Tree diversity depending on environmental gradients promotes biomass stability via species asynchrony in China's forest ecosystems

There is mounting evidence that biodiversity promotes ecological stability in changing environments. However, understanding diversity–stability relationships and their underlying mechanisms across large-scale tree diversity and natural environmental gradients are still controversial and largely lacking. We used thirty-nine 0.12 ha long-term permanent forest plots spanning China's various forest types to test the effects of multiple abiotic (climate, soil, age and topography) and biotic factors (taxonomic and structural diversity, functional diversity and community-mean traits, and species asynchrony) on biomass stability and its components (mean biomass and biomass variability) over time. We used multiple analytical methods to identify the best explanatory variables and complicated causal relationships for community biomass stability. Our results showed that species richness increased biomass stability by promoting species asynchrony. Structural and functional diversity had a weaker effect on biomass stability. Forest age and structural diversity increased mean biomass and biomass variability significantly and simultaneously. Communities dominated by tree species with high wood density had high biomass stability. Soil nitrogen enhanced biomass stability directly and indirectly through its effects on mean biomass. Soil nitrogen to phosphorus ratio increased biomass stability via increasing species asynchrony. Precipitation indirectly increased biomass stability by affecting tree diversity. Moreover, the direct and indirect effects of soil nutrients on biomass stability were greater than that of climate variables. Our results suggest that species asynchrony is the main mechanism proposed to explain the stabilizing effect of diversity on community biomass, supporting two mechanisms, namely, the biodiversity insurance hypothesis and complementary dynamics. Soil and climate factors also play an important role in shaping diversity–stability relationships. Our results provide a new insight into how tree diversity affects ecosystem stability across diverse community types and large-scale environmental gradients

Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Heterogeneity in gene expression and epigenetic states exists across individual cells. Here, the authors develop scCAT-seq, a technique for simultaneously performing ATAC-seq and RNA-seq within the same single cell

Clinical characteristics and outcomes in patients with traumatic brain injury in China: a prospective, multicentre, longitudinal, observational study

Background Large-scale studies are required to better characterise traumatic brain injury (TBI) and to identify the most effective treatment approaches for TBI. However, evidence is scarce and mostly originates from high-income countries. We aimed to describe the existing care for patients with TBI and the outcomes in China. Methods The Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China registry is a prospective, multicentre, longitudinal, observational study done in 56 neurosurgical centres across China. We collected data of patients who were admitted to hospital with a clinical diagnosis of TBI and an indication for CT. Patients who were discharged directly from the emergency room were excluded. The primary endpoint was survival on discharge. Prognostic analyses were applied to identify predictors of mortality. Variations in mortality were compared between centres and provinces within China. Mortality was compared with expected mortality, estimated using the CRASH basic model. This study was registered with ClinicalTrials.gov, NCT02210221. Findings From Dec 22, 2014, to Aug 1, 2017, 13 627 patients with TBI from 56 centres were enrolled in the registry. Data from 13 138 patients from 52 hospitals in 22 provinces of China were analysed. Most patients were male (9782 [74%]), with a median age of 48 years (IQR 33–61). The median Glasgow Coma Scale (GCS) score was 13 (IQR 9–15), and the leading cause of injury was road-traffic incident (6548 [50%]). Overall, 637 (5%) patients died, including 552 (20%) patients with severe TBI. Age, GCS score, injury severity score, pupillary light reflex, CT findings (compressed basal cistern and midline shift ≥5 mm), presence of hypoxia, systemic hypotension, altitude higher than >500 m, and GDP per capita were significantly associated with survival in all patients with TBI. Variation in mortality existed between centres and regions. The expected 14-day mortality was 1116 (13%), but 544 (7%) deaths within 14 days were observed (observed to expected ratio 0·49 [95% CI 0·45–0·53]). Interpretation The results show differences in mortality between centres and regions across China, which indicates potential for identifying best practices through comparative effectiveness research. The risk factors identified in prognostic analyses might contribute to developing benchmarks for assessing quality of care. Funding None

Multi-Tasking for Aspect-Based Sentiment Analysis via Constructing Auxiliary Self-Supervision ACOP Task

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. In aspect-based sentiment analysis, the semantic understanding of sentences is critical. The previous approach had to design modules to improve the understanding of sentence semantics. However, We adopt a multi-task approach to learn sentence semantics to avoid design modules. Thus we propose the Aspect Word and Context Order Prediction Task (ACOP) as an auxiliary task. We implement the ACOP task with the global and local way for aspect-based sentiment analysis and adopt the self-supervised method to train our model. Our model improves accuracy and F1 values over the best baseline model on the Rest14 dataset by 1.96% and 1.76%, on the Lap14 dataset by 0.24% and 0.24%, and on the Twitter dataset by 1.45% and 2.03%. Our experiments on three public datasets demonstrate that our approach is effective and that using a multi-task approach is a good choice instead of designing corresponding modules to extract semantic features

Simulation Analysis of Concrete Pumping Based on Smooth Particle Hydrodynamics and Discrete Elements Method Coupling

With an increase of suction efficiency of fresh concrete pumping in confined spaces, the laminar flow state will be damaged by the return flow caused by distribution value direction changes and concrete gravity. This is a fact, but one which is rarely studied. In this work, the flow state, flow velocity, and suction efficiency of fresh concrete pumping are simulated using the coupled smooth particle hydrodynamics and Discrete Elements Method (SPH-DEM). The rheological parameters and Herschel-Bulkley-Papanastasiou (HBP) rheological model are adopted to simulate fresh concrete in the numerical simulation model. The study reveals that the error between the slump experimental result and that obtained by the HBP model is negligible. A model is therefore established for numerical simulations of the suction efficiency of fresh concrete pumping. An experimental concrete pumping platform is built, and the pressure and efficiency data during pumping are collected. A comparison of the numerical simulation with experimental results shows that the error is less than 10%

A Novel Pyramid Network with Feature Fusion and Disentanglement for Object Detection

In order to alleviate the scale variation problem in object detection, many feature pyramid networks are developed. In this paper, we rethink the issues existing in current methods and design a more effective module for feature fusion, called multiflow feature fusion module (MF3M). We first construct gate modules and multiple information flows in MF3M to avoid information redundancy and enhance the completeness and accuracy of information transfer between feature maps. Furtherore, in order to reduce the discrepancy of classification and regression in object detection, a modified deformable convolution which is termed task adaptive convolution (TaConv) is proposed in this study. Different offsets and masks are predicted to achieve the disentanglement of features for classification and regression in TaConv. By integrating the above two designs, we build a novel feature pyramid network with feature fusion and disentanglement (FFAD) which can mitigate the scale misalignment and task misalignment simultaneously. Experimental results show that FFAD can boost the performance in most models