A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.

Simulating the evolution of a clonal trait in plants with sexual and vegetative reproduction

Aims Phenotypic optimality models neglect genetics. However, especially when heterozygous genotypes ire fittest, evolving allele, genotype and phenotype frequencies may not correspond to predicted optima. This was not previously addressed for organisms with complex life histories. Methods Therefore, we modelled the evolution of a fitness-relevant trait of clonal plants, stolon internode length. We explored the likely case of air asymmetric unimodal fitness profile with three model types. In constant selection models (CSMs), which are gametic, but not spatially explicit, evolving allele frequencies in the one-locus and five-loci cases did not correspond to optimum stolon internode length predicted by the spatially explicit, but not gametic, phenotypic model. This deviation was due to the asymmetry of the fitness profile. Gametic, spatially explicit individual-based (SEIB) modeling allowed us relaxing the CSM assumptions of constant selection with exclusively sexual reproduction. Important findings For entirely vegetative or sexual reproduction, predictions. of the gametic SEIB model were close to the ones of spatially explicit CSMs gametic phenotypic models, hut for mixed modes of reproduction they appoximated those of gametic, not spatially explicit CSMs. Thus, in contrast to gametic SEIB models, phenotypic models and, especially for few loci, also CSMs can be very misleading. We conclude that the evolution of trails governed by few quantitative trait loci appears hardly predictable by simple models, that genetic algorithms aiming at technical optimization may actually, miss the optimum and that selection may lead to loci with smaller effects, in derived compared with ancestral lines.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

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.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

Genetic diversity, phenotypic variation and local adaptation in the alpine landscape: case studies with alpine plant species

Plant survival in alpine landscapes is constantly challenged by the harsh and often unpredictable environmental conditions. Steep environmental gradients and patchy distribution of habitats lead to small size and spatial isolation of populations and restrict gene flow. Agricultural land use has further increased the diversity of habitats below and above the treeline. We studied the consequences of the highly structured alpine landscape for evolutionary processes in four study plants: Epilobium fleischeri, Geum reptans, Campanula thyrsoides and Poa alpina. The main questions were: (1) How is genetic diversity distributed within and among populations and is it affected by altitude, population size or land use? (2) Do reproductive traits such as allocation to sexual or vegetative reproduction vary with altitude or land use? Furthermore, we studied if seed weight increases with altitude. Within-population genetic diversity of the four species was high and mostly not related to altitude and population size. Nevertheless, genetic differentiation among populations was pronounced and strongly increasing with distance. In Poa alpina genetic diversity was affected by land use. Results suggest considerable genetic drift among populations of alpine plants. Reproductive allocation was affected by altitude and land use in Poa alpina and by succession in Geum reptans. Seed weight was usually higher in alpine species than in related lowland species. We conclude that the evolutionary potential to respond to global change is mostly intact in alpine plants, even at high altitude. Phenotypic variability is shaped by adaptive as well as by random evolutionary processes; moreover plastic responses to growth conditions seem to be crucial for survival of plants in the alpine landscape.

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.

Tracking the origins of Yakutian horses and the genetic basis for their fast adaptation to subarctic environments

Yakutia, Sakha Republic, in the Siberian Far East, represents one of the coldest places on Earth, with winter record temperatures dropping below -70 °C. Nevertheless, Yakutian horses survive all year round in the open air due to striking phenotypic adaptations, including compact body conformations, extremely hairy winter coats, and acute seasonal differences in metabolic activities. The evolutionary origins of Yakutian horses and the genetic basis of their adaptations remain, however, contentious. Here, we present the complete genomes of nine present-day Yakutian horses and two ancient specimens dating from the early 19th century and ∼5,200 y ago. By comparing these genomes with the genomes of two Late Pleistocene, 27 domesticated, and three wild Przewalski's horses, we find that contemporary Yakutian horses do not descend from the native horses that populated the region until the mid-Holocene, but were most likely introduced following the migration of the Yakut people a few centuries ago. Thus, they represent one of the fastest cases of adaptation to the extreme temperatures of the Arctic. We find cis-regulatory mutations to have contributed more than nonsynonymous changes to their adaptation, likely due to the comparatively limited standing variation within gene bodies at the time the population was founded. Genes involved in hair development, body size, and metabolic and hormone signaling pathways represent an essential part of the Yakutian horse adaptive genetic toolkit. Finally, we find evidence for convergent evolution with native human populations and woolly mammoths, suggesting that only a few evolutionary strategies are compatible with survival in extremely cold environments.

Endocarditis due to Tropheryma whipplei: rapid detection, limited genetic diversity, and long-term clinical outcome in a local experience

The characteristic features of Whipple's disease include abdominal pain, diarrhoea, wasting, and arthralgias, with the causative agent, Tropheryma whipplei, being detected mainly in intestinal biopsies. PCR technology has led to the identification of T. whipplei in specimens from various other locations, including the central nervous system and the heart. T. whipplei is now recognized as one of the causes of culture-negative endocarditis, and endocarditis can be the only manifestation of the infection with T. whipplei. Although it is considered a rare disease, the true incidence of endocarditis due to T. whipplei is not clearly established. With the increasing use of molecular methods, it is likely that T. whipplei will be more frequently identified. Questions also remain about the genetic variability of T. whipplei strains, optimal diagnostic procedures and therapeutic options. In the present study, we provide clinical data on four new patients with documented endocarditis due to T. whipplei in the context of the available published literature. There was no clinical involvement of the gastrointestinal tract. Genetic analysis of the T. whipplei strains with DNA isolated from the excised heart valves revealed little to no genetic variability. In a selected case, we describe acridine orange staining for early detection of the disease, prompting early adaptation of the antibiotic therapy. We provide long-term follow-up data on the patients. In our hands, an initial 2-week course of intravenous antibiotics followed by cotrimoxazole for at least 1 year was a suitable treatment option for T. whipplei endocarditis.

Genetic evaluation of short stature

After a proper medical history, growth analysis and physical examination of a short child, followed by radiological and laboratory screening, the clinician may decide to perform genetic testing. We propose several clinical algorithms that can be used to establish the diagnosis. GH1 and GHRHR should be tested in children with severe isolated growth hormone deficiency and a positive family history. A multiple pituitary dysfunction can be caused by defects in several genes, of which PROP1 and POU1F1 are most common. GH resistance can be caused by genetic defects in GHR, STAT5B, IGF1, IGFALS, which all have their specific clinical and biochemical characteristics. IGF-I resistance is seen in heterozygous defects of the IGF1R. If besides short stature additional abnormalities are present, these should be matched with known dysmorphic syndromes. If no obvious candidate gene can be determined, a whole genome approach can be taken to check for deletions, duplications and/or uniparental disomies.

Conserving indigenous animal genetic resources as a coping strategy to adapt to climate change: the Azikheli Buffalo in Northern Mountains of Pakistan

There are clear signs that the agro-pastoralists in the Himalayan and Hindu-Kush mountain ranges will have less cropping opportunities due to reduced possibilities for irrigated agriculture as a result of climate change. The importance of extensive livestock production based on well adapted livestock species may once again increase. This calls for a better documentation and understanding of the adaptation capabilities of indigenous breeds considering a changing environment. The current study investigates the adaptive traits of the Azikheli buffalo to mountain environments through calculating mean, standard error and percentages for different variables. Results from this study suggest that the brown coat color, the small body size and the high fertility are adaptive traits of the Azikheli buffalo that may well suit harsh mountainous environment conditions with greater climate variability. Local farmers find it hard to sustain the Azikheli buffalo’s key adaptive traits because of a low bull to buffalo ratio, possibility of insemination with semen from imported breeds and a lack of institutional support to conserve the Azikheli breed. The breed is crucial for sustaining custodian communities in these mountains and thus needs to be conserved.

Thermal adaptation and ecological speciation

Ecological speciation is defined as the emergence of reproductive isolation as a direct or indirect consequence of divergent ecological adaptation. Several empirical examples of ecological speciation have been reported in the literature which very often involve adaptation to biotic resources. In this review, we investigate whether adaptation to different thermal habitats could also promote speciation and try to assess the importance of such processes in nature. Our survey of the literature identified 16 animal and plant systems where divergent thermal adaptation may underlie (partial) reproductive isolation between populations or may allow the stable coexistence of sibling taxa. In many of the systems, the differentially adapted populations have a parapatric distribution along an environmental gradient. Isolation often involves extrinsic selection against locally maladapted parental or hybrid genotypes, and additional pre- or postzygotic barriers may be important. Together, the identified examples strongly suggest that divergent selection between thermal environments is often strong enough to maintain a bimodal genotype distribution upon secondary contact. What is less clear from the available data is whether it can also be strong enough to allow ecological speciation in the face of gene flow through reinforcement-like processes. It is possible that intrinsic features of thermal gradients or the genetic basis of thermal adaptation make such reinforcement-like processes unlikely but it is equally possible that pertinent systems are understudied. Overall, our literature survey highlights (once again) the dearth of studies that investigate similar incipient species along the continuum from initial divergence to full reproductive isolation and studies that investigate all possible reproductive barriers in a given system.

Evidence of neutral and adaptive genetic divergence between European trout populations sampled along altitudinal gradients

Species with a wide geographical distribution are often composed of distinct subgroups which may be adapted to their local environment. European trout (Salmo trutta species complex) provide an example of such a complex consisting of several genetically and ecologically distinct forms. However, trout populations are strongly influenced by human activities, and it is unclear to what extent neutral and adaptive genetic differences have persisted. We sampled 30 Swiss trout populations from heterogeneous environments along replicated altitudinal gradients in three major European drainages. More than 850 individuals were genotyped at 18 microsatellite loci which included loci diagnostic for evolutionary lineages and candidate markers associated with temperature tolerance, reproductive timing and immune defence. We find that the phylogeographic structure of Swiss trout populations has not been completely erased by stocking. Distinct genetic clusters corresponding to the different drainages could be identified, although nonindigenous alleles were clearly present, especially in the two Mediterranean drainages. We also still detected neutral genetic differentiation within rivers which was often associated with the geographical distance between populations. Five loci showed evidence of divergent selection between populations with several drainage-specific patterns. Lineage-diagnostic markers, a marker linked to a quantitative trait locus for upper temperature tolerance in other salmonids and a marker linked to the major histocompatibility class I gene were implicated in local adaptation and some patterns were associated with altitude. In contrast, tentative evidence suggests a signal of balancing selection at a second immune relevant gene (TAP2). Our results confirm the persistence of both neutral and potentially adaptive genetic differences between trout populations in the face of massive human-mediated dispersal.