Unraveling the Composition of the Root-Associated Bacterial Microbiota of <i>Phragmites australis</i> and <i>Typha latifolia</i>

Phragmites australis and Typha latifolia are two macrophytes commonly present in natural and artificial wetlands. Roots of these plants engage in interactions with a broad range of microorganisms, collectively referred to as the microbiota. The microbiota contributes to the natural process of phytodepuration, whereby pollutants are removed from contaminated water bodies through plants. The outermost layer of the root corpus, the rhizoplane, is a hot-spot for these interactions where microorganisms establish specialized aggregates designated biofilm. Earlier studies suggest that biofilm-forming members of the microbiota play a crucial role in the process of phytodepuration. However, the composition and recruitment cue of the Phragmites, and Typha microbiota remain poorly understood. We therefore decided to investigate the composition and functional capacities of the bacterial microbiota thriving at the P. australis and T. latifolia root–soil interface. By using 16S rRNA gene Illumina MiSeq sequencing approach we demonstrated that, despite a different composition of the initial basin inoculum, the microbiota associated with the rhizosphere and rhizoplane of P. australis and T. latifolia tends to converge toward a common taxonomic composition dominated by members of the phyla Actinobacteria, Firmicutes, Proteobacteria, and Planctomycetes. This indicates the existence of a selecting process acting at the root–soil interface of these aquatic plants reminiscent of the one observed for land plants. The magnitude of this selection process is maximum at the level of the rhizoplane, where we identified different bacteria enriched in and discriminating between rhizoplane and rhizosphere fractions in a species-dependent and -independent way. This led us to hypothesize that the structural diversification of the rhizoplane community underpins specific metabolic capabilities of the microbiota. We tested this hypothesis by complementing the sequencing survey with a biochemical approach and scanning electron microscopy demonstrating that rhizoplane-enriched bacteria have a bias for biofilm-forming members. Together, our data will be critical to facilitate the rational exploitation of plant–microbiota interactions for phytodepuration

Software for replicating the results with the SKC and the KPC-A methods

Training and testing of clustering models with the KPC-A* and with the SKC** methods * Kernel clustering with approximate pseudo-centres (KPC-A) ** Semi-supervised kernel clustering with sample-to-cluster weights (SKC) The main purpose of this software is to replicate the experiments done in the publications listed below. The second purpose of this software is to allow others to re-use this software under the MIT license (see LICENSE). In case of re-use I kindly ask to cite the references below, where appropriate. Warning: The source code may consist of dead code, unused code (including unused parameters), wrongly documented code, or simply not working code parts. In short, it is (mainly) written for replicating the experiments and has not been (much) cleaned-up afterwards. No guarantees whatsoever. Currently supported baseline kernel methods: Kernel K-means (KKM), kernel fuzzy C-means (KFCM), relational neural gas (RNG). Supported kernel functions: Linear kernel, (normalized) polynomial kernel, Gaussian kernel. Evaluated data sets (all available from UCI): Gas, Pen, Cardiotocography, Activity, MiniBooNE. The software were used for the following articles: [1] Faußer, S. and Schwenker, F. (2012). "Clustering large datasets with kernel methods". In: Proceedings 21st International Conference on Pattern Recognition. (Tsukuba, Japan). ICPR ’12. IEEE Computer Society, pp. 501–504. [2] Faußer, S. and Schwenker, F. (2012). "Semi-Supervised Kernel Clustering with Sample-to-cluster Weights". In: Proceedings 1st IAPR TC3 Conference on Partially Supervised Learning. (Ulm, Germany). PSL’11. Springer, pp. 72–81. doi: 10.1007/978-3-642-28258-4_8. [3] Faußer, S. and Schwenker, F. (2014) "Semi-supervised Clustering of Large Data Sets with Kernel Methods". In: Pattern Recognition Letters 37, pp. 78–84. doi: 10.1016/j.patrec.2013.01.007. [4] Faußer, S. (2015). "Large state spaces and large data: Utilizing neural network ensembles in reinforcement learning and kernel methods for clustering". Doctoral thesis. URN: urn:nbn:de:bsz:289-vts-96149. URL: http://vts.uni-ulm.de/doc.asp?id=9614. In [1], the KPC-A method was introduced and in [2,3], the SKC method were proposed. Note that this software can be used to replicate the results of [4] only. Due to a lost seed, however, you won't get the very same results as in [4]. Still, with the seed set in this software, the results are in many cases identical to [4] or very close to them

Large state spaces and large data: Utilizing neural network ensembles in reinforcement learning and kernel methods for clustering

With reinforcement learning techniques, an agent learns an optimal policy by trial-and-error interaction with an environment. The integration of function approximation methods into reinforcement learning models allows for learning state-action values in large state spaces. Ensemble models can achieve more accurate and robust predictions than single learners. In this work, reinforcement learning ensembles are considered, where the members are artificial neural networks. It is analytically shown that the committees benefit from the diversity on the value estimations. The empirical evaluations on two large state space environments confirmed the theoretical results. A selective ensemble may further improve the predictions by selecting a subset of the models from the entire ensemble. In the thesis, an algorithm for ensemble subset selection is proposed. Experimentally, we found that selecting an informative subset of many agents may be more efficient than training full ensembles. In clustering, a model is built for discovering group-like structures in unobserved data. Over the last years, real-world data sets have become larger. However, an exact-solution model training method may not be able to learn from full large data sets due to the time complexity. Partitioning clustering methods with a linear time complexity can handle large data sets but mostly assume spherically-shaped clusters in the input space. In contrast, kernel-based methods may group the data in arbitrary shapes in the input space, but have a quadratic time complexity. This work focuses on an approximate kernel clustering approach and empirically evaluates it on five real-world data sets. In semi-supervised clustering, external information is partially used for improving the clustering results. A method (SKC) is proposed that exploits the class labels to influence the positions in the centres. In the experiments, SKC outperformed the baseline methods in the external cluster validation measures

Oxygen as dynamic parameter in internal aeration and the rhizosphere of wetland plants

In wetland ecosystems, actively ventilating plants strongly influence the oxidation of reduced substances from anaerobic soil layers by releasing oxygen (O2) from the roots to the substrate. The objective of this thesis was to connect in-situ plant-mediated O2 transport to submerged organs with O2 usage associated with the roots. Plant-internal O2 and carbon dioxide (CO2) concentrations were measured in-situ continuously and concomitantly over several days. A greenhouse experiment was performed to reveal the influence of water vapour pressure deficit (VPD) on aeration efficiency of submerged organs and the rhizosphere. The roots were analysed for associated biofilms and aerobic methane-oxidizing bacteria (MOB). The O2 concentrations assessed inside culms and rhizomes of Phragmites australis (Cav.) Trin. ex Steud. showed active pressurization of fresh air to submerged organs. Regulation mechanisms for aerobic metabolism were assumed during times with reduced O2 supply. The observed negative correlation of plant-internal O2 to CO2 emphasizes the strong inter-dependency of plant-mediated O2 supply and O2 consuming processes in the rhizosphere. Changing of VPD influenced the aeration patterns of rhizomes and rhizospheres of Typha angustifolia L. strongly. This effect is assumed to be induced by stomatal narrowing in response to increasing VPD in the surrounding atmosphere. Dense biofilms were detected on the roots of P. australis and T. latifolia L.. The proportions of MOB were higher than one third of total biofilm bacteria which demonstrates that the rhizosphere is highly influenced by methane diffusing from anaerobic soil layers. The present study confirms that the combined assessment of plant-internal O2 and CO2 concentrations can be used to estimate O2 supply to and O2 consumption in wetland soils. The large number of root-associated MOB indicates the high potential of methane consumption mediated through plants in wetland ecosystems

Selective Neural Network Ensembles in Reinforcement Learning

Abstract. Ensemble models can achieve more accurate predictions than single learners. Selective ensembles further improve the predictions by selecting an informative subset of the full ensemble. We consider reinforcement learning ensembles, where the members are neural networks. In this context we study a new algorithm for ensemble subset selection in reinforcement learning scenarios. The aim of the proposed learning strategy is to minimize the Bellman errors of the collected states. In the empirical evaluation, two benchmark applications with large state spaces have been considered, namely SZ-Tetris and generalized maze. Here, our selective ensemble algorithm significantly outperforms other approaches.