Effect of Acceptor-Type Traps in GaN Buffer Layer on Current Collapse of ε-Ga₂O₃/GaN HEMTs

In this paper, we investigate the effects of acceptor-type traps in the GaN buffer layer on current collapse in ε-Ga2O3/GaN high-electron-mobility transistors (HEMTs). Numerical simulations were conducted across a wide range of trap densities (1 × 1015 cm−3 to 1 × 1018 cm−3) and energy levels (0.4 eV to 1.0 eV). The results show that as trap density increased, current dispersion increased to a peak value of 0.34 A/mm, with a dispersion percentage of 30.91%. Higher trap energy levels (0.6 eV, 0.8 eV, and 1.0 eV) reduced current collapse due to limited electron trapping. Conversely, at a lower energy level of 0.4 eV, rapid recovery prevented significant net current loss despite initial current collapse. For comparison, Al0.28Ga0.72N/GaN HEMTs were also analyzed, showing a similar trend in the effect of trap energy levels, but with a non-monotonic dependence on trap density due to the lower two-dimensional electron gas (2DEG) concentration. These findings highlight the importance of optimizing trap density and energy levels to mitigate current collapse and improve device performance. Such optimizations can make ε-Ga2O3/GaN HEMTs more reliable and efficient for high-power applications requiring stability and robustness. © The Minerals, Metals & Materials Society 2025

SputumLocator: Enhancing Airway Clearance with Auscultation-based Sputum Localization

Airway clearance is essential for managing Muco-Obstructive Lung Diseases (MOLDs). Percussion, a widely used airway clearance technique (ACT) in community and home care settings, is favored for its ease of implementation compared to other complex techniques. However, percussion is time-consuming and physically demanding for both caregivers and patients, as caregivers typically perform percussion on the entire back to avoid missing accumulated sputum when its exact location is unknown. Therefore, accurate sputum localization can significantly enhance the percussion experience. Current clinical methods for sputum localization typically rely on imaging techniques, which are costly, expose patients to radiation, and are usually performed only once during diagnosis, thereby limiting their application to inpatient settings. Alternatively, some medical professionals combine auscultation with other clinical assessments, but this approach requires substantial clinical experience and is impractical for community or home care settings where medical experts are unavailable. To address these limitations, we introduce SputumLocator, an innovative sputum localization system based on digital stethoscopes. SputumLocator leverages standard auscultation procedures to detect accumulated sputum in the four quadrants of the back, which is straightforward and highly practical. SputumLocator comprises two components: SputumEmbedder, which extracts key abnormal sounds and their spatial features using a Transformer-based feature extractor, and SputumClassifier, which maps these features to determine sputum presence in each region via a Convolutional Block Attention Module (CBAM). Given the limited availability of annotated sputum data, we developed a pretraining method based on Embedding on Masked Data (EOM) and enhanced model robustness through a Teacher-Student Architecture (TSA) that integrates noisy data. In collaboration with a medical institution, we evaluate SputumLocator on 43 patients with diverse physiological characteristics and under varying recording conditions. Experimental results demonstrate that SputumLocator achieves high accuracy with an overall sensitivity of 0.97, specificity of 0.82, and F1-Score of 0.83, maintaining robustness across different thoracic regions, genders, and disease types

A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model

Remarkable strides in computational pathology have been made in the task-agnostic foundation model that advances the performance of a wide array of downstream clinical tasks. Despite the promising performance, there are still several challenges. First, prior works have resorted to either vision-only or image-caption data, disregarding pathology reports with more clinically authentic information from pathologists and gene expression profiles which respectively offer distinct knowledge for versatile clinical applications. Second, the current progress in pathology FMs predominantly concentrates on the patch level, where the restricted context of patch-level pretraining fails to capture whole-slide patterns. Even recent slide-level FMs still struggle to provide whole-slide context for patch representation. In this study, for the first time, we develop a pathology foundation model incorporating three levels of modalities: pathology slides, pathology reports, and gene expression data, which resulted in 26,169 slide-level modality pairs from 10,275 patients across 32 cancer types, amounting to over 116 million pathological patch images. To leverage these data for CPath, we propose a novel whole-slide pretraining paradigm that injects the multimodal whole-slide context into the patch representation, called Multimodal Self-TAught PRetraining (mSTAR). The proposed paradigm revolutionizes the pretraining workflow for CPath, enabling the pathology FM to acquire the whole-slide context. To the best of our knowledge, this is the first attempt to incorporate three modalities at the whole-slide context for enhancing pathology FMs. To systematically evaluate the capabilities of mSTAR, we built the largest spectrum of oncological benchmark, spanning 7 categories of oncological applications in 15 types of 97 practical oncological tasks

Corrigendum to: Atmospheric microplastic input into wetlands: Spatiotemporal patterns, drivers, and unique ecological impacts

The authors regret < The legend of Figure 6A needs correction. (1) The correct unit for the microplastic exposure dose should be mg·m-2, as described in the Materials and Methods section.(2) The correct dose for MP1 group is 2.6 mg·m-2, while the originally labeled value corresponds to the concentration for the 4-month dose.(3) The microplastic size indicated in the legend reflects the expected size based on the supplier's specifications. However, upon measurement, we are able to provide a more accurate description of the actual size distribution, as outlined in the Materials and Methods section.The legend in this figure was created early in the experimental design phase, whereas the actual experimental procedures were redesigned and conducted as described in the Materials and Methods section.[Formula presented] The authors would like to apologise for any inconvenience caused. © 2024 Elsevier Lt

Structural insights into the molecular mechanism of broad-spectrum neutralization of SARS-CoV-2 variants by bnAbs

The COVID-19 pandemic, driven by the rapid evolution of SARS-CoV-2, continues to pose significant global health challenges nowadays. The virus’s ability to evolve to variants of concern (VOCs) such as Alpha, Beta, Delta, and notably Omicron, has enhanced its immune escape capabilities, complicating prevention and treatment efforts. Our study focuses on broadly neutralizing antibodies (bnAbs) isolated from vaccinated patients who experienced Omicron BA.2/BA.5 breakthrough infections. These bnAbs have demonstrated potent neutralizing activities against a range of SARS-CoV-2 variants, including the newly identified JN.1 and KP.2. Our research reveals the neutralization mechanism by cryo-EM based structural determination. All the four identified bnAbs, namely ZCP3B4, ZCP4C9, ZCP4D5, and CUP2G3, target the receptor-binding domain (RBD) of the Spike (S) protein located on the virus membrane, effectively blocking its interaction with the ACE2 receptor. This blockade is facilitated by the antibodies recognizing the RBD in its “up” conformation, which inhibits the receptor engagement and viral entry. Epitope mapping indicated that the interactions are predominantly mediated by highly conserved residues in the RBD, explaining the antibodies’ broad neutralization capabilities. Detailed interaction studies highlighted that the binding involves nearly all variable regions of the antibodies, including CDR-H1, CHR-H2, CDR-H3, CDRL1, and CDRL3, showcasing their high efficiency and specificity. The large buried surface areas of these interactions correlate with the high affinity observed between the antibodies and the RBD. These findings underscore the potential of these bnAbs as therapeutic candidates for COVID-19, capable of neutralizing current and emergent variants. This study not only provides a deeper understanding of the neutralization breadth of bnAbs but also supports ongoing efforts in vaccine and therapeutic antibody development against SARS-CoV-2

Identifying genetic susceptibility loci associated with human coronary artery disease

Coronary artery disease (CAD) is a multigenic condition influenced by both nature and nurture (60% to 40%). Prognosis of CAD is based on familial patterns. This study examined and analyzed the susceptibility of CAD to genetic variants in various Pakistani families. A total of 50 families, 308 participants (79 affected and 229 unaffected were genotyped for NOS3 (rs1799983, rs2070744), PON1 (rs662), LPA-PLA2 (rs105193, rs1805017), APOE (rs429358, rs7412), PCSK9 (rs505151), MEF2A (rs325400), TNF (rs1800629) and LDLR (rs1122608, rs2228671) genes. The family-based association in CAD associated genes SNPs were NOS3 (rs1799983), PON1 (rs662), LPA-PLA2 (rs1805017), MEF2A (rs325400), and LDLR (rs1122608, rs222867) showed transmission within families p≤ 0.05 whereas NOS3 (rs2070744), APOE (rs429358, rs7412) and TNF (rs1800629) showed no association TDT asymptotic p-value >0.05. In DFAM and QFAM test NOS3 (rs1799983), PON1 (rs662), MEF2A (rs325400), and LDLR (rs1122608, rs222867) showed positive association p≤ 0.05 in both whereas NOS3 (rs2070744), APOE (rs429358, rs7412), LPA-PLA2 (rs1805017) and TNF (rs1800629) showed low risk of transmission asymptotic p-value >0.05 in DFAM but NOS3(rs2070744), APOE(rs7412), LPA-PLAG2(rs1805017) also showed association p≤ 0.05 whereas APOE (rs429358) and TNF (rs1800629) showed no association EMP1 p-value >0.05 in QFAM. In linkage analysis Chromosome 6 (Position 70.810): LOD = 3.16, Chromosome 7 (Position 107.190): LOD = 3.16, and chromosome 19 (Position 31.470): LOD = 3.90 also showed significant association with disease as p < 0.05. This discovery enhances the understanding about genetic variants of CAD and also facilitates early detection, targeted interventions, pattern of inheritance in population. This ultimately improving patient outcomes and guiding future research to highlight its significance as a potential diagnostic marker

Survey of distributed algorithms for resource allocation over multi-agent systems

Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems. It covers a significant area of research at the intersection of optimization, multi-agent systems, and distributed consensus-based computing. The paper begins by presenting a mathematical formulation of the DRA problem, establishing a solid foundation for further exploration. Real-world applications of DRA in various domains are examined to underscore the importance of efficient resource allocation, and relevant distributed optimization formulations are presented. The survey then delves into existing solutions for DRA, encompassing linear, nonlinear, primal-based, and dual-formulation-based approaches. Furthermore, this paper evaluates the features and properties of DRA algorithms, addressing key aspects such as feasibility, convergence rate, and network reliability. The analysis of mathematical foundations, diverse applications, existing solutions, and algorithmic properties contributes to a broader comprehension of the challenges and potential solutions for this domain. © 2024 Elsevier Lt