MRI newsletter 8: Georeferenced biological databases - A tool for understanding mountain biodiversity

The Mountain Research Initiative invited Dr Eva Spehn, Director of the Global Mountain Biodiversity Assessment (GMBA), and Dr Antoine Guisan, head of the Spatial Ecology Group at the University of Lausanne, to introduce the reader to their coordinated efforts to advance understanding and prediction of mountain biodiversity. Antoine Guisan's EUROMONT project is one of the many scientific projects that may potentially provide data for the new GMBA initiative for a GIS mountain biodiversity database.

Prediction of infant drug exposure through breastfeeding: population PK modeling and simulation of fluoxetine

BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]

IL28B expression depends on a novel TT/-G polymorphism which improves HCV clearance prediction.

Approximately 3% of the world population is chronically infected with the hepatitis C virus (HCV), with potential development of cirrhosis and hepatocellular carcinoma. Despite the availability of new antiviral agents, treatment remains suboptimal. Genome-wide association studies (GWAS) identified rs12979860, a polymorphism nearby IL28B, as an important predictor of HCV clearance. We report the identification of a novel TT/-G polymorphism in the CpG region upstream of IL28B, which is a better predictor of HCV clearance than rs12979860. By using peripheral blood mononuclear cells (PBMCs) from individuals carrying different allelic combinations of the TT/-G and rs12979860 polymorphisms, we show that induction of IL28B and IFN-γ-inducible protein 10 (IP-10) mRNA relies on TT/-G, but not rs12979860, making TT/-G the only functional variant identified so far. This novel step in understanding the genetic regulation of IL28B may have important implications for clinical practice, as the use of TT/G genotyping instead of rs12979860 would improve patient management.

Wavelet analysis residual kriging vs. neural network residual kriging

This paper deals with the problem of spatial data mapping. A new method based on wavelet interpolation and geostatistical prediction (kriging) is proposed. The method - wavelet analysis residual kriging (WARK) - is developed in order to assess the problems rising for highly variable data in presence of spatial trends. In these cases stationary prediction models have very limited application. Wavelet analysis is used to model large-scale structures and kriging of the remaining residuals focuses on small-scale peculiarities. WARK is able to model spatial pattern which features multiscale structure. In the present work WARK is applied to the rainfall data and the results of validation are compared with the ones obtained from neural network residual kriging (NNRK). NNRK is also a residual-based method, which uses artificial neural network to model large-scale non-linear trends. The comparison of the results demonstrates the high quality performance of WARK in predicting hot spots, reproducing global statistical characteristics of the distribution and spatial correlation structure.

Prediction of clinical response after renal angioplasty: respective value of renal Doppler sonography and scintigraphy.

OBJECTIVE: The goal of our study was to compare Doppler sonography and renal scintigraphy as tools for predicting the therapeutic response in patients after undergoing renal angioplasty. SUBJECTS AND METHODS. Seventy-four hypertensive patients underwent clinical examination, Doppler sonography, and renal scintigraphy before and after receiving captopril in preparation for renal revascularization. The patients were evaluated for the status of hypertension 3 months after the procedure. The predictive values of the findings of clinical examination, Doppler sonography, renal scintigraphy, and angiography were assessed. RESULTS: For prediction of a favorable therapeutic outcome, abnormal results from renal scintigraphy before and after captopril administration had a sensitivity of 58% and specificity of 57%. Findings of Doppler sonography had a sensitivity of 68% and specificity of 50% before captopril administration and a sensitivity of 81% and specificity of 32% after captopril administration. Significant predictors of a cure or reduction of hypertension after revascularization were low unilateral (p = 0.014) and bilateral resistive (p = 0.016) indexes on Doppler sonography before (p = 0.009) and after (p = 0.028) captopril administration. On multivariate analysis, the best predictors were a unilateral resistive index of less than 0.65 (odds ratio [OR] = 3.7) after captopril administration and a kidney longer than 93 mm (OR = 7.8). The two best combined criteria to predict the favorable therapeutic outcome were a bilateral resistive index of less than 0.75 before captopril administration combined with a unilateral resistive index of less than 0.70 after captopril administration (sensitivity, 76%; specificity, 58%) or a bilateral resistive index of less than 0.75 before captopril administration and a kidney measuring longer than 90 mm (sensitivity, 81%; specificity, 50%). CONCLUSION: Measurements of kidney length and unilateral and bilateral resistive indexes before and after captopril administration were useful in predicting the outcome after renal angioplasty. Renal scintigraphy had no significant predictive value.

Response Prediction in Chronic Hepatitis C by Assessment of IP-10 and IL28B-Related Single Nucleotide Polymorphisms.

BACKGROUND: High baseline levels of IP-10 predict a slower first phase decline in HCV RNA and a poor outcome following interferon/ribavirin therapy in patients with chronic hepatitis C. Several recent studies report that single nucleotide polymorphisms (SNPs) adjacent to IL28B predict spontaneous resolution of HCV infection and outcome of treatment among HCV genotype 1 infected patients. METHODS AND FINDINGS: In the present study, we correlated the occurrence of variants at three such SNPs (rs12979860, rs12980275, and rs8099917) with pretreatment plasma IP-10 and HCV RNA throughout therapy within a phase III treatment trial (HCV-DITTO) involving 253 Caucasian patients. The favorable SNP variants (CC, AA, and TT, respectively) were associated with lower baseline IP-10 (P = 0.02, P = 0.01, P = 0.04) and were less common among HCV genotype 1 infected patients than genotype 2/3 (P<0.0001, P<0.0001, and P = 0.01). Patients carrying favorable SNP genotypes had higher baseline viral load than those carrying unfavorable variants (P = 0.0013, P = 0.029, P = 0.0004 respectively). Among HCV genotype 1 infected carriers of the favorable C, A, or T alleles, IP-10 below 150 pg/mL significantly predicted a more pronounced reduction of HCV RNA from day 0 to 4 (first phase decline), which translated into increased rates of RVR (62%, 53%, and 39%) and SVR (85%, 76%, and 75% respectively) among homozygous carriers with baseline IP-10 below 150 pg/mL. In multivariate analyses of genotype 1-infected patients, baseline IP-10 and C genotype at rs12979860 independently predicted the first phase viral decline and RVR, which in turn independently predicted SVR. CONCLUSIONS: Concomitant assessment of pretreatment IP-10 and IL28B-related SNPs augments the prediction of the first phase decline in HCV RNA, RVR, and final therapeutic outcome.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Biotic and abiotic variables show little redundancy in explaining tree species distributions

Abiotic factors such as climate and soil determine the species fundamental niche, which is further constrained by biotic interactions such as interspecific competition. To parameterize this realized niche, species distribution models (SDMs) most often relate species occurrence data to abiotic variables, but few SDM studies include biotic predictors to help explain species distributions. Therefore, most predictions of species distributions under future climates assume implicitly that biotic interactions remain constant or exert only minor influence on large-scale spatial distributions, which is also largely expected for species with high competitive ability. We examined the extent to which variance explained by SDMs can be attributed to abiotic or biotic predictors and how this depends on species traits. We fit generalized linear models for 11 common tree species in Switzerland using three different sets of predictor variables: biotic, abiotic, and the combination of both sets. We used variance partitioning to estimate the proportion of the variance explained by biotic and abiotic predictors, jointly and independently. Inclusion of biotic predictors improved the SDMs substantially. The joint contribution of biotic and abiotic predictors to explained deviance was relatively small (similar to 9%) compared to the contribution of each predictor set individually (similar to 20% each), indicating that the additional information on the realized niche brought by adding other species as predictors was largely independent of the abiotic (topo-climatic) predictors. The influence of biotic predictors was relatively high for species preferably growing under low disturbance and low abiotic stress, species with long seed dispersal distances, species with high shade tolerance as juveniles and adults, and species that occur frequently and are dominant across the landscape. The influence of biotic variables on SDM performance indicates that community composition and other local biotic factors or abiotic processes not included in the abiotic predictors strongly influence prediction of species distributions. Improved prediction of species' potential distributions in future climates and communities may assist strategies for sustainable forest management.

Urinary incontinence in the prediction of falls in hospitalized elderly

Objective Analyzing the effect of urinary incontinence as a predictor of the incidence of falls among hospitalized elderly. Method Concurrent cohort study where 221 elderly inpatients were followed from the date of admission until discharge, death or fall. The Kaplan-Meier methods, the incidence density and the Cox regression model were used for the survival analysis and the assessment of the association between the exposure variable and the other variables. Results Urinary incontinence was a strong predictor of falls in the surveyed elderly, and was associated with shorter time until the occurrence of event. Urinary incontinence, concomitant with gait and balance dysfunction and use of antipsychotics was associated with falls. Conclusion Measures to prevent the risk of falls specific to hospitalized elderly patients who have urinary incontinence are necessary.



Effects of pro-cholinergic treatment in patients suffering from spatial neglect.

Spatial neglect is a neurological condition characterized by a breakdown of spatial cognition contralateral to hemispheric damage. Deficits in spatial attention toward the contralesional side are considered to be central to this syndrome. Brain lesions typically involve right fronto-parietal cortices mediating attentional functions and subcortical connections in underlying white matter. Convergent findings from neuroimaging and behavioral studies in both animals and humans suggest that the cholinergic system might also be critically implicated in selective attention by modulating cortical function via widespread projections from the basal forebrain. Here we asked whether deficits in spatial attention associated with neglect could partly result from a cholinergic deafferentation of cortical areas subserving attentional functions, and whether such disturbances could be alleviated by pro-cholinergic therapy. We examined the effect of a single-dose transdermal nicotine treatment on spatial neglect in 10 stroke patients in a double-blind placebo-controlled protocol, using a standardized battery of neglect tests. Nicotine-induced systematic improvement on cancellation tasks and facilitated orienting to single visual targets, but had no significant effect on other tests. These results support a global effect of nicotine on attention and arousal, but no effect on other spatial mechanisms impaired in neglect.

Morphostructural analysis of an alpine debris flows catchment: implication for debris supply

Rock slope instabilities are implicitly linked to the supply of sediment and debris recharging channels prone to debris flow. Hence, the incorporation of bedrock structure and terrain morphology can be relevant in the analysis of sediment budget and debris flow hazard assessment. Here, the mode of debris production of the Manival catchment (northern French Alps) is documented by the study of its morphostructural aspects extracted from high resolution DEM. Terrain implication in the process of debris supply is evaluated by: a) A systematic classification of the major morphological units based on the slope gradient that enables a spatial analysis of zones of debris production and deposition. b) A detailed structural analysis performed on DEM in order to identify potential unstable slopes. c) An analysis of the gullies orientation that informs in term of structural control of the sources zones. d) Localisation of high density joints sets that document about whether sources of continuous debris production are controlled by the structural setting of the catchment. These DEM-based indicators can be used as proxies for assessing the influences of the current topography and enable to quantify a degree of susceptibility to mass wasting and hillslope erosion activity. This present contribution suggests some directions for characterizing sediment flux dynamic in small alpine catchment.

Dispersal strategies, few dominating or many coexisting: the effect of environmental spatial structure and multiple sources of mortality.

Interspecific competition, life history traits, environmental heterogeneity and spatial structure as well as disturbance are known to impact the successful dispersal strategies in metacommunities. However, studies on the direction of impact of those factors on dispersal have yielded contradictory results and often considered only few competing dispersal strategies at the same time. We used a unifying modeling approach to contrast the combined effects of species traits (adult survival, specialization), environmental heterogeneity and structure (spatial autocorrelation, habitat availability) and disturbance on the selected, maintained and coexisting dispersal strategies in heterogeneous metacommunities. Using a negative exponential dispersal kernel, we allowed for variation of both species dispersal distance and dispersal rate. We showed that strong disturbance promotes species with high dispersal abilities, while low local adult survival and habitat availability select against them. Spatial autocorrelation favors species with higher dispersal ability when adult survival and disturbance rate are low, and selects against them in the opposite situation. Interestingly, several dispersal strategies coexist when disturbance and adult survival act in opposition, as for example when strong disturbance regime favors species with high dispersal abilities while low adult survival selects species with low dispersal. Our results unify apparently contradictory previous results and demonstrate that spatial structure, disturbance and adult survival determine the success and diversity of coexisting dispersal strategies in competing metacommunities.

MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field.

The large spatial inhomogeneity in transmit B(1) field (B(1)(+)) observable in human MR images at high static magnetic fields (B(0)) severely impairs image quality. To overcome this effect in brain T(1)-weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T(1)-weighted images where the result image was free of proton density contrast, T(2) contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B(1)(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T(1)-weighted images, acquired within 12 min, high-resolution 3D T(1) maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T(1) maps were validated in phantom experiments. In humans, the T(1) values obtained at 7 T were 1.15+/-0.06 s for white matter (WM) and 1.92+/-0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T(1) values obtained (0.81+/-0.03 s for WM and 1.35+/-0.05 for GM) were once again found to be in very good agreement with values in the literature.