A study of the time complexity of an Elitist Genetic Algorithm used for associating optical observations of MEO and GEO Space Debris

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both the correct associations among the observations, and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. Where S stands for the number of ’fences’ used in the problem, each fence consists of a set of observations that all originate from dierent targets. For a dimension of S ˃ the MTT problem becomes NP-hard. As of now no algorithm exists that can solve an NP-hard problem in an optimal manner within a reasonable (polynomial) computation time. However, there are algorithms that can approximate the solution with a realistic computational e ort. To this end an Elitist Genetic Algorithm is implemented to approximately solve the S ˃ MTT problem in an e cient manner. Its complexity is studied and it is found that an approximate solution can be obtained in a polynomial time. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to e ciently process large data sets with minimal manual intervention.

A Genetic Algorithm to associate optical measurements and estimate orbits of geocentric objects

Cataloging geocentric objects can be put in the framework of Multiple Target Tracking (MTT). Current work tends to focus on the S = 2 MTT problem because of its favorable computational complexity of O(n²). The MTT problem becomes NP-hard for a dimension of S˃3. The challenge is to find an approximation to the solution within a reasonable computation time. To effciently approximate this solution a Genetic Algorithm is used. The algorithm is applied to a simulated test case. These results represent the first steps towards a method that can treat the S˃3 problem effciently and with minimal manual intervention.

Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images

Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az: 0.802+/-0.083) in the discrimination of hepatic tissue.

Analyzing diffuse scattering with supercomputers

Two new approaches to quantitatively analyze diffuse diffraction intensities from faulted layer stacking are reported. The parameters of a probability-based growth model are determined with two iterative global optimization methods: a genetic algorithm (GA) and particle swarm optimization (PSO). The results are compared with those from a third global optimization method, a differential evolution (DE) algorithm [Storn & Price (1997). J. Global Optim. 11, 341–359]. The algorithm efficiencies in the early and late stages of iteration are compared. The accuracy of the optimized parameters improves with increasing size of the simulated crystal volume. The wall clock time for computing quite large crystal volumes can be kept within reasonable limits by the parallel calculation of many crystals (clones) generated for each model parameter set on a super- or grid computer. The faulted layer stacking in single crystals of trigonal three-pointedstar- shaped tris(bicylco[2.1.1]hexeno)benzene molecules serves as an example for the numerical computations. Based on numerical values of seven model parameters (reference parameters), nearly noise-free reference intensities of 14 diffuse streaks were simulated from 1280 clones, each consisting of 96 000 layers (reference crystal). The parameters derived from the reference intensities with GA, PSO and DE were compared with the original reference parameters as a function of the simulated total crystal volume. The statistical distribution of structural motifs in the simulated crystals is in good agreement with that in the reference crystal. The results found with the growth model for layer stacking disorder are applicable to other disorder types and modeling techniques, Monte Carlo in particular.

Deletions of 11q22.3-q25 are associated with atypical lung carcinoids and poor clinical outcome

Carcinoids are slow-growing neuroendocrine tumors that, in the lung, can be subclassified as typical (TC) or atypical (AC). To identify genetic alterations that improve the prediction of prognosis, we investigated 34 carcinoid tumors of the lung (18 TCs, 15 ACs, and 1 unclassified) by using array comparative genomic hybridization (array CGH) on 3700 genomic bacterial artificial chromosome arrays (resolution ?1 Mb). When comparing ACs with TCs, the data revealed: i) a significant difference in the average number of chromosome arms altered (9.6 versus 4.2, respectively; P = 0.036), with one subgroup of five ACs having more than 15 chromosome arms altered; ii) chromosomal changes in 30% of ACs or more with additions at 9q (?1 Mb) and losses at 1p, 2q, 10q, and 11q; and iii) 11q deletions in 8 of 15 ACs versus 1 of 18 TCs (P = 0.004), which was confirmed via fluorescence in situ hybridization. The four critical regions of interest in 45% ACs or more comprised 11q14.1, 11q22.1-q22.3, 11q22.3-q23.2, and 11q24.2-q25, all telomeric of MEN1 at 11q13. Results were correlated with patient clinical data and long-term follow-up. Thus, there is a strong association of 11q22.3-q25 loss with poorer prognosis, alone or in combination with absence of 9q34.11 alterations (P = 0.0022 and P = 0.00026, respectively).

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

Distributed sensor failure detection in sensor networks

We investigate the problem of distributed sensors' failure detection in networks with a small number of defective sensors, whose measurements differ significantly from the neighbor measurements. We build on the sparse nature of the binary sensor failure signals to propose a novel distributed detection algorithm based on gossip mechanisms and on Group Testing (GT), where the latter has been used so far in centralized detection problems. The new distributed GT algorithm estimates the set of scattered defective sensors with a low complexity distance decoder from a small number of linearly independent binary messages exchanged by the sensors. We first consider networks with one defective sensor and determine the minimal number of linearly independent messages needed for its detection with high probability. We then extend our study to the multiple defective sensors detection by modifying appropriately the message exchange protocol and the decoding procedure. We show that, for small and medium sized networks, the number of messages required for successful detection is actually smaller than the minimal number computed theoretically. Finally, simulations demonstrate that the proposed method outperforms methods based on random walks in terms of both detection performance and convergence rate.

Defined morphological criteria allow reliable diagnosis of colorectal serrated polyps and predict polyp genetics.

Criteria for the diagnosis of serrated colorectal lesions (hyperplastic polyp, sessile serrated adenoma without or with dysplasia--which we called mixed polyp--and traditional serrated adenoma) for which consensus has been reached should be validated for applicability in daily practice in terms of inter-observer reproducibility and their association with clinical features and (epi)genetic events. A study set was created from a consecutive series of colorectal polyps (n = 1,926) by selecting all sessile serrated adenomas, traditional serrated adenomas and mixed polyps. We added consecutive series of hyperplastic polyps, classical adenomas and normal mucosa samples for a total of 200 specimens. With this series, we conducted an inter-observer study, encompassing ten pathologists with gastrointestinal pathology experience from five European countries, in three rounds in which all cases were microscopically evaluated. An assessment of single morphological criteria was included, and these were correlated with clinical parameters and the mutation status of KRAS, BRAF and PIK3CA and the methylation status of MLH1. Gender, age and localisation were significantly associated with certain types of lesions. Kappa statistics revealed moderate to good inter-observer agreement for polyp classification (κ = 0.56 to 0.63), but for single criteria, this varied considerably (κ = 0.06 to 0.82). BRAF mutations were frequently found in hyperplastic polyps (86 %, 62/72) and sessile serrated adenomas (80 %, 41/51). KRAS mutations occurred more frequently in traditional serrated adenomas (78 %, 7/9) and less so in classical adenomas (20 %, 10/51). Single morphological criteria for sessile serrated adenomas showed significant correlation with BRAF mutation (all p ≤ 0.001), and those for classical adenomas or traditional serrated adenoma correlated significantly with KRAS mutation (all p < 0.001). Therefore, single well-defined morphological criteria are predictive for genetic alterations in colorectal polyps.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

Prognostic value of the Geneva prediction rule in patients in whom pulmonary embolism is ruled out

The prognosis of patients in whom pulmonary embolism (PE) is suspected but ruled out is poorly understood. We evaluated whether the initial assessment of clinical probability of PE could help to predict the prognosis for these patients.

Prediction of codeine toxicity in infants and their mothers using a novel combination of maternal genetic markers

Substantial variation exists in response to standard doses of codeine ranging from poor analgesia to life-threatening central nervous system (CNS) depression. We aimed to discover the genetic markers predictive of codeine toxicity by evaluating the associations between polymorphisms in cytochrome P450 2D6 (CYP2D6), UDP-glucuronosyltransferase 2B7 (UGT2B7), P-glycoprotein (ABCB1), mu-opioid receptor (OPRM1), and catechol O-methyltransferase (COMT) genes, which are involved in the codeine pathway, and the symptoms of CNS depression in 111 breastfeeding mothers using codeine and their infants. A genetic model combining the maternal risk genotypes in CYP2D6 and ABCB1 was significantly associated with the adverse outcomes in infants (odds ratio (OR) 2.68; 95% confidence interval (CI) 1.61-4.48; P(trend) = 0.0002) and their mothers (OR 2.74; 95% CI 1.55-4.84; P(trend) = 0.0005). A novel combination of the genetic and clinical factors predicted 87% of the infant and maternal CNS depression cases with a sensitivity of 80% and a specificity of 87%. Genetic markers can be used to improve the outcome of codeine therapy and are also probably important for other opioids sharing common biotransformation pathways.

Spatial isolation and genetic differentiation in naturally fragmented plant populations of the Swiss Alps

Aims The effect Of anthropogenic landscape fragmentation on the genetic diversity and adaptive potential of plant populations is a major issue in conservation biology. However, little is known about the partitioning of genetic diversity in alpine species, which occur in naturally fragmented habitats. Here, we, investigate molecular patterns of three alpine plants (Epilobium fleischeri, Geum reptans and Campanula thyrsoides) across Switzerland and ask whether Spatial isolation has led to high levels of populations differentiation, increasing over distance, and a decrease of within-population variability. We further hypothesize that file contrasting potential for long-distance dispersal (LDD) of Seed in these Species will considerably influence and explain diversity partitioning. Methods For each study species, we Sampled 20-23 individuals from each of 20-32 populations across entire Switzerland. We applied Random Amplified Polymorphic Dimorphism markers to assess genetic diversity within (Nei's expected heterozygosity, H-e; percentage of polymorphic hands, P-P) and among (analysis of molecular variance, Phi(st)) populations and correlated population size and altitude with within-populalion diversity. Spatial patterns of genetic relatedness were investigated using Mantel tests and standardized major axis regression as well as unweighted pair group method with arithmetic mean cluster analyses and Monmonier's algorithm. To avoid known biases, We standardized the numbers of populations, individuals and markers using multiple random reductions. We modelled LDD with a high alpine wind data set using the terminal velocity and height of seed release as key parameters. Additionally, we assessed a number of important life-history traits and factors that potentially influence genetic diversity partitioning (e.g. breeding system, longevity and population size). Important findings For all three species, We found a significant isolation-by-distance relationship but only a moderately high differentiation among populations (Phi(st): 22.7, 48 and 16.8%, for E. fleischeri, G. reptans and C. thyrsoides, respectively). Within-population diversity (H-c: 0.19-0.21, P-p: 62-75%) was not reduced in comparison to known results from lowland species and even small populations with < 50 reproductive individuals contained high levels of genetic diversity. We further found no indication that a high long-distance seed dispersal potential enhances genetic connectivity among populations. Gene flow seems to have a strong stochastic component causing large dissimilarity between population pairs irrespective of the spatial distance. Our results suggest that other life-history traits, especially the breeding System, may play an important role in genetic diversity partitioning. We conclude that spatial isolation in the alpine environment has a strong influence on population relatedness but that a number of factors can considerably influence the strength of this relationship.

Phylogeny and prediction of genetic similarity of Cronobacter and related taxa by multilocus sequence analysis (MLSA)

Multilocus sequence analysis (MLSA) based on recN, rpoA and thdF genes was done on more than 30 species of the family Enterobacteriaceae with a focus on Cronobacter and the related genus Enterobacter. The sequences provide valuable data for phylogenetic, taxonomic and diagnostic purposes. Phylogenetic analysis showed that the genus Cronobacter forms a homogenous cluster related to recently described species of Enterobacter, but distant to other species of this genus. Combining sequence information on all three genes is highly representative for the species' %GC-content used as taxonomic marker. Sequence similarity of the three genes and even of recN alone can be used to extrapolate genetic similarities between species of Enterobacteriaceae. Finally, the rpoA gene sequence, which is the easiest one to determine, provides a powerful diagnostic tool to identify and differentiate species of this family. The comparative analysis gives important insights into the phylogeny and genetic relatedness of the family Enterobacteriaceae and will serve as a basis for further studies and clarifications on the taxonomy of this large and heterogeneous family.