Genome-wide analysis of adaptive molecular evolution in the carnivorous plant Utricularia gibba

The genome of the bladderwort Utricularia gibba provides an unparalleled opportunity to uncover the adaptive landscape of an aquatic carnivorous plant with unique phenotypic features such as absence of roots, development of water-filled suction bladders, and a highly ramified branching pattern. Despite its tiny size, the U. gibba genome accommodates approximately as many genes as other plant genomes. To examine the relationship between the compactness of its genome and gene turnover, we compared the U. gibba genome with that of four other eudicot species, defining a total of 17,324 gene families (orthogroups). These families were further classified as either 1) lineage-specific expanded/contracted or 2) stable in size. The U. gibba-expanded families are generically related to three main phenotypic features: 1) trap physiology, 2) key plant morphogenetic/developmental pathways, and 3) response to environmental stimuli, including adaptations to life in aquatic environments. Further scans for signatures of protein functional specialization permitted identification of seven candidate genes with amino acid changes putatively fixed by positive Darwinian selection in the U. gibba lineage. The Arabidopsis orthologs of these genes (AXR, UMAMIT41, IGS, TAR2, SOL1, DEG9, and DEG10) are involved in diverse plant biological functions potentially relevant for U. gibba phenotypic diversification, including 1) auxin metabolism and signal transduction, 2) flowering induction and floral meristem transition, 3) root development, and 4) peptidases. Taken together, our results suggest numerous candidate genes and gene families as interesting targets for further experimental confirmation of their functional and adaptive roles in the U. gibba's unique lifestyle and highly specialized body plan.

Neural mechanisms underlying adaptive actions after slips

An increase in cognitive control has been systematically observed in responses produced immediately after the commission of an error. Such responses show a delay in reaction time (post-error slowing) and an increase in accuracy. To characterize the neurophysiological mechanism involved in the adaptation of cognitive control, we examined oscillatory electrical brain activity by electroencephalogram and its corresponding neural network by event-related functional magnetic resonance imaging in three experiments. We identified a new oscillatory thetabeta component related to the degree of post-error slowing in the correct responses following an erroneous trial. Additionally, we found that the activity of the right dorsolateral prefrontal cortex, the right inferior frontal cortex, and the right superior frontal cortex was correlated with the degree of caution shown in the trial following the commission of an error. Given the overlap between this brain network and the regions activated by the need to inhibit motor responses in a stop-signal manipulation, we conclude that the increase in cognitive control observed after the commission of an error is implemented through the participation of an inhibitory mechanism.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

Exploring Adaptive Implementation of On-Chip Networks

As technology geometries have shrunk to the deep submicron regime, the communication delay and power consumption of global interconnections in high performance Multi- Processor Systems-on-Chip (MPSoCs) are becoming a major bottleneck. The Network-on- Chip (NoC) architecture paradigm, based on a modular packet-switched mechanism, can address many of the on-chip communication issues such as performance limitations of long interconnects and integration of large number of Processing Elements (PEs) on a chip. The choice of routing protocol and NoC structure can have a significant impact on performance and power consumption in on-chip networks. In addition, building a high performance, area and energy efficient on-chip network for multicore architectures requires a novel on-chip router allowing a larger network to be integrated on a single die with reduced power consumption. On top of that, network interfaces are employed to decouple computation resources from communication resources, to provide the synchronization between them, and to achieve backward compatibility with existing IP cores. Three adaptive routing algorithms are presented as a part of this thesis. The first presented routing protocol is a congestion-aware adaptive routing algorithm for 2D mesh NoCs which does not support multicast (one-to-many) traffic while the other two protocols are adaptive routing models supporting both unicast (one-to-one) and multicast traffic. A streamlined on-chip router architecture is also presented for avoiding congested areas in 2D mesh NoCs via employing efficient input and output selection. The output selection utilizes an adaptive routing algorithm based on the congestion condition of neighboring routers while the input selection allows packets to be serviced from each input port according to its congestion level. Moreover, in order to increase memory parallelism and bring compatibility with existing IP cores in network-based multiprocessor architectures, adaptive network interface architectures are presented to use multiple SDRAMs which can be accessed simultaneously. In addition, a smart memory controller is integrated in the adaptive network interface to improve the memory utilization and reduce both memory and network latencies. Three Dimensional Integrated Circuits (3D ICs) have been emerging as a viable candidate to achieve better performance and package density as compared to traditional 2D ICs. In addition, combining the benefits of 3D IC and NoC schemes provides a significant performance gain for 3D architectures. In recent years, inter-layer communication across multiple stacked layers (vertical channel) has attracted a lot of interest. In this thesis, a novel adaptive pipeline bus structure is proposed for inter-layer communication to improve the performance by reducing the delay and complexity of traditional bus arbitration. In addition, two mesh-based topologies for 3D architectures are also introduced to mitigate the inter-layer footprint and power dissipation on each layer with a small performance penalty.

Applications and Benefits of Adaptive Pulsed GMAW

In ship and offshore terminal construction, welded cross sections are thick and the number of welds very high. Consequently, there are two aspects of great importance; cost and heat input. Reduction in the welding operation time decreases the costs of the work force and avoids excessive heat, preventing distortion and other weld defects. The need to increase productivity while using a single wire in the GMAW process has led to the use of a high current and voltage to improve the melting rate. Unfortunately, this also increases the heat input. Innovative GMAW processes, mostly implemented for sheet plate sections, have shown significant reduction in heat input (Q), low distortion and increase in welding speed. The aim of this study is to investigate adaptive pulsed GMAW processes and assess relevant applications in the high power range, considering possible benefits when welding thicker sections and high yield strength steel. The study experimentally tests the usability of adaptive welding processes and evaluates their effects on weld properties, penetration and shapes of the weld bead.The study first briefly reviews adaptive GMAW to evaluate different approaches and their applications and to identify benefits in adaptive pulsed. Experiments are then performed using Synergic Pulsed GMAW, WiseFusionTM and Synergic GMAW processes to weld a T-joint in a horizontal position (PB). The air gap between the parts ranges from 0 to 2.5 mm. The base materials are structural steel grade S355MC and filler material G3Si1. The experiment investigates heat input, mechanical properties and microstructure of the welded joint. Analysis of the literature reveals that different approaches have been suggested using advanced digital power sources with accurate waveform, current, voltage, and feedback control. In addition, studies have clearly indicated the efficiency of lower energy welding processes. Interest in the high power range is growing and a number of different approaches have been suggested. The welding experiments in this study reveal a significant reduction of heat input and a weld microstructure with the presence of acicular ferrite (AF) beneficial for resistance to crack propagation. The WiseFusion bead had higher dilution, due to the weld bead shape, and low defects. Adaptive pulse GMAW processes can be a favoured choice when welding structures with many welded joints. The total heat reduction mitigates residual stresses and the bead shape allows a higher amperage limit. The stability of the arc during the process is virtually spatter free and allows an increase in welding speed.

Adaptive Routing Approaches for Networked Many-Core Systems

Through advances in technology, System-on-Chip design is moving towards integrating tens to hundreds of intellectual property blocks into a single chip. In such a many-core system, on-chip communication becomes a performance bottleneck for high performance designs. Network-on-Chip (NoC) has emerged as a viable solution for the communication challenges in highly complex chips. The NoC architecture paradigm, based on a modular packet-switched mechanism, can address many of the on-chip communication challenges such as wiring complexity, communication latency, and bandwidth. Furthermore, the combined benefits of 3D IC and NoC schemes provide the possibility of designing a high performance system in a limited chip area. The major advantages of 3D NoCs are the considerable reductions in average latency and power consumption. There are several factors degrading the performance of NoCs. In this thesis, we investigate three main performance-limiting factors: network congestion, faults, and the lack of efficient multicast support. We address these issues by the means of routing algorithms. Congestion of data packets may lead to increased network latency and power consumption. Thus, we propose three different approaches for alleviating such congestion in the network. The first approach is based on measuring the congestion information in different regions of the network, distributing the information over the network, and utilizing this information when making a routing decision. The second approach employs a learning method to dynamically find the less congested routes according to the underlying traffic. The third approach is based on a fuzzy-logic technique to perform better routing decisions when traffic information of different routes is available. Faults affect performance significantly, as then packets should take longer paths in order to be routed around the faults, which in turn increases congestion around the faulty regions. We propose four methods to tolerate faults at the link and switch level by using only the shortest paths as long as such path exists. The unique characteristic among these methods is the toleration of faults while also maintaining the performance of NoCs. To the best of our knowledge, these algorithms are the first approaches to bypassing faults prior to reaching them while avoiding unnecessary misrouting of packets. Current implementations of multicast communication result in a significant performance loss for unicast traffic. This is due to the fact that the routing rules of multicast packets limit the adaptivity of unicast packets. We present an approach in which both unicast and multicast packets can be efficiently routed within the network. While suggesting a more efficient multicast support, the proposed approach does not affect the performance of unicast routing at all. In addition, in order to reduce the overall path length of multicast packets, we present several partitioning methods along with their analytical models for latency measurement. This approach is discussed in the context of 3D mesh networks.

The genetics of behaviour and other adaptive traits in nine-spined sticklebacks (Pungitius pungitius)

One of the main goals in current evolutionary biology research is to identify genes behind adaptive phenotypic variations. The advances in genomic technologies have made it possible to identify genetic loci behind these variations, also concerning non-model species. This thesis investigates the genetics of the behaviour and other adaptive traits of the nine-spined stickleback (Pungitius pungitius) through the application of different genetic approaches. Fennoscandian nine-spined stickleback populations express large phenotypical differences especially in behaviour, life –history traits and morphology. However the underlying genetic bases for these phenotypical differences have not been studied in detail. The results of the project will lay the foundation for further genetics studies and provide valuable information for our understanding of the genetics of the adaptive divergence of the nine-spined stickleback. A candidate gene approach was used to develop microsatellite markers situating close to candidate genes for behaviour in the nine-spined stickleback. Altogether 13 markers were developed and these markers were used in the subsequent studies with the anonymous random markers and physiologically important gene markers which are already currently available for nine-spined sticklebacks. It was shown that heterozygosity correlated with behaviour in one of the marine nine-spined stickleback populations but with contrasting effects: correlations with behaviour were negative when using physiological gene markers and positive with random markers. No correlation was found between behavioural markers and behaviour. From the physiological gene markers, a strong correlation was found between osmoregulation-related gene markers and behaviour. These results indicate that both local (physiological) and general (random) effects are important in the shaping of behaviour and that heterozygosity– behaviour correlations are population dependent. In this thesis a second linkage map for nine-spined sticklebacks was constructed. Compared to the earlier nine-spined stickleback linkage map, genomic rearrangements were observed between autosomal (LG7) and sex-determing (LG12) linkage groups. This newly constructed map was used in QTL mapping studies in order to locate genomic regions associated with pelvic structures, behaviour and body size/growth. One major QTL was found for pelvic structures and Pitx1 gene was related to these traits as was predicted from three-spined stickleback studies, but this was in contrast to earlier nine-spined stickleback study. The QTL studies also revealed that behaviour and body size/growth were genetically more complex by having more QTL than pelvic traits. However, in many cases, pelvic structure, body size/growth and behaviour were linked to similar map locations indicating possible pleiotropic effects of genes locating in these QTL regions. Many of the gene related markers resided in the QTL area. In the future, studying these possible candidate genes in depth might reveal the underlying mechanism behind the measured traits.

An Hp-Adaptive Hierarchical Formulation for the Boundary Element Method Applied to Elasticity in Two Dimensions

This paper presents an HP-Adaptive Procedure with Hierarchical formulation for the Boundary Element Method in 2-D Elasticity problems. Firstly, H, P and HP formulations are defined. Then, the hierarchical concept, which allows a substantial reduction in the dimension of equation system, is introduced. The error estimator used is based on the residual computation over each node inside an element. Finally, the HP strategy is defined and applied to two examples.

Mathematics inspired by Darwin. Adaptive dynamics of dispersal and cooperation

In 1859, Charles Darwin published his theory of evolution by natural selection, the process occurring based on fitness benefits and fitness costs at the individual level. Traditionally, evolution has been investigated by biologists, but it has induced mathematical approaches, too. For example, adaptive dynamics has proven to be a very applicable framework to the purpose. Its core concept is the invasion fitness, the sign of which tells whether a mutant phenotype can invade the prevalent phenotype. In this thesis, four real-world applications on evolutionary questions are provided. Inspiration for the first two studies arose from a cold-adapted species, American pika. First, it is studied how the global climate change may affect the evolution of dispersal and viability of pika metapopulations. Based on the results gained here, it is shown that the evolution of dispersal can result in extinction and indeed, evolution of dispersalshould be incorporated into the viability analysis of species living in fragmented habitats. The second study is focused on the evolution of densitydependent dispersal in metapopulations with small habitat patches. It resulted a very surprising unintuitive evolutionary phenomenon, how a non-monotone density-dependent dispersal may evolve. Cooperation is surprisingly common in many levels of life, despite of its obvious vulnerability to selfish cheating. This motivated two applications. First, it is shown that density-dependent cooperative investment can evolve to have a qualitatively different, monotone or non-monotone, form depending on modelling details. The last study investigates the evolution of investing into two public-goods resources. The results suggest one general path by which labour division can arise via evolutionary branching. In addition to applications, two novel methodological derivations of fitness measures in structured metapopulations are given.

Adaptive value of ryegrass biotypes with low-level resistance and susceptible to the herbicide fluazifop and competitive ability with the wheat culture

Ryegrass is the main weed in wheat crop, causing yield loss due to competition by environmental resources. The objectives of this study were to estimate the fitness cost of ryegrass biotypes with low-level resistance and susceptible to fluazifop and to investigate the relative competitive ability of these biotypes between themselves and against the crop. Thus, fitness cost and competitive ability experiments were conducted under greenhouse conditions. For the fitness cost experiments, the low-level resistant ryegrass biotypes and those susceptible to fluazifop were used. For competitive ability, the treatments were arranged in a replacement series, with five proportions of the wheat cultivar FUNDACEP Horizonte and the low-level resistant and susceptible ryegrass biotypes 100:0, 75:25, 50:50, 25:75 and 0:100. Competitive analysis was carried out through diagrams applied to the replacement experiments and use of relative competitiveness indices. Variables evaluated were plant height, in the fitness cost experiment, and leaf area and shoot dry biomass in both experiments. The ryegrass biotypes show overall similar fitness cost and competitive ability. The wheat cultivar FUNDACEP Horizonte is superior in competitive ability to the ryegrass biotype with low-level resistance and equivalent to the susceptible biotype.

Adaptive control of Hydraulic Drive

Adaptive control systems are one of the most significant research directions of modern control theory. It is well known that every mechanical appliance’s behavior noticeably depends on environmental changes, functioning-mode parameter changes and changes in technical characteristics of internal functional devices. An adaptive controller involved in control process allows reducing an influence of such changes. In spite of this such type of control methods is applied seldom due to specifics of a controller designing. The work presented in this paper shows the design process of the adaptive controller built by Lyapunov’s function method for the Hydraulic Drive. The calculation needed and the modeling were conducting with MATLAB® software including Simulink® and Symbolic Math Toolbox™ etc. In the work there was applied the Jacobi matrix linearization of the object’s mathematical model and derivation of the suitable reference models based on Newton’s characteristic polynomial. The intelligent adaptive to nonlinearities algorithm for solving Lyapunov’s equation was developed. Developed algorithm works properly but considered plant is not met requirement of functioning with. The results showed confirmation that adaptive systems application significantly increases possibilities in use devices and might be used for correction a system’s behavior dynamics.