Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2

Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management

A general physics-based data-driven framework for numerical simulation and history matching of reservoirs

This paper proposed a general physics-based data-driven framework for numerical mod-eling and history matching of reservoirs that achieves a good balance of flow physics and actual field data. Underground reservoir is easily discretized in this framework as a flow network composed of one-dimensional connection elements, each of which is defined by two flow characteristic parameters. Each one-dimensional connection element is divided into some grids, and the cross-sectional area and permeability of the grids on the same connection element are equal. The fully implicit scheme of flow equations and the Newton iteration nonlinear solver concurrently solve all unknown quantities. Then, using actual field data, the simultaneous perturbation stochastic approximation algorithm is used to invert flow characteristic parameters of each connection element, and the unequal constraint that the volume of connection elements should not exceed the total reservoir volume is added to control the data-driven process. To demonstrate the unequal constraint is physical, a test case of a waterflooding reservoir with a high permeability zone is given. A waterflooding reservoir example with five injectors and four producers is used to demonstrate that this framework outperforms earlier techniques, and another case with single-phase depletion development is used to demonstrate that this framework has a high generalization for flow models. In addition, this data-driven framework based on physics is expected to serve as a reference for other fields of science and engineering.Cited as: Rao, X., Xu, Y., Liu, D., Liu, Y., Hu, Y. A general physics-based data-driven framework for numerical simulation and history matching of reservoirs. Advances in Geo-Energy Research, 2021, 5(4): 422-436, doi: 10.46690/ager.2021.04.0

Resource Efficiency Optimization Engine in Smart Production Networks via Intelligent Cloud Manufacturing Platforms

Abstract The aim of this paper is to develop an optimization engine to be implemented in a cloud manufacturing platform to promote resource efficiency in sharing of manufacturing services related to sheet metal cutting. The optimization engine allows to properly select the manufacturing service requests collected through the cloud platform, analyse the possible pairings with the supplier ongoing production orders and dynamically choose the best production strategy (e.g. incorporate, queue, prioritize or reject) considering the surface utilization rate of the metal sheets as key performance index. A simulation of different possible scenarios in terms of customer and supplier orders is reported to exemplify the diverse decision-making scheduling strategies proposed by the platform and the related quantification of resource efficiency improvement

Distance-Decay Relationship for Biological Wastewater Treatment Plants.

UnlabelledPatterns in the spatial distribution of organisms provide important information about mechanisms underlying biodiversity and the complexity of ecosystems. One of the most well-documented spatial patterns is the distance-decay relationship, which is a universal biogeographic pattern observed repeatedly for plant and animal communities, particularly for microorganisms in natural ecosystems such as soil, ocean, and salt marsh sediment. However, it is uncertain whether the microorganisms exhibit a distance-decay pattern in engineered ecosystems. Therefore, we measured the distance-decay relationship across various microbial functional and phylogenetic groups in 26 biological wastewater treatment plants (WWTPs) in China using a functional gene array (GeoChip 4.2). We found that microbial communities of activated sludge in WWTPs exhibited a significant but very weak distance-decay relationship. The taxon-area z values for different functional and phylogenetic groups were <0.0065, which is about 1 to 2 orders of magnitude lower than those observed in microbial communities elsewhere. Variation-partitioning analysis (VPA) showed that the relationships were driven by both environmental heterogeneity and geographic distance. Collectively, these results provided new insights into the spatial scaling of microbial communities in engineering ecosystems and highlighted the importance of environmental heterogeneity and geographic distance in shaping biogeographic patterns.ImportanceDetermining the distance-decay relationship of microbial biodiversity is important but challenging in microbial ecology. All studies to date are based on natural environments; thus, it remains unclear whether there is such a relationship in an engineered ecosystem. The present study shows that there is a very weak distance-decay relationship in an engineered ecosystem (WWTPs) at the regional-to-continental scale. This study makes fundamental contributions to a mechanistic, predictive understanding of microbial biogeography

Model and Data Agreement for Learning with Noisy Labels

Learning with noisy labels is a vital topic for practical deep learning as models should be robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning approach JoCoR fails when faced with a large ratio of noisy labels. Moreover, selecting small-loss samples can also cause error accumulation as once the noisy samples are mistakenly selected as small-loss samples, they are more likely to be selected again. In this paper, we try to deal with error accumulation in noisy label learning from both model and data perspectives. We introduce mean point ensemble to utilize a more robust loss function and more information from unselected samples to reduce error accumulation from the model perspective. Furthermore, as the flip images have the same semantic meaning as the original images, we select small-loss samples according to the loss values of flip images instead of the original ones to reduce error accumulation from the data perspective. Extensive experiments on CIFAR-10, CIFAR-100, and large-scale Clothing1M show that our method outperforms state-of-the-art noisy label learning methods with different levels of label noise. Our method can also be seamlessly combined with other noisy label learning methods to further improve their performance and generalize well to other tasks. The code is available in https://github.com/zyh-uaiaaaa/MDA-noisy-label-learning.Comment: Accepted by AAAI2023 Worksho

Nonlinear characteristic analysis of high-speed spatial parallel mechanism

In order to grasp the nonlinear characteristic of high-speed spatial parallel mechanism, the analysis of nonlinear characteristics for spatial parallel mechanism is investigated. The nonlinear elastic dynamic equation of 4-UPS-UPU high-speed spatial parallel mechanism is derived by kineto-elastodynamics theory, the dynamic equation is solved by numerical method, the nonlinear characteristic of the parallel mechanism is analyzed by phase diagram. Numerical results show that 4-UPS-UPU high-speed spatial parallel mechanism exhibits typical nonlinear characteristic during exercise, the factors, such as the motion trajectory of parallel mechanism, the material of driving limbs, the diameter of driving limbs and the mass of moving platform, are also have effect on nonlinear characteristics of parallel mechanism. Therefore the reasonable choice of the above factors can weaken the chaos motion. This researches provide important theoretical base of the chaos suppression for spatial parallel mechanism

The normal-auxeticity mechanical phase transition in graphene

When a solid object is stretched, in general, it shrinks transversely. However, the abnormal ones are auxetic, which exhibit lateral expansion, or negative Poisson ratio. While graphene is a paradigm 2D material, surprisingly, graphene converts from normal to auxetic at certain strains. Here, we show via molecular dynamics simulations that the normal-auxeticity mechanical phase transition only occurs in uniaxial tension along the armchair direction or the nearest neighbor direction. Such a characteristic persists at temperatures up to 2400 K. Besides monolayer, bilayer and multi-layer graphene also possess such a normal-auxeticity transition. This unique property could extend the applications of graphene to new horizons

Downramp-assisted underdense photocathode electron bunch generation in plasma wakefield accelerators

It is shown that the requirements for high quality electron bunch generation and trapping from an underdense photocathode in plasma wakefield accelerators can be substantially relaxed through localizing it on a plasma density downramp. This depresses the phase velocity of the accelerating electric field until the generated electrons are in phase, allowing for trapping in shallow trapping potentials. As a consequence the underdense photocathode technique is applicable by a much larger number of accelerator facilities. Furthermore, dark current generation is effectively suppressed.Comment: 4 pages, 3 figure

Laboratory-based Surveillance of Extensively Drug-Resistant Tuberculosis, China

To estimate the prevalence of extensively drug-resistant tuberculosis (XDR TB) in China, we retrospectively analyzed drug-resistance profiles of 989 clinical Mycobacterium tuberculosis isolates. We found 319 (32.3%) isolates resistant to >1 first-line drugs; 107 (10.8%) isolates were multidrug resistant, of which 20 (18.7%) were XDR. XDR TB is of major concern in China