Learning Cross-modal Context Graph for Visual Grounding

Visual grounding is a ubiquitous building block in many vision-language tasks and yet remains challenging due to large variations in visual and linguistic features of grounding entities, strong context effect and the resulting semantic ambiguities. Prior works typically focus on learning representations of individual phrases with limited context information. To address their limitations, this paper proposes a language-guided graph representation to capture the global context of grounding entities and their relations, and develop a cross-modal graph matching strategy for the multiple-phrase visual grounding task. In particular, we introduce a modular graph neural network to compute context-aware representations of phrases and object proposals respectively via message propagation, followed by a graph-based matching module to generate globally consistent localization of grounding phrases. We train the entire graph neural network jointly in a two-stage strategy and evaluate it on the Flickr30K Entities benchmark. Extensive experiments show that our method outperforms the prior state of the arts by a sizable margin, evidencing the efficacy of our grounding framework. Code is available at "https://github.com/youngfly11/LCMCG-PyTorch".Comment: AAAI-202

The Gut Microbiome Signatures Discriminate Healthy From Pulmonary Tuberculosis Patients

Cross talk occurs between the human gut and the lung through a gut-lung axis involving the gut microbiota. However, the signatures of the human gut microbiota after active Mycobacterium tuberculosis infection have not been fully understood. Here, we investigated changes in the gut microbiota in tuberculosis (TB) patients by shotgun sequencing the gut microbiomes of 31 healthy controls and 46 patients. We observed a dramatic changes in gut microbiota in tuberculosis patients as reflected by significant decreases in species number and microbial diversity. The gut microbiota of TB patients were mostly featured by the striking decrease of short-chain fatty acids (SCFAs)-producingbacteria as well as associated metabolic pathways. A classification model based on the abundance of three species, Haemophilus parainfluenzae, Roseburia inulinivorans, and Roseburia hominis, performed well for discriminating between healthy and diseased patients. Additionally, the healthy and diseased states can be distinguished by SNPs in the species of B. vulgatus. We present a comprehensive profile of changes in the microbiota in clinical TB patients. Our findings will shed light on the design of future diagnoses and treatments for M. tuberculosis infections

Gender-Specific Differences in Clinical Profile and Biochemical Parameters in Patients with Cushing's Disease: A Single Center Experience

Cushing's disease (CD) is remarkably prevalent among females; however, more severe clinical presentation and adverse outcomes have been found in males. The purpose of this study was to investigate the overall clinical profile and biochemical parameters in patients with CD to identify the gender differences. Here we describe our series of CD patients referred to our medical center during 2012-2013. Among 73 cases, females presented a marked preponderance compared to males. Males had significantly higher ACTH, BMI, HbA1c, systolic blood pressure, and hemoglobin than females. For the first time, the incidence of fatty liver and hepatic function was also shown to be elevated in males. Multiple linear regression analysis was performed to further investigate the correlation of risk factors with hypokalemia, HbA1c, and systolic blood pressure. Gender and serum cortisol were associated with hypokalemia. Age, gender, and serum cortisol were significantly associated with HbA1c. Additionally, only gender was significantly associated with systolic blood pressure. Regarding clinical presentation, purple striae seemed to occur more frequently in males than in females. Thus, more severe clinical presentation, biochemical parameters, and complications were found in males than in females. Clinical professionals should pay more attention to the diagnosis and management of males with CD

Variation in carbon isotope discrimination in Cleistogenes squarrosa (Trin.) Keng: patterns and drivers at tiller, local, catchment, and regional scales

Understanding the patterns and drivers of carbon isotope discrimination in C4 species is critical for predicting the effects of global change on C3/C4 ratio of plant community and consequently on ecosystem functioning and services. Cleistogenes squarrosa (Trin.) Keng is a dominant C4 perennial bunchgrass of arid and semi-arid ecosystems across the Mongolian plateau of the Eurasian steppe. Its carbon isotope discrimination (13Δ) during photosynthesis is relatively large among C4 species and it is variable. Here the 13Δ of C. squarrosa and its potential drivers at a nested set of scales were examined. Within cohorts of tillers, 13Δ of leaves increased from 5.1‰ to 8.1‰ from old to young leaves. At the local scale, 13Δ of mature leaves varied from 5.8‰ to 8.4‰, increasing with decreasing grazing intensity. At the catchment scale, 13Δ of mature leaves varied from 6.2‰ to 8.5‰ and increased with topsoil silt content. At the regional scale, 13Δ of mature leaves varied from 5.5‰ to 8.9‰, increasing with growing-season precipitation. At all scales, 13Δ decreased with increasing leaf nitrogen content (Nleaf). Nleaf was positively correlated with grazing intensity and leaf position along tillers, but negatively correlated with precipitation. The presence of the correlations across a range of different environmental contexts strongly implicates Nleaf as a major driver of 13Δ in C. squarrosa and, possibly, other C4 species

The GAAS Metagenomic Tool and Its Estimations of Viral and Microbial Average Genome Size in Four Major Biomes

Metagenomic studies characterize both the composition and diversity of uncultured viral and microbial communities. BLAST-based comparisons have typically been used for such analyses; however, sampling biases, high percentages of unknown sequences, and the use of arbitrary thresholds to find significant similarities can decrease the accuracy and validity of estimates. Here, we present Genome relative Abundance and Average Size (GAAS), a complete software package that provides improved estimates of community composition and average genome length for metagenomes in both textual and graphical formats. GAAS implements a novel methodology to control for sampling bias via length normalization, to adjust for multiple BLAST similarities by similarity weighting, and to select significant similarities using relative alignment lengths. In benchmark tests, the GAAS method was robust to both high percentages of unknown sequences and to variations in metagenomic sequence read lengths. Re-analysis of the Sargasso Sea virome using GAAS indicated that standard methodologies for metagenomic analysis may dramatically underestimate the abundance and importance of organisms with small genomes in environmental systems. Using GAAS, we conducted a meta-analysis of microbial and viral average genome lengths in over 150 metagenomes from four biomes to determine whether genome lengths vary consistently between and within biomes, and between microbial and viral communities from the same environment. Significant differences between biomes and within aquatic sub-biomes (oceans, hypersaline systems, freshwater, and microbialites) suggested that average genome length is a fundamental property of environments driven by factors at the sub-biome level. The behavior of paired viral and microbial metagenomes from the same environment indicated that microbial and viral average genome sizes are independent of each other, but indicative of community responses to stressors and environmental conditions

Two ultraviolet radiation datasets that cover China

Ultraviolet (UV) radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2005 to 2015. CUV3 broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization's measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2014. The mean absolute bias error and root-mean-square error are smaller than 30% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes