Assessing the Firing Properties of the Electrically Stimulated Auditory Nerve Using a Convolution Model

The electrically evoked compound action potential (eCAP) is a routinely performed measure of the auditory nerve in cochlear implant users. Using a convolution model of the eCAP, additional information about the neural firing properties can be obtained, which may provide relevant information about the health of the auditory nerve. In this study, guinea pigs with various degrees of nerve degeneration were used to directly relate firing properties to nerve histology. The same convolution model was applied on human eCAPs to examine similarities and ultimately to examine its clinical applicability. For most eCAPs, the estimated nerve firing probability was bimodal and could be parameterised by two Gaussian distributions with an average latency difference of 0.4 ms. The ratio of the scaling factors of the late and early component increased with neural degeneration in the guinea pig. This ratio decreased with stimulation intensity in humans. The latency of the early component decreased with neural degeneration in the guinea pig. Indirectly, this was observed in humans as well, assuming that the cochlear base exhibits more neural degeneration than the apex. Differences between guinea pigs and humans were observed, among other parameters, in the width of the early component: very robust in guinea pig, and dependent on stimulation intensity and cochlear region in humans. We conclude that the deconvolution of the eCAP is a valuable addition to existing analyses, in particular as it reveals two separate firing components in the auditory nerve.

Improved Peak Detection and Deconvolution of Native Electrospray Mass Spectra from Large Protein Complexes

Native electrospray-ionization mass spectrometry (native MS) measures biomolecules under conditions that preserve most aspects of protein tertiary and quaternary structure, enabling direct characterization of large intact protein assemblies. However, native spectra derived from these assemblies are often partially obscured by low signal-to-noise as well as broad peak shapes because of residual solvation and adduction after the electrospray process. The wide peak widths together with the fact that sequential charge state series from highly charged ions are closely spaced means that native spectra containing multiple species often suffer from high degrees of peak overlap or else contain highly interleaved charge envelopes. This situation presents a challenge for peak detection, correct charge state and charge envelope assignment, and ultimately extraction of the relevant underlying mass values of the noncovalent assemblages being investigated. In this report, we describe a comprehensive algorithm developed for addressing peak detection, peak overlap, and charge state assignment in native mass spectra, called PeakSeeker. Overlapped peaks are detected by examination of the second derivative of the raw mass spectrum. Charge state distributions of the molecular species are determined by fitting linear combinations of charge envelopes to the overall experimental mass spectrum. This software is capable of deconvoluting heterogeneous, complex, and noisy native mass spectra of large protein assemblies as demonstrated by analysis of (1) synthetic mononucleosomes containing severely overlapping peaks, (2) an RNA polymerase II/α-amanitin complex with many closely interleaved ion signals, and (3) human TriC complex containing high levels of background noise. Graphical Abstract ᅟ.

3D Tumor Models and Time-Lapse Analysis by Multidimensional Microscopy

The 3D culture is advantageous in reflecting the in vivo condition compared to the 2D culture; however, imaging 3D-cultured cells may be a challenge due to technical restrictions. Recent development of confocal spinning disc microscope system as well as sophisticated software has enabled us to monitor dynamism of cell movement in multiple dimensions. Here we describe the method for time-lapse imaging of 3D-cultured cancer cells co-cultured with non-cancerous cells and discuss current limitations and future perspectives.

Localisation-based imaging of malarial antigens during erythrocyte entry reaffirms a role for AMA1 but not MTRAP in invasion

Microscopy-based localisation of proteins during malaria parasite (Plasmodium) invasion of the erythrocyte is widely used for tentative assignment of protein function. To date, however, imaging has been limited by the rarity of invasion events and the poor resolution available, given the micron size of the parasite, which leads to a lack of quantitative measures for definitive localisation. Here, using computational image analysis we have attempted to assign relative protein localisation during invasion using wide-field deconvolution microscopy. By incorporating three-dimensional information we present a detailed assessment of known parasite effectors predicted to function during entry but as yet untested or for which data are equivocal. Our method, termed longitudinal intensity profiling, resolves confusion surrounding the localisation of apical membrane antigen 1 (AMA1) at the merozoite–erythrocyte junction and predicts that the merozoite thrombospondin-related anonymous protein (MTRAP) is unlikely to play a direct role in the mechanics of entry, an observation supported with additional biochemical evidence. This approach sets a benchmark for imaging of complex micron-scale events and cautions against simplistic interpretations of small numbers of representative images for the assignment of protein function or prioritisation of candidates as therapeutic targets.

Highlighted Article: Here we develop a high-definition imaging approach to dissect and assign function to proteins involved in the rapid process of malaria parasite invasion of the human erythrocyte.

Using patient-specific hemodynamic response function in epileptic spike analysis of human epilepsy: a study based on EEG-fNIRS

Functional near-infrared spectroscopy (fNIRS) can be combined with electroencephalography (EEG) to continuously monitor the hemodynamic signal evoked by epileptic events such as seizures or interictal epileptiform discharges (IEDs, aka spikes). As estimation methods assuming a canonical shape of the hemodynamic response function (HRF) might not be optimal, we sought to model patient-specific HRF (sHRF) with a simple deconvolution approach for IED-related analysis with EEG-fNIRS data. Furthermore, a quadratic term was added to the model to account for the nonlinearity in the response when IEDs are frequent. Prior to analyzing clinical data, simulations were carried out to show that the HRF was estimable by the proposed deconvolution methods under proper conditions. EEG-fNIRS data of five patients with refractory focal epilepsy were selected due to the presence of frequent clear IEDs and their unambiguous focus localization. For each patient, both the linear sHRF and the nonlinear sHRF were estimated at each channel. Variability of the estimated sHRFs was seen across brain regions and different patients. Compared with the SPM8 canonical HRF (cHRF), including these sHRFs in the general linear model (GLM) analysis led to hemoglobin activations with higher statistical scores as well as larger spatial extents on all five patients. In particular, for patients with frequent IEDs, nonlinear sHRFs were seen to provide higher sensitivity in activation detection than linear sHRFs. These observations support using sHRFs in the analysis of IEDs with EEG-fNIRS data.

Using Cox cluster processes to model latent pulse location patterns in hormone concentration data

Many hormones, including stress hormones, are intermittently secreted as pulses. The pulsatile location process, describing times when pulses occur, is a regulator of the entire stress system. Characterizing the pulse location process is particularly difficult because the pulse locations are latent; only hormone concentration at sampled times is observed. In addition, for stress hormones the process may change both over the day and relative to common external stimuli. This potentially results in clustering in pulse locations across subjects. Current approaches to characterizing the pulse location process do not capture subject-to-subject clustering in locations. Here we show how a Bayesian Cox cluster process may be adapted as a model of the pulse location process. We show that this novel model of pulse locations is capable of detecting circadian rhythms in pulse locations, clustering of pulse locations between subjects, and identifying exogenous controllers of pulse events. We integrate our pulse location process into a model of hormone concentration, the observed data. A spatial birth-and-death Markov chain Monte Carlo algorithm is used for estimation. We exhibit the strengths of this model on simulated data and adrenocorticotropic and cortisol data collected to study the stress axis in depressed and non-depressed women.

A deconvolution-based approach to identifying large-scale effective connectivity

Rapid, robust computation of effective connectivity between neural regions is an important next step in characterizing the brain's organization, particularly in the resting state. However, recent work has called into question the value of causal inference computed directly from BOLD, demonstrating that valid inferences require transformation of the BOLD signal into its underlying neural events as necessary for accurate causal inference. In this work we develop an approach for effective connectivity estimation directly from deconvolution-based features and estimates of inter-regional communication lag. We then test, in both simulation as well as whole-brain fMRI BOLD signal, the viability of this approach. Our results show that deconvolution precision and network size play outsized roles in effective connectivity estimation performance. Idealized simulation conditions allow for statistically significant effective connectivity estimation of networks of up to four hundred regions-of-interest (ROIs). Under simulation of realistic recording conditions and deconvolution performance, however, our result indicates that effective connectivity is viable in networks containing up to approximately sixty ROIs. We then validated the ability for the proposed method to reliably detect effective connectivity in whole-brain fMRI signal parcellated into networks of viable size.

Improving the precision of fMRI BOLD signal deconvolution with implications for connectivity analysis

An important, open problem in neuroimaging analyses is developing analytical methods that ensure precise inferences about neural activity underlying fMRI BOLD signal despite the known presence of confounds. Here, we develop and test a new meta-algorithm for conducting semi-blind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that estimates, via bootstrapping, both the underlying neural events driving BOLD as well as the confidence of these estimates. Our approach includes two improvements over the current best performing deconvolution approach; 1) we optimize the parametric form of the deconvolution feature space; and, 2) we pre-classify neural event estimates into two subgroups, either known or unknown, based on the confidence of the estimates prior to conducting neural event classification. This knows-what-it-knows approach significantly improves neural event classification over the current best performing algorithm, as tested in a detailed computer simulation of highly-confounded fMRI BOLD signal. We then implemented a massively parallelized version of the bootstrapping-based deconvolution algorithm and executed it on a high-performance computer to conduct large scale (i.e., voxelwise) estimation of the neural events for a group of 17 human subjects. We show that by restricting the computation of inter-regional correlation to include only those neural events estimated with high-confidence the method appeared to have higher sensitivity for identifying the default mode network compared to a standard BOLD signal correlation analysis when compared across subjects.

Structural and functional analysis of tunneling nanotubes (TnTs) using gCW STED and gconfocal approaches

BACKGROUND INFORMATION

Tunneling nanotubes (TnTs) are thin plasma membrane bridges mediating transfers of materials and signals between cells. Heterogeneity of heterocellular and homocellular TnTs is largely described but ultrafine imaging of these light-sensitive floating nanometric structures represents a real challenge in microscopy. We propose here imaging strategies designed to dissect structural and dynamic aspects of TnT formation and function in fixed or living PC12 cells.

RESULTS

Through time-gated Continuous Wave STimulated Emission Depletion (gCW STED) nanoscopy associated with deconvolution, we provided nanoscale details of membrane and cytoskeleton organisations in two subtypes of TnTs, namely type 1 TnT (TnT1) and type 2 TnT (TnT2). In fixed PC12 cells, TnT1 (length, several tens of micrometres; diameter, 100-650 nm) exhibited a large trumpet-shaped origin, a clear cytosolic tunnel and different bud-shaped connections from closed-ended to open-ended tips. TnT1 contained both actin and tubulin. TnT2 (length, max 20 μm, diameter, 70-200 nm) only contained actin without clear cytosolic tunnel. In living PC12 cells, we observed through gCW STED additional details, unrevealed so far, including a filament spindle emerging from an organising centre at the origin of TnT1 and branched or bulbous attachments of TnT2. However, the power of depletion laser in STED nanoscopy was deleterious for TnTs and prolonged time-lapse experiments were almost prohibited. By circumventing the hazard of photoxicity, we were able to monitor dynamics of bud-shaped tips and intercellular transfer of wheat germ agglutinin labelled cellular elements through time-gated confocal microscopy.

CONCLUSIONS

Our work identified new structural characteristics of two subtypes of TnTs in PC12 cells as well as dynamics of formation and transfer through complementary imaging methods combined with image processing. Therefore, we could achieve maximum lateral resolution and sample preservation during acquisitions to reveal new insights into TnT studies.

SIGNIFICANCE

Due to large disparity of TnT-like structures in neuronal, immune, cancer or epithelial cells, high- and superresolution approaches can be utilised for full characterisation of these yet poorly understood routes of cell-to-cell communication.

Fourier Deconvolution Methods for Resolution Enhancement in Continuous-Wave EPR Spectroscopy

An overview of resolution enhancement of conventional, field-swept, continuous-wave electron paramagnetic resonance spectra using Fourier transform-based deconvolution methods is presented. Basic steps that are involved in resolution enhancement of calculated spectra using an implementation based on complex discrete Fourier transform algorithms are illustrated. Advantages and limitations of the method are discussed. An application to an experimentally obtained spectrum is provided to illustrate the power of the method for resolving overlapped transitions.

Tissue-specific sparse deconvolution for brain CT perfusion

Enhancing perfusion maps in low-dose computed tomography perfusion (CTP) for cerebrovascular disease diagnosis is a challenging task, especially for low-contrast tissue categories where infarct core and ischemic penumbra usually occur. Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra are likely to occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we propose a tissue-specific sparse deconvolution approach to preserve the subtle perfusion information in the low-contrast tissue classes. We first build tissue-specific dictionaries from segmentations of high-dose perfusion maps using online dictionary learning, and then perform deconvolution-based hemodynamic parameters estimation for block-wise tissue segments on the low-dose CTP data. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method compared to state-of-art, and potentially improve diagnostic accuracy by increasing the differentiation between normal and ischemic tissues in the brain.

On the demultiplexing of chromosome capture conformation data

How to describe the multiple chromosome structures that underlie interactions among genome loci and how to quantify the occurrence of these structures in a cell population remain important challenges to solve, which can be addressed via a proper demultiplexing of chromosome capture conformation related data. Here, we first aim to review two main methodologies that have been proposed to tackle this problem: restrained-based methods, in which the resulting chromosome structures stem from the multiple solutions of a distance satisfaction problem; and thermodynamic-based methods, in which the structures stem from the simulation of polymer models. Next, we propose a novel demultiplexing method based on a matrix decomposition of contact maps. To this end, we extend the notion of topologically associated domains (TADs) by introducing that of statistical interaction domains (SIDs). SIDs can overlap and occur in a cell population at certain frequencies, and we propose a simple method to estimate these frequency values. As an application, we show that SIDs that measure 100kb to tens of Mb long occur both frequently and specifically in the human genome.

Deconvolution procedure of the UV-vis spectra. A powerful tool for the estimation of the binding of a model drug to specific solubilisation loci of bio-compatible aqueous surfactant-forming micelle

UV-vis-spectra evolution of Nile Red loaded into Tween 20 micelles with pH and [Tween 20] have been analysed in a non-conventional manner by exploiting the deconvolution method. The number of buried sub-bands has been found to depend on both pH and bio-surfactant concentration, whose positions have been associated to Nile Red confined in aqueous solution and in the three micellar solubilisation sites. For the first time, by using an extended classical two-pseudo-phases-model, the robust treatment of the spectrophotometric data allows the estimation of Nile Red binding constant to the available loci. Hosting capability towards Nile Red is exalted by the pH enhancement. Comparison between binding constant values classically evaluated and those estimated by the deconvolution protocol unveiled that overall binding values perfectly match with the mean values of the local binding sites. This result suggests that deconvolution procedure provides more precise and reliable values, which are more representative of drug confinement.

A comparison of manual and automated methods of quantitation of oestrogen/progesterone receptor expression in breast carcinoma

BACKGROUND

Oestrogen/progesterone receptor expression in breast carcinoma is associated with good response to hormonal therapy and overall better prognosis. The predictive and prognostic capabilities of these receptors are enhanced by quantitation of immunoreaction. There are several manual and automated methods for this purpose. Whether they yield comparable results that can be used interchangeably is not yet clear.

AIM

To compare the manual methods (H-score and Allred score) with automated methods (Immunoratio) for quantifying immunohistochemical (IHC) reaction for ER/PR in breast carcinoma.

MATERIALS AND METHODS

Samples from established cases of breast carcinoma were processed and stained by immunohistochemical methods to demonstrate oestrogen receptor (ER) and progesterone receptor (PR). Receptor expression was quantified by manual methods (H-score, modified H-score and Allred score) and automated methods (basic and advanced Immunoratio). In modified H score, the intensity of reaction was assessed by measurement of mean grey value {H (MGV)} or optical density {H (DC-OD)} of deconvoluted image. The manual counting was done with cell counter plugin of Image-J (NIH). The scores were compared and Pearson's correlation coefficient was determined.

RESULTS

Both manual and automated methods produced results that were comparable. There was a statistically significant positive correlation among all methods (p<0.02). The strongest correlation was observed between advanced immunoratio and H (DC-OD) (p=<0.001). Basic immunoratio appeared to be less reliable than the other methods. Staining intensity measurements by various methods did not significantly affect correlation. However, intensity measurements by optical density resulted in lower H-scores but led to more reliable detection of negative immunoreaction.

CONCLUSION

Both manual and automated methods of quantitation are comparable. Advanced immunoratio is a reliable alternative to manual methods. Cell Counter plugin is a useful tool for manual counting and quantitation.

Using deconvolution to understand the mechanism for variable plasma concentration-time profiles after intramuscular injection

To introduce better antibiotics for the treatment of some infectious diseases in sheep and to expand the range of antibiotics available for veterinary medicine, pharmacokinetics of two antibiotics marbofloxacin (MBX) and trovafloxacin (TVX) were investigated in sheep after intramuscular injection. Variable and irregular plasma concentration-time profiles were observed for TVX but not for MBX. To understand the mechanism of this phenomenon, intravenous studies were performed for both drugs and data were analyzed using a population approach. Deconvolution was then performed using various approaches to obtain absorption profiles of both drugs in sheep after intramuscular injection. The Loo-Riegelman and staircase deconvolution function methods were found to provide more reliable estimates of absorption rate than the Spath-spline and B-spline constraining break points deconvolution methods. The absorption profiles resulting from deconvolution indicated a zero-order absorption process for TVX and a first-order process for MBX. Precipitation of TVX at the injection site was suspected to cause the pseudo zero-order absorption. This hypothesis was supported by the observation of crystalline deposits of TVX in sheep meat after direct injection, using reflectance confocal microscopy.

Impact of Adiposity and Fat Distribution on the Dynamics of Adrenocorticotropin and Cortisol Rhythms

Obesity impacts many hormonal systems, including pituitary hormones, as well as insulin and leptin. In this review we discuss articles which investigate the influence of obesity on the hypothalamic-pituitary-adrenal (HPA) axis. Different techniques have been used to assess the function of the HPA-axis in obesity, including measuring fasting and/or late evening levels of adrenocorticotropic hormone (ACTH) and (free) cortisol in plasma and saliva, studying feedback with dexamethasone or cortisol, and evaluating responsiveness of the system to corticotropin-releasing hormone (CRH) and arginine vasopressin (AVP) or ACTH 1-29. In addition, more elaborate studies investigated 24-h secretion patterns, analyzed with deconvolution techniques to quantitate pulsatile secretion rates of cortisol and less often ACTH. Other investigators used timed infusions of labeled cortisol for the estimation of the 24-h secretion rate, clearance rate and distribution volume. Many studies relied on the 24-h urinary excretion of free cortisol, but for quantitation of the 24-h secretion, measurement of all cortisol-derived metabolites is required. Several studies have applied modern liquid chromatography-tandem-mass spectrometry techniques to measure these metabolites. The picture emerging from all these studies is that, first, ACTH secretion is amplified, likely via enhanced forward drive; and, second, serum cortisol levels are normal or even low, associated with a normal 24-h cortisol secretion per liter distribution volume determined by deconvolution, but enhanced when based on the increased total distribution volume associated with obesity. Increased cortisol secretion was also established by isotope dilution studies and reports based on the measurement of all urinary cortisol metabolites. The responsiveness of the adrenal gland to ACTH is diminished. The studies do not address quantitative aspects of cortisol-cortisone metabolism on individual organs, including liver, central and peripheral fat, intestine, skin, and muscle.

Improved peak shape fitting in alpha spectra

Peak overlap is a recurrent issue in alpha-particle spectrometry, not only in routine analyses but also in the high-resolution spectra from which reference values for alpha emission probabilities are derived. In this work, improved peak shape formulae are presented for the deconvolution of alpha-particle spectra. They have been implemented as fit functions in a spreadsheet application and optimum fit parameters were searched with built-in optimisation routines. Deconvolution results are shown for a few challenging spectra with high statistical precision. The algorithm outperforms the best available routines for high-resolution spectrometry, which may facilitate a more reliable determination of alpha emission probabilities in the future. It is also applicable to alpha spectra with inferior energy resolution.

Deconvolution of overlapping spectral polymer signals in size exclusion separation-diode array detection separations by implementing a multivariate curve resolution method optimized by alternating least square

Peaks eluting from a size exclusion separation (SEC) are often not completely baseline-separated due to the inherent dispersity of the polymer. Lowering the flow rate is sometimes a solution to obtain a better physical separation, but results in a longer retention time, which is often not desirable. The chemometrical deconvolution method discussed in this work provides the possibility of calculating the contribution of each peak separately in the total chromatogram of overlapping peaks. An in-house-developed MATLAB script differentiates between compounds based on their difference in UV-spectrum and retention time, using the entire 3D retention time UV-spectrum. Consequently, the output of the script offers the calculated chromatograms of the separate compounds as well as their respective UV-spectrum, of which the latter can be used for peak identification. This approach is of interest to quantitate contributions of different polymer types with overlapping UV-spectra and retention times, as is often the case in, for example, copolymer or polymer blend analysis. The applicability has been proven on mixtures of different polymer types: polystyrene, poly(methyl methacrylate) and poly(ethoxyethyl acrylate). This paper demonstrates that both qualitative and quantitative analyses are possible after deconvolution and that alternating concentrations of adjacent peaks do not significantly influence the obtained accuracy.

A locally convoluted cluster model for nucleosome positioning signals in chemical map

Nucleosome is the fundamental packing unit of DNA in eukaryotic cells, and its positioning plays a critical role in regulation of gene expression and chromosome functions. Using a recently developed chemical mapping method, nucleosomes can be potentially mapped with an unprecedented single-base-pair resolution. Existence of overlapping nucleosomes due to cell mixture or cell dynamics, however, causes convolution of nucleosome positioning signals. In this paper, we introduce a locally convoluted cluster model and a maximum likelihood deconvolution approach, and illustrate the effectiveness of this approach in quantification of the nucleosome positional signal in the chemical mapping data.

Deconvolution filtering: temporal smoothing revisited

Inferences made from analysis of BOLD data regarding neural processes are potentially confounded by multiple competing sources: cardiac and respiratory signals, thermal effects, scanner drift, and motion-induced signal intensity changes. To address this problem, we propose deconvolution filtering, a process of systematically deconvolving and reconvolving the BOLD signal via the hemodynamic response function such that the resultant signal is composed of maximally likely neural and neurovascular signals. To test the validity of this approach, we compared the accuracy of BOLD signal variants (i.e., unfiltered, deconvolution filtered, band-pass filtered, and optimized band-pass filtered BOLD signals) in identifying useful properties of highly confounded, simulated BOLD data: (1) reconstructing the true, unconfounded BOLD signal, (2) correlation with the true, unconfounded BOLD signal, and (3) reconstructing the true functional connectivity of a three-node neural system. We also tested this approach by detecting task activation in BOLD data recorded from healthy adolescent girls (control) during an emotion processing task. Results for the estimation of functional connectivity of simulated BOLD data demonstrated that analysis (via standard estimation methods) using deconvolution filtered BOLD data achieved superior performance to analysis performed using unfiltered BOLD data and was statistically similar to well-tuned band-pass filtered BOLD data. Contrary to band-pass filtering, however, deconvolution filtering is built upon physiological arguments and has the potential, at low TR, to match the performance of an optimal band-pass filter. The results from task estimation on real BOLD data suggest that deconvolution filtering provides superior or equivalent detection of task activations relative to comparable analyses on unfiltered signals and also provides decreased variance over the estimate. In turn, these results suggest that standard preprocessing of the BOLD signal ignores significant sources of noise that can be effectively removed without damaging the underlying signal.

Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function

Functional magnetic resonance imaging (fMRI) is a powerful and broadly used means of non-invasively mapping human brain activity. However fMRI is an indirect measure that rests upon a mapping from neuronal activity to the blood oxygen level dependent (BOLD) signal via hemodynamic effects. The quality of estimated neuronal activity hinges on the validity of the hemodynamic model employed. Recent work has demonstrated that the hemodynamic response has non-separable spatiotemporal dynamics, a key property that is not implemented in existing fMRI analysis frameworks. Here both simulated and empirical data are used to demonstrate that using a physiologically based model of the spatiotemporal hemodynamic response function (stHRF) results in a quantitative improvement of the estimated neuronal response relative to unphysical space-time separable forms. To achieve this, an integrated spatial and temporal deconvolution is established using a recently developed stHRF. Simulated data allows the variation of key parameters such as noise and the spatial complexity of the neuronal drive, while knowing the neuronal input. The results demonstrate that the use of a spatiotemporally integrated HRF can avoid "ghost" neuronal responses that can otherwise be falsely inferred. Applying the spatiotemporal deconvolution to high resolution fMRI data allows the recovery of neuronal responses that are consistent with independent electrophysiological measures.

An image analysis method to quantify CFTR subcellular localization

Aberrant protein subcellular localization caused by mutation is a prominent feature of many human diseases. In Cystic Fibrosis (CF), a recessive lethal disorder that results from dysfunction of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), the most common mutation is a deletion of phenylalanine-508 (pF508del). Such mutation produces a misfolded protein that fails to reach the cell surface. To date, over 1900 mutations have been identified in CFTR gene, but only a minority has been analyzed at the protein level. To establish if a particular CFTR variant alters its subcellular distribution, it is necessary to quantitatively determine protein localization in the appropriate cellular context. To date, most quantitative studies on CFTR localization have been based on immunoprecipitation and western blot. In this work, we developed and validated a confocal microscopy-image analysis method to quantitatively examine CFTR at the apical membrane of epithelial cells. Polarized MDCK cells transiently transfected with EGFP-CFTR constructs and stained for an apical marker were used. EGFP-CFTR fluorescence intensity in a region defined by the apical marker was normalized to EGFP-CFTR whole cell fluorescence intensity, rendering "apical CFTR ratio". We obtained an apical CFTR ratio of 0.67 ± 0.05 for wtCFTR and 0.11 ± 0.02 for pF508del. In addition, this image analysis method was able to discriminate intermediate phenotypes: partial rescue of the pF508del by incubation at 27 °C rendered an apical CFTR ratio value of 0.23 ± 0.01. We concluded the method has a good sensitivity and accurately detects milder phenotypes. Improving axial resolution through deconvolution further increased the sensitivity of the system as rendered an apical CFTR ratio of 0.76 ± 0.03 for wild type and 0.05 ± 0.02 for pF508del. The presented procedure is faster and simpler when compared with other available methods and it is therefore suitable as a screening method to identify mutations that completely or mildly affect CFTR processing. Moreover, it could be extended to other studies on the biology underlying protein subcellular localization in health and disease.

Unwrapping of transient responses from high rate overlapping pattern electroretinograms by deconvolution

OBJECTIVE

Due to overlapping, temporal information is mostly lost in high rate steady-state pattern electroretinograms (PERGSS). This study develops a deconvolution method and a display/recording system to "unwrap" PERGSS and obtain a transient, "per stimulus" response (PERGtr) regardless of reversal rate.

METHODS

Processing and instrumentation, including high temporal resolution display and acquisition were developed for deconvolving PERGs acquired at high rates by slight jittering of reversal onsets at a given mean rate.

RESULTS

The system was successfully tested at eight rates from 2.2 to 78.1rps. At medium rates (17.4-41.2rps) recordings with conventional morphology (N35-P50-N95) but earlier peaks and higher amplitudes were extracted up to 40rps. At higher rates, smaller triphasic responses were obtained, exhibiting similar peak latencies, but reversed polarity. Oscillating potentials (OPs) were also recorded at all rates after deconvolution.

CONCLUSIONS

Transient PERGs and OPs can be extracted from quasi steady-state PERG recordings obtained at high rates with a deconvolution algorithm using high temporal resolution display and acquisition systems.

SIGNIFICANCE

The methodology to extract transient and oscillatory responses from steady-state PERGs could be useful in understanding high rate responses and diagnosis of various retinal diseases by revealing temporal information on waveform components which cannot be normally observed.

Arterial input function in perfusion MRI: a comprehensive review

Cerebral perfusion, also referred to as cerebral blood flow (CBF), is one of the most important parameters related to brain physiology and function. The technique of dynamic-susceptibility contrast (DSC) MRI is currently the most commonly used MRI method to measure perfusion. It relies on the intravenous injection of a contrast agent and the rapid measurement of the transient signal changes during the passage of the bolus through the brain. Central to quantification of CBF using this technique is the so-called arterial input function (AIF), which describes the contrast agent input to the tissue of interest. Due to its fundamental role, there has been a lot of progress in recent years regarding how and where to measure the AIF, how it influences DSC-MRI quantification, what artefacts one should avoid, and the design of automatic methods to measure the AIF. The AIF is also directly linked to most of the major sources of artefacts in CBF quantification, including partial volume effect, bolus delay and dispersion, peak truncation effects, contrast agent non-linearity, etc. While there have been a number of good review articles on DSC-MRI over the years, these are often comprehensive but, by necessity, with limited in-depth discussion of the various topics covered. This review article covers in greater depth the issues associated with the AIF and their implications for perfusion quantification using DSC-MRI.

NMR in pulsed high-field magnets and application to high-T(C) superconductors

This article deals with the implementation of Nuclear Magnetic Resonance (NMR) experiments in pulsed magnetic fields at the pulsed-field facility of the Laboratoire National des Champs Magnétiques Intenses and its application to the high-T(C) superconductor YBa2Cu3O6.51. The experimental setup is described in detail, including a low-temperature probe head adapted for pulsed fields. An entire paragraph is dedicated to the discussion of NMR in pulsed field and the introduction of an advanced deconvolution technique making use of the induction voltage in an additional pick-up coil. The (63)Cu/(65)Cu NMR experiments on an YBa2Cu3O6.51 single crystal were performed at 2.5K during a field pulse of 46.8-T-amplitude. In the recorded spectrum the (63)Cu center line and high-frequency satellites as well as the (65)Cu center line are identified and are compared with results in literature.