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PubMed2024

Universal Identification of Pathogenic Viruses by Liquid Chromatography Coupled with Tandem Mass Spectrometry Proteotyping.

Accurate and rapid identification of viruses is crucial for an effective medical diagnosis when dealing with infections. Conventional methods, including DNA amplification techniques or lateral-flow assays, are constrained to a specific set of targets to search for. In this study, we introduce a novel tandem mass spectrometry proteotyping-based method that offers a universal approach for the identification of pathogenic viruses and other components, eliminating the need for a priori knowledge of the sample composition. Our protocol relies on a time and cost-efficient peptide sample preparation, followed by an analysis with liquid chromatography coupled to high-resolution tandem mass spectrometry. As a proof of concept, we first assessed our method on publicly available shotgun proteomics datasets obtained from virus preparations and fecal samples of infected individuals. Successful virus identification was achieved with 53 public datasets, spanning 23 distinct viral species. Furthermore, we illustrated the method's capability to discriminate closely related viruses within the same sample, using alphaviruses as an example. The clinical applicability of our method was demonstrated by the accurate detection of the vaccinia virus in spiked saliva, a matrix of paramount clinical significance due to its non-invasive and easily obtainable nature. This innovative approach represents a significant advancement in pathogen detection and paves the way for enhanced diagnostic capabilities.

Tandem Mass Spectrometry↗
PubMed2004

Strategies for brain shift evaluation.

For the analysis of the brain shift phenomenon different strategies were applied. In 32 glioma cases pre- and intraoperative MR datasets were acquired in order to evaluate the maximum displacement of the brain surface and the deep tumor margin. After rigid registration using the software of the neuronavigation system, a direct comparison was made with 2D- and 3D visualizations. As a result, a great variability of the brain shift was observed ranging up to 24 mm for cortical displacement and exceeding 3 mm for the deep tumor margin in 66% of all cases. Following intraoperative imaging the neuronavigation system was updated in eight cases providing reliable guidance. For a more comprehensive analysis a voxel-based nonlinear registration was applied. Aiming at improved speed of alignment we performed all interpolation operations with 3D texture mapping based on OpenGL functions supported in graphics hardware. Further acceleration was achieved with an adaptive refinement of the underlying control point grid focusing on the main deformation areas. For a quick overview the registered datasets were evaluated with different 3D visualization approaches. Finally, the results were compared to the initial measurements contributing to a better understanding of the brain shift phenomenon. Overall, the experiments clearly demonstrate that deformations of the brain surface and deeper brain structures are uncorrelated.

Brain Neoplasms↗
PubMed2004

Feature characterization in fMRI data: the Information Bottleneck approach.

Clustering is a well-known technique for the analysis of Functional Magnetic Resonance Imaging (fMRI) data, whose main advantage is certainly flexibility: given a metric on the dataset, it "summarizes" the main characteristics of the data. But intrinsic to this approach are also the problem of defining correctly the quantization accuracy, and the number of clusters necessary to describe the data. The Information Bottleneck (IB) approach to vector quantization, proposed by Bialek and Tishby, addresses these difficulties: (1) it deals with an explicit trade-off between quantization and data fidelity; (2) it does so during the clustering procedure and not post hoc; (3) it takes into account the full statistical distribution of the features within the feature space and not only their most likely value; last, it is principled through an information theoretic formulation, which is relevant in many situations. In this paper, we present how to benefit from this method to analyze fMRI data. Our application is the clustering of voxels according to the magnitude of their responses to several experimental conditions. The IB quantization provides a consistent representation of the data, allowing for an easy interpretation and comparison of datasets.

Algorithms↗
PubMed2004

Thoracic non-rigid registration combining self-organizing maps and radial basis functions.

An automatic three-dimensional non-rigid registration scheme is proposed in this paper and applied to thoracic computed tomography (CT) data of patients with stage III non-small cell lung cancer (NSCLC). According to the registration scheme, initially anatomical set of points such as the vertebral spine, the ribs, and shoulder blades are automatically segmented slice by slice from the two CT scans of the same patient in order to serve as interpolant points. Based on these extracted features, a rigid-body transformation is then applied to provide a pre-registration of the data. To establish correspondence between the feature points, the novel application of the self-organizing maps (SOMs) is adopted. In particular, the automatic correspondence of the interpolant points is based on the initialization of the Kohonen neural network model capable to identify 500 corresponding pairs of points approximately in the two CT sets. Then, radial basis functions (RBFs) using the shifted log function is subsequently employed for elastic warping of the image volume, using the correspondence between the interpolant points, as obtained in the previous phase. Quantitative and qualitative results are also presented to validate the performance of the proposed elastic registration scheme resulting in an alignment error of 6 mm, on average, over 15 CT paired datasets. Finally, changes of the tumor volume in respect to each reference dataset are estimated for all patients, which indicate inspiration and/or movement of the patient during acquisition of the data. Thus, the practical implementation of this scheme could provide estimations of lung tumor volumes during radiotherapy treatment planning.

Adult↗
PubMed2006

Cerebrovascular segmentation from TOF using stochastic models.

In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either blood vessels or background noise. The observed volume data is modeled by two stochastic processes. The low level process characterizes the intensity distribution of the data, while the high level process characterizes their statistical dependence among neighboring voxels. The low level process of the background signal is modeled by a finite mixture of one Rayleigh and two normal distributions, while the blood vessels are modeled by one normal distribution. The parameters of the low level process are estimated using the expectation maximization (EM) algorithm. Since the convergence of the EM is sensitive to the initial estimate of the model parameters, an automatic method for parameter initialization, based on histogram analysis, is provided. To improve the quality of segmentation achieved by the proposed low level model especially in the regions of significantly vascular signal loss, the high level process is modeled as a Markov random field (MRF). Since MRF is sensitive to edges and the intracranial vessels represent roughly 5% of the intracranial volume, 2D MRF will destroy most of the small and medium sized vessels. Therefore, to reduce this limitation, we employed 3D MRF, whose parameters are estimated using the maximum pseudo likelihood estimator (MPLE), which converges to the true likelihood under large lattice. Our proposed model exhibits a good fit to the clinical data and is extensively tested on different synthetic vessel phantoms and several 2D/3D TOF datasets acquired from two different MRI scanners. Experimental results showed that the proposed model provides good quality of segmentation and is capable of delineating vessels down to 3 voxel diameters.

Algorithms↗
PubMed2006

A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine.

We have developed a new hierarchical 3D technique to segment the vertebral bodies in order to measure bone mineral density (BMD) with high trueness and precision in volumetric CT datasets. The hierarchical approach starts with a coarse separation of the individual vertebrae, applies a variety of techniques to segment the vertebral bodies with increasing detail and ends with the definition of an anatomic coordinate system for each vertebral body, relative to which up to 41 trabecular and cortical volumes of interest are positioned. In a pre-segmentation step constraints consisting of Boolean combinations of simple geometric shapes are determined that enclose each individual vertebral body. Bound by these constraints viscous deformable models are used to segment the main shape of the vertebral bodies. Volume growing and morphological operations then capture the fine details of the bone-soft tissue interface. In the volumes of interest bone mineral density and content are determined. In addition, in the segmented vertebral bodies geometric parameters such as volume or the length of the main axes of inertia can be measured. Intra- and inter-operator precision errors of the segmentation procedure were analyzed using existing clinical patient datasets. Results for segmented volume, BMD, and coordinate system position were below 2.0%, 0.6%, and 0.7%, respectively. Trueness was analyzed using phantom scans. The bias of the segmented volume was below 4%; for BMD it was below 1.5%. The long-term goal of this work is improved fracture prediction and patient monitoring in the field of osteoporosis. A true 3D segmentation also enables an accurate measurement of geometrical parameters that may augment the clinical value of a pure BMD analysis.

Absorptiometry, Photon↗
PubMed2006

Extraction of the midsagittal plane from morphological neuroimages using the Kullback-Leibler's measure.

A theoretically simple and computationally efficient method to extract the midsagittal plane (MSP) from volumetric neuroimages is presented. The method works in two stages (coarse and fine) and is based on calculation of the Kullback and Leibler's (KL) measure, which characterizes the difference between two distributions. Slices along the sagittal direction are analyzed with respect to a reference slice to determine the coarse MSP. To calculate the final MSP, a local search algorithm is applied. The proposed method does not need any preprocessing, like reformatting, skull stripping, etc. The algorithm was validated quantitatively on 75 MRI datasets of different pulse sequences (T1WI, T2WI, FLAIR and SPGR) and MRA. The angular and distance errors between the calculated MSP and the ground truth lines marked by the expert were calculated. The average distance and angular deviation were 1.25 pixels and 0.63 degrees , respectively. In addition, the algorithm was tested qualitatively on PD, FLAIR, MRA, and CT datasets. To analyze the robustness of the method against rotation, inhomogeneity and noise, the phantom data were used.

Arachnoid Cysts↗
PubMed2007

Knowledge-based interpolation of curves: application to femoropopliteal arterial centerline restoration.

We present a novel algorithm, Partial Vector Space Projection (PVSP), for estimation of missing data given a database of similar datasets, and demonstrate its use in restoring the centerlines through simulated occlusions of femoropopliteal arteries, derived from CT angiography data. The algorithm performs Principal Component Analysis (PCA) on a database of centerlines to obtain a set of orthonormal basis functions defined in a scaled and oriented frame of reference, and assumes that any curve not in the database can be represented as a linear combination of these basis functions. Using a database of centerlines derived from 30 normal femoropopliteal arteries, we evaluated the algorithm, and compared it to a correlation-based linear Minimum Mean Squared Error (MMSE) method, by deleting portions of a centerline for several occlusion lengths (OL: 10 mm, 25 mm, 50 mm, 75 mm, 100 mm, 125 mm, 150 mm, 175 mm and 200 mm). For each simulated occlusion, we projected the partially known dataset on the set of basis functions derived from the remaining 29 curves to restore the missing segment. We calculated the maximum point-wise distance (Maximum Departure or MD) between the actual and estimated centerline as the error metric. Mean (standard deviation) of MD increased from 0.18 (0.14) to 4.35 (2.23) as OL increased. The results were fairly accurate even for large occlusion lengths and are clinically useful. The results were consistently better than those using the MMSE method. Multivariate regression analysis found that OL and the root-mean-square error in the 2 cm proximal and distal to the occlusion accounted for most of the error.

Algorithms↗
PubMed2026

Auditing bacterial dark-gene screens for superimposed open reading frame artefacts: A multi-layer analysis of Rv2438A in Mycobacterium tuberculosis.

Essentiality and knockdown-vulnerability screens can promote spurious bacterial open reading frames when those frames overlap essential genes, because such a frame inherits its neighbour's signals undiluted and therefore satisfies the screen's criteria better than a genuine small gene. We present a multi-layer audit that tests this failure mode across genome annotation, transposon mutagenesis, CRISPR interference, homology, transcript mapping, proteomics, and population variation. We apply it to Rv2438A, a 92-codon conserved hypothetical open reading frame of Mycobacterium tuberculosis ranked first by our own dark-gene target screen. Rv2438A is superimposed on the essential NAD synthetase locus nadE: 44% lies within its coding sequence on the opposite strand, and the remainder covers its promoter and transcription start site. Consequently, three of five Himar1 sites lie within nadE, no CRISPRi guide can target Rv2438A without binding nadE, and the cross-species hit maps to the same nadE start junction. Rv2438A lacks its own transcription start site and is absent from every proteomic dataset that detects nadE. A genome-wide scan identifies six short, overlapping, uncharacterised loci among 3907 annotated genes, but only Rv2438A combines overlap and essentiality with non-detection across all proteomic datasets; rare genome-wide, it ranked first among screen hits. We provide an implementable audit workflow and a codon-position control, but measure the control's sensitivity as only two of five genes with attested protein, limiting it to confirmatory use. Overlap coordinates and neighbour-specific experimental resolution should therefore be reported before bacterial dark genes are prioritised.

CRISPR interference↗
PubMed2004

Generation and analysis of expressed sequence tags from Trypanosoma cruzi trypomastigote and amastigote cDNA libraries.

We have generated 2771 expressed sequence tags (ESTs) from two cDNA libraries of Trypanosoma cruzi CL-Brener. The libraries were constructed from trypomastigote and amastigotes, using a spliced leader primer to synthesize the cDNA second strand, thus selecting for full-length cDNAs. Since the libraries were not normalized nor pre-screened, we compared the representation of transcripts between the two using a statistical test and identify a subset of transcripts that show apparent differential representation. A non-redundant set of 1619 reconstructed transcripts was generated by sequence clustering. This dataset was used to perform similarity searches against protein and nucleotide databases. Based on these searches, 339 sequences could be assigned a putative identity. One thousand one-hundred and sixteen sequences in the non-redundant clustered dataset (68.8%) are new expression tags, not represented in the T. cruzi epimastigote ESTs that are in the public databases. Additional information is provided online at http://genoma.unsam.edu.ar/projects/tram. To the best of our knowledge these are the first ESTs reported for the life cycle stages of T. cruzi that occur in the vertebrate host.

Animals↗
PubMed2006

Self-organizing neural networks to support the discovery of DNA-binding motifs.

Identification of the short DNA sequence motifs that serve as binding targets for transcription factors is an important challenge in bioinformatics. Unsupervised techniques from the statistical learning theory literature have often been applied to motif discovery, but effective solutions for large genomic datasets have yet to be found. We present here three self-organizing neural networks that have applicability to the motif-finding problem. The core system in this study is a previously described SOM-based motif-finder named SOMBRERO. The motif-finder is integrated in this work with a SOM-based method that automatically constructs generalized models for structurally related motifs and initializes SOMBRERO with relevant biological knowledge. A self-organizing tree method that displays the relationships between various motifs is also presented, and it is shown that such a method can act as an effective structural classifier of novel motifs. The performance of the three self-organizing neural networks is evaluated here using various datasets.

Algorithms↗
PubMed2006

Association studies between the plasmin genes and late-onset Alzheimer's disease.

The plasmin system is involved in the degradation of Abeta peptides, the accumulation of which in brain is a hallmark of Alzheimer's disease (AD). In a North European case-control AD dataset we studied 14 common variations in the PLG, PAI-1, PLAT and PLI genes encoding components of the plasmin system. Among the four polymorphisms in the PLAT, PAI-1 and PLI genes showing nominally significant evidence for an association with AD (allele p-value=0.01-0.00003) the strongest association was detected for the deletion allele in the Alu-repeat region of the PLAT gene. However, none of these positive results were confirmed in follow-up studies using an independent Canadian case-control cohort and two familial AD datasets of North European and Caribbean Hispanic origin. Thus, the current survey does not support the notion that common polymorphisms in the plasmin genes influence the development of AD.

Aged↗
PubMed2005

Unified SPM-ICA for fMRI analysis.

A widely used tool for functional magnetic resonance imaging (fMRI) data analysis, statistical parametric mapping (SPM), is based on the general linear model (GLM). SPM therefore requires a priori knowledge or specific assumptions about the time courses contributing to signal changes. In contradistinction, independent component analysis (ICA) is a data-driven method based on the assumption that the causes of responses are statistically independent. Here we describe a unified method, which combines ICA, temporal ICA (tICA), and SPM for analyzing fMRI data. tICA was applied to fMRI datasets to disclose independent components, whose number was determined by the Bayesian information criterion (BIC). The resulting components were used to construct the design matrix of a GLM. Parameters were estimated and regionally-specific statistical inferences were made about activations in the usual way. The sensitivity and specificity were evaluated using Monte Carlo simulations. The receiver operating characteristic (ROC) curves indicated that the unified SPM-ICA method had a better performance. Moreover, SPM-ICA was applied to fMRI datasets from twelve normal subjects performing left and right hand movements. The areas identified corresponded to motor (premotor, sensorimotor areas and SMA) areas and were consistently task related. Part of the frontal lobe, parietal cortex, and cingulate gyrus also showed transiently task-related responses. The unified method requires less supervision than the conventional SPM and enables classical inference about the expression of independent components. Our results also suggest that the method has a higher sensitivity than SPM analyses.

Adult↗
PubMed2005

Fully-automated detection of cerebral water content changes: study of age- and gender-related H2O patterns with quantitative MRI.

We present a simple and robust method for the automated image analysis of quantitative cerebral water content maps acquired with MRI. The method is based on a new approach for the absolute and quantitative mapping of water content in vivo. Water content maps were automatically segmented into grey and white matter by employing the quantitative T1 information acquired as part of the water content mapping procedure. Based on the segmented maps, twenty-two parameters sensitive to both absolute water content and its spatial organisation are automatically extracted without user interaction. The parameters include, amongst others, absolute water content in grey and white matter and spatial asymmetries of the cerebral water content distribution. Significant age- and gender-related changes in the parameters determined were observed in a study of forty-four healthy subjects. Most notably, the grey matter water content decreases at a rate of 0.034%/year for females between the 3rd and 8th decade of life, whilst a much stronger decrease is observed in males which sets in after the 5th decade of life. In addition, female grey matter water content is, on average, 1.2% higher than the respective male grey matter water content. In contrast to the heterogeneity observed in grey matter, no significant physiological variation was observed for white matter water content. In addition to absolute grey matter water content, characteristic age- and gender-specific variations were also observed in most of the other variables. To check the potential loss of information associated with the large reduction of the dimensionality of the dataset to 22 parameters only, the age and gender of each individual subject were predicted by employing robust linear discriminant analysis based on only the determined twenty-two variables. The median deviation between predicted and real age was 6.3 years resulting in a high correlation coefficient between both values (r = 0.69). Gender is correctly predicted in 68.2% of all cases which improves to 87.5% when age-dependent effects are first corrected, demonstrating the high information content present in the variables even though the dimension of the dataset was significantly reduced. These results form the baseline for future studies of cerebral pathology. The method presented is fully automated, robust and flexible, making it an ideal tool for routine application in both neuroscientific studies and clinical diagnosis based on the quantitative measurement of cerebral water content.

Adult↗
PubMed2006

An adaptive filter for suppression of cardiac and respiratory noise in MRI time series data.

The quality of MRI time series data, which allows the study of dynamic processes, is often affected by confounding sources of signal fluctuation, including the cardiac and respiratory cycle. An adaptive filter is described, reducing these signal fluctuations as long as they are repetitive and their timing is known. The filter, applied in image domain, does not require temporal oversampling of the artifact-related fluctuations. Performance is demonstrated for suppression of cardiac and respiratory artifacts in 10-minute brain scans on 6 normal volunteers. Experimental parameters resemble a typical fMRI experiment (17 slices; 1700 ms TR). A second dataset was acquired at a rate well above the Nyquist frequency for both cardiac and respiratory cycle (single slice; 100 ms TR), allowing identification of artifacts specific to the cardiac and respiratory cycles, aiding assessment of filtering performance. Results show significant reduction in temporal standard deviation (SD(t)) in all subjects. For all 6 datasets with 1700 ms TR combined, the filtering method resulted in an average reduction in SD(t) of 9.2% in 2046 voxels substantially affected by respiratory artifacts, and 12.5% for the 864 voxels containing substantial cardiac artifacts. The maximal SD(t) reduction achieved was 52.7% for respiratory and 55.3% for cardiac filtering. Performance was found to be at least equivalent to the previously published RETROICOR method. Furthermore, the interaction between the filter and fMRI activity detection was investigated using Monte Carlo simulations, demonstrating that filtering algorithms introduce a systematic error in the detected BOLD-related signal change if applied sequentially. It is demonstrated that this can be overcome by combining physiological artifact filtering and detection of BOLD-related signal changes simultaneously. Visual fMRI data from 6 volunteers were analyzed with and without the filter proposed here. Inclusion of the cardio-respiratory regressors in the design matrix yielded a 4.6% t-score increase and 4.0% increase in the number of significantly activated voxels.

Adult↗
PubMed2006

A comment on the severity of the effects of non-white noise in fMRI time-series.

Analysis of fMRI time-series data is usually performed within the general linear model (GLM), and conclusions are based on the probability (p) of a false-positive voxel. In its simplest form, this process assumes that temporal noise in the time-series is random (white); if it is not, then an error in the estimation of p will occur. Various methods for correcting the problem have been developed, but they are not always used. Analysis packages vary in their ability to deal with this problem, and there is a lack of consistent advice to non-experts concerning whether this issue is serious or is small enough to be ignored. The variability of expert opinion partly reflects the fact that the magnitude of the error that occurs when estimating p in fMRI data is largely undocumented. In this discussion, aimed more at "neuroscience users" than methods experts, we try to clarify the position by documenting the scale of the error in a simple block-design experiment. Based on a fixed-effects group analysis of null datasets (8 participants scanned while at rest), we show that the magnitude of the problem can be very substantial. Without correction, it was necessary to set a nominal probability threshold of about p=0.000001 in order to achieve an actual probability of a false positive of 0.001. This means that if task-related activations were present in such a dataset, only if they reached the former, very low nominal p value could they be relied upon. With the use of standard correction methods, the error was largely removed. Our results suggest that some form of pre-whitening or correction for effects of non-white noise is essential in single-subject GLM analyses and fixed-effects group analyses of fMRI data. It is less important but probably prudent to apply a correction at the first level of random-effects group analyses. We also evaluate the effect of high-pass filtering. We find that the number of false positives in a fixed-effects analysis increases markedly as the filter cut-off frequency is increased. The sensitivity benefits derived from high-pass filtering may thus be partially offset by a significant increase in the number of false positives.

Adult↗
PubMed2005

Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data.

We propose a method for characterizing spatial region data. The method efficiently constructs a k-dimensional feature vector using concentric spheres in 3D (circles in 2D) radiating out of a region's center of mass. These signatures capture structural and internal volume properties. We evaluate our approach by performing experiments on classification and similarity searches, using artificial and real datasets. To generate artificial regions we introduce a region growth model. Similarity searches on artificial data demonstrate that our technique, although straightforward, compares favorably to mathematical morphology, while being two orders of magnitude faster. Experiments with real datasets show its effectiveness and general applicability.

Journal Article↗
PubMed2026

Translating single-cell RNA sequencing into monocyte direct leukocyte subpopulation-transcript abundance assay ratio-based biomarkers (IFI27/PSAP or IFI27/CTSS) for clinical detection of viral infection.

A rapid method for triaging febrile patients by aetiology (e.g., viral or bacterial infection) using gene expression in peripheral blood (PB) is an intensively researched area. However, gene expression in blood represents a composite sum of gene expression of all the component cell types present in the sample. As a result, numerous genes are measured in most proposed signatures. Herein, we propose a simple ratio-based biomarker (RBB) called direct leukocyte subpopulation-transcript abundance assay (DIRECT LS-TA) that recapitulates gene expressions of a single cell type in PB (i.e., monocytes). Based on single-cell RNA sequencing (scRNAseq) data and bulk expression data, IFI27 and SIGLEC1 are found as interferon-stimulated genes (ISGs) predominantly expressed by monocytes. The DIRECT LS-TA method can use a simple ratio of two genes measured in PB as an RBB to represent the target gene expression in monocytes without the need for monocyte purification. Both scRNAseq and bulk RNA sequencing datasets were used to evaluate the correlation between ISG expression in monocytes and PB, with a particular focus on monocyte expression of IFI27. An iceberg plot of bulk transcriptome data was used to identify genes that were predominantly expressed by monocytes in PB. DIRECT LS-TA RBBs of the three genes (IFI27, IFI44L and SIGLEC1) were evaluated by group-wise comparison, receiver operating characteristic and meta-analysis. In addition, the conventional interferon (IFN) score was evaluated for comparison of diagnostic performance. In viral infection datasets, DIRECT LS-TA of IFI27 (IFI27/PSAP or IFI27/CTSS) was most intensely activated (p value by t test <1e-9) and had the best area under the curve (0.94) among the three potential monocyte ISGs analysed. DIRECT LS-TA SIGLEC1 was also another monocyte biomarker but showed a lower activation (p<9e-5). IFI27/PSAP showed better diagnostic performance than the conventional IFN score. On the other hand, IFI44L was not a predominant monocyte expression gene. DIRECT LS-TA of IFI27 (IFI27/PSAP or IFI27/CTSS) measured in PB was the best biomarker of viral infection and IFN activation among ISGs predominantly expressed by monocytes. It performed even better than the conventional IFN score which required quantification of eight genes. The results suggest that DIRECT LS-TA of IFI27 is a monocyte-informative biomarker which is easy to determine in PB without the need for cell sorting.

Humans↗