Land-use, landform, and seasonal-dependent changes in microbial communities and their impact on nitrous oxide emission activities

The greenhouse gas nitrous oxide (N2O) is produced mainly by the microbial processes of nitrification and denitrification. I hypothesized that microbial community structure (composition and abundance) is linked to differences in soil N2O emissions from these two processes. Microbial community composition (type and number of nitrifier and denitrifier genotypes), abundance and N2O emission activity were determined and compared for soils from two landscapes characteristic of the North American “prairie pothole region” (cultivated vs. uncultivated wetlands). The landscape difference in composition of individual microbial communities was not predictive of soil N2O emissions, indicating that there is redundancy in each microbial community in relation to N2O emission activities. However, community factors influenced the pattern and distribution of N2O emission from the soils of the study site. For example, nitrification was the dominant N2O emitting process for soils of all landforms. However, neither nitrifier amoA abundance nor community composition had predictive relationships with nitrification associated N2O emissions. This lack of relationship may be a consequence of using amoA as the gene target to characterize nitrifiers. For denitrifying bacteria, there was a temporal relationship between community composition and N2O emissions. However, this may be related to the change in water-filled pore space over time. Alternatively, the presence of fungi can be linked directly to N2O emissions from water accumulating landform elements. Under hypoxic conditions, there may be two fungal pathways contributing to N2O release: fungal denitrification via P450nor and fungal heterotrophic nitrification. Results suggest that the relative importance of these two processes is linked to root exudates such as formate. It is the interaction between the seasonal fluctuations of the microbial and environmental factors that determine the level of N2O emissions from soils

Tanimoto metric in Tree-SOM for improved representation of mass spectrometry data with an underlying taxonomic structure

Simmuteit S, Schleif F-M, Villmann T, Elssner T. Tanimoto metric in Tree-SOM for improved representation of mass spectrometry data with an underlying taxonomic structure. In: Proceedings of ICMLA 2009. IEEE Press; 2009: 563--567

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp