Short- and long-term changes in chromosomal inversion polymorphism and global warming: Drosophila subobscura from the Balkans

The chromosomal inversion polymorphism of Drosophila subobscura is adaptive to environmental changes. The population of Petnica, Serbia, was chosen to analyze short- and long-term changes in this polymorphism. Short-term changes were studied in the samples collected in May, June, and August of 1995. The inversion polymorphism varied over these months, although various interpretations are possible. To analyze long-term changes, samples obtained in May 1995 and May 2010 were compared. The frequency of the 'cold' adapted inversions (Ast, Jst, Ust, Est, and Ost) decreased and that of the 'warm' adapted inversions (A2, J1, U1+2, and O3+4) increased, from 1995 to 2010. These changes are consistent with the general increase in temperature recorded in Petnica for the same period. Finally, the possible response of chromosomal polymorphism to global warming was analyzed at the regional level (Balkan peninsula). This polymorphism depends on the ecological conditions of the populations, and the changes observed appear to be consistent with global warming expectations. Natural selection seems to be the main mechanism responsible for the evolution of this chromosomal polymorphism.

Short- and long-term changes in chromosomal inversion polymorphism and global warming: Drosophila subobscura from the Balkans

The chromosomal inversion polymorphism of Drosophila subobscura is adaptive to environmental changes. The population of Petnica, Serbia, was chosen to analyze short- and long-term changes in this polymorphism. Short-term changes were studied in the samples collected in May, June, and August of 1995. The inversion polymorphism varied over these months, although various interpretations are possible. To analyze long-term changes, samples obtained in May 1995 and May 2010 were compared. The frequency of the 'cold' adapted inversions (Ast, Jst, Ust, Est, and Ost) decreased and that of the 'warm' adapted inversions (A2, J1, U1+2, and O3+4) increased, from 1995 to 2010. These changes are consistent with the general increase in temperature recorded in Petnica for the same period. Finally, the possible response of chromosomal polymorphism to global warming was analyzed at the regional level (Balkan peninsula). This polymorphism depends on the ecological conditions of the populations, and the changes observed appear to be consistent with global warming expectations. Natural selection seems to be the main mechanism responsible for the evolution of this chromosomal polymorphism.

3D coronary vessel wall imaging utilizing a local inversion technique with spiral image acquisition.

Current 2D black blood coronary vessel wall imaging suffers from a relatively limited coverage of the coronary artery tree. Hence, a 3D approach facilitating more extensive coverage would be desirable. The straightforward combination of a 3D-acquisition technique together with a dual inversion prepulse can decrease the effectiveness of the black blood preparation. To minimize artifacts from insufficiently suppressed blood signal of the nearby blood pools, and to reduce residual respiratory motion artifacts from the chest wall, a novel local inversion technique was implemented. The combination of a nonselective inversion prepulse with a 2D selective local inversion prepulse allowed for suppression of unwanted signal outside a user-defined region of interest. Among 10 subjects evaluated using a 3D-spiral readout, the local inversion pulse effectively suppressed signal from ventricular blood, myocardium, and chest wall tissue in all cases. The coronary vessel wall could be visualized within the entire imaging volume.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Quasi-Nonparametric Blind Inversion of Wiener Systems

An e cient procedure for the blind inversion of a nonlinear Wiener system is proposed. We proved that the problem can be expressed as a problem of blind source separation in nonlinear mixtures, for which a solution has been recently proposed. Based on a quasi-nonparametric relative gradient descent, the proposed algorithm can perform e ciently even in the presence of hard distortions.

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.

Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"

Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.

A Canonical Genetic Algorithm for Blind Inversion of Linear Channels

It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic Algorithm (GA) to blindly invert linear channels. The use of GA is justified in the case of small number of samples, where other gradient-like methods fails because of poor estimation of statistics.

Initialisation of Nonlinearities for PNL and Wiener systems Inversion

This paper proposes a very fast method for blindly initial- izing a nonlinear mapping which transforms a sum of random variables. The method provides a surprisingly good approximation even when the basic assumption is not fully satis¯ed. The method can been used success- fully for initializing nonlinearity in post-nonlinear mixtures or in Wiener system inversion, for improving algorithm speed and convergence.

Blind Inversion of Wiener Systems

A system in which a linear dynamic part is followed by a non linear memoryless distortion a Wiener system is blindly inverted This kind of systems can be modelised as a postnonlinear mixture and using some results about these mixtures an e cient algorithm is proposed Results in a hard situation are presented and illustrate the e ciency of this algorithm

Performance aux tests d'intelligence : vers une inversion de l'effet Flynn ?

Des nombreuses études ont montré une augmentation des scores aux tests d'aptitudes à travers les générations (« effet Flynn »). Différentes hypothèses d'ordre biologique, social et/ou éducationnels ont été élaborées afin d'expliquer ce phénomène. L'objectif de cette recherche est d'examiner l'évolution des performances aux tests d'aptitudes sur la base d'étalonnages datant de 1991 et de 2002. Les résultats suggèrent une inversion non homogène de l'effet Flynn. La diminution concerne plus particulièrement les tests d'aptitudes scolaires, comme ceux évaluant le facteur verbal et numérique. Cette étude pourrait refléter un changement de l'importance accordée aux différentes aptitudes peu évaluées en orientation scolaire et professionnelle.

A non-linear Bayesian approach for upscaling local-scale hydraulic conductivity measurements based on local- and regional-scale geophysical data

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Flow-targeted inversion-prepared b-TFE coronary MR angiography: initial results in patients.

PURPOSE: Visualization of coronary blood flow in the right and left coronary system in volunteers and patients by means of a modified inversion-prepared bright-blood coronary magnetic resonance angiography (cMRA) sequence. MATERIALS AND METHODS: cMRA was performed in 14 healthy volunteers and 19 patients on a 1.5 Tesla MR system using a free-breathing 3D balanced turbo field echo (b-TFE) sequence with radial k-space sampling. For magnetization preparation a slab selective and a 2D selective inversion pulse were used for the right and left coronary system, respectively. cMRA images were evaluated in terms of clinically relevant stenoses (< 50 %) and compared to conventional catheter angiography. Signal was measured in the coronary arteries (coro), the aorta (ao) and in the epicardial fat (fat) to determine SNR and CNR. In addition, maximal visible vessel length, and vessel border definition were analyzed. RESULTS: The use of a selective inversion pre-pulse allowed direct visualization of the coronary blood flow in the right and left coronary system. The measured SNR and CNR, vessel length, and vessel sharpness in volunteers (SNR coro: 28.3 +/- 5.0; SNR ao: 37.6 +/- 8.4; CNR coro-fat: 25.3 +/- 4.5; LAD: 128.0 cm +/- 8.8; RCA: 74.6 cm +/- 12.4; Sharpness: 66.6 % +/- 4.8) were slightly increased compared to those in patients (SNR coro: 24.1 +/- 3.8; SNR ao: 33.8 +/- 11.4; CNR coro-fat: 19.9 +/- 3.3; LAD: 112.5 cm +/- 13.8; RCA: 69.6 cm +/- 16.6; Sharpness: 58.9 % +/- 7.9; n.s.). In the patient study the assessment of 42 coronary segments lead to correct identification of 10 clinically relevant stenoses. CONCLUSION: The modification of a previously published inversion-prepared cMRA sequence allowed direct visualization of the coronary blood flow in the right as well as in the left coronary system. In addition, this sequence proved to be highly sensitive regarding the assessment of clinically relevant stenotic lesions.

Web Services for Bayesian Learning

In this demonstration we present our web services to perform Bayesian learning for classification tasks.