Overcoming Dimensionality Constraints: A Gershgorin Circle Theorem-Based Feature Extraction for Weighted Laplacian Matrices in Computer Vision Applications.

In graph theory, the weighted Laplacian matrix is the most utilized technique to interpret the local and global properties of a complex graph structure within computer vision applications. However, with increasing graph nodes, the Laplacian matrix's dimensionality also increases accordingly. Therefore, there is always the "curse of dimensionality"; In response to this challenge, this paper introduces a new approach to reducing the dimensionality of the weighted Laplacian matrix by utilizing the Gershgorin circle theorem by transforming the weighted Laplacian matrix into a strictly diagonal domain and then estimating rough eigenvalue inclusion of a matrix. The estimated inclusions are represented as reduced features, termed GC features; The proposed Gershgorin circle feature extraction (GCFE) method was evaluated using three publicly accessible computer vision datasets, varying image patch sizes, and three different graph types. The GCFE method was compared with eight distinct studies. The GCFE demonstrated a notable positive Z-score compared to other feature extraction methods such as I-PCA, kernel PCA, and spectral embedding. Specifically, it achieved an average Z-score of 6.953 with the 2D grid graph type and 4.473 with the pairwise graph type, particularly on the E_Balanced dataset. Furthermore, it was observed that while the accuracy of most major feature extraction methods declined with smaller image patch sizes, the GCFE maintained consistent accuracy across all tested image patch sizes. When the GCFE method was applied to the E_MNSIT dataset using the K-NN graph type, the GCFE method confirmed its consistent accuracy performance, evidenced by a low standard deviation (SD) of 0.305. This performance was notably lower compared to other methods like Isomap, which had an SD of 1.665, and LLE, which had an SD of 1.325; The GCFE outperformed most feature extraction methods in terms of classification accuracy and computational efficiency. The GCFE method also requires fewer training parameters for deep-learning models than the traditional weighted Laplacian method, establishing its potential for more effective and efficient feature extraction in computer vision tasks.

Gershgorin circle theorem-based feature extraction for biomedical signal analysis.

Recently, graph theory has become a promising tool for biomedical signal analysis, wherein the signals are transformed into a graph network and represented as either adjacency or Laplacian matrices. However, as the size of the time series increases, the dimensions of transformed matrices also expand, leading to a significant rise in computational demand for analysis. Therefore, there is a critical need for efficient feature extraction methods demanding low computational time. This paper introduces a new feature extraction technique based on the Gershgorin Circle theorem applied to biomedical signals, termed Gershgorin Circle Feature Extraction (GCFE). The study makes use of two publicly available datasets: one including synthetic neural recordings, and the other consisting of EEG seizure data. In addition, the efficacy of GCFE is compared with two distinct visibility graphs and tested against seven other feature extraction methods. In the GCFE method, the features are extracted from a special modified weighted Laplacian matrix from the visibility graphs. This method was applied to classify three different types of neural spikes from one dataset, and to distinguish between seizure and non-seizure events in another. The application of GCFE resulted in superior performance when compared to seven other algorithms, achieving a positive average accuracy difference of 2.67% across all experimental datasets. This indicates that GCFE consistently outperformed the other methods in terms of accuracy. Furthermore, the GCFE method was more computationally-efficient than the other feature extraction techniques. The GCFE method can also be employed in real-time biomedical signal classification where the visibility graphs are utilized such as EKG signal classification.

Feasibility Study of Developing a Saline-Based Antiviral Nanoformulation Containing Lipid-Soluble EGCG: A Potential Nasal Drug to Treat Long COVID.

A recent estimate indicates that up to 23.7 million Americans suffer from long COVID, and approximately one million workers may be out of the workforce each day due to associated symptoms, leading to a USD 50 billion annual loss of salary. Post-COVID (Long COVID) neurologic symptoms are due to the initial robust replication of SARS-CoV-2 in the nasal neuroepithelial cells, leading to inflammation of the olfactory epithelium (OE) and the central nervous system (CNS), and the OE becoming a persistent infection site. Previously, our group showed that Epigallocatechin-3-gallate-palmitate (EC16) nanoformulations possess strong antiviral activity against human coronavirus, suggesting this green tea-derived compound in nanoparticle formulations could be developed as an intranasally delivered new drug to eliminate the persistent SARS-CoV-2 infection, leading to restored olfactory function and reduced inflammation in the CNS. The objective of the current study was to determine the compatibility of the nanoformulations with human nasal primary epithelial cells (HNpECs). METHODS: Nanoparticle size was measured using the ZetaView Nanoparticle Tracking Analysis (NTA) system; contact antiviral activity was determined by TCID50 assay for cytopathic effect on MRC-5 cells; post-infection inhibition activity was determined in HNpECs; and cytotoxicity for these cells was determined using an MTT assay. The rapid inactivation of OC43 (a ß-coronavirus) and 229E (α-coronavirus) viruses was further characterized by transmission electron microscopy. RESULTS: A saline-based nanoformulation containing 0.1% w/v EC16 was able to inactivate 99.9999% ß-coronavirus OC43 on direct contact within 1 min. After a 10-min incubation of infected HNpECs with a formulation containing drug-grade EC16 (EGCG-4' mono-palmitate or EC16m), OC43 viral replication was inhibited by 99%. In addition, all nanoformulations tested for their effect on cell viability were comparable to normal saline, a regularly used nasal irrigation solution. A 1-min incubation of an EC16 nanoformulation with either OC43 or 229E showed an altered viral structure. CONCLUSION: Nanoformulations containing EC16 showed properties compatible with nasal application to rapidly inactivate SARS-CoV-2 residing in the olfactory mucosa and to reduce inflammation in the CNS, pending additional formulation and safety studies.

Non-stationary neural signal to image conversion framework for image-based deep learning algorithms.

This paper presents a time-efficient preprocessing framework that converts any given 1D physiological signal recordings into a 2D image representation for training image-based deep learning models. The non-stationary signal is rasterized into the 2D image using Bresenham's line algorithm with time complexity O(n). The robustness of the proposed approach is evaluated based on two publicly available datasets. This study classified three different neural spikes (multi-class) and EEG epileptic seizure and non-seizure (binary class) based on shapes using a modified 2D Convolution Neural Network (2D CNN). The multi-class dataset consists of artificially simulated neural recordings with different Signal-to-Noise Ratios (SNR). The 2D CNN architecture showed significant performance for all individual SNRs scores with (SNR/ACC): 0.5/99.69, 0.75/99.69, 1.0/99.49, 1.25/98.85, 1.5/97.43, 1.75/95.20 and 2.0/91.98. Additionally, the binary class dataset also achieved 97.52% accuracy by outperforming several others proposed algorithms. Likewise, this approach could be employed on other biomedical signals such as Electrocardiograph (EKG) and Electromyography (EMG).

Close yet so far away: a look into the management strategies of genetic imprinting disorders.

Genetic imprinting is the process of epigenetic labelling or silencing of particular genes, based on the maternal or paternal origin of the gene, in a heritable pattern. The incidence of imprinting disorders has become a growing concern due to the potential association between these congenital syndromes and assisted reproductive technologies (ARTs). This review presents a general summary of the imprinting process as well as the current knowledge surrounding the genetic and epigenetic underpinnings of the most prevalent imprinting disorders: Beckwith-Wiedemann syndrome (BWS), Silver-Russell syndrome (SRS), Prader-Willi syndrome (PWS), and Angelman syndrome (AS). As research continues to elucidate the molecular pathways that characterize genetic imprinting, efforts have been made to establish guidelines that incorporate phenotypic manifestations as well as genetic testing to ensure safe and effective management of symptoms. While these efforts are likely to benefit future clinical management, their efficacy cannot yet be generalized to all patients diagnosed with these syndromes, as many of the genetic abnormalities and the associated phenotypic manifestations have yet to be characterized. Furthermore, future advances in the knowledge of epigenetic processes and genetic loci involved in the development of these syndromes may allow for the development of curative therapies.