Identification and Characterization of Two Novel KCNH2 Mutations Contributing to Long QT Syndrome

We identified two different inherited mutations in KCNH2 gene, or human ether-a-go-go related gene (hERG), which are linked to Long QT Syndrome. The first mutation was in a 1-day-old infant, whereas the second was in a 14-year-old girl. The two KCNH2 mutations were transiently transfected into either human embryonic kidney (HEK) cells or human induced pluripotent stem-cell derived cardiomyocytes. We performed associated multiscale computer simulations to elucidate the arrhythmogenic potentials of the KCNH2 mutations. Genetic screening of the first and second index patients revealed a heterozygous missense mutation in KCNH2, resulting in an amino acid change (P632L) in the outer loop of the channel and substitution at position 428 from serine to proline (S428P), respectively. Heterologous expression of P632L and S428P into HEK cells produced no hERG current compared to the wild type (WT). Moreover, the co-transfection of WT and P632L yielded no hERG current; however, the co-transfection of WT and S428P yielded partial hERG current. Action potentials were prolonged in a complete or partial blockade of hERG current from computer simulations which was more severe in Purkinje than ventricular myocytes. Three dimensional simulations revealed a higher susceptibility to reentry in the presence of hERG current blockade. Our experimental findings suggest that both P632L and S428P mutations may impair the KCNH2 gene. The Purkinje cells exhibit a more severe phenotype than ventricular myocytes, and the hERG current blockade renders the ventricles an arrhythmogenic substrate from computer modeling

Quantification of Antiviral Drug Tenofovir (TFV) by Surface-Enhanced Raman Spectroscopy (SERS) Using Cumulative Distribution Functions (CDFs)

Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive spectroscopic technique that generates signal-enhanced fingerprint vibrational spectra of small molecules. However, without rigorous control of SERS substrate active sites, geometry, surface area, or surface functionality, SERS is notoriously irreproducible, complicating the consistent quantitative analysis of small molecules. While evaporatively prepared samples yield significant SERS enhancement resulting in lower detection limits, the distribution of these enhancements along the SERS surface is inherently stochastic. Acquiring spatially resolved SERS spectra of these dried surfaces, we have shown that this enhancement is governed by a power law as a function of analyte concentration. Consequently, by definition, there is no true mean of SERS enhancement, requiring an alternative approach to achieve reproducible quantitative results. In this study, we introduce a new method of analysis of SERS data using a cumulative distribution function (CDF). The antiviral drug tenofovir (TFV) in an aqueous matrix was quantified down to a clinically relevant concentration of 25 ng/mL using hydroxylamine-reduced silver colloids evaporated to dryness. The data presented in this study provide a rationale for the benefits of combining a novel statistical approach using CDFs with simple and inexpensive experimental techniques to increase the precision, accuracy, and analytical sensitivity of aqueous TFV quantification by SERS. TFV calibration curves generated using CDF analysis showed higher analytical sensitivity (in the form of a normalized calibration curve average slope increase of 0.25) compared to traditional SERS intensity calculations. A second aliquot of nanoparticles and analyte dried on the SERS surface followed by CDF analysis showed further analytical sensitivity with a normalized calibration curve slope increase of 0.23 and decreased variation among replicates represented by an average standard deviation decrease of 0.02 with a second aliquot. The quantitative analysis of SERS data using CDFs presented here shows promise to be a reproducible method for quantitative analysis of SERS data, a significant step toward implementing SERS as an analytical method in clinical and industrial settings

Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization

Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities by integrating feature extraction, machine learning, and visual analysis based on EEG signals collected from individuals with neurological and mental disorders. The classification performance of four feature approaches—EEG frequency band, raw data, power spectral density, and wavelet transform—is assessed using machine learning techniques to evaluate their capability to differentiate neurological disabilities in short EEG segmentations (one second and two seconds). In detail, the classification analysis is conducted under two conditions: single-channel-based classification and region-based classification. While a clear demarcation between normal (healthy) and abnormal (neurological disabilities) EEG metrics may not be evident, their similarities and distinctions are observed through visualization, employing wavelet features. Notably, the frontal brain region (frontal lobe) emerges as a crucial area for distinguishing abnormalities among different brain regions. Also, the integration of wavelet features and visual analysis proves effective in identifying and understanding neurological disabilities

Nanosecond Pulsed Electric Fields Increase Antibiotic Susceptibility in Methicillin-Resistant \u3ci\u3eStaphylococcus aureus\u3c/i\u3e

Staphylococcus aureus is the leading cause of skin and soft-tissue infections (SSTIs). SSTIs caused by bacteria resistant to antimicrobials, such as methicillin-resistant S. aureus (MRSA), are increasing in incidence and have led to higher rates of hospitalization. In this study, we measured MRSA inactivation by nanosecond pulsed electric fields (nsPEF), a promising new cell ablation technology. Our results show that treatment with 120 pulses of 600 ns duration (28 kV/cm, 1 Hz), caused modest inactivation, indicating cellular damage. We anticipated that the perturbation created by nsPEF could increase antibiotic efficacy if nsPEF were applied as a co-treatment. To test this hypothesis, we used three antibiotics approved to treat SSTI, daptomycin, doxycycline, and vancomycin, and compared the cytotoxic effects of these antibiotics administered either before or after nsPEF. Co-treatment with nsPEF and daptomycin greatly potentiated the effects of each monotherapy regardless of their order. Conversely, the sensitivity of MRSA to both doxycycline and vancomycin was increased only when nsPEF preceded the antibiotic incubation. Finally, MRSA cells grown in biofilms were efficiently killed by co-treatment with nsPEF/vancomycin, suggesting that their mutual enhancement is maintained even when treating sessile communities known for their inherent antimicrobial resistance. Altogether our results show that MRSA perturbation by nsPEF potentiates the effect of multiple antibiotics and that the order of the combined treatment can have a major impact on efficacy. Since SSTIs are accessible for physical interventions such as nsPEF stimulus, combinatorial treatments could be used to increase the efficacy of antibiotics used to treat such infections

Book Challenges Popping Up All Over: What Do School Principals Need to Know?

This chapter provides practical advice and reasons for school leaders to support students\u27 intellectual freedom through their support of school libraries and school librarians. The chapter begins with a short but critical literature review that includes case law on the topic of censorship in schools. The concerns of teachers and librarians from a recent study are summarized and help build the foundation for practical and ready to use advice for any school leaders to uphold the intellectual freedom of all students

Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security

Due to the rapid advancement of quantum computers, there has been a furious race for quantum technologies in academia and industry. Quantum cryptography is an important tool for achieving security services during quantum communication. Designated verifier signature, a variant of quantum cryptography, is very useful in applications like the Internet of Things (IoT) and auctions. An identity-based quantum-designated verifier signature (QDVS) scheme is suggested in this work. Our protocol features security attributes like eavesdropping, non-repudiation, designated verification, and hiding sources attacks. Additionally, it is protected from attacks on forgery, inter-resending, and impersonation. The proposed scheme benefits from the traditional designated verifier signature schemes. In the proposed scheme, the signer encrypts a message with his or her private key, and the designated verifier validates the accompanying QDVS using the signer’s public key, which is the signer’s name or email address, which makes the quantum signature system’s key management simpler. It uses an entangled state while signing and verifying the signature; however, the verifier is not required to compare quantum states. A detailed comparison analysis with other similar schemes provides more security for the proposed scheme. Furthermore, the proposed scheme’s effectiveness and feasibility are validated using quantum simulations

Forward-Focused Together: A Strategic Plan for the Old Dominion University Libraries, 2023-2028

[From Dean Hackman\u27s Introduction] The Old Dominion University Libraries are pleased to present our 2023-2028 Strategic Plan, Forward-Focused Together. The plan is a result of many hours of careful thought, discussion, debate, research, writing, and revising by the Libraries’ dedicated employees. It represents our vision for the coming half-decade and identifies exciting opportunities for the Libraries to support the mission and vision of Old Dominion University

A Survey on Few-Shot Class-Incremental Learning

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective

Reliability of Popliteal Artery Flow-Mediated Dilation in the Seated Position

Flow-mediated dilation (FMD) is a noninvasive measurement of endothelial function, which is a useful prognostic tool for cardiovascular disease risk. Despite its widespread use since 1992, the reproducibility of FMD varies widely between studies. This variability in reproducibility is especially significant in the case of the popliteal artery due to different methodological approaches. Studies perform popliteal FMD in various body positions, with the prone and seated positions most common. However, no studies have examined the reproducibility of both the seated and prone positions of the popliteal artery FMD. Therefore, the aim of this study is to examine the test-retest and visit-to-visit reliability of the popliteal artery FMD in the seated position and to see whether differences in % FMD exist between seated and prone positions. The popliteal artery FMD was measured on two occasions in twenty healthy young adults, both in seated and prone positions. Popliteal artery diameter was measured at baseline, during 5 minutes of cuff occlusion at 220 mmHg, and following cuff deflation. FMD was calculated as the percent change from baseline diameter to peak diameter. The reliability of FMD measures were assessed in the prone and seated positions via intraclass correlation coefficient (ICC). Further, differences in FMD measures between the prone and seated positions were assessed via the three-way repeated measures analysis of variance (body position x visit x trial). The results demonstrate that the popliteal artery %FMD is reliable in the seated position both within and between visits (ICC value from 0.67 to 0.89), whereas the prone position has poor-to-moderate reliability within and between visits (ICC value from 0.25 to 0.74). To conclude, the popliteal artery FMD has a good reliability when measured in the seated position which can contribute to the development of a standard protocol to measure the FMD in the seated position

In Pursuit of Consumption-Based Forecasting

[Introduction] Today\u27s most mature, most sophisticated, best-in-class forecasting is what we call consumption-based forecasting (CBF). In contrast, the least sophisticated companies typically do not forecast at all, but rather set financial targets based on management expectations. Companies beginning to use statistical forecasting techniques usually take a supply-centric orientation, relying on time series techniques applied to shipment and/or order history. The next stage of progression is to incorporate promotions data, economic data, and market data alongside supply-centric data so that regression and other advanced analytics can be used. Companies pursing CBF utilize even more advanced capabilities to capture, examine, and understand true demand and combine market planning, operational planning, and forecasting. These capabilities include the areas of analytics, data, people, process, and technology