RESPONSES OF SOIL MICROBIAL COMMUNITIES TO CLIMATE WARMING

Strong scientific evidence supports that anthropogenic activities since industrialization have caused instability in earth’s climate, featured by increasing global surface temperature, increasing greenhouse gas concentration in the atmosphere. Climate change has then caused a series of changes in the earth’s ecosystems, which can have significant impacts on the biosphere and our human being. Although huge efforts have been put into the research in climate science since the past century, due to the complexity of the climate system and its broad and long-lasting influence, there are still countless question marks and uncertainties in our understanding of climate change and its influence on earth and human society. Microorganisms are among the tiniest groups of life, but play important roles in the cycling of carbon and other nutrient elements in the biosphere. However, their response and feedback to climate warming in different ecosystems is still difficult to predict, limited by the lack of mechanistic understanding of the complex microbial community, their functions, their interactions among themselves and under warming perturbation. With the fast advance of high-throughput metagenomic technologies and the development of environmental microbiology, deep and detailed characterization of microbial diversity and functions became available, which provided great chances in promoting our insights into the mechanisms by which microbial communities mediate the carbon balance in a warmer world. This dissertation applied several metagenomic technologies to probe the soil microbial community responses to warming and permafrost thaw based on field observations and experiments in two ecosystems, a permafrost underlain Alaska tundra, and a temperate tall grass prairie in Oklahoma. Microbial decomposition of soil carbon in high latitude tundra underlain with permafrost is one of the most important, but poorly understood, potential positive feedbacks of greenhouse gas emissions from terrestrial ecosystems into the atmosphere in a warmer world. On the other hand, temperate grassland provided a contrast to the cold weather and huge soil carbon storage in the tundra, allowing the comparison of different ecosystems in terms of their sensitivity and vulnerability to warming. In the beginning of this work, we sought answers to the question that how microbial functional diversity was affected by regional warming induced long-term permafrost thaw. Soil columns were collected from a tundra site where three locations with different lengths of permafrost degradation history were on record. A functional gene array (i.e. GeoChip 4.2) was used to analyze the functional capacities of soil microbial communities in these samples. Compared with the minimally thawed site, the number of detected functional gene probes across the 15-65 cm depth profile at the moderately and extensively thawed sites decreased by 25 % and 5 %, while the community functional gene β-diversity increased by 34% and 45%, respectively, revealing decreased functional gene richness but increased community heterogeneity along the thaw progression. Particularly, the moderately thawed site contained microbial communities with the highest abundances of many genes involved in prokaryotic C degradation, ammonification, and nitrification processes, but lower abundances of fungal C decomposition and anaerobic-related genes. Significant correlations were observed between functional gene abundance and vascular plant primary productivity, suggesting that plant growth and species composition could be co-evolving traits together with microbial community composition. This study reveals the complex responses of microbial functional potentials to thaw related soil and plant changes, and provides information on potential microbially mediated biogeochemical cycles in tundra ecosystems. Next, a field warming experiment was set up to increase the winter soil temperature in tundra by snow cover coupled with spring snow removal. Using integrated metagenomic technologies, we showed that the microbial functional community structure in the active layer of tundra soil was significantly altered after only 1.5 years of warming, a rapid response demonstrating the high sensitivity of this ecosystem to warming. The abundance of microbial functional genes involved in both aerobic and anaerobic C decomposition was also markedly increased by this short-term warming. Consistent with this, ecosystem respiration (Reco) increased up to 38%. In addition, warming enhanced genes involved in nutrient cycling, which likely contributed to an observed increase (30%) in gross primary productivity (GPP). However, the GPP increase did not offset the extra Reco, resulting in significantly more net C loss in warmed plots compared to control plots. Altogether, our results demonstrate the vulnerability of active layer soil C in this permafrost-based tundra ecosystem to climate warming and the importance of microbial communities in mediating such vulnerability. Then, we conducted quantitative comparisons of the responses of soil microbial communities to warming at tundra and the prairie ecosystems. Climate warming has been differentially increasing the global surface temperature, with the greatest temperature elevation observed in the northern high-latitude regions. Although tundra and underlain permafrost in those areas were predicted vulnerable to climate warming, few quantitative comparisons were reported between tundra and other grassland ecosystems, especially of the composition and structure of soil microbial communities and their functional diversity. We compared the early responses of soil microbial composition and functional gene abundance to experimental warming between a tundra site and a temperate tall grass prairie using several metagenomic technologies, including functional gene microarray, amplicon sequencing, and metagenomic shotgun sequencing. Despite distinct species and functional gene pools in soils from the two ecosystems, genes involved in carbon and nitrogen cycling showed positive responses to warming at both sites, but with 36% more significantly responding genes and a greater magnitude of response for 10 genes at the tundra site. The functional gene compositions were correlated with temperature, moisture, ecosystem respiration and gross primary production at the tundra sites, but mostly with substrate related variables, plant biomass and nitrate concentration, at the prairie, implying different limiting factors in microbial growth and functions. These results revealed the higher sensitivity of tundra soil microbial communities to warming, compared with those from temperature prairie, and provided field evidence in supporting that northern high-latitude regions might be more vulnerable to climate warming. At last, we extended our exploration of the warming influence on the microbial community to the interaction of microorganism in the community by constructing co-occurrence networks for a time series sample set from the temperate prairie. Although intensive reports have shown that warming can influence the soil microbial community composition and structure, little is clear about how the microbial interactions among themselves would be influenced. Here, soil microbial co-occurrence networks were constructed using 16S rRNA gene amplicon sequences extracted from monthly samples collected in a long-term field warming experiment on a Central Oklahoma grassland. We observed substantially larger and more connected networks for warmed communities compared with control, despite huge variation in network structures along season. The increase in network complexity under warming was concurrent to decreased phylogenetic diversity, reflecting environmental filtering and increased functional association in altered soil and vegetation conditions. A portion of identified keystone taxa, which play important roles in network topology, reoccur in different networks, representing a preserved prominent group in grassland soils across the season and under warming. The structure of microbial networks introduced a dimension beyond species abundance, which revealed more complicated responses of microbial communities to climate warming. Overall, this work provided valuable field evidence on microbial community’s response to climate warming, revealed different sensitivities of these responses in tundra and prairie, and captured the seasonal dynamics of soil microbial interactions under warming influence. Many of these findings represent novel insights into our understanding of the microbial-mediated carbon cycle in a warmer world, from which new hypothesis could be formulated, tested, and generate knowledge essential for including the microbial contributions to earth system models, eventually a better prediction of the future climate

Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition

Cross-Database Micro-Expression Recognition (CDMER) aims to develop the Micro-Expression Recognition (MER) methods with strong domain adaptability, i.e., the ability to recognize the Micro-Expressions (MEs) of different subjects captured by different imaging devices in different scenes. The development of CDMER is faced with two key problems: 1) the severe feature distribution gap between the source and target databases; 2) the feature representation bottleneck of ME such local and subtle facial expressions. To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i.e., the salient facial region selection. Compared with previous transfer group sparse methods, our proposed TGSR has the ability to select the salient facial regions, which is effective in alleviating the aforementioned problems for better performance and reducing the computational cost at the same time. We use two public ME databases, i.e., CASME II and SMIC, to evaluate our proposed TGSR method. Experimental results show that our proposed TGSR learns the discriminative and explicable regions, and outperforms most state-of-the-art subspace-learning-based domain-adaptive methods for CDMER

Dual antiplatelet therapy after percutaneous coronary intervention in patients at high bleeding risk: A systematic review and meta-analysis

Background: To date, it has not been ascertained whether shortening the duration of dual antiplatelet therapy (DAPT) can benefit high bleeding risk (HBR) patients. This systematic review and meta-analysis was performed to investigate the safety and efficacy of short (≤ 3 months) DAPT in HBR patients after percutaneous coronary intervention (PCI). Methods: The PubMed, Embase, and Clinical Trials databases were searched from inception until November 2021 to identify studies that evaluated the safety and efficacy of short DAPT in HBR patients implanted with new-generation drug-eluting stents (DES). Primary endpoints included major bleeding, definite or probable stent thrombosis (ST), and myocardial infarction (MI), while secondary endpoints included all-cause death and ischemic stroke. Based on the fixed and random effect model, the risk ratio (RR) and 95% confidence interval of each endpoint were measured. Results: Five observational studies and one randomized controlled trial were included, involving 15,432 HBR patients. Short DAPT for HBR patients undergoing PCI had a lower incidence of major bleeding in comparison with standard (> 3 months) DAPT (2.3% vs. 3.2%, RR 0.64 [0.44, 0.95], p = 0.03), while short DAPT was comparable to standard DAPT with regard to definite or probable ST (0.4% vs. 0.3%, RR 1.31 [0.77, 2.23], p = 0.32) and MI (2.4% vs. 2.0%, RR 1.17 [0.95, 1.45], p = 0.14). Conclusions: Among HBR patients implanted with new-generation DES, short DAPT was associated with reduced risk of major bleeding without significantly increasing the risk of definite or probable ST and MI in comparison with standard DAPT

Successional change in species composition alters climate sensitivity of grassland productivity.

Succession theory predicts altered sensitivity of ecosystem functions to disturbance (i.e., climate change) due to the temporal shift in plant community composition. However, empirical evidence in global change experiments is lacking to support this prediction. Here, we present findings from an 8-year long-term global change experiment with warming and altered precipitation manipulation (double and halved amount). First, we observed a temporal shift in species composition over 8 years, resulting in a transition from an annual C3 -dominant plant community to a perennial C4 -dominant plant community. This successional transition was independent of any experimental treatments. During the successional transition, the response of aboveground net primary productivity (ANPP) to precipitation addition magnified from neutral to +45.3%, while the response to halved precipitation attenuated substantially from -17.6% to neutral. However, warming did not affect ANPP in either state. The findings further reveal that the time-dependent climate sensitivity may be regulated by successional change in species composition, highlighting the importance of vegetation dynamics in regulating the response of ecosystem productivity to precipitation change

Functional Gene Array-Based Ultrasensitive and Quantitative Detection of Microbial Populations in Complex Communities.

While functional gene arrays (FGAs) have greatly expanded our understanding of complex microbial systems, specificity, sensitivity, and quantitation challenges remain. We developed a new generation of FGA, GeoChip 5.0, using the Agilent platform. Two formats were created, a smaller format (GeoChip 5.0S), primarily covering carbon-, nitrogen-, sulfur-, and phosphorus-cycling genes and others providing ecological services, and a larger format (GeoChip 5.0M) containing the functional categories involved in biogeochemical cycling of C, N, S, and P and various metals, stress response, microbial defense, electron transport, plant growth promotion, virulence, gyrB, and fungus-, protozoan-, and virus-specific genes. GeoChip 5.0M contains 161,961 oligonucleotide probes covering >365,000 genes of 1,447 gene families from broad, functionally divergent taxonomic groups, including bacteria (2,721 genera), archaea (101 genera), fungi (297 genera), protists (219 genera), and viruses (167 genera), mainly phages. Computational and experimental evaluation indicated that designed probes were highly specific and could detect as little as 0.05 ng of pure culture DNAs within a background of 1 μg community DNA (equivalent to 0.005% of the population). Additionally, strong quantitative linear relationships were observed between signal intensity and amount of pure genomic (∼99% of probes detected; r > 0.9) or soil (∼97%; r > 0.9) DNAs. Application of the GeoChip to a contaminated groundwater microbial community indicated that environmental contaminants (primarily heavy metals) had significant impacts on the biodiversity of the communities. This is the most comprehensive FGA to date, capable of directly linking microbial genes/populations to ecosystem functions.IMPORTANCE The rapid development of metagenomic technologies, including microarrays, over the past decade has greatly expanded our understanding of complex microbial systems. However, because of the ever-expanding number of novel microbial sequences discovered each year, developing a microarray that is representative of real microbial communities, is specific and sensitive, and provides quantitative information remains a challenge. The newly developed GeoChip 5.0 is the most comprehensive microarray available to date for examining the functional capabilities of microbial communities important to biogeochemistry, ecology, environmental sciences, and human health. The GeoChip 5 is highly specific, sensitive, and quantitative based on both computational and experimental assays. Use of the array on a contaminated groundwater sample provided novel insights on the impacts of environmental contaminants on groundwater microbial communities

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates

Microbial functional diversity covaries with permafrost thaw-induced environmental heterogeneity in tundra soil.

Permafrost soil in high latitude tundra is one of the largest terrestrial carbon (C) stocks and is highly sensitive to climate warming. Understanding microbial responses to warming-induced environmental changes is critical to evaluating their influences on soil biogeochemical cycles. In this study, a functional gene array (i.e., geochip 4.2) was used to analyze the functional capacities of soil microbial communities collected from a naturally degrading permafrost region in Central Alaska. Varied thaw history was reported to be the main driver of soil and plant differences across a gradient of minimally, moderately, and extensively thawed sites. Compared with the minimally thawed site, the number of detected functional gene probes across the 15-65 cm depth profile at the moderately and extensively thawed sites decreased by 25% and 5%, while the community functional gene β-diversity increased by 34% and 45%, respectively, revealing decreased functional gene richness but increased community heterogeneity along the thaw progression. Particularly, the moderately thawed site contained microbial communities with the highest abundances of many genes involved in prokaryotic C degradation, ammonification, and nitrification processes, but lower abundances of fungal C decomposition and anaerobic-related genes. Significant correlations were observed between functional gene abundance and vascular plant primary productivity, suggesting that plant growth and species composition could be co-evolving traits together with microbial community composition. Altogether, this study reveals the complex responses of microbial functional potentials to thaw-related soil and plant changes and provides information on potential microbially mediated biogeochemical cycles in tundra ecosystems