Epigenetic processes, particularly disruptions in DNA methylation profiles, are associated with many disease states. Traditional approaches for DNA methylation biomarker discovery focusing on individual CpG sites do not account for fragment-level methylation states. Methylation haplotype analysis offers a more comprehensive approach leading to increased distinction capability between reads originating from tissues with diverse methylation profiles. This can be particularly valuable in liquid biopsy where detecting small amounts of disease-specific cell-free DNA (cfDNA) amidst a bulk of healthy cfDNA is challenging. To address limitations of existing metrics for quantifying methylation patterns in a region from sequencing data, we propose the Methylation Pattern Consistency Index (MPCI), a novel metric that captures consistent methylation patterns across sequencing reads, accounting for both methylated and unmethylated blocks of CpGs. Using whole-genome bisulfite sequencing data, we demonstrate that MPCI outperforms MHL and its symmetric counterpart, dMHL (MHL - uMHL), across several benchmarks: distinguishing closely related cell types (CD4 vs. CD8; AUC 0.915), multi-tissue classification (0.92 accuracy), and detection of in-silico cfDNA spike-ins at abundances as low as 1%. Notably, in a clinical liquid-biopsy cohort of liver transplant patients, MPCI achieved significantly higher classification performance than dMHL (Accuracy: MPCI: 0.868 ± 0.023 vs. dMHL: 0.768 ± 0.027, p = 0.014) in discriminating pre- from post-transplant cfDNA profiles. These findings position MPCI as a reliable quantification approach for biomarker selection or diagnostic testing in epigenetic studies. We have made MPCI available as an R function for usage convenience.

DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.

Epilepsy is a neurological disorder identified by sudden and recurrent seizures, which are believed to be accompanied by distinct changes in brain dynamics. Exploring the dynamic changes of brain network states during seizures can pave the way for improving the diagnosis and treatment of patients with epilepsy. In this paper, the connectivity brain network is constructed using the phase lag index (PLI) measurement within five frequency bands, and graph-theoretic techniques are employed to extract topological features from the brain network. Subsequently, an unsupervised clustering approach is used to examine the state transitions of the brain network during seizures. Our findings demonstrate that the level of brain synchrony during the seizure period is higher than the preseizure and post-seizure periods in the theta, alpha, and beta bands, while it decreases in the gamma bands. These changes in synchronization also lead to alterations in the topological features of functional brain networks during seizures. Additionally, our results suggest that the dynamics of the brain during seizures are more complex than the traditional three-state model (pre-seizure, seizure, and post-seizure) and the brain network state exhibits a slower rate of change during the seizure period compared to the pre-seizure and post-seizure periods.

Recent years have seen an increasing trend in developing 3D action recognition methods. However, despite the advances, existing models still suffer from some major drawbacks including the lack of any provision for recognizing action sequences with some missing frames. This significantly hampers the applicability of these methods for online scenarios, where only an initial part of sequences are already provided. In this paper, we introduce a novel sequence-to-sequence representation-based algorithm in which a query sample is characterized using a collaborative frame representation of all the training sequences. This way, an optimal classifier is tailored for the existing frames of each query sample, making the model robust to the effect of missing frames in sequences (e.g. in online scenarios). Moreover, due to the collaborative nature of the representation, it implicitly handles the problem of varying styles during the course of activities. Experimental results on three publicly available databases, UTKinect, TST fall, and UTD-MHAD, respectively, show 95.48%, 90.91%, and 91.67% accuracy when using the beginning 75% portion of query sequences and 84.42%, 60.98%, and 87.27% accuracy for their initial 50%.

Recently a few methods have been proposed in the literature for non-invasive cuff-less estimation of systolic and diastolic blood pressures. One of the most prominent methods is to use the Pulse Transit Time (PTT). Although it is proven that PTT has a strong correlation with the systolic and diastolic blood pressures, this relation is highly dependent to each individuals physiological properties. Therefore, it requires per person calibration for accurate and reliable blood pressure estimation from PTT, which is a big drawback. To alleviate this issue, in this paper, a novel method is proposed for accurate and reliable estimation of blood pressure that is calibration-free. This goal is accomplished by extraction of several physiological parameters from Photoplethysmography (PPG) signal as well as utilizing signal processing and machine learning algorithms. The results show that the accuracy of the proposed method achieves grade B for the estimation of the diastolic blood pressure and grade C for the estimation of the mean arterial pressure under the standard British Hypertension Society (BHS) protocol.

The common approach for 3D face recognition is to register a probe face to each of the gallery faces and then calculate the sum of the distances between their points. This approach is computationally expensive and sensitive to facial expression variation. In this paper, we introduce the iterative closest normal point method for finding the corresponding points between a generic reference face and every input face. The proposed correspondence finding method samples a set of points for each face, denoted as the closest normal points. These points are effectively aligned across all faces, enabling effective application of discriminant analysis methods for 3D face recognition. As a result, the expression variation problem is addressed by minimizing the within-class variability of the face samples while maximizing the between-class variability. As an important conclusion, we show that the surface normal vectors of the face at the sampled points contain more discriminatory information than the coordinates of the points. We have performed comprehensive experiments on the Face Recognition Grand Challenge database, which is presently the largest available 3D face database. We have achieved verification rates of 99.6 and 99.2 percent at a false acceptance rate of 0.1 percent for the all versus all and ROC III experiments, respectively, which, to the best of our knowledge, have seven and four times less error rates, respectively, compared to the best existing methods on this database.

Discriminant analysis methods are powerful tools for face recognition. However, these methods cannot be used for the single sample per person scenario because the within-subject variability cannot be estimated in this case. In the generic learning solution, this variability is estimated using images of a generic training set, for which more than one sample per person is available. However, because of rather poor estimation of the within-subject variability using a generic set, the performance of discriminant analysis methods is yet to be satisfactory. This problem particularly exists when images are under drastic facial expression variation. In this paper, we show that images with the same expression are located on a common subspace, which here we call it the expression subspace. We show that by projecting an image with an arbitrary expression into the expression subspaces, we can synthesize new expression images. By means of the synthesized images for subjects with one image sample, we can obtain more accurate estimation of the within-subject variability and achieve significant improvement in recognition. We performed comprehensive experiments on two large face databases: the Face Recognition Grand Challenge and the Cohn-Kanade AU-Coded Facial Expression database to support the proposed methodology.

This paper discusses ECG biometric recognition in a distributed system, such as smart cards. In a setting where every card is equipped with an ECG sensor to record heart beats from the fingers, and to subsequently perform identity verification, the interest is in protecting the card holder from a set of unknown/unseen biometric traits. Prior works have examined ECG biometrics in settings where a particular subject was to be identified among a set of enrollees. However, this treatment limits the applicability of this biometric. The Autocorrelation - Linear Discriminant Analysis (AC/LDA) is revisited, to propose a strategic extension of the methodology, in order to account for recognition among unknown individuals (blind verification). The discriminant is trained individually for every smart card, on the samples of the subject to be enrolled, as well as a generic dataset of ECG recordings. This enables the recognizer to protect the template against attacks by biometric samples that have not been used to train the discriminant. In addition, we present a methodology for the selection of the matching threshold, which targets to control false acceptance while being experimentally optimized for a particular smart card.

This work presents a novel methodology for the transformation of facial expressions, to assist face biometrics. It is known that identification using only one image per subject poses a great challenge to recognizers. This is because drastic facial expressions introduce variability, on which the recognizer is not trained. The proposed framework uses only one image per subject to predict intra-class variability, by synthesizing new expressions, which are subsequently used to train the discriminant. The expression of the gallery is transformed using the bivariate empirical mode decomposition (BEMD), which allows for simultaneous analysis of the probe image and a targeted expression mask. We advocate that 2D BEMD is a powerful tool for multi-resolution face analysis. The performance of the proposed framework, tested over a database of 96 individuals, is 90% for an FAR of 1%.

A novel two-dimensional (2D) filtering operation, involving both curl and divergence, is applied to the 2D Clifford Fourier transform (CFT) in order to simultaneously enhance important features of a 2D vector field, such as vortices and pairs of sources and sinks. Applications are envisaged for enhancing vector field images in such sampled vector field images as fluid flow, gas flow and biomedical image processing.