Prediction of spatial distribution characteristics of ecosystem functions based on a minimum data set of functional traits of desert plants

The relationship between plant functional traits and ecosystem function is a hot topic in current ecological research, and community-level traits based on individual plant functional traits play important roles in ecosystem function. In temperate desert ecosystems, which functional trait to use to predict ecosystem function is an important scientific question. In this study, the minimum data sets of functional traits of woody (wMDS) and herbaceous (hMDS) plants were constructed and used to predict the spatial distribution of C, N, and P cycling in ecosystems. The results showed that the wMDS included plant height, specific leaf area, leaf dry weight, leaf water content, diameter at breast height (DBH), leaf width, and leaf thickness, and the hMDS included plant height, specific leaf area, leaf fresh weight, leaf length, and leaf width. The linear regression results based on the cross-validations (FTEIW - L, FTEIA - L, FTEIW - NL, and FTEIA - NL) for the MDS and TDS (total data set) showed that the R2 (coefficients of determination) for wMDS were 0.29, 0.34, 0.75, and 0.57, respectively, and those for hMDS were 0.82, 0.75, 0.76, and 0.68, respectively, proving that the MDSs can replace the TDS in predicting ecosystem function. Then, the MDSs were used to predict the C, N, and P cycling in the ecosystem. The results showed that non-linear models RF and BPNN were able to predict the spatial distributions of C, N and P cycling, and the distributions showed inconsistent patterns between different life forms under moisture restrictions. The C, N, and P cycling showed strong spatial autocorrelation and were mainly influenced by structural factors. Based on the non-linear models, the MDSs can be used to accurately predict the C, N, and P cycling, and the predicted values of woody plant functional traits visualized by regression kriging were closer to the kriging results based on raw values. This study provides a new perspective for exploring the relationship between biodiversity and ecosystem function

Evolution of triclosan resistance modulates bacterial permissiveness to multidrug resistance plasmids and phages

The horizontal transfer of plasmids has been recognized as one of the key drivers for the worldwide spread of antimicrobial resistance (AMR) across bacterial pathogens. However, knowledge remain limited about the contribution made by environmental stress on the evolution of bacterial AMR by modulating horizontal acquisition of AMR plasmids and other mobile genetic elements. Here we combined experimental evolution, whole genome sequencing, reverse genetic engineering, and transcriptomics to examine if the evolution of chromosomal AMR to triclosan (TCS) disinfectant has correlated effects on modulating bacterial pathogen (Klebsiella pneumoniae) permissiveness to AMR plasmids and phage susceptibility. Herein, we show that TCS exposure increases the evolvability of K. pneumoniae to evolve TCS-resistant mutants (TRMs) by acquiring mutations and altered expression of several genes previously associated with TCS and antibiotic resistance. Notably, nsrR deletion increases conjugation permissiveness of K. pneumoniae to four AMR plasmids, and enhances susceptibility to various Klebsiella-specific phages through the downregulation of several bacterial defense systems and changes in membrane potential with altered reactive oxygen species response. Our findings suggest that unrestricted use of TCS disinfectant imposes a dual impact on bacterial antibiotic resistance by augmenting both chromosomally and horizontally acquired AMR mechanisms

Precise segmentation of densely interweaving neuron clusters using G-Cut

脑是宇宙间最为复杂的系统之一，成人的脑中有约1000亿个神经元，单个神经元通常与其它神经元有成千上万个“突触”连接节点，形成拥有百万亿级连接的极其复杂的脑神经网络。当前多数神经元三维重建和分析工具仅适用于单个神经元的形态学重建，难以从神经元簇图像中正确追踪重建出多个神经元，而神经元的重建质量又影响到量化分析神经元的形态学特征及其功能。针对这一问题，课题组提出一种新的三维神经元簇重建工具G-Cut。具体地，为了度量神经元胞体与神经突起间的关联性，课题组从已有的带有标注的大规模神经元形态学数据集统计分析得到其规律和形态学信息。然后将神经元簇的重建问题转化为神经突起之间连接所形成的拓扑连接图的图分割问题，并结合神经元形态学规律和信息，在所有的神经突起与神经元胞体的关联性中寻找重建问题的最优解。通过在不同的合成数据集以及真实的脑组织图像数据集上测试，和已有的方法相比，G-Cut在不同密度和不同规模的神经元簇图像上均获得了更高的重建正确率。该项研究工作由厦门大学，南加州大学，加州大学洛杉矶分校等高校课题组合作完成，厦门大学信息学院智能科学与技术系为第一完成单位，厦门大学博士生李睿和USC博士生Muye Zhu为论文共同第一作者，张俊松博士和南加州大学的Hong-Wei Dong教授为论文共同通讯作者。厦门大学周昌乐教授和南加州大学的Arthur Toga教授为研究提供了大力支持。【Abstract】Characterizing the precise three-dimensional morphology and anatomical context of neurons is crucial for neuronal cell type classification and circuitry mapping. Recent advances in tissue clearing techniques and microscopy make it possible to obtain image stacks of intact, interweaving neuron clusters in brain tissues. As most current 3D neuronal morphology reconstruction methods are only applicable to single neurons, it remains challenging to reconstruct these clusters digitally. To advance the state of the art beyond these challenges, we propose a fast and robust method named G-Cut that is able to automatically segment individual neurons from an interweaving neuron cluster. Across various densely interconnected neuron clusters, G-Cut achieves significantly higher accuracies than other state-of-the-art algorithms. G-Cut is intended as a robust component in a high throughput informatics pipeline for large-scale brain mapping projects.This work was supported by NIH/NIMH MH094360-01A1 (H.W.D.), MH094360-06 (H.W.D.), NIH/NCI U01CA198932-01 (H.W.D.), NIH/NIMH MH106008 (X.W.Y. and H.W.D.), National Nature Science Foundation of China No. 61772440 (J.S.Z.), and National Basic Research Program of China 2013CB329502 (J.S.Z. and C.L.Z.). We thank a support of Graduate Student International Exchange Project of Xiamen University to R.L. and State Scholarship Fund of China Scholarship Council (No. 201406315023) to J.S.Z. 该项研究得到国家自然科学基金、国家重点基础研究发展计划973项目、国家留学基金、厦门大学研究生国际交流项目、美国脑计划和NIH等课题资助

Horizontal gene transfer and shifts in linked bacterial community composition are associated with maintenance of antibiotic resistance genes during food waste composting

About 1.3 billion tons of food waste (FW) is annually produced at a global scale. A major fraction of FW is deposited into landfills thereby contributing to environmental pollution and emission of greenhouse gasses. While increasing amounts of FW are recycled more sustainably into fertilizers in industrial-scale composting, very little is known about the antibiotic resistance genes (ARGs) present in FW and how their abundance is affected by composting. To study this, we quantified the diversity and abundance of ARGs, mobile genetic elements (MGEs) and bacterial communities in the beginning, during and at the end of the FW composting. All targeted 27 ARGs and 5 MGEs were detected in every sample suggesting that composted FW remains a reservoir of ARGs and MGEs. While the composting drastically changed the abundance, composition and diversity of bacterial communities, an increase in total ARG and MGE abundances was observed. Changes in ARGs were linked with shifts in the composition of bacterial communities as revealed by a Procrustes analysis (P < 0.01). Crucially, even though the high composting temperatures reduced the abundance and diversity of initially ARG-associated bacterial taxa, ARG abundances were maintained in other associated bacterial taxa. This was likely driven by horizontal gene transfer and physicochemical composting properties as revealed by a clear positive correlation between ARGs, MGEs, pH, NO 3 − and moisture. Together our findings suggest that traditional composting is not efficient at removing ARGs and MGEs from FW. More effective composting strategies are thus needed to minimize ARG release from composted FW into agricultural environments

Scale Change and Correlation of Plant Functional Characteristics in the Desert Community of Ebinur Lake

The difference of functional traits among different species is the basis of species coexistence in natural ecosystems, and the variation of traits among individuals within species also plays an important role in species coexistence and distribution. Taking the desert plant community of Ebinur Lake as the research object, five plant functional characteristics were measured in 13 plants of 25 quadrats in the study area. The changes of these five functional characteristics by the method of character gradient analysis and the scale variation of plant functional traits and the correlation between their environments were studied. The results showed that: (1) the range of α value of the five plant functional characteristics in the community was larger than that of β value; that is, the change of the character value of a species relative to related symbiotic species was larger than that along the average character gradient of the community. (2) The correlations between leaf thickness and leaf area as well as between leaf thickness and leaf dry matter content were the strongest with correlation coefficients. That is, the correlations between LTH and SLA as well as between LTH and LDMC were stronger than that between the two species in the community, suggesting that the development of succession had no significant effect. The strategies used by dominant species to adapt to the environment changed from high-speed growth to improving resource utilization efficiency, while the coexisting species in the same community adopted different character combinations to adapt to the common community environment

Structural Stability Monitoring of a Physical Model Test on an Underground Cavern Group during Deep Excavations Using FBG Sensors

Fiber Bragg Grating (FBG) sensors are comprehensively recognized as a structural stability monitoring device for all kinds of geo-materials by either embedding into or bonding onto the structural entities. The physical model in geotechnical engineering, which could accurately simulate the construction processes and the effects on the stability of underground caverns on the basis of satisfying the similarity principles, is an actual physical entity. Using a physical model test of underground caverns in Shuangjiangkou Hydropower Station, FBG sensors were used to determine how to model the small displacements of some key monitoring points in the large-scale physical model during excavation. In the process of building the test specimen, it is most successful to embed FBG sensors in the physical model through making an opening and adding some quick-set silicon. The experimental results show that the FBG sensor has higher measuring accuracy than other conventional sensors like electrical resistance strain gages and extensometers. The experimental results are also in good agreement with the numerical simulation results. In conclusion, FBG sensors could effectively measure small displacements of monitoring points in the whole process of the physical model test. The experimental results reveal the deformation and failure characteristics of the surrounding rock mass and make some guidance for the in situ engineering construction