Microbial diversity in hummock and hollow soils of three wetlands on the Qinghai-Tibetan plateau revealed by 16S rRNA pyrosequencing

The wetlands of the Qinghai-Tibetan Plateau are believed to play an important role in global nutrient cycling, but the composition and diversity of microorganisms in this ecosystem are poorly characterized. An understanding of the effects of geography and microtopography on microbial populations will provide clues to the underlying mechanisms that structure microbial communities. In this study, we used pyrosequencing-based analysis of 16S rRNA gene sequences to assess and compare the composition of soil microbial communities present in hummock and hollow soils from three wetlands (Dangxiong, Hongyuan and Maduo) on the Qinghai-Tibetan Plateau, the world’s highest plateau. A total of 36 bacterial phyla were detected. Proteobacteria (34.5% average relative abundance), Actinobacteria (17.3%) and Bacteroidetes (11%) had the highest relative abundances across all sites. Chloroflexi, Acidobacteria, Verrucomicrobia, Firmicutes, and Planctomycetes were also relatively abundant (1–10%). In addition, archaeal sequences belonging to Euryarchaea, Crenarchaea and Thaumarchaea were detected. Alphaproteobacteria sequences, especially of the order Rhodospirillales, were significantly more abundant in Maduo than Hongyuan and Dangxiong wetlands. Compared with Hongyuan soils, Dangxiong and Maduo had significantly higher relative abundances of Gammaproteobacteria sequences (mainly order Xanthomonadales). Hongyuan wetland had a relatively high abundance of methanogens (mainly genera Methanobacterium, Methanosarcina and Methanosaeta) and methanotrophs (mainly Methylocystis) compared with the other two wetlands. Principal coordinate analysis (PCoA) indicated that the microbial community structure differed between locations and microtopographies and canonical correspondence analysis indicated an association between microbial community structure and soil properties or geography. These insights into the microbial community structure and the main controlling factors in wetlands of the Qinghai-Tibetan Plateau provide a valuable background for further studies on biogeochemical processes in this distinct ecosystem

Surgical treatment of traumatic frontal hematoma: comparison of the endoscopic supraorbital approach with frontotemporal approach

BackgroundThe objective of this study was to compare the efficacy, safety, and outcomes of the endoscopic supraorbital approach and frontotemporal approach for the treatment of traumatic frontal hematoma, with the aim of demonstrating the feasibility of the endoscopic supraorbital approach.MethodsA total of 24 cases underwent hematoma evacuation, including 10 cases using the endoscopic supraorbital approach and 14 cases using the frontotemporal approach. Baseline demographic data, hematoma clearance rate, blood loss, postoperative complications, and 6-month outcomes were retrospectively analyzed.ResultsBoth approaches effectively evacuated the hematoma, with hematoma clearance rates of 90.97 ± 10.23% in the endoscopic supraorbital group and 85.29 ± 16.15% in the frontotemporal approach group (p > 0.05). The supraorbital approach group demonstrated significantly shorter operation times compared to the frontotemporal approach group (116.50 ± 28.19 min vs. 193.29 ± 72.55 min, p < 0.05), as well as significantly less blood loss (55.00 ± 33.08 mL vs. 685.71 ± 840.20 mL, p < 0.05). There was no significant difference in the rate of postoperative complications between the two groups, and the majority of patients achieved favorable outcomes with a Glasgow Outcome Scale score of 4 or 5 in both groups.ConclusionCompared to the frontotemporal approach, the endoscopic supraorbital approach offers advantages such as shorter operation times, reduced blood loss, similar treatment effects, and comparable complication rates. Therefore, the endoscopic supraorbital approach may serve as a viable alternative for the treatment of traumatic frontal hematoma

Atlantic hurricane surge response to geoengineering

Devastating floods due to Atlantic hurricanes are relatively rare events. However, the frequency of the most intense storms is likely to increase with rises in sea surface temperatures. Geoengineering by stratospheric sulfate aerosol injection cools the tropics relative to the polar regions, including the hurricane Main Development Region in the Atlantic, suggesting that geoengineering may mitigate hurricanes. We examine this hypothesis using eight earth system model simulations of climate under the Geoengineering Model Intercomparison Project (GeoMIP) G3 and G4 schemes that use stratospheric aerosols to reduce the radiative forcing under the Representative Concentration Pathway (RCP) 4.5 scenario. Global mean temperature increases are greatly ameliorated by geoengineering, and tropical temperature increases are at most half of those temperature increases in the RCP4.5. However, sulfate injection would have to double (to nearly 10 teragrams of SO2 per year) between 2020 and 2070 to balance the RCP4.5, approximately the equivalent of a 1991 Pinatubo eruption every 2 y, with consequent implications for stratospheric ozone. We project changes in storm frequencies using a temperature-dependent generalized extreme value statistical model calibrated by historical storm surges and observed temperatures since 1923. The number of storm surge events as big as the one caused by the 2005 Katrina hurricane are reduced by about 50% compared with no geoengineering, but this reduction is only marginally statistically significant. Nevertheless, when sea level rise differences in 2070 between the RCP4.5 and geoengineering are factored into coastal flood risk, we find that expected flood levels are reduced by about 40 cm for 5-y events and about halved for 50-y surges

Soil diazotrophic abundance, diversity, and community assembly mechanisms significantly differ between glacier riparian wetlands and their adjacent alpine meadows

Global warming can trigger dramatic glacier area shrinkage and change the flux of glacial runoff, leading to the expansion and subsequent retreat of riparian wetlands. This elicits the interconversion of riparian wetlands and their adjacent ecosystems (e.g., alpine meadows), probably significantly impacting ecosystem nitrogen input by changing soil diazotrophic communities. However, the soil diazotrophic community differences between glacial riparian wetlands and their adjacent ecosystems remain largely unexplored. Here, soils were collected from riparian wetlands and their adjacent alpine meadows at six locations from glacier foreland to lake mouth along a typical Tibetan glacial river in the Namtso watershed. The abundance and diversity of soil diazotrophs were determined by real-time PCR and amplicon sequencing based on nifH gene. The soil diazotrophic community assembly mechanisms were analyzed via iCAMP, a recently developed null model-based method. The results showed that compared with the riparian wetlands, the abundance and diversity of the diazotrophs in the alpine meadow soils significantly decreased. The soil diazotrophic community profiles also significantly differed between the riparian wetlands and alpine meadows. For example, compared with the alpine meadows, the relative abundance of chemoheterotrophic and sulfate-respiration diazotrophs was significantly higher in the riparian wetland soils. In contrast, the diazotrophs related to ureolysis, photoautotrophy, and denitrification were significantly enriched in the alpine meadow soils. The iCAMP analysis showed that the assembly of soil diazotrophic community was mainly controlled by drift and dispersal limitation. Compared with the riparian wetlands, the assembly of the alpine meadow soil diazotrophic community was more affected by dispersal limitation and homogeneous selection. These findings suggest that the conversion of riparian wetlands and alpine meadows can significantly alter soil diazotrophic community and probably the ecosystem nitrogen input mechanisms, highlighting the enormous effects of climate change on alpine ecosystems

Unimodal productivity-biodiversity relationship along the gradient of multidimensional resources across Chinese grasslands

Resources can affect plant productivity and biodiversity simultaneously and thus are key drivers of their relationships in addition to plant-plant interactions. However, most previous studies only focused on a single resource while neglecting the nature of resource multidimensionality. Here we integrated four essential resources for plant growth into a single metric of resource diversity (RD) to investigate its effects on the productivity-biodiversity relationship (PBR) across Chinese grasslands. Results showed that habitats differing in RD have different PBRs − positive in low-resource habitats, but neutral in medium- and high-resource ones—while collectively, a weak positive PBR was observed. However, when excluding direct effects of RD on productivity and biodiversity, PBR in high-resource habitats became negative, which leads to a unimodal instead of a positive PBR along the RD gradient. By integrating resource effects and changing plant-plant interactions into a unified framework with the RD gradient, our work contributes to uncovering underlying mechanisms for inconsistent PBRs at large scales

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

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe