Assessment of two non-invasive techniques for measuring turbulent benthic fluxes in a shallow lake.

Benthic fluxes refer to the exchange rates of nutrients and other compounds between the water column and the sediment bed in aquatic ecosystems. Their quantification contributes to our understanding of aquatic ecosystem functioning. Near-bed hydrodynamics plays an important role at the sediment-water interface, especially in shallow lakes, but it is poorly considered by traditional measuring techniques of flux quantification, such as sediment incubations. Thus, alternative sampling techniques are needed to characterize key benthic fluxes under in-situ hydrodynamic conditions. This study aimed to evaluate the performance of two promising methods: relaxed eddy accumulation (REA) and mass transfer coefficient (MTC). We applied them in a hyper-eutrophic shallow lake to measure the fluxes of ammonium, phosphate, iron, and manganese ions. For the first time, REA revealed hourly nutrient flux variations, indicating a strong lake biogeochemical dynamics at short time-scales. Daily average fluxes are of similar orders of magnitude for REA and MTC for ammonium (24 and 42 mmol m2 d-1), manganese (1.0 and 0.8), and iron (0.8 and 0.7) ions. They are one order of magnitude higher than fluxes estimated from sediment incubations, due to the difficulty in reproducing in-situ oxygen and hydrodynamic conditions in the laboratory. Although the accuracy of both techniques needs to be improved, the results revealed their potential: REA follows the short-term biogeochemical dynamics of sediments, while MTC could be widely used for lake monitoring because of its simpler implementation.

Bioaccumulation of chemical elements at post-industrial freshwater sites varies predictably between habitats, elements and taxa: A power law approach.

Elevated environmental levels of elements originating from anthropogenic activities threaten natural communities and public health, as these elements can persist and bioaccumulate in the environment. However, their environmental risks and bioaccumulation patterns are often habitat-, species- and element-specific. We studied the bioaccumulation patterns of 11 elements in seven freshwater taxa in post-mining habitats in the Czech Republic, ranging from less polluted mining ponds to highly polluted fly ash lagoons. We found nonlinear, power-law relationships between the environmental and tissue concentrations of the elements, which may explain differences in bioaccumulation factors (BAF) reported in the literature. Tissue concentrations were driven by the environmental concentrations in non-essential elements (Al, As, Co, Cr, Ni, Pb and V), but this dependence was limited in essential elements (Cu, Mn, Se and Zn). Tissue concentrations of most elements were also more closely related to substrate than to water concentrations. Bioaccumulation was habitat specific in eight elements: stronger in mining ponds for Al and Pb, and stronger in fly ash lagoons for As, Cu, Mn, Pb, Se, V and Zn, although the differences were often minor. Bioaccumulation of some elements further increased in mineral-rich localities. Proximity to substrate, rather than trophic level, drove increased bioaccumulation levels across taxa. This highlights the importance of substrate as a pollutant reservoir in standing freshwaters and suggests that benthic taxa, such as molluscs (e.g., Physella) and other macroinvertebrates (e.g., Nepa), constitute good bioindicators. Despite the higher environmental risks in fly ash lagoons than in mining ponds, the observed ability of freshwater biota to sustain pollution supports the conservation potential of post-industrial sites. The power law approach used here to quantify and disentangle the effects of various bioaccumulation drivers may be helpful in additional contexts, increasing our ability to predict the effects of other contaminants and environmental hazards on biota.

Muddying the unexplored post-industrial waters: Biodiversity and conservation potential of freshwater habitats in fly ash sedimentation lagoons.

Deposits of fly ash and other coal combustion wastes are common remnants of the energy industry. Despite their environmental risks from heavy metals and trace elements, they have been revealed as refuges for threatened terrestrial biodiversity. Surprisingly, freshwater biodiversity of fly ash sedimentation lagoons remains unknown despite such lack of knowledge strongly limits the efficient restoration of fly ash deposits. We bring the first comprehensive survey of freshwater biodiversity, including nekton, benthos, zooplankton, phytoplankton, and macrophytes, in fly ash lagoons across industrial regions of the Czech Republic. To assess their conservation potential, we compared their biodiversity with abandoned post-mining ponds, the known strongholds of endangered aquatic species in the region with a shortage of natural ponds. Of 28 recorded threatened species, 15 occurred in the studied fly ash lagoons, some of which were less abundant or even absent in the post-mining ponds. These are often species of nutrient-poor, fishless waters with rich vegetation, although some are specialised extremophiles. Species richness and conservation value of most groups in the fly ash lagoons did not significantly differ from the post-mining ponds, except for species richness of benthos, zooplankton, and macrophytes, which were slightly lower in the fly ash lagoons. Although the concentrations of some heavy metals (mainly Se, V, and As) were significantly higher in the fly ash lagoons, they did not significantly affect species richness or conservation value of the local communities. The differences in species composition therefore does not seem to be caused by water chemistry. Altogether, we have shown that fly ash lagoons are refuges for threatened aquatic species, and we thus suggest maintaining water bodies during site restoration after the cessation of fly ash deposition. Based on our analyses of environmental variables, we discuss suitable restoration practices that efficiently combine biodiversity protection and environmental risk reduction.

Contrasting catchment soil pH and Fe concentrations influence DOM distribution and nutrient dynamics in freshwater systems.

Organic matter (OM) quantity, quality, and nutrient dynamics within twelve shallow lakes in the Czech Republic were assessed in the context of catchment soil pH and iron (Fe) concentration. The catchments of the lakes were classified into two categories: (i) slightly acidic (soil pH = 5.1-6.3) with Fe-rich soils (H_Fe; Fe = 315-344 mg kg-1 in Mehlich 3 extract); and (ii) neutral (soil pH = 6.8-7.6) with Fe-poor soils (L_Fe; Fe = 126-259 mg kg-1 in Mehlich 3 extract). The quality of OM in the two lake types was characterized using a combination of spectroscopic techniques (UV-Vis, fluorescence, and Fourier Transform Infrared spectroscopy). We show that dissolved nutrient and dissolved organic carbon (DOC) concentrations, as well as the amount of aromatic and protein-like compounds in the water column and sediment porewater were significantly (p < 0.01) lower in the H_Fe lakes compared to the waterbodies located within L_Fe catchments. The FTIR analyses of the H_Fe sediments contained higher relative concentrations of aromatic compounds with hydroxyl-containing functional groups and carbohydrates, while more aliphatic and oxidised OM was found in the L_Fe lake sediments. These results suggest that the pH value of catchment soils and, particularly, their Fe content have profound geochemical effects on the mobility of OM and nutrients in the sediments of recipient waters. Because the OM-Fe association stabilises OM in sediments, waterbodies within L_Fe catchments are likely more vulnerable to increasing eutrophication and oxygen depletion compared to those in H_Fe catchments and this has important implications for water quality management, risk assessment, and predictions of aquatic ecosystem vulnerability under conditions of accelerating climate change.

Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?

Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ 4 band and low ß 1 band) demonstrated significant negative linear relationship (p FWE < 0.05) to the frequent stimulus and three patterns (two low δ 2 and δ 3 bands, and narrow θ 1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ 4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ 4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related ß 1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ 1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ 1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ 4, ß 1, and θ 1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.

Learning-based vertebra localization and labeling in 3D CT data of possibly incomplete and pathological spines.

BACKGROUND AND OBJECTIVE: We present a fully automatic system based on learning approaches, which aims to localization and identification (labeling) of vertebrae in 3D computed tomography (CT) scans of possibly incomplete spines in patients with bone metastases and vertebral compressions. METHODS: The framework combines a set of 3D algorithms for i) spine detection using a convolution neural network (CNN) ii) spinal cord tracking based on combination of a CNN and a novel growing sphere method with a population optimization, iii) intervertebral discs localization using a novel approach of spatially variant filtering of intensity profiles and iv) vertebra labeling using a CNN-based classification combined with global dynamic optimization. RESULTS: The proposed algorithm has been validated in testing databases, including also a publicly available dataset. The mean error of intervertebral discs localization is 4.4 mm, and for vertebra labeling, the average rate of correctly identified vertebrae is 87.1%, which can be considered a good result with respect to the large share of highly distorted spines and incomplete spine scans. CONCLUSIONS: The proposed framework, which combines several advanced methods including also three CNNs, works fully automatically even with incomplete spine scans and with distorted pathological cases. The achieved results allow including the presented algorithms as the first phase to the fully automated computer-aided diagnosis (CAD) system for automatic spine-bone lesion analysis in oncological patients.

EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions.

BACKGROUND: Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization. NEW METHOD: We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 1st and 2nd temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during "resting-state", visual oddball and semantic decision paradigms. RESULTS: The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated. COMPARISON WITH EXISTING METHOD(S): Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI. CONCLUSIONS: This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.

Deep-learning-based fully automatic spine centerline detection in CT data.

In this contribution, we present a fully automatic approach, that is based on two convolution neural networks (CNN) together with a spine tracing algorithm utilizing a population optimization algorithm. Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.

Iterative machine learning based rotational alignment of brain 3D CT data.

The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard position has a crucial importance for both automatic and manual diagnostic analysis. In this contribution, we present a novel two-step iterative approach for the automatic 3D rotational alignment of brain CT data. The angles of axial and coronal rotations are determined by an unsupervised by localisation of the Midsagittal Plane (MSP) method. This includes detection and pairing of medially symmetrical feature points. The sagittal rotation angle is subsequently estimated by regression convolutional neural network (CNN). The proposed methodology has been evaluated on a dataset of CT data manually aligned by radiologists. It has been shown that the algorithm achieved the low error of estimated rotations (≈1 degree) and in a significantly shorter time than the experts (≈2 minutes per case).

Iron and nitrogen cycling, bacterioplankton community composition and mineral transformations involving phosphorus stabilisation in the ferruginous hypolimnion of a post-mining lake.

Lake Medard is an oligotrophic post-mining lake characterised by ferruginous bottom waters, with marked redox gradients resulting from iron (Fe) and nitrogen (N) speciation and accompanying depth-dependent variations in the abundance of volatile fatty acids (VFAs), pH and alkalinity. The lacustrine system is meromictic, featuring a dysoxic hypolimnion and an anoxic monimolimnion with relatively high concentrations of sulfate (SO42-, 19 ± 2 mM) and Fe(ii) (127 ± 17 µM). An increase in dissolved manganese is also observed with increasing depth, together with a general lack of sulfide, which can only be detected at the sediment-water interface at concentrations of ∼0.30 µM. In the hypolimnion, nitrate (NO3-) becomes progressively depleted and ammonium (NH4+) dominates the dissolved N inventory (up to 185 ± 13 µM). Here we describe the biogeochemical disequilibrium conditions governing critical mineralogical transformations involving Fe and phosphorus (P) co-precipitation in the dysoxic-to-anoxic bottom water column. A combination of mineral equilibrium modelling and synchrotron-based diffraction and spectroscopic techniques was applied to investigate the minerals comprising the upper anoxic sediments. The combined dataset indicates that elemental recycling on and below the hypolimnion promote the precipitation of FeOOH polymorphs that accumulate as heterogeneous mineral clusters. Changes in the relative abundance of bacterioplankton taxa with increasing water depth point to a link between the activity of certain members of Proteobacteria and the co-recycling of carbon (C), N, and Fe stocks. Such a redox recycling process seems to lead to P stabilisation into organic-rich Fe-(oxyhydr)oxides near and above the anoxic sediment-water interface (SWI).

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data.

This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesions are ill-defined and consequently it is difficult to find relevant image features that would enable detection and classification of lesions by classical methods of texture and shape analysis, the problem is solved by automatic feature extraction provided by a deep Convolutional Neural Network (CNN). Our main contributions are: (i) individual CNN architecture, and pre-processing steps that are dependent on a patient data and a scan protocol - it enables work with different types of CT scans; (ii) medial axis transform (MAT) post-processing for shape simplification of segmented lesion candidates with Random Forest (RF) based meta-analysis; and (iii) usability of the proposed method on whole-spine CTs (cervical, thoracic, lumbar), which is not treated in other published methods (they work with thoracolumbar segments of spine only). Our proposed method has been tested on our own dataset annotated by two mutually independent radiologists and has been compared to other published methods. This work is part of the ongoing complex project dealing with spine analysis and spine lesion longitudinal studies.

Approach for predicting P sorption/desorption behaviour of potentially eroded topsoil in watercourses.

This study focuses on the quantification of the impact of potentially eroded topsoil particles on the available watercourse P concentration. We used 56 topsoil samples for determining the relation existing between the molar ratio of sorption-active Fe- and Al-(hydr)oxides to plant available P, as determined by the commonly used oxalate and Mehlich 3 extractions (PM3). Our sample set covers the most common combinations of non-calcareous soil types, land and agricultural uses. By using Freundlich isotherms, we found that the concentration of adsorbed/desorbed P (Q) by soil particles in water with specific soluble reactive phosphorus (SRP) concentrations are significantly correlated to PM3 (Q=a∗PM3+b) and also to the degree of P saturation as measured by oxalate extraction (DPSox; Q=c∗DPSox+d). The observed relations varied in slope and intercept parameters for discrete equilibrium P concentrations in water. However, in the environmentally important range of PO4-P concentrations, i.e., from 20 to 220µgL-1, a strong non-linear correlation was found between the individual parameters and equilibrium P concentration. Accordingly, we derived equations useful for quantitatively predict P exchange between topsoil particles when potentially eroded into watercourse and the recipient water of either known or targeted P concentrations. This approach might refine erosion models, and thus facilitates the quantification of the impact of soil erosion on the in-stream bioavailable P concentrations.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics.

OBJECTIVE: Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. APPROACH: The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component's time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. MAIN RESULTS: We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. SIGNIFICANCE: Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA.

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Accurate micro-computed tomography imaging of pore spaces in collagen-based scaffold.

In this work we have used X-ray micro-computed tomography (µCT) as a method to observe the morphology of 3D porous pure collagen and collagen-composite scaffolds useful in tissue engineering. Two aspects of visualizations were taken into consideration: improvement of the scan and investigation of its sensitivity to the scan parameters. Due to the low material density some parts of collagen scaffolds are invisible in a µCT scan. Therefore, here we present different contrast agents, which increase the contrast of the scanned biopolymeric sample for µCT visualization. The increase of contrast of collagenous scaffolds was performed with ceramic hydroxyapatite microparticles (HAp), silver ions (Ag(+)) and silver nanoparticles (Ag-NPs). Since a relatively small change in imaging parameters (e.g. in 3D volume rendering, threshold value and µCT acquisition conditions) leads to a completely different visualized pattern, we have optimized these parameters to obtain the most realistic picture for visual and qualitative evaluation of the biopolymeric scaffold. Moreover, scaffold images were stereoscopically visualized in order to better see the 3D biopolymer composite scaffold morphology. However, the optimized visualization has some discontinuities in zoomed view, which can be problematic for further analysis of interconnected pores by commonly used numerical methods. Therefore, we applied the locally adaptive method to solve discontinuities issue. The combination of contrast agent and imaging techniques presented in this paper help us to better understand the structure and morphology of the biopolymeric scaffold that is crucial in the design of new biomaterials useful in tissue engineering.

Exploring task-related variability in fMRI data using fluctuations in power spectrum of simultaneously acquired EEG.

BACKGROUND: The paper deals with joint analysis of fMRI and scalp EEG data, simultaneously acquired during event-related oddball experiment. The analysis is based on deriving temporal sequences of EEG powers in individual frequency bands for the selected EEG electrodes and using them as regressors in the general linear model (GLM). NEW METHOD: Given the infrequent use of EEG spectral changes to explore task-related variability, we focused on the aspects of parameter setting during EEG regressor calculation and searched for such parameters that can detect task-related variability in EEG-fMRI data. We proposed a novel method that uses relative EEG power in GLM. RESULTS: Parameter, the type of power value, has a direct impact as to whether task-related variability is detected or not. For relative power, the final results are sensitive to the choice of frequency band of interest. The electrode selection also has certain impact; however, the impact is not crucial. It is insensitive to the choice of EEG power series temporal weighting step. Relative EEG power characterizes the experimental task activity better than the absolute power. Absolute EEG power contains broad spectrum component. Task-related relative power spectral formulas were derived. COMPARISON WITH EXISTING METHODS: For particular set of parameters, our results are consistent with previously published papers. Our work expands current knowledge by new findings in spectral patterns of different brain processes related to the experimental task. CONCLUSIONS: To make analysis to be sensitive to task-related variability, the parameters type of power value and frequency band should be set properly.

Thickness related textural properties of retinal nerve fiber layer in color fundus images.

Images of ocular fundus are routinely utilized in ophthalmology. Since an examination using fundus camera is relatively fast and cheap procedure, it can be used as a proper diagnostic tool for screening of retinal diseases such as the glaucoma. One of the glaucoma symptoms is progressive atrophy of the retinal nerve fiber layer (RNFL) resulting in variations of the RNFL thickness. Here, we introduce a novel approach to capture these variations using computer-aided analysis of the RNFL textural appearance in standard and easily available color fundus images. The proposed method uses the features based on Gaussian Markov random fields and local binary patterns, together with various regression models for prediction of the RNFL thickness. The approach allows description of the changes in RNFL texture, directly reflecting variations in the RNFL thickness. Evaluation of the method is carried out on 16 normal ("healthy") and 8 glaucomatous eyes. We achieved significant correlation (normals: ρ=0.72±0.14; p≪0.05, glaucomatous: ρ=0.58±0.10; p≪0.05) between values of the model predicted output and the RNFL thickness measured by optical coherence tomography, which is currently regarded as a standard glaucoma assessment device. The evaluation thus revealed good applicability of the proposed approach to measure possible RNFL thinning.

Novel registration-based framework for CT angiography in lower legs.

Proper subtraction and visualization of contrast-enhanced blood vessels in lower extremities using computed tomography angiography (CTA) is based on precise masking of all non-contrasted structures in the area, and it is the main prerequisite for correct diagnosis and decision on treatment for peripheral arterial occlusive disease (PAOD). Because of possible motion of patients during the CTA examination, precise elimination of non-contrasted tissues, including bones, calcifications, and soft tissue, is still very challenging for lower legs, that is, from knees to toes. We propose novel registration-based framework for detection and correction of the motion in lower legs, which typically occurs between and during CTA pre-contrast and post-contrast acquisitions. Within the framework, two registration cores are proposed as alternatives, and resulting CTA subtraction images are compared with Advanced Vessel Analysis considered one of the reference commercial tools among clinical applications for CTA of lower extremities. The CTA subtraction images of 55 patients examined for PAOD are evaluated visually by four expert observers on the Philips Extended Brilliance Workspace using four criteria assessing the overall robustness of tested methods. According to the complex evaluation, the proposed framework enabled valuable improvements of CTA examination of lower legs.

Registration of FA and T1-weighted MRI data of healthy human brain based on template matching and normalized cross-correlation.

In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.

What do results of common sequential fractionation and single-step extractions tell us about P binding with Fe and Al compounds in non-calcareous sediments?

Correct identification of P forms together with their main Fe and Al binding partners in non-calcareous sediments is of crucial importance for evaluation of P cycling in water bodies. In this paper, we assess extraction methods frequently used for this purpose, i.e., a sequential five-step fractionation (water, bicarbonate buffered dithionite solution (BD), NaOH, HCl, nitric-perchloric acid), ascorbate extraction (pH ~7.5), and oxalate extraction (pH ~3), directly on a range of laboratory prepared Fe and Al minerals enriched with adsorbed P. Extraction selectivity and efficiency for particular P, Fe and Al forms were also verified by specific combinations of these extraction methods applied on freshwater sediment samples. In the sequential fractionation, BD was highly effective in dissolving both amorphous and crystalline Fe (hydr)oxides and the associated P, while neither FeS nor Al (hydr)oxides were dissolved. The following NaOH extraction effectively dissolved both amorphous and crystalline Al (hydr)oxides. The high solubilizing power of BD and NaOH to dissolve crystalline Fe and Al oxides that have only a small P-sorption ability prevents the use of resulting Fe/P and Al/P ratios as simple predictors of total P sorption capacity of sediments and soils. Ascorbate non-selectively extracted small proportions of FeS and amorphous Fe and Al (hydr)oxides, but significant amounts of adsorbed P, which hinders its use for the characterization of P forms in non-calcareous sediments. Similar nonselective characteristics were found for oxalate extractions. As oxalate extracts most of the adsorbed phosphate, it is not possible to use it unambiguously to determine specific Fe/P and Al/P ratios of active complexes. However, this method is convenient (and more selective than NaOH step in the sequential fractionation) for the determination of amorphous Al (hydr)oxides.