Responses of above- and below-ground carbon stocks to environmental drivers in Tibetan Alpine grasslands

Paucity in the knowledge of responses of grassland carbon dynamics to environmental variables constrains our ability to predict future ecosystem productivity. The aim of this study was to investigate differential responses of above- and below-ground carbon stocks to environmental drivers in Tibetan alpine Plateau at both regional and local scales. Variance partitioning and non-linear regression between carbon stocks and environmental driving variables suggested that both above- and below-ground carbon stocks showed a significant negative relationship with temperature and a positive relationship with soil moisture. Annual accumulated temperature constrained above-ground carbon at regional scale (r2 = 0.50, P < 0.0001), while soil moisture controlled below-ground carbon at local scale (r2 = 0.48, P < 0.0001). Scale-specific responses of above- and belowground carbon storage to temperature and soil moisture complicated the influences of abiotic environmental variables on ecosystem productivity. Soil carbon had significant unimodal (r2 = 0.11, P = 0.0073) and linear (r2 = 0.37, P < 0.0001) relationships with mean annual temperature and soil moisture, respectively. Since the driving factors of aboveground and soil carbon content are specific to spatial scales, the relationships of grassland carbon storage and environmental factors at small scales are not applicable to a large spatial scale

Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation

Document-level Neural Machine Translation (DocNMT) has been proven crucial for handling discourse phenomena by introducing document-level context information. One of the most important directions is to input the whole document directly to the standard Transformer model. In this case, efficiency becomes a critical concern due to the quadratic complexity of the attention module. Existing studies either focus on the encoder part, which cannot be deployed on sequence-to-sequence generation tasks, e.g., Machine Translation (MT), or suffer from a significant performance drop. In this work, we keep the translation performance while gaining 20\% speed up by introducing extra selection layer based on lightweight attention that selects a small portion of tokens to be attended. It takes advantage of the original attention to ensure performance and dimension reduction to accelerate inference. Experimental results show that our method could achieve up to 95\% sparsity (only 5\% tokens attended) approximately, and save 93\% computation cost on the attention module compared with the original Transformer, while maintaining the performance.Comment: Accepted by AACL 202

Robust Visual Question Answering: Datasets, Methods, and Future Challenges

Visual question answering requires a system to provide an accurate natural language answer given an image and a natural language question. However, it is widely recognized that previous generic VQA methods often exhibit a tendency to memorize biases present in the training data rather than learning proper behaviors, such as grounding images before predicting answers. Therefore, these methods usually achieve high in-distribution but poor out-of-distribution performance. In recent years, various datasets and debiasing methods have been proposed to evaluate and enhance the VQA robustness, respectively. This paper provides the first comprehensive survey focused on this emerging fashion. Specifically, we first provide an overview of the development process of datasets from in-distribution and out-of-distribution perspectives. Then, we examine the evaluation metrics employed by these datasets. Thirdly, we propose a typology that presents the development process, similarities and differences, robustness comparison, and technical features of existing debiasing methods. Furthermore, we analyze and discuss the robustness of representative vision-and-language pre-training models on VQA. Finally, through a thorough review of the available literature and experimental analysis, we discuss the key areas for future research from various viewpoints.Comment: IEEE TPAMI (Under Review

A curve model for association of serum homocysteine with carotid artery hemodynamics

Purpose: To investigate the correlation between carotid artery hemodynamics and serum homocysteine.Methods: A total of 894 participants made up of 439 male (49.11 %) and 455 female (50.89 %) from Ma’anshan, China, enrolled in the cross-sectional study. Data collection included demographics, blood sample and carotid ultrasonography. Piecewise linear regression analysis was used to analyze the relationship between serum homocysteine and carotid artery hemodynamics.Results: Homocysteine (Hcy) levels were divided into four groups by quartiles. The populations of the groups were 226, 220, 222, 226; and their mean ages were 56.52 ± 10.49, 62.27 ± 10.06, 63.42 ± 9.81 and 65.38 ± 10.56 years, respectively. After adjustment for blood biochemical and demographics factors, U-shaped and S-shaped curves were as observed between Hcy and carotid artery hemodynamics. The adjusted regression analysis showed that the threshold values of Hcy with end diastolic velocity (EDV) of right common carotid artery (CCA) were 12.50 and 19.00, while for the EDV of right internal carotid artery (ICA), the values were 11.50 and 22.00. U-shaped curves were observed between Hcy and peak systolic velocity (PSV) of left CCA, EDV of left CCA, PSV of left ICA and EDV of left ICA. The threshold values of Hcy with PSV of left CCA, EDV of left CCA, PSV of left ICA and EDV of left ICA were 14.00, 14.00, 14.00 and 13.50, respectively.Conclusion: These results indicate that a significant correlation exists between homocysteine at different concentrations and carotid artery hemodynamics.Keywords: Homocysteine, Hemodynamics, End diastolic velocity, Peak systolic velocit

Adaptive loose optimization for robust question answering

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalizability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in \url{https://github.com/reml-group/ALO}.Comment: 13 pages,8 figure

The role of lysosomal peptidases in glioma immune escape: underlying mechanisms and therapeutic strategies

Glioblastoma is the most common primary malignant tumor of the central nervous system, which has the characteristics of strong invasion, frequent recurrence, and rapid progression. These characteristics are inseparable from the evasion of glioma cells from immune killing, which makes immune escape a great obstacle to the treatment of glioma, and studies have confirmed that glioma patients with immune escape tend to have poor prognosis. The lysosomal peptidase lysosome family plays an important role in the immune escape process of glioma, which mainly includes aspartic acid cathepsin, serine cathepsin, asparagine endopeptidases, and cysteine cathepsins. Among them, the cysteine cathepsin family plays a prominent role in the immune escape of glioma. Numerous studies have confirmed that glioma immune escape mediated by lysosomal peptidases has something to do with autophagy, cell signaling pathways, immune cells, cytokines, and other mechanisms, especially lysosome organization. The relationship between protease and autophagy is more complicated, and the current research is neither complete nor in-depth. Therefore, this article reviews how lysosomal peptidases mediate the immune escape of glioma through the above mechanisms and explores the possibility of lysosomal peptidases as a target of glioma immunotherapy

Association between cystatin C and the interaction of pulmonary tuberculosis with chronic diseases

Purpose: To determine the association between Cystatin C (Cys C) levels and the interaction of pulmonary tuberculosis (PTB) with chronic diseases (CD).Methods: Participants (n = 356) were selected randomly from The First Affiliated Hospital of Wannan Medical College, China, and divided into 4 groups: normal control group (n = 80), PTB group (n = 98), chronic disease group (n = 146), and PTB combined with chronic disease group (PTB+CD, n = 31). The investigation included information on demographics and analysis of blood samples for Cys C, liver function, renal function, blood glucose and other biochemical indices.Results: The highest level of Cys C was obtained in PTB + CD group. Before and after adjusting eGFR, there was no association between Cys C and PTB or/and chronic disease. However abnormal levels of Cys C were significantly higher in PTB+CD group after adjusting eGFR (OR = 4.014, p = 0.0125).Conclusion: Higher levels of Cys C may be associated with chronic diseases co existing with PTB.Keywords: Cystatin C, Pulmonary tuberculosis, Chronic diseases, Inflammatio

Au@h-Al2O3 Analogic Yolk–Shell Nanocatalyst for Highly Selective Synthesis of Biomass-Derived D-xylonic Acid via Regulation of Structure Effects

Selective oxidation of biomass-based monosaccharides into value-added sugar acids is highly desired, but limited success of producing D-xylonic acid has been achieved. Herein, we report an efficient catalyst system, viz., Au nanoparticles anchored on the inner walls of hollow Al2O3 nanospheres (Au@h- Al2O3), which could catalyze the selective oxidation of D-xylose into D-xylonic acid under base-free conditions. The mesoporous Al2O3 shell as the adsorbent first adsorbed D-xylose. Then, the interface of Au nanoparticles and Al2O3 as active sites spontaneously dissociated O2, and the exposed Au nanoparticle surface as the catalytic site drove the transformation. With this catalyst system, the valuable D-xylonic acid was produced with excellent yields in the aerobic oxidation of D-xylose. Extensive investigation showed that Au@h- Al2O3 is an efficient catalyst with high stability and recyclability

Genome-wide eQTLs and heritability for gene expression traits in unrelated individuals

BACKGROUND: While the possible sources underlying the so-called ‘missing heritability’ evident in current genome-wide association studies (GWAS) of complex traits have been actively pursued in recent years, resolving this mystery remains a challenging task. Studying heritability of genome-wide gene expression traits can shed light on the goal of understanding the relationship between phenotype and genotype. Here we used microarray gene expression measurements of lymphoblastoid cell lines and genome-wide SNP genotype data from 210 HapMap individuals to examine the heritability of gene expression traits. RESULTS: Heritability levels for expression of 10,720 genes were estimated by applying variance component model analyses and 1,043 expression quantitative loci (eQTLs) were detected. Our results indicate that gene expression traits display a bimodal distribution of heritability, one peak close to 0% and the other summit approaching 100%. Such a pattern of the within-population variability of gene expression heritability is common among different HapMap populations of unrelated individuals but different from that obtained in the CEU and YRI trio samples. Higher heritability levels are shown by housekeeping genes and genes associated with cis eQTLs. Both cis and trans eQTLs make comparable cumulative contributions to the heritability. Finally, we modelled gene-gene interactions (epistasis) for genes with multiple eQTLs and revealed that epistasis was not prevailing in all genes but made a substantial contribution in explaining total heritability for some genes analysed. CONCLUSIONS: We utilised a mixed effect model analysis for estimating genetic components from population based samples. On basis of analyses of genome-wide gene expression from four HapMap populations, we demonstrated detailed exploitation of the distribution of genetic heritabilities for expression traits from different populations, and highlighted the importance of studying interaction at the gene expression level as an important source of variation underlying missing heritability. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-13) contains supplementary material, which is available to authorized users