Analysis of genetic diversity of Trypanosoma cruzi: an application of riboprinting and gradient gel electrophoresis methods

Analysis of restriction fragment length polymorphism (RFLP) profiles derived from digestion of polymerase chain reaction (PCR) products of the ribosomal 18S from Trypanosoma cruzi yields a typical `riboprint' profile that can vary intraspecifically. A selection of 21 stocks of T. cruzi and three outgroup taxa: T. rangeli, T. conorhini and Leishmania braziliensis were analysed by riboprinting to assess divergence within and between taxa. T. rangeli, T. conorhini and L. braziliensis could be easily differentiated from each other and from T. cruzi. Phenetic analysis of PCR-RFLP profiles indicated that, with one or two exceptions, stocks of T. cruzi could be broadly partitioned into two groups that formally corresponded to T. cruzi I and T. cruzi II respectively. To test if ribosomal 18S sequences were homogeneous within each taxon, gradient gel electrophoresis methods were employed utilising either chemical or temperature gradients. Upon interpretation of the melting profiles of riboprints and a section of the 18S independently amplified by PCR, there would appear to be at least two divergent 18S types present within T. cruzi. Heterogeneity within copies of the ribosomal 18S within a single genome has therefore been demonstrated and interestingly, this dimorphic arrangement was also present in the outgroup taxa. Presumably the ancestral duplicative event that led to the divergent 18S types preceded that of speciation within this group. These divergent 18S paralogues may have, or had, different functional pressures or rates of molecular evolution. Whether or not these divergent types are equally transcriptionally active throughout the life cycle, remain to be assessed.

Extensional flow of nematic liquid crystal under electric field gradient

Systematic asymptotic methods are used to formulate a model for the extensional flow of a thin sheet of nematic liquid crystal. With no external body forces applied, the model is found to be equivalent to the so-called Trouton model for Newtonian sheets (and fi bers), albeit with a modi fied "Trouton ratio". However, with a symmetry-breaking electric field gradient applied, behavior deviates from the Newtonian case, and the sheet can undergo fi nite-time breakup if a suitable destabilizing field is applied. Some simple exact solutions are presented to illustrate the results in certain idealized limits, as well as sample numerical results to the full model equations.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Association of adiposity genetic variants with menarche timing in 92,105 women of European descent.

Obesity is of global health concern. There are well-described inverse relationships between female pubertal timing and obesity. Recent genome-wide association studies of age at menarche identified several obesity-related variants. Using data from the ReproGen Consortium, we employed meta-analytical techniques to estimate the associations of 95 a priori and recently identified obesity-related (body mass index (weight (kg)/height (m)(2)), waist circumference, and waist:hip ratio) single-nucleotide polymorphisms (SNPs) with age at menarche in 92,116 women of European descent from 38 studies (1970-2010), in order to estimate associations between genetic variants associated with central or overall adiposity and pubertal timing in girls. Investigators in each study performed a separate analysis of associations between the selected SNPs and age at menarche (ages 9-17 years) using linear regression models and adjusting for birth year, site (as appropriate), and population stratification. Heterogeneity of effect-measure estimates was investigated using meta-regression. Six novel associations of body mass index loci with age at menarche were identified, and 11 adiposity loci previously reported to be associated with age at menarche were confirmed, but none of the central adiposity variants individually showed significant associations. These findings suggest complex genetic relationships between menarche and overall obesity, and to a lesser extent central obesity, in normal processes of growth and development.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Gender difference in the response to an angiotensin-converting enzyme inhibitor and a diurectic in hypertensive patients of African descent

Résumé de la thèse en français Titre : Différence entre hommes et femmes dans la réponse à un inhibiteur de l'enzyme de conversion de l'angiotensine et un diurétique chez des patients hypertendus d'origine Africaine. Introduction: L'efficacité des inhibiteurs de l'enzyme de conversion de l'angiotensine (ACEI) dans le traitement de l'hypertension artérielle chez les patients africains est controversée. Objectif: Nous avons examiné la baisse de la tension artérielle ambulatoire (ABP) en réponse à un diurétique et un ACEI chez des patients hypertendus d'origine africaine et nous avons évalué les différentes caractéristiques déterminant l'efficacité du traitement. Méthodes: Etude en simple-aveugle randomisée, en crossover AB/BA. Arrangement Familles hypertendues d'origine africaine de la population générale des Seychelles. Participants : 52 patients (29 hommes et 23 femmes) sur 62 patients hypertendus éligibles ont été inclus. Le principal résultat était la mesure de la réponse de l'ABP à 20 mg de lisinopril (LIS) ou 25 mg d'hydrochlorothiazide (HCT) quotidiennement pendant quatre semaines. Résultats: Le jour, la réponse systolique/diastolique de l'ABP sous HCT était de 4.9 (95% intervalle de confiance (IC) 1.2-8.6)/3.6 (1.0-6.2) mm Hg pour les hommes et 12.9 (9.216.6)/6.3 (3.7-8.8) mm Hg pour les femmes. Sous LIS, la réponse était de 18.8 (15.022.5)/14.6 (12.0-17.1) mm Hg pour les hommes et de 12.4 (8.7-16.2)/7.7 (5.1-10.2) mm Hg pour les femmes. La nuit, la réponse systolique/diastolique sous HCT était de 5.0 (0.6-9.4)/2.7 ((-0.4)-5.7) mm Hg pour les hommes et de 11.5 (7.1-16.0)/5.7 (2.6-8.8) mm Hg pour les femmes, et sous LIS était de 18.7 (14.2-22.1)/15.4 (12.4-18.5) mm Hg pour les hommes et de 3.5 ((-1.0)-7.9)/2.3 ((-0.8)-5.4) mm Hg pour les femmes. L'analyse de régression linéaire multiple a montré que le sexe est un prédicteur indépendant de la réponse tensionnelle à l'HCT et au LIS. Conclusions : Les patients hypertendus d'origine africaine ont présenté une baisse tensionnelle plus grande en réponse au LIS qu'à l'HCT. Les hommes ont mieux répondu au LIS qu'à l'HCT alors que les femmes ont répondu de manière similaire aux deux traitements.

UOC Algorithms

Aplicació per a iPad a mode de repositori de continguts relacionats amb l'ensenyament d'assignatures d'informàtica.

Evaluation of organ-specific peripheral doses after 2-dimensional, 3-dimensional and hybrid intensity modulated radiation therapy for breast cancer based on Monte Carlo and convolution/superposition algorithms: implications for secondary cancer risk assessment.

To make a comprehensive evaluation of organ-specific out-of-field doses using Monte Carlo (MC) simulations for different breast cancer irradiation techniques and to compare results with a commercial treatment planning system (TPS). Three breast radiotherapy techniques using 6MV tangential photon beams were compared: (a) 2DRT (open rectangular fields), (b) 3DCRT (conformal wedged fields), and (c) hybrid IMRT (open conformal+modulated fields). Over 35 organs were contoured in a whole-body CT scan and organ-specific dose distributions were determined with MC and the TPS. Large differences in out-of-field doses were observed between MC and TPS calculations, even for organs close to the target volume such as the heart, the lungs and the contralateral breast (up to 70% difference). MC simulations showed that a large fraction of the out-of-field dose comes from the out-of-field head scatter fluence (>40%) which is not adequately modeled by the TPS. Based on MC simulations, the 3DCRT technique using external wedges yielded significantly higher doses (up to a factor 4-5 in the pelvis) than the 2DRT and the hybrid IMRT techniques which yielded similar out-of-field doses. In sharp contrast to popular belief, the IMRT technique investigated here does not increase the out-of-field dose compared to conventional techniques and may offer the most optimal plan. The 3DCRT technique with external wedges yields the largest out-of-field doses. For accurate out-of-field dose assessment, a commercial TPS should not be used, even for organs near the target volume (contralateral breast, lungs, heart).

Use of a saline gradient for the diagnosis of schistosomiasis

The development of novel methods for parasitological diagnosis that are both highly sensitive and low in cost has been strongly recommended by the World Health Organization. In this study, a new technique for diagnosis of schistosomiasis mansoni is proposed based on the differential sedimentation of eggs when subjected to a slow continuous flux of 3% saline solution through a porous plaque. This influx suspends low-density faecal material, effectively cleaning the sample. The remaining sediment covering the porous plaque surface is then transferred to a glass slide and examined under a bright field microscope. Twelve Kato-Katz slides were used for comparison in the present study. Our results suggest that the saline gradient method detects a signifi-cantly higher number of eggs than the 12 Kato-Katz slides (p < 0.0001). We also found microscopic inspection to be quicker and easier with our newly described method. After cleaning the sample, the obtained sediment can also be conserved in a 10% formaldehyde solution and examined for at least 45 days later without statistically significant egg count differences.

Enhancing fault management performance of two-step QoS routing algorithms in GMPLS networks

In this paper a novel methodology aimed at minimizing the probability of network failure and the failure impact (in terms of QoS degradation) while optimizing the resource consumption is introduced. A detailed study of MPLS recovery techniques and their GMPLS extensions are also presented. In this scenario, some features for reducing the failure impact and offering minimum failure probabilities at the same time are also analyzed. Novel two-step routing algorithms using this methodology are proposed. Results show that these methods offer high protection levels with optimal resource consumption

Enhancing MPLS QoS routing algorithms by using the network protection degree paradigm

IP based networks still do not have the required degree of reliability required by new multimedia services, achieving such reliability will be crucial in the success or failure of the new Internet generation. Most of existing schemes for QoS routing do not take into consideration parameters concerning the quality of the protection, such as packet loss or restoration time. In this paper, we define a new paradigm to develop new protection strategies for building reliable MPLS networks, based on what we have called the network protection degree (NPD). This NPD consists of an a priori evaluation, the failure sensibility degree (FSD), which provides the failure probability and an a posteriori evaluation, the failure impact degree (FID), to determine the impact on the network in case of failure. Having mathematical formulated these components, we point out the most relevant components. Experimental results demonstrate the benefits of the utilization of the NPD, when used to enhance some current QoS routing algorithms to offer a certain degree of protection