Bridging the gap between omics and earth system science to better understand how environmental change impacts marine microbes

The advent of genomic-, transcriptomic- and proteomic-based approaches has revolutionized our ability to describe marine microbial communities, including biogeography, metabolic potential and diversity, mechanisms of adaptation, and phylogeny and evolutionary history. New interdisciplinary approaches are needed to move from this descriptive level to improved quantitative, process-level understanding of the roles of marine microbes in biogeochemical cycles and of the impact of environmental change on the marine microbial ecosystem. Linking studies at levels from the genome to the organism, to ecological strategies and organism and ecosystem response, requires new modelling approaches. Key to this will be a fundamental shift in modelling scale that represents micro-organisms from the level of their macromolecular components. This will enable contact with omics data sets and allow acclimation and adaptive response at the phenotype level (i.e. traits) to be simulated as a combination of fitness maximization and evolutionary constraints. This way forward will build on ecological approaches that identify key organism traits and systems biology approaches that integrate traditional physiological measurements with new insights from omics. It will rely on developing an improved understanding of ecophysiology to understand quantitatively environmental controls on microbial growth strategies. It will also incorporate results from experimental evolution studies in the representation of adaptation. The resulting ecosystem-level models can then evaluate our level of understanding of controls on ecosystem structure and function, highlight major gaps in understanding and help prioritize areas for future research programs. Ultimately, this grand synthesis should improve predictive capability of the ecosystem response to multiple environmental drivers

Community Detection in Networks using Bio-inspired Optimization: Latest Developments, New Results and Perspectives with a Selection of Recent Meta-Heuristics

Detecting groups within a set of interconnected nodes is a widely addressed prob- lem that can model a diversity of applications. Unfortunately, detecting the opti- mal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to pro- viding an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti-Fortunato-Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform com- petitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms

Mutable composite firefly algorithm for gene selection in microarray based cancer classification

Cancer classification is critical due to the strenuous effort required in cancer treatment and the rising cancer mortality rate. Recent trends with high throughput technologies have led to discoveries in terms of biomarkers that successfully contributed to cancerrelated issues. A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. However, the existing hybrid approaches with metaheuristic optimization algorithms in feature selection (specifically in gene selection) are not generalized enough to efficiently classify most cancer microarray data while maintaining a small set of genes. This leads to the classification accuracy and genes subset size problem. Hence, this study proposed to modify the Firefly Algorithm (FA) along with the Correlation-based Feature Selection (CFS) filter for the gene selection task. An improved FA was proposed to overcome FA slow convergence by generating mutable size solutions for the firefly population. In addition, a composite position update strategy was designed for the mutable size solutions. The proposed strategy was to balance FA exploration and exploitation in order to address the local optima problem. The proposed hybrid algorithm known as CFS-Mutable Composite Firefly Algorithm (CFS-MCFA) was evaluated on cancer microarray data for biomarker selection along with the deployment of Support Vector Machine (SVM) as the classifier. Evaluation was performed based on two metrics: classification accuracy and size of feature set. The results showed that the CFS-MCFA-SVM algorithm outperforms benchmark methods in terms of classification accuracy and genes subset size. In particular, 100 percent accuracy was achieved on all four datasets and with only a few biomarkers (between one and four). This result indicates that the proposed algorithm is one of the competitive alternatives in feature selection, which later contributes to the analysis of microarray data

Molecular Phylogenetics and Historical Biogeography of Basal Angiosperms : A Case Study in Nymphaeales

Scientific progress during the last two decades has greatly improved our knowledge on phylogenetic relationships among major lineages of flowering plants. Besides the two major groups of angiosperms, the eudicots and the monocots, there are several not closely related lineages that are generally referred to as the "basal angiosperms". Among those lineages, Amborella, Nymphaeales and Austrobaileyales are currently assumed to be successive sisters to the rest of angiosperms, thus forming a "basal grade". However, the phylogenetic relationships among and within the basal angiosperm lineages are still not convincingly resolved, which is to a large extent due to the persisting need for efficient molecular markers at this taxonomic level. The focus of the present thesis is on the phylogenetic and biogeographic history of the order Nymphaeales, a relatively small order comprising water lilies and other water plants. To clarify the phylogeny of Nymphaeales a new molecular marker, the petD intron, was developed. The molecular evolution of this non-coding region of the chloroplast genome was examined and its suitability as a new marker for resolving basal angiosperm relationships was proved. This study also revealed a great potential of microstructural changes as phylogenetic markers. In a second step the petD intron was chosen as a marker - together with other fast evolving chloroplast regions (rpl16 intron, trnK intron, matK gene, and the trnT-trnF region) to elucidate details of phylogenetic relationships in Nymphaeales. With this comprehensive analysis of Nymphaeales the monophyly of the Cabombaceae could be confirmed, but there is no convincing support for the monophyly of Nymphaeaceae with respect to Nuphar. Furthermore, the genus Nymphaea is inferred to be polyphyletic with respect to the genera Ondinea, Victoria and Euryale. In fact, Victoria and Euryale are inferred to be closely related to a clade comprising all night blooming water lilies (Nymphaea subgenera Hydrocallis and Lotos). The Australian endemic Ondinea forms a highly supported clade with the Australian water lilies Nymphaea subg. Anecphya. A detailed examination of relationships among Australian water lilies using chloroplast and nuclear markers (ITS, trnT-trnF) confirmed the close affinity of Ondinea to N. subg. Anecphya, and within this subgenus especially to Nymphaea hastifolia. The ITS data set resolved two well supported clades in Anecphya, the small-seeded and large-seeded group, with Ondinea and N. hastifolia being part of the small-seeded group. Observed polymorphisms among ITS paralogues points to recent hybridisation or introgression in this group. The rather young radiation of water lilies in Australia gave rise to one of the centres of diversity in Nymphaeales. Other centres of diversity are northern South America and South-Central Africa, which correspond to likewise recent radiations in the water lily subgenera Hydrocallis and Brachyceras. The radiation of core Nymphaeaceae, i.e. Nymphaea, Victoria, Euryale and Ondinea, occurred in the Tertiary around the Eocene-Oligocene boundary and is correlated with strong global cooling, the demise of the boreotropical flora and the segregation of northern hemispheric continents. The northern hemisphere, and possibly also South America, is inferred to be the ancestral range of Nymphaeales. Other Gondwanan continents such as Africa or Australia have been invaded by water lilies rather recently. Some findings of this thesis are of more general significance - beyond Nymphaeales or basal angiosperms: The present study provides another peace of evidence for the general utility of non-coding, fast-evolving chloroplast genomic regions and of microstructural changes as phylogenetic markers. Furthermore, the Nymphaeales study exemplifies the importance of judicious taxon sampling for correct phylogenetic inference. Only the combination of well-supported evidence from molecular phylogenetics, earth history and the fossil record with a thorough consideration of biological and ecological factors allows reasonable conclusions on the evolution of a lineage in space and time

免疫学的および進化的アルゴリズムに基づく改良された群知能最適化に関する研究

富山大学・富理工博甲第175号・楊玉・2020/3/24富山大学202

Ecological and Genetic Consequences of Climate Change Impacting Species Distributions, with Specific Cases in Whitebark Pine (Pinus albicaulis Engelm.)

Climate is widely recognized as a primary factor contributing to species distribution limits. In turn, species’ geographic ranges have contracted and expanded in concert with Earth’s climatic history and are predicted to shift in response to future climate change. We investigated the ecological and genetic consequences of species distributions in flux with their historical, current, and projected future climatic environments. Chapter 1 introduces themes and background information related to the thesis research found in Chapters 2 through 4. In Chapter 2, we questioned the extent to which ancestral climate-related traits are conserved through vast periods of evolutionary time spanning the Cenozoic. We found a significant correlation between mean annual temperatures experienced by ancestral plant species and those of their modern descendants, supporting the theory that plant lineages have largely tracked suitable climates throughout Earth’s history. In Chapter 3, we used niche modeling applications to predict the locations where whitebark pine (Pinus albicaulis Engelm.), a threatened high elevation tree species, may seek refuge with future climate change. We found regions dominated by Wilderness Areas where whitebark pine is predicted to perform well, and our results supported the species future climatic niche shift with respect to elevation but not latitude. We suggested offsetting the species’ expected reduction in high elevation habitat by returning to a more prevalent fire regime, which was historically important to the species survival among competitive shade tolerant taxa at lower elevations. In Chapter 4, we examined the genetic consequences of the Last Glacial Maximum on phylogeographic patterns of whitebark pine. Significant patterns of isolation by distance were detected by latitude and longitude, and species population structure was poorly defined. Whitebark pine genetic partitioning was low within and among populations, which may benefit the species as it becomes increasingly endangered by pests, pathogens, climate change, and reduced fire frequency. As the threat of anthropogenic climate change is on the rise, future studies will benefit conservation efforts by further untangling the array of ecological and genetic impacts associated with a changing climate. Chapter 5 presents the thesis conclusion, tying together research findings from Chapters 2 through 4