Ornaments for efficient allele-specific expression estimation with bias correction.

Allele-specific expression plays a crucial role in unraveling various biological mechanisms, including genomic imprinting and gene expression controlled by cis-regulatory variants. However, existing methods for quantification from RNA-sequencing (RNA-seq) reads do not adequately and efficiently remove various allele-specific read mapping biases, such as reference bias arising from reads containing the alternative allele that do not map to the reference transcriptome or ambiguous mapping bias caused by reads containing the reference allele that map differently from reads containing the alternative allele. We present Ornaments, a computational tool for rapid and accurate estimation of allele-specific transcript expression at unphased heterozygous loci from RNA-seq reads while correcting for allele-specific read mapping biases. Ornaments removes reference bias by mapping reads to a personalized transcriptome and ambiguous mapping bias by probabilistically assigning reads to multiple transcripts and variant loci they map to. Ornaments is a lightweight extension of kallisto, a popular tool for fast RNA-seq quantification, that improves the efficiency and accuracy of WASP, a popular tool for bias correction in allele-specific read mapping. In experiments with simulated and human lymphoblastoid cell-line RNA-seq reads with the genomes of the 1000 Genomes Project, we demonstrate that Ornaments improves the accuracy of WASP and kallisto, is nearly as efficient as kallisto, and is an order of magnitude faster than WASP per sample, with the additional cost of constructing a personalized index for multiple samples. Additionally, we show that Ornaments finds imprinted transcripts with higher sensitivity than WASP, which detects imprinted signals only at gene level.

Attojoule Hexagonal Boron Nitride-Based Memristor for High-Performance Neuromorphic Computing.

In next-generation neuromorphic computing applications, the primary challenge lies in achieving energy-efficient and reliable memristors while minimizing their energy consumption to a level comparable to that of biological synapses. In this work, hexagonal boron nitride (h-BN)-based metal-insulator-semiconductor (MIS) memristors operating is presented at the attojoule-level tailored for high-performance artificial neural networks. The memristors benefit from a wafer-scale uniform h-BN resistive switching medium grown directly on a highly doped Si wafer using metal-organic chemical vapor deposition (MOCVD), resulting in outstanding reliability and low variability. Notably, the h-BN-based memristors exhibit exceptionally low energy consumption of attojoule levels, coupled with fast switching speed. The switching mechanisms are systematically substantiated by electrical and nano-structural analysis, confirming that the h-BN layer facilitates the resistive switching with extremely low high resistance states (HRS) and the native SiOx on Si contributes to suppressing excessive current, enabling attojoule-level energy consumption. Furthermore, the formation of atomic-scale conductive filaments leads to remarkably fast response times within the nanosecond range, and allows for the attainment of multi-resistance states, making these memristors well-suited for next-generation neuromorphic applications. The h-BN-based MIS memristors hold the potential to revolutionize energy consumption limitations in neuromorphic devices, bridging the gap between artificial and biological synapses.

A Causality Assessment Framework for COVID-19 Vaccines and Adverse Events at the COVID-19 Vaccine Safety Research Center.

During the coronavirus disease 2019 (COVID-19) pandemic, conclusively evaluating possible associations between COVID-19 vaccines and potential adverse events was of critical importance. The National Academy of Medicine of Korea established the COVID-19 Vaccine Safety Research Center (CoVaSC) with support from the Korea Disease Control and Prevention Agency to investigate the scientific relationship between COVID-19 vaccines and suspected adverse events. Although determining whether the COVID-19 vaccine was responsible for any suspected adverse event necessitated a systematic approach, traditional causal inference theories, such as Hill's criteria, encountered certain limitations and criticisms. To facilitate a systematic and evidence-based evaluation, the United States Institute of Medicine, at the request of the Centers for Disease Control and Prevention, offered a detailed causality assessment framework in 2012, which was updated in the recent report by the National Academies of Sciences, Engineering, and Medicine (NASEM) in 2024. This framework, based on a weight-of-evidence approach, allows the independent evaluation of both epidemiological and mechanistic evidence, culminating in a comprehensive conclusion about causality. Epidemiological evidence derived from population studies is categorized into four levels-high, moderate, limited, or insufficient-while mechanistic evidence, primarily from biological and clinical studies in animals and individuals, is classified as strong, intermediate, weak, or lacking. The committee then synthesizes these two types of evidence to draw a conclusion about the causal relationship, which can be described as "convincingly supports" ("evidence established" in the 2024 NASEM report), "favors acceptance," "favors rejection," or "inadequate to accept or reject." The CoVaSC has established an independent committee to conduct causality assessments using the weight-of-evidence framework, specifically for evaluating the causality of adverse events associated with COVID-19 vaccines. The aim of this study is to provide an overview of the weight-of-evidence framework and to detail the considerations involved in its practical application in the CoVaSC.

Effects of Rehabilitation Robot Training on Physical Function, Functional Recovery, and Daily Living Activities in Patients with Sub-Acute Stroke.

Stroke often results in sensory deficits, muscular weakness, and diminished postural control, thereby restricting mobility and functional capabilities. It is important to promote neuroplasticity by implementing task-oriented exercises that induce changes in patients. Therefore, this study aimed to investigate the effects of rehabilitation robot training on physical function, functional recovery, and activities of daily living (ADLs) in patients with subacute stroke. The study participants were patients with subacute stroke receiving treatment at Hospitals A and B. They were selected as research subjects based on selection and exclusion criteria. The experimental group received rehabilitation robot training in sessions of 30 min, five times weekly, for a total of 20 sessions over four weeks. Conversely, the control group underwent standard rehabilitation equipment training with an identical frequency, duration, and number of sessions. Measurements were taken before and after the training period to assess changes in physical function, functional recovery, and activities of daily living using tools such as the MMT, BBS, FBG, FAC, FIM, and MBI. The results were as follows: in the within-group comparison, the rehabilitation robot training group showed significant differences in MMT, BBS, FBG, FAC, FIM, and MBI (p < 0.05), while the control group showed significant differences in FIM (p < 0.05). Statistically significant differences were observed in the time, group, and time × group interaction effects among the MMT, static seated FBG, dynamic seated FBG, FIM, and MBI (p < 0.05). Based on these results, rehabilitation robotic training resulted in significant improvements in physical function, functional recovery, and activities of daily living in patients with subacute stroke. Based on these findings, providing a basic protocol for a rehabilitation program that applies rehabilitation robot training to patients with subacute stroke may offer more effective treatment and outcomes in the future.

Extremely Rare Case of Successful Treatment of Foot Ulcer Associated with Evans' Syndrome and Buerger's Disease.

Evans Syndrome (ES) is a rare autoimmune disorder characterized by the simultaneous occurrence of immune thrombocytopenia (ITP) and autoimmune hemolytic anemia (AIHA). Thrombotic complications in ES patients are uncommon, particularly involving Buerger's Disease (BD). We report a case of a 49-year-old male with ES and a history of diabetes and heavy smoking, presenting with a necrotic wound on his right great toe. Diagnostic evaluations revealed severe stenosis and thrombosis in the lower limb arteries, diagnosed as BD. The patient underwent successful popliteal-tibioperoneal artery bypass surgery and the subsequent disarticulation and revision of the distal phalanx, followed by the application of an acellular dermal matrix (ADM) to promote healing. Post-surgery, the patient showed significant improvement in blood flow and complete epithelialization without complications. This case highlights the importance of a multidisciplinary approach to managing complex wounds in ES patients, suggesting potential treatment pathways for future cases involving BD.

Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing.

Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.

Comparison of the Fatty Acid Composition in Phospholipid Species from Korean Human Milk.

In this study, the phospholipid species [i.e., phosphatidylethanolamine (PE), phosphatidylcholine (PC), and sphingomyelin (SM)] in human milk (HM) were compared according to their fatty acid (FA) composition. 34 HM samples were collected and classified into three groups (A < B < C) according to their fat content. Stearic acid (C18:0) was the main FA in PE, PC, and SM. The highest concentrations of arachidonic acid (AA), eicosapentaenoic acid (EPA), and docosapentaenoic acid (DPA) were observed in PE, whereas docosahexaenoic acid (DHA) was predominant in SM. Although PC exhibited the highest total saturated FAs (SFAs) and PE contained the highest unsaturated FAs (UFAs), very long-chain SFAs and monounsaturated FAs (MUFAs) were preferentially distributed in SM. PC and SM had higher saturation compared to PE. Regarding the effect of the fat content of HM on the FA composition of the phospholipid species, a limited influence was observed on the composition of SFAs and MUFAs of PE, SM, and particularly PC. However, a more pronounced effect on the composition of polyunsaturated FAs (PUFAs) in phospholipids was observed, especially for linoleic acid (LA), α-linolenic acid (ALA), EPA, and DHA, indicating that the composition of FAs in the phospholipid species was probably affected by the maternal diet.

High-purity 1,2-dimyristoyl-sn-glycero-3-phosphocholine: synthesis and emulsifying performance evaluation.

Introduction: 1,2-Dimyristoyl-sn-glycero-3-phosphocholine (DMPC) is a promising emulsifier for bioactive delivery systems, but its industrial applications are limited by the lack of cost-effective and scalable synthetic routes. The purpose of this study was to economically produce high-purity DMPC by replacing commonly used column chromatography methods and to evaluate the emulsifying performance. Methods: DMPC was synthesized from sn-glycero-3-phosphocholine using Steglich esterification followed by sequential recrystallization from ethyl acetate and acetone. The structure of DMPC was identified and its purity was confirmed using various spectroscopy and chromatography techniques. The emulsifying performance was evaluated by examining the effects of storage on the properties of o/w emulsions prepared using soybean oil with (i) soy PC, (ii) soy PC + DMPC (1:1, w/w), and (iii) DMPC as emulsifiers. Results: The chemical impurities formed during the synthesis of DMPC was removed, and its final purity was 96%, and the melt transition temperature was 37.6°C. No visible difference between the three emulsions (soy PC, soy PC+DMPC, and DMPC) was observed during two-week storage, and the DMPC-based emulsion was more stable than soy PC emulsion, showing smaller particle size distribution during 6 months. Discussion: The highly pure DMPC was synthesized by an economical method, and DMPC-based emulsions demonstrated physicochemical stable, highlighting its potential for food and pharmaceutical industry-related applications. Our findings suggest that DMPC holds promise as an emulsifier with broad applications in the food industry.

Retention-aware zero-shifting technique for Tiki-Taka algorithm-based analog deep learning accelerator.

We present the fabrication of 4 K-scale electrochemical random-access memory (ECRAM) cross-point arrays for analog neural network training accelerator and an electrical characteristic of an 8 × 8 ECRAM array with a 100% yield, showing excellent switching characteristics, low cycle-to-cycle, and device-to-device variations. Leveraging the advances of the ECRAM array, we showcase its efficacy in neural network training using the Tiki-Taka version 2 algorithm (TTv2) tailored for non-ideal analog memory devices. Through an experimental study using ECRAM devices, we investigate the influence of retention characteristics on the training performance of TTv2, revealing that the relative location of the retention convergence point critically determines the available weight range and, consequently, affects the training accuracy. We propose a retention-aware zero-shifting technique designed to optimize neural network training performance, particularly in scenarios involving cross-point devices with limited retention times. This technique ensures robust and efficient analog neural network training despite the practical constraints posed by analog cross-point devices.