provide an unprecedented resource to rapidly develop modern crops. A recent paper by Varshney et al. provides genome variation maps of 3366 chickpea accessions. Here, we highlight how this breakthrough research can fundamentally change breeding practices of chickpea and potentially other crops
Serum lipidomic signatures of type 2 diabetes and early surgical diabetes resolution: Basic science and research in bariatric surgery
No full textAbstract taken from the 25th IFSO World Congress Silver Anniversary #O-35
Gut-Testis Axis: Microbiota prime metabolome to increase sperm quality in young Type 2 Diabetes
No full textYoung type 2 diabetes (T2D) affects 15% of the population, with a noted increase in cases, and T2D-related male infertility has become a serious issue in recent years. The current study aimed to explore the improvements of alginate oligosaccharide (AOS)-modified gut microbiota on semen quality in T2D. The T2D was established in young mice of 5 weeks of age with a blood glucose level of 21.2 ± 2.2 mmol/L, while blood glucose was 8.7 ± 1.1 mM in control animals. We discovered that fecal microbiota transplantation (FMT) of AOS-improved microbiota (A10-FMT) significantly decreased blood glucose, while FMT of gut microbiota from control animals (Con-FMT) did not. Sperm concentration and motility were decreased in T2D to 10% to 20% of those in the control group, while A10-FMT brought about a recovery of around 5- to 10-fold. A10-FMT significantly increased small intestinal Allobaculum, while it elevated small intestinal and cecal Lactobacillus in some extent, blood butyric acid and derivatives and eicosapentaenoic acid (EPA), and testicular docosahexaenoic acid (DHA), EPA, and testosterone and its derivatives. Furthermore, A10-FMT improved liver functions and systemic antioxidant environments. Most importantly, A10-FMT promoted spermatogenesis through the improvement in the expression of proteins important for spermatogenesis to increase sperm concentration and motility. The underlying mechanisms may be that A10-FMT increased gut-beneficial microbes Lactobacillus and Allobaculum to elevate blood and/or testicular butyric acid, DHA, EPA, and testosterone to promote spermatogenesis and thus to ameliorate sperm concentration and motility. AOS-improved gut microbes could emerge as attractive candidates to treat T2D-diminished semen quality.
IMPORTANCE A10-FMT benefits gut microbiota, liver function, and systemic environment via improvement in blood metabolome, consequently to favor the testicular microenvironment to improve spermatogenesis process and to boost T2D-diminished semen quality. We established that AOS-improved gut microbiota may be used to boost T2D-decreased semen quality and metabolic disease-related male subfertility
Robust prescribed trajectory tracking control of a robot manipulator using adaptive finite-time sliding mode and extreme learning machine method
Get PDFThis study aims to provide a robust trajectory tracking controller which guarantees the prescribed performance of a robot manipulator, both in transient and steady-state modes, experiencing parametric uncertainties. The main core of the controller is designed based on the adaptive finite-time sliding mode control (SMC) and extreme learning machine (ELM) methods to collectively estimate the parametric model uncertainties and enhance the quality of tracking performance. Accordingly, the global estimation with a fast convergence rate is achieved while the tracking error and the impact of chattering on the control input are mitigated significantly. Following the control design, the stability of the overall control system along with the finite-time convergence rate is proved, and the effectiveness of the proposed method is investigated via extensive simulation studies. The results of simulations confirm that the prescribed transient and steady-state performances are obtained with enough accuracy, fast convergence rate, robustness, and smooth control input which are all required for practical implementation and applications
City-wide wastewater genomic surveillance through the successive emergence of SARS-CoV-2 Alpha and Delta variants
Get PDFGenomic surveillance of SARS-CoV-2 has provided a critical evidence base for public health decisions throughout the pandemic. Sequencing data from clinical cases has helped to understand disease transmission and the spread of novel variants. Genomic wastewater surveillance can offer important, complementary information by providing frequency estimates of all variants circulating in a population without sampling biases. Here we show that genomic SARS-CoV-2 wastewater surveillance can detect fine-scale differences within urban centres, specifically within the city of Liverpool, UK, during the emergence of Alpha and Delta variants between November 2020 and June 2021. Furthermore, wastewater and clinical sequencing match well in the estimated timing of new variant rises and the first detection of a new variant in a given area may occur in either clinical or wastewater samples. The study's main limitation was sample quality when infection prevalence was low in spring 2021, resulting in a lower resolution of the rise of the Delta variant compared to the rise of the Alpha variant in the previous winter. The correspondence between wastewater and clinical variant frequencies demonstrates the reliability of wastewater surveillance. However, discrepancies in the first detection of the Alpha variant between the two approaches highlight that wastewater monitoring can also capture missing information, possibly resulting from asymptomatic cases or communities less engaged with testing programmes, as found by a simultaneous surge testing effort across the city
Comparison of primers for the detection of Phytophthora (and other oomycetes) from environmental samples
Get PDFMany oomycetes are important plant pathogens that cause devastating diseases in agricultural fields, orchards, urban areas, and natural ecosystems. Limitations and difficulties associated with isolating these pathogens have led to a strong uptake of DNA metabarcoding and mass parallel sequencing. At least 21 primer combinations have been designed to amplify oomycetes, or more specifically, Phytophthora species, from environmental samples. We used the Illumina sequencing platform to compare 13 primer combinations on mock communities and environmental samples. The primer combinations tested varied significantly in their ability to amplify Phytophthora species in a mock community and from environmental samples; this was due to either low sensitivity (unable to detect species present in low concentrations) or a lack of specificity (an inability to amplify some species even if they were present in high concentrations). Primers designed for oomycetes underestimated the Phytophthora community compared to Phytophthora-specific primers. We recommend using technical replicates, primer combinations, internal controls, and a phylogenetic approach for assigning a species identity to OTUs or ASVs. Particular care must be taken if sampling substrates where hybrid species could be expected. Overall, the choice of primers should depend upon the hypothesis being tested