Genetic Control of Plant Development by Overriding a Geometric Division Rule

Formative cell divisions are critical for multicellular patterning. In the early plant embryo, such divisions follow from orienting the division plane. A major unanswered question is how division plane orientation is genetically controlled, and in particular whether this relates to cell geometry. We have generated a complete 4D map of early Arabidopsis embryogenesis and used computational analysis to demonstrate that several divisions follow a rule that uses the smallest wall area going through the center of the cell. In other cases, however, cell division clearly deviates from this rule, which invariably leads to asymmetric cell division. By analyzing mutant embryos and through targeted genetic perturbation, we show that response to the hormone auxin triggers a deviation from the ``shortest wall'' rule. Our work demonstrates that a simple default rule couples division orientation to cell geometry in the embryo and that genetic regulation can create patterns by overriding the default rule.

Optimal ground control points for geometric correction using genetic algorithm with glboal accuracy

Establishing the ground control point (GCP) network is a pre-requisite for georeferencing raw image data. Given current typical digital spatial database quality, much interest among users is about the accuracy of the geometric correction model that yields the final product. This paper reports an approach to optimizing GCP assembly using a genetic/evolution algorithm. The paper also suggests an optimal criterion for accuracy assessment through appraisal of global accuracy of the transformation, which is computed at each point of the image space. Experimental results demonstrate that the proposed approach has a great potential for selection of the best GCPs, and considerable improvement to the accuracy of geometric correction models can be expected when it is implemented.

QoS-Based Web Service Scheduling Accommodating Inter-Service Dependencies Using Minimal-Conflict Hill-Climbing Repair Genetic Algorithm

In the filed of semantic grid, QoS-based Web service scheduling for workflow optimization is an important problem.However, in semantic and service rich environment like semantic grid, the emergence of context constraints on Web services is very common making the scheduling consider not only quality properties of Web services, but also inter service dependencies which are formed due to the context constraints imposed on Web services. In this paper, we present a repair genetic algorithm, namely minimal-conflict hill-climbing repair genetic algorithm, to address scheduling optimization problems in workflow applications in the presence of domain constraints and inter service dependencies. Experimental results demonstrate the scalability and effectiveness of the genetic algorithm.

QoS-based web service composition accommodating inter-service dependencies using minimal-conflict , hill-climbing repair genetic algorithm

In the field of semantic grid, QoS-based Web service composition is an important problem. In semantic and service rich environment like semantic grid, the emergence of context constraints on Web services is very common making the composition consider not only QoS properties of Web services, but also inter service dependencies and conflicts which are formed due to the context constraints imposed on Web services. In this paper, we present a repair genetic algorithm, namely minimal-conflict hill-climbing repair genetic algorithm, to address the Web service composition optimization problem in the presence of domain constraints and inter service dependencies and conflicts. Experimental results demonstrate the scalability and effectiveness of the genetic algorithm.

QoS-aware web service composition using genetic algorithms

Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.

Semantic service discovery in the service ecosystem

Electronic services are a leitmotif in ‘hot’ topics like Software as a Service, Service Oriented Architecture (SOA), Service oriented Computing, Cloud Computing, application markets and smart devices. We propose to consider these in what has been termed the Service Ecosystem (SES). The SES encompasses all levels of electronic services and their interaction, with human consumption and initiation on its periphery in much the same way the ‘Web’ describes a plethora of technologies that eventuate to connect information and expose it to humans. Presently, the SES is heterogeneous, fragmented and confined to semi-closed systems. A key issue hampering the emergence of an integrated SES is Service Discovery (SD). A SES will be dynamic with areas of structured and unstructured information within which service providers and ‘lay’ human consumers interact; until now the two are disjointed, e.g., SOA-enabled organisations, industries and domains are choreographed by domain experts or ‘hard-wired’ to smart device application markets and web applications. In a SES, services are accessible, comparable and exchangeable to human consumers closing the gap to the providers. This requires a new SD with which humans can discover services transparently and effectively without special knowledge or training. We propose two modes of discovery, directed search following an agenda and explorative search, which speculatively expands knowledge of an area of interest by means of categories. Inspired by conceptual space theory from cognitive science, we propose to implement the modes of discovery using concepts to map a lay consumer’s service need to terminologically sophisticated descriptions of services. To this end, we reframe SD as an information retrieval task on the information attached to services, such as, descriptions, reviews, documentation and web sites - the Service Information Shadow. The Semantic Space model transforms the shadow's unstructured semantic information into a geometric, concept-like representation. We introduce an improved and extended Semantic Space including categorization calling it the Semantic Service Discovery model. We evaluate our model with a highly relevant, service related corpus simulating a Service Information Shadow including manually constructed complex service agendas, as well as manual groupings of services. We compare our model against state-of-the-art information retrieval systems and clustering algorithms. By means of an extensive series of empirical evaluations, we establish optimal parameter settings for the semantic space model. The evaluations demonstrate the model’s effectiveness for SD in terms of retrieval precision over state-of-the-art information retrieval models (directed search) and the meaningful, automatic categorization of service related information, which shows potential to form the basis of a useful, cognitively motivated map of the SES for exploratory search.

An application of fuzzy DL-Based semantic perception to soil container classification

Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.

Genetic influences on sulcal patterns of the brain

We present a shape-space approach for analyzing genetic influences on the shapes of the sulcal folding patterns on the cortex. Sulci are represented as continuously parameterized functions in a shape space, and shape differences between sulci are obtained via geodesics between them. The resulting statistical shape analysis framework is used not only to construct populations averages, but also used to compute meaningful correlations within and across groups of sulcal shapes. More importantly, we present a new algorithm that extends the traditional Euclidean estimate of the intra-class correlation to the geometric shape space, thereby allowing us to study heritability of sulcal shape traits for a population of 193 twin pairs. This new methodology reveals strong genetic influences on the sulcal geometry of the cortex.

Traditional and geometric morphometrics detect morphological variation of lower pharyngeal jaw inCoris julis(Teleostei, Labridae)

In the present study, variation in the morphology of the lower pharyngeal element between two Sicilian populations of the rainbow wrasse Coris julis has been explored by the means of traditional morphometrics for size and geometric morphometrics for shape. Despite close geographical distance and probable high genetic flow between the populations, statistically significant differences have been found both for size and shape. In fact, one population shows a larger lower pharyngeal element that has a larger central tooth. Compared to the other population, this population also has medially enlarged lower pharyngeal jaws with a more pronounced convexity of the medial-posterior margin. The results are discussed in the light of a possible more pronounced durophagy of this population.

Phylogeographical relationships of Sicilian brown trout and the effects of genetic introgression on morphospace occupation

Genetic introgression of aquaculture stocks in local forms is well documented in many fish species but their evolutionary consequences for the local populations have not been thoroughly explored. Due to its wide geographical range, the existence of many locally adapted forms and the frequent occurrence of introgression of aquaculture stocks in local forms, brown trout represents the ideal system to study the effects of such introgressions. Here, we focus on a group of rivers and streams in Sicily (Italy), and, by using molecular tools, we show that autochthonous populations are probably derived from the Southern Atlantic clade, which is present in the Iberian peninsula and North Africa. Three out of the four studied rivers reveal signs of genetic introgression of domestic stocks. Finally, by using advanced geometric morphometric analyses, we show that genetic introgression produces a higher degree of morphological variability relative to that observed in non-introgressed populations.

A dynamic programming model for optimising feeding and slaughter decisions regarding fattening pigs

Costs of purchasing new piglets and of feeding them until slaughter are the main variable expenditures in pig fattening. They both depend on slaughter intensity, the nature of feeding patterns and the technological constraints of pig fattening, such as genotype. Therefore, it is of interest to examine the effect of production technology and changes in input and output prices on feeding and slaughter decisions. This study examines the problem by using a dynamic programming model that links genetic characteristics of a pig to feeding decisions and the timing of slaughter and takes into account how these jointly affect the quality-adjusted value of a carcass. The model simulates the growth mechanism of a pig under optional feeding and slaughter patterns and then solves the optimal feeding and slaughter decisions recursively. The state of nature and the genotype of a pig are known in the analysis. The main contribution of this study is the dynamic approach that explicitly takes into account carcass quality while simultaneously optimising feeding and slaughter decisions. The method maximises the internal rate of return to the capacity unit. Hence, the results can have vital impact on competitiveness of pig production, which is known to be quite capital-intensive. The results suggest that producer can significantly benefit from improvements in the pig's genotype, because they improve efficiency of pig production. The annual benefits from obtaining pigs of improved genotype can be more than €20 per capacity unit. The annual net benefits of animal breeding to pig farms can also be considerable. Animals of improved genotype can reach optimal slaughter maturity quicker and produce leaner meat than animals of poor genotype. In order to fully utilise the benefits of animal breeding, the producer must adjust feeding and slaughter patterns on the basis of genotype. The results suggest that the producer can benefit from flexible feeding technology. The flexible feeding technology segregates pigs into groups according to their weight, carcass leanness, genotype and sex and thereafter optimises feeding and slaughter decisions separately for these groups. Typically, such a technology provides incentives to feed piglets with protein-rich feed such that the genetic potential to produce leaner meat is fully utilised. When the pig approaches slaughter maturity, the share of protein-rich feed in the diet gradually decreases and the amount of energy-rich feed increases. Generally, the optimal slaughter weight is within the weight range that pays the highest price per kilogram of pig meat. The optimal feeding pattern and the optimal timing of slaughter depend on price ratios. Particularly, an increase in the price of pig meat provides incentives to increase the growth rates up to the pig's biological maximum by increasing the amount of energy in the feed. Price changes and changes in slaughter premium can also have large income effects. Key words: barley, carcass composition, dynamic programming, feeding, genotypes, lean, pig fattening, precision agriculture, productivity, slaughter weight, soybeans

Genetic background of bipolar disorder and related cognitive impairments

Bipolar disorder (BP) is a complex psychiatric disorder characterized by episodes of mania and depression. BP affects approximately 1% of the world’s population and shows no difference in lifetime prevalence between males and females. BP arises from complex interactions among genetic, developmental and environmental factors, and it is likely that several predisposing genes are involved in BP. The genetic background of BP is still poorly understood, although intensive and long-lasting research has identified several chromosomal regions and genes involved in susceptibility to BP. This thesis work aims to identify the genetic variants that influence bipolar disorder in the Finnish population by candidate gene and genome-wide linkage analyses in families with many BP cases. In addition to diagnosis-based phenotypes, neuropsychological traits that can be seen as potential endophenotypes or intermediate traits for BP were analyzed. In the first part of the thesis, we examined the role of the allelic variants of the TSNAX/DISC1 gene cluster to psychotic and bipolar spectrum disorders and found association of distinct allelic haplotypes with these two groups of disorders. The haplotype at the 5’ end of the Disrupted-in-Schizophrenia-1 gene (DISC1) was over-transmitted to males with psychotic disorder (p = 0.008; for an extended haplotype p = 0.0007 with both genders), whereas haplotypes at the 3’ end of DISC1 associated with bipolar spectrum disorder (p = 0.0002; for an extended haplotype p = 0.0001). The variants of these haplotypes also showed association with different cognitive traits. The haplotypes at the 5’ end associated with perseverations and auditory attention, while the variants at the 3’ end associated with several cognitive traits including verbal fluency and psychomotor processing speed. Second, in our complete set of BP families with 723 individuals we studied six functional candidate genes from three distinct signalling systems: serotonin-related genes (SLC6A4 and TPH2), BDNF -related genes (BDNF, CREB1 and NTRK2) and one gene related to the inflammation and cytokine system (P2RX7). We replicated association of the functional variant Val66Met of BDNF with BP and better performance in retention. The variants at the 5’ end of SLC6A4 also showed some evidence of association among males (p = 0.004), but the widely studied functional variants did not yield any significant results. A protective four-variant haplotype on P2RX7 showed evidence of association with BP and executive functions: semantic and phonemic fluency (p = 0.006 and p = 0.0003, respectively). Third, we analyzed 23 bipolar families originating from the North-Eastern region of Finland. A genome-wide scan was performed using the 6K single nucleotide polymorphism (SNP) array. We identified susceptibility loci at chromosomes 7q31 with a LOD score of 3.20 and at 9p13.1 with a LOD score of 4.02. We followed up both linkage findings in the complete set of 179 Finnish bipolar families. The finding on chromosome 9p13 was supported (maximum LOD score of 3.02), but the susceptibility gene itself remains unclarified. In the fourth part of the thesis, we wanted to test the role of the allelic variants that have associated with bipolar disorder in recent genome-wide association studies (GWAS). We could confirm findings for the DFNB31, SORCS2, SCL39A3, and DGKH genes. The best signal in this study comes from DFNB31, which remained significant after multiple testing corrections. Two variants of SORCS2 were allelic replications and presented the same signal as the haplotype analysis. However, no association was detected with the PALB2 gene, which was the most significantly associated region in the previous GWAS. Our results indicate that BP is heterogeneous and its genetic background may accordingly vary in different populations. In order to fully understand the allelic heterogeneity that underlies common diseases such as BP, complete genome sequencing for many individuals with and without the disease is required. Identification of the specific risk variants will help us better understand the pathophysiology underlying BP and will lead to the development of treatments with specific biochemical targets. In addition, it will further facilitate the identification of environmental factors that alter risk, which will potentially provide improved occupational, social and psychological advice for individuals with high risk of BP.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Optimal reservoir operation for irrigation of multiple crops using genetic algorithms

This paper presents a genetic algorithm (GA) model for obtaining an optimal operating policy and optimal crop water allocations from an irrigation reservoir. The objective is to maximize the sum of the relative yields from all crops in the irrigated area. The model takes into account reservoir inflow, rainfall on the irrigated area, intraseasonal competition for water among multiple crops, the soil moisture dynamics in each cropped area, the heterogeneous nature of soils. and crop response to the level of irrigation applied. The model is applied to the Malaprabha single-purpose irrigation reservoir in Karnataka State, India. The optimal operating policy obtained using the GA is similar to that obtained by linear programming. This model can be used for optimal utilization of the available water resources of any reservoir system to obtain maximum benefits.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.