Search Results (374)

While Neural Architecture Search (NAS) has revolutionized the automation of deep learning model design, gradient-based approaches like DARTS often suffer from high computational overheads, the collapse of skip-connections, and optimization instability. To address these limitations, we propose Efficient and Lightweight Differentiable Architecture Search (EL-DARTS). EL-DARTS constructs a compact and redundancy-reduced search space, integrates a partial channel strategy to lower memory usage, employs a Dynamic Coefficient Scheduling Strategy to balance edge importance, and introduces entropy regularization to sharpen operator selection. Experiments on CIFAR-10 and ImageNet demonstrate that EL-DARTS substantially improves both search efficiency and accuracy. Remarkably, it attains a 2.47% error rate on CIFAR-10, requiring merely 0.075 GPU-days for the search. On ImageNet, the discovered architecture achieves a 26.2% top-1 error while strictly adhering to the mobile setting (<600 M MACs). These findings confirm that EL-DARTS effectively stabilizes the search process and pushes the efficiency frontier of differentiable NAS. Full article

Marine flora is a significant source of bioactive metabolites. These compounds have been demonstrated to have outstanding bioactivity and biocompatibility, enabling their use in various therapeutic applications. Therefore, examining the biological potential of marine natural compounds remains important, with particular emphasis on their interaction profiles to identify the macromolecular partners they can modulate. This study focused on the interactome profiling of the marine alkaloid caulerpin (CAU), isolated from the alga Caulerpa cylindracea. Along with the discovery of its antitumor properties, this metabolite has garnered attention for its potential therapeutic applications, including modulation of MAO-B and PPARs involved in inflammatory responses, as well as the discovery of its antitumor properties. Two complementary MS-based proteomic approaches were used to identify CAU target proteins in cancer cells: DARTS, which enabled proteome-wide screening to identify proteins interacting with the compound, and t-LIP-MRM-MS, which pinpointed the target protein regions involved in ligand binding. RUVB-like 1 (RUVBL1), a protein that regulates the essential mechanism of carcinogenesis, including chromatin remodeling, DNA repair, and transcriptional control, was discovered as an intriguing CAU target. These results were corroborated via in silico and biological investigations that elucidated CAU role in the regulation of RUVBL1 activity, highlighting its promising therapeutic relevance. Full article

Wheat (Triticum aestivum L.) is one of the most important staple crops in the world. Iron (Fe) plays a vital role in the growth and development of wheat as an essential nutrient. Meanwhile, Fe is closely associated with human health, as Fe deficiency anemia can cause fatigue, weakness, heart problems, and so on. In this study, quantitative trait loci (QTLs) for grain Fe content (GFeC) were detected in two populations: a recombinant inbred line (RIL) population with 175 lines derived from a cross between Avocet and Huites (AH population) genotyped with diversity array technology (DArT) and a natural population of 243 varieties (CH population) genotyped by using the 660K single-nucleotide polymorphism (SNP). Three stable QTLs (QGFe.haust-AH-5B, QGFe.haust-AH-6A, and QGFe.haust-AH-7A.2) were identified through QTL mapping with phenotypic variations of 11.55–13.63%, 3.58–9.89%, and 4.81–11.12% in the AH population in four environments. Genetic effects of QGFe.haust-AH-5B, QGFe.haust-AH-6A, and QGFe.haust-AH-7A.2 were shown to significantly increase GFeC by 8.11%, 14.05%, and 5.25%, respectively. One hundred and thirty-three significant SNPs were identified (p < 0.001) through a genome-wide association study (GWAS) for GFeC on chromosomes 1B, 2B, 3A, 3B, 5D, and 7A with phenotypic variations of 5.26–9.88% in the CH population. A novel locus was co-located within the physical interval 689.86 Mb-690.01 Mb in five environments through QTL mapping and GWAS, with one high-confidence gene, TraesCS7A02G499500, which was temporarily designated as TaqFe-7A, involved in GFeC regulation. A Kompetitive allele-specific PCR, KAFe-7A-2, was developed, which was validated in 181 natural populations. Genetic effect analysis revealed that favorable haplotype AA significantly increased GFeC by 4.64% compared to an unfavorable haplotype (p < 0.05). Therefore, this study provides the theoretical basis for cloning the GFeC gene and nutritional fortification breeding. Full article

Tsunamis and submarine earthquakes pose severe risks to coastal regions, demanding rapid and reliable monitoring systems. While the Deep-ocean Assessment and Reporting of Tsunamis (DART) system has been globally deployed, its dependence on pressure sensors and one-to-one communication limits its applicability to the Korean East Sea. This paper introduces the Korean Tsunami and Earthquake Monitoring System, which integrates seafloor seismometers and proposes a dedicated Medium Access Control (MAC) protocol optimized for multi-node underwater acoustic communication. The study performs a comprehensive analytical derivation of closed-form expressions for channel utilization and energy consumption under diverse node configurations and acoustic conditions. The analytical results confirm that the proposed MAC protocol maintains stable performance, supports multi-node operation, and enables long-term monitoring within the limited energy budget of underwater devices. The derived results also provide practical design implications for underwater network planning, including guidelines on node placement, frame duration, and control packet timing for efficient data delivery. Although empirical validation remains as future work, the findings establish theoretical benchmarks and engineering insights for the design of next-generation underwater monitoring systems tailored to Korean coastal environments. Full article

Background/Objectives: To explore the protective effects of 7-Ketolithocholic acid (7-KLCA) against renal fibrosis and its mechanism, focusing on its interaction with farnesoid X receptor (FXR). Methods: In vitro, TGF-β-induced fibrosis in HK-2/NRK-49F cells and LPS-induced inflammation in HK-2 cells were detected by CCK-8, Western blot, and qPCR. In vivo, unilateral ureteral obstruction (UUO) and adenine (Ade)-induced mouse models were treated with a low/high-dose 7-KLCA or losartan. Renal injury was evaluated via H&E/Masson staining, serum creatinine (SCR), and blood urea nitrogen (BUN) levels. The 7-KLCA-FXR interaction was verified by molecular docking, CETSA, and DARTS. FXR downstream genes and related proteins were measured by WB and qPCR. Results: 7-KLCA inhibited the expression of fibrotic proteins (fibronectin, collagen-I) and reduced the LPS-induced release of inflammatory factors (IL-1β, IL-6). In mice, it alleviated renal swelling, collagen deposition, and tubular damage, while lowering serum SCR and BUN levels. Mechanistically, 7-KLCA stably bound to the FXR ligand-binding domain, enhanced its thermal stability and degradation resistance. It upregulated FXR and its downstream genes SHP and FGF15, thereby inhibiting the activation of TGF-β/Smad and Wnt/β-catenin pathways. Conclusions: This is the first study to clarify the molecular mechanism through which 7-KLCA targets FXR and dually suppresses the key pro-fibrotic pathways TGF-β/Smad and Wnt/β-catenin, thereby exerting anti-renal fibrosis effects. Full article

Pod dehiscence is one of the main factors which play a vital role on the final yield of many crops including soybean and, therefore, it is important to elucidate genetic mechanisms associated with this trait. In this study, morphological, physiological and biochemical analysis was conducted for pod and pod-related traits on 170 soybean genotypes with diverse origins. Subsequently, a genome-wide association study (GWAS) was performed using Silico-DArT and DArT SNPs markers. In total, 48 QTLs were identified with 14 stable QTLs, mostly located on chromosomes 6, 13 and 16, corresponding to pod dehiscence and pod-related traits. From putative candidate genes, two most stable and important genes for pod dehiscence with known functions were emphasised from the QTLs: Glyma.13G184500 and Glyma.16G141100, encoding transcription factors DNA-binding bromodomain-containing protein and C2H2 zinc finger protein, respectively. Finally, a genomic prediction approach was implemented to select genotypes most resistant to pod dehiscence. GWAS-derived markers confirmed the stable prediction of pod dehiscence in studied accessions from different populations and the best non-dehiscent soybean genotypes were successfully selected. Full article

Calmodulin (CaM) plays a central role in cardiac excitation–contraction coupling by regulating ion channels, including the L-type calcium (Ca²⁺) channel Ca_v1.2 and the voltage-gated potassium (K⁺) channel K_v7.1. Mutations in CaM are linked to severe arrhythmogenic disorders such as Long QT syndrome (LQTS), yet the molecular mechanisms remain incompletely understood. Here, we investigate the structural and functional consequences of the arrhythmia-associated CaM variant D133H. Biophysical analysis revealed that D133H destabilises Ca²⁺ binding at the C-terminal lobe of CaM, altering its Ca²⁺-dependent conformational changes. Electrophysiological recordings demonstrated that CaM D133H impairs Ca²⁺-dependent inactivation (CDI) of Ca_v1.2, prolonging Ca²⁺ influx, while also reducing activation of K_v7.1, thereby limiting repolarising K⁺ currents. Together, these dual defects converge to prolong action potential duration, providing a mechanistic basis for arrhythmogenesis in LQTS. Our findings establish that CaM D133H perturbs both Ca²⁺ and K⁺ channel regulation, highlighting a shared pathway by which calmodulinopathy mutations disrupt cardiac excitability. Full article

Row crops are regarded as a transitional type between continuous and discrete vegetation. Previous studies idealized row crops as periodic hedgerows with rectangular cross-sections. However, these models relied on oversimplified assumptions, failing to capture the intrinsic heterogeneity of canopy or its dynamic evolution over the life cycle. In 2020, we proposed a row crop model with a gradual decrease in leaf area volume density (LAVD) from the center of the row to the edge, partially overcoming these limitations. Building on this previous model, this paper introduces the leaf shape factor proposed by Mõttus et al. into the model. Three control parameters, a leaf width control parameter (β), leaf length control parameter (ψ), and leaf azimuth control parameter (e), are proposed to regulate the spatial distribution of LAVD. Additionally, an empirical exponential function from Watanabe et al. is adopted to describe the leaf zenith angle distribution, enabling the realistic calculation of the G-function and Γ-function in conjunction with leaf azimuth distribution. The LAVD is formulated at three hierarchical scales: individual scale, row scale, and scene scale. The model delivers two key advancements: enabling pronounced spatial heterogeneity and high tunability of the LAVD, and accurately simulating row crops throughout the life cycle, which bridges the row structure and continuous stages of row crops. Radiative transfer simulations are conducted to derive the bidirectional reflectance distribution function (BRDF), which is validated against the discrete anisotropic radiative transfer (DART) model. Comparisons across three growth stages demonstrated good consistency. Furthermore, this paper investigates the sensitivity of the BRDF to three control parameters (β, ψ, and e). The results indicate that changes in three parameters significantly affect the reflectance in the darkspot direction, leading to a maximum error of 22.6%. In carrying out remote sensing applications such as parameter inversion and yield estimation for row crops, the new model is recommended for more accurate BRDF simulations. Full article

The optimization capabilities of Kolmogorov–Arnold Networks (KANs) remain largely unexplored, which has limited their practical use in video anomaly recognition compared to conventional 3D-CNNs. To address this gap, we introduce a novel hybrid optimization framework that combines a Minimax Ant System (MMAS) for hyperparameter selection with a modified DARTS strategy for adaptive tuning of the 3D KAN architecture. Unlike existing approaches, our method simultaneously optimizes both learning dynamics and architectural configurations, enabling KANs to better exploit their expressive power in spatiotemporal feature extraction. Applied to a three-class video dataset, the proposed approach improved model accuracy to 87%, surpassing the performance of a standard 3D-CNN by 6%. Full article

Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma (NHL) worldwide. Currently, approximately sixty percent of patients are cured with R-CHOP as frontline treatment, while the remaining patients experience primary refractory or relapsed (R/R) disease. Recently, the introduction of Pola-R-CHP as front-line therapy has represented a major advance in the management of DLBCL, resulting in improved outcomes. Prognosis of R/R DLBCL patients is poor, particularly for those eligible neither for chimeric antigen receptor (CAR) T-cell therapy nor autologous stem cell transplantation (ASCT), representing a significant unmet clinical need. The advent of bispecific monoclonal antibodies (BsAbs), such as bispecific T-cell engagers (BiTEs), dual affinity retargeting (DART) molecules and IgG-like bispecific antibodies, offers a novel promising therapeutic approach in the treatment of DLBCL, both as frontline treatment and in the R/R setting. BsAbs simultaneously engage two different antigens, a tumor-associated antigen and an immune cell antigen, redirecting T-cells against malignant cells and enhancing the immune response. Most BsAbs developed for the treatment of NHLs engage T-cells via CD3 and malignant B-cells via CD20, a surface antigen expressed on most lymphomatous cells. Engagement of malignant B-cells by BsAbs activates T-cells, leading to the release of multiple cytokines and potentially to two characteristic adverse events: cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). The most extensively studied BsAbs, in both the frontline and relapsed/refractory (R/R) settings, include epcoritamab, glofitamab, mosunetuzumab, and odronextamab. Epcoritamab and glofitamab have received FDA and EMA approval for R/R DLBCL after two or more systemic line of therapies. EMA has also approved glofitamab in combination with gemcitabine and oxaliplatin (GemOx) for patients with R/R DLBCL ineligible for ASCT, whereas this indication has not been approved by FDA. Odronextamab is approved by EMA for R/R DLBCL and FL in patients who have received at least two prior lines of therapy, but it has not been approved by FDA. Mosunetuzumab is approved by both agencies—but only for R/R follicular lymphoma (FL). BsAbs represent a breakthrough therapy in the treatment of DLBCL, especially in R/R diseases. The purpose of this article is to review the landscape of BsAbs in DLBCL. Full article

The treatment of acute myeloid leukemia (AML) remains challenging, largely due to high relapse rates following standard therapies. T-cell engagers (TCEs) offer a promising immunotherapeutic approach by redirecting T cells to recognize and kill AML cells. These therapeutic proteins bind CD3 to T cells and a tumor-associated antigen to AML cells, facilitating targeted immune activation. While CD33 and CD123 are the most commonly targeted AML antigens, others such as CD135, CD38, and CLEC12A/CLL-1 are being evaluated in preclinical and clinical studies. In parallel, various TCE formats—including BiTEs, DuoBodies, DARTs, and DARPin-based constructs—have been developed to optimize pharmacokinetics, stability, and immune engagement. Despite the growing number of TCEs entering clinical evaluation, none have advanced beyond early Phase (I/II) trials, primarily due to the lack of optimal target antigens and challenges in balancing antileukemic activity with the risks of immune-related toxicities such as cytokine release syndrome (CRS). This review aims to summarize the current landscape of TCE development in AML, highlighting key targets, formats, and challenges. Full article

This paper contains an analysis of methods for person classification based on signals from wearable IMU sensors during sports. While this problem has been investigated in prior work, existing approaches have not addressed it within the context of few-shot or minimal-data scenarios. A few-shot scenario is especially useful as the main use case for person identification in sports systems is to be integrated into personalised biofeedback systems in sports. Such systems should provide personalised feedback that helps athletes learn faster. When introducing a new user, it is impractical to expect them to first collect many recordings. We demonstrate that the problem can be solved with over 90% accuracy in both open-set and closed-set scenarios using established methods. However, the challenge arises when applying few-shot methods, which do not require retraining the model to recognise new people. Most few-shot methods perform poorly due to feature extractors that learn dataset-specific representations, limiting their generalizability. To overcome this, we propose a combination of an unsupervised feature extractor and a prototypical network. This approach achieves 91.8% accuracy in the five-shot closed-set setting and 81.5% accuracy in the open-set setting, with a 99.6% rejection rate for unknown athletes. Full article

Nowadays, Flutter with the Dart programming language has become widely popular in mobile developments, allowing developers to build multi-platform applications using one codebase. An increasing number of companies are adopting these technologies to create scalable and maintainable mobile applications. Despite this increasing relevance, university curricula often lack structured resources for Flutter/Dart, limiting opportunities for students to learn it in academic environments. To address this gap, we previously developed the Flutter Programming Learning Assistance System (FPLAS), which supports self-learning through interactive problems focused on code comprehension through code-based exercises and visual interfaces. However, it was observed that many students completed the exercises without fully understanding even basic concepts, if they already had some knowledge of object-oriented programming (OOP). As a result, they may not be able to design and implement Flutter/Dart codes independently, highlighting a mismatch between the system’s outcomes and intended learning goals. In this paper, we propose a guided self-study approach of integrating documentation, code, visual output, and exercise in FPLAS. Two existing problem types, namely, Grammar Understanding Problems (GUP) and Element Fill-in-Blank Problems (EFP), are combined together with documentation, code, and output into a new format called Integrated Introductory Problems (INTs). For evaluations, we generated 16 INT instances and conducted two rounds of evaluations. The first round with 23 master students in Okayama University, Japan, showed high correct answer rates but low usability ratings. After revising the documentation and the system design, the second round with 25 fourth-year undergraduate students in the same university demonstrated high usability and consistent performances, which confirms the effectiveness of the proposal. Full article

Background/Objectives: Cutaneous angiosarcoma (CAS) is a rare and aggressive endothelial malignancy with a high rate of local recurrence and distant metastasis. In advanced cases, where surgical resection is not feasible, systemic therapy remains the cornerstone of treatment. This review aims to summarize the current landscape of systemic therapies for unresectable or metastatic CAS and discuss emerging strategies, particularly focusing on immune checkpoint inhibitors (ICIs). Methods: A comprehensive review of the literature was conducted, including clinical trials, retrospective studies, and case series focusing on systemic treatments for advanced CAS. Therapeutic approaches covered include cytotoxic chemotherapy, molecular targeted therapies, and ICIs, as well as combination strategies. Special attention was given to biomarker studies and ongoing clinical trials. Results: Taxane-based chemotherapy, particularly paclitaxel, has demonstrated clinical activity and remains a standard option. Molecular targeted agents such as pazopanib have yielded modest efficacy. Recent trials of ICIs, including the SWOG S1609 DART and AngioCheck studies, have shown encouraging results in select subgroups, especially tumors from sun-exposed regions associated with high tumor mutational burden (TMB). Although AngioCheck did not meet its predefined response criteria, a subset of patients achieved disease control. Biomarkers such as TMB, PD-L1 expression, and tumor-infiltrating lymphocytes are under investigation to guide patient selection. Combination therapies with ICIs and tyrosine kinase inhibitors (TKIs) are being actively explored. Conclusions: While systemic therapies for CAS remain limited in efficacy, ICIs—particularly in combination with TKIs—represent a promising avenue. Future trials should emphasize biomarker-driven, CAS-specific strategies to improve clinical outcomes in this challenging malignancy. Full article

The Necrópolis de Wari Kayan, at the Paracas site in the coastal desert of south–central Peru, is a large archeologically excavated mortuary complex with fine textile preservation, dated approximately to 2000 BP. This study investigates loose yarns associated with textiles from Wari Kayan tomb 12 (bundle 382), collected by the late Dr. Anne Paul in 1985 at what is now the Museo Nacional de Arqueología Antropología e Historia del Perú (MNAAHP). Sequencing multiple state-of-the-art analyses, including direct analysis in real time mass spectrometry (DART-MS), high performance liquid chromatography (HPLC) with diode array detection, and accelerator mass spectrometry, on the same small sample, seeks to “squeeze out every drop” of information. Six mantles from the outer layer include different sets of color hues and values, representing either different time periods or different producer groups. Plasma oxidation at low temperature (<100 °C) prepared carbon dioxide for AMS radiocarbon analysis. Fibers remaining after oxidation were combusted for light-stable isotope analysis. The sequential analysis results in fiber and dye composition, radiocarbon age, and stable isotope fractionation values may suggest fiber origin, continuing and updating a project started over 40 years ago. Full article