Search Results (18,122)

Autonomous vehicles at level 3 and above must maintain high navigation accuracy, particularly in global navigation satellite system (GNSS)-denied environments. The main innovations of this work are threefold. First, we integrate visual inertial odometry (VIO) and light detection and ranging (LiDAR) inertial odometry (LIO) as external updates to mitigate the rapid drift of micro-electromechanical system (MEMS)-based industrial-grade inertial measurement units (IMUs) during long-term GNSS outages. Second, we adopt a redundant IMU (RIMU) approach that fuses multiple low-cost IMUs to reduce sensor noise and improve reliability. Third, we propose a system calibration methodology using both static and dynamic vehicle motion to estimate extrinsic parameters (boresight angles and lever arms) of the sensors, achieving an overall boresight angle root-mean-square error of 0.04 degrees in the simulation. Experiments were conducted under a 7 min GNSS-denied scenario in an underground parking lot, allowing for comparison of the error characteristics of multi-sensor fusion schemes against a navigation-grade reference. The INS/GNSS/LIO framework achieved a two-dimensional root-mean-square position error of 1.22 m (95% position error within 2.5 m), meeting the lane-level (1.5 m) accuracy requirement under a GNSS outage exceeding 7 min without prior maps. In contrast, the RINS/GNSS/VIO framework yielded a 4.71 m 2D mean position error under the same conditions. This paper provides a quantitative comparison of the baseline error characteristics of VIO-, LIO-, and RIMU-assisted INS/GNSS fusion under a GNSS-denied navigation scenario. Full article

Trichothecene mycotoxins, especially T-2 toxin, represent a significant threat to food safety and public health. Although the enzymatic degradation of deoxynivalenol has been extensively investigated, there are few reports of enzymes capable of efficiently degrading T-2 toxin. This study identified that the aldo-keto reductase AKR13B3 from Devosia A6-243 exhibits 3-keto-DON-degrading and a little T-2 toxin-degrading activity. To address this limitation, a rational design strategy targeting the substrate-binding pocket was employed to enhance its activity. Utilizing site-directed and combinatorial mutagenesis, a double mutant R134F/D217A was successfully screened. R134F/D217A retains catalytic activity towards 3-keto-DON while significantly enhancing its catalytic capacity for T-2. Specifically, the R134F/D217A variant exhibited a 2.88-fold increase in catalytic activity and a 3.15-fold enhancement in catalytic efficiency (k_cat/K_m) relative to the wild type enzyme. Notably, a substantial improvement in thermal stability was also observed. After incubation at 55 °C, the residual activity of the R134F/D217A mutant was 2.63 times that of the wild type. Molecular dynamics (MD) simulations and three-dimensional structural modeling suggested the mechanistic basis for the enhanced performance of the R134F/D217A double mutant. Catalytic enhancement stems from a shortened nucleophilic attack distance, a positively biased electrostatic environment, combined with an enlarged pocket and reduced binding free energy. Concurrently, the increased thermal stability results from decreased flexibility and a more rigid structural architecture. This work presents the first report of AKR13B3 as an effective enzyme for T-2 toxin transformation, and its catalytic activity was significantly enhanced through rational design. Thus, a novel enzymatic strategy was proposed, and could inform future approaches to study issues related to T-2 toxin contamination. Full article

Background/Objectives: Thinness is widely idealized as a standard of beauty, and late school-age girls are increasingly exposed to sociocultural pressures that may be associated with excessive drive for thinness and maladaptive body esteem. These body-related cognitive distortions often emerge regardless of actual weight status and may precede unhealthy dieting behaviors and emotional difficulties. This study aimed to develop and evaluate a school-based Rational Emotive Behavior Therapy (REBT) intervention designed to modify excessive drive for thinness and maladaptive body esteem among late school-age girls. Methods: A quasi-experimental, non-equivalent control group design with repeated measures was employed. Participants were 62 girls in grades 5–6 recruited from two public elementary schools in South Korea (experimental group: n = 30; control group: n = 32). The experimental group participated in a five-week REBT intervention consisting of 10 structured sessions grounded in the A-B-C-D-E model, while the control group received no intervention. Outcomes were assessed at pretest, posttest, and follow-up, including drive for thinness, body esteem, positive affect, negative affect, disordered eating behaviors, and social media overuse. Data were analyzed using repeated measures ANOVA. Results: Significant group × time interaction effects were observed for drive for thinness, body esteem, positive affect, negative affect, and disordered eating behaviors, with greater changes observed in the experimental group. No significant group × time interaction was found for social media overuse. Conclusions: The school-based REBT intervention was associated with reductions in excessive drive for thinness and improvements in body esteem and was also associated with changes in emotional outcomes and reductions in disordered eating behaviors among late school-age girls. These findings support early cognitive modification as a preventive strategy within school settings. Full article

Background: There is growing recognition that certain medical conditions, such as type 2 diabetes (T2D), can be effectively addressed through comprehensive lifestyle changes, thereby reducing reliance on medications; however, little guidance exists on deprescribing following lifestyle change. This study aimed to develop a framework that can be used to better define and standardize across research studies which medication changes in T2D care can be classified as deprescribing. Methods: An iterative development process began with a review of medication data exported from electronic health records (EHR) for n = 650 patients with T2D, 18–89 years, from two primary care practices with LM board-certified physicians. Included patients were seen during the period of 15 May 2014 to 13 March 2023. All reported T2D medications were grouped into the following categories: insulin, non-insulin, or metformin. A consensus-based review process was employed, facilitated by weekly meetings with the research team, whereby patients were classified as “potentially deprescribed,” “not deprescribed,” or “unclear” (not enough information based on limited, exported EHR data). Patients identified as potentially deprescribed or “unclear” were then further assessed through a more detailed review of their EHR. Results: Using the results of this chart review, a framework was developed to identify types of deprescribing, as follows: (1) insulin dose reduced; (2) change from insulin to other non-insulin medication; (3) insulin discontinued; (4) non-insulin T2D medication stopped; (5) dose reduced of the same non-insulin T2D medication; (6) change from any non-insulin medication to metformin or multiple medications + metformin to metformin only; (7) metformin stopped; (8) metformin dose reduced. A total of n = 193 patients were identified as having been potentially deprescribed based on the exported EHR data, and after a more detailed review of individual EHR records, 41 were confirmed as deprescribed. Conclusions: This study is the first to present a novel framework for classifying deprescribing in the context of positive health outcomes. The framework will facilitate future research evaluating the impact of lifestyle changes on diabetes management and promote comparability across settings for medication outcomes. Future research is needed to apply this framework to quantify deprescribing across various settings with greater precision. Full article

This work presents the design of a compact, wideband circular microstrip patch antenna for microwave imaging-based brain tumor detection. The main contribution is the development of a compact antenna structure incorporating enhanced ground-plane slot modifications, which significantly improves impedance bandwidth while maintaining a small electrical size, making it highly suitable for medical imaging systems. In addition, the study integrates antenna design, safety evaluation, and microwave imaging analysis within a unified framework to assess tumor localization feasibility using a realistic head model in CST Microwave Studio. The proposed antenna is fabricated on an FR-4 substrate with dimensions of 37 × 54.5 × 1.6 mm³, corresponding to an electrical size of 0.176λ × 0.260λ × 0.0076λ at the lowest operating frequency of 1.43 GHz. Ground-plane slot enhancements are introduced to achieve wideband performance, resulting in an impedance bandwidth from 1.43 to 4 GHz and a fractional bandwidth of 94.7%. The antenna exhibits a maximum realized gain of 3.7 dB. To evaluate its suitability for medical applications, specific absorption rate (SAR) analysis is performed using a realistic human head model at multiple antenna positions and at 1.5, 2.1, 2.5, 3.3, and 3.9 GHz frequencies. The computed SAR values range from 0.109 to 1.56 W/kg averaged over 10 g of tissue, satisfying the IEEE C95.1 safety guideline limit of 2 W/kg. For tumor detection assessment, time-domain simulations are conducted in CST Microwave Studio using a monostatic radar configuration, where the antenna operates as both transmitter and receiver at twelve angular positions around the head with 30° increments. The collected scattered signals are processed using the Delay-and-Sum (DAS) beamforming algorithm to reconstruct dielectric contrast maps and localize the tumor. It should be noted that the tumor-imaging demonstrations presented in this work are based on numerical simulations, while experimental validation is limited to the characterization of the fabricated antenna. Nevertheless, the findings indicate that the proposed antenna is a promising candidate for noninvasive, low-cost microwave brain tumor imaging applications. Full article

Background: Protein phosphatase 2Cs (PP2Cs) constitutes the largest phosphatase family in plants, playing a pivotal role in signal transduction. Within this family, the PP2C.D subfamily exerts significant influence on cell elongation and stress adaptation by mediating the ‘SAUR-PP2C.D-H+-ATPase’ regulatory module in the auxin signaling pathway. In rice, OsPP2C61 is a PP2C member whose molecular features and potential regulatory context remain unclear. Methods: Our study conducted a preliminary characterization of OsPP2C61 through integrated bioinformatics analysis, spatiotemporal expression profiling, and subcellular localization experiments in tobacco leaf cell. Results: OsPP2C61 encodes a 377-amino-acid protein predicted to be hydrophilic, basic, and structurally unstable. Secondary-structure prediction identified three major elements with random coils as the predominant component, whereas 3D modeling indicated alternating α-helices and β-sheets consistent with a canonical PP2C fold. Phylogenetic inference placed OsPP2C61 within the PP2C.D clade and revealed conserved motifs shared with OsPP2C25, OsPP2C28, and OsPP2C39. Promoter analysis showed enrichment of abscisic acid (ABA)- and methyl jasmonate (MeJA)-responsive elements along with multiple stress-related cis-regulatory motifs. Spatiotemporal expression analysis showed that OsPP2C61 is highly expressed in roots. Subcellular localization assays further demonstrated that the OsPP2C61-GFP fusion protein localizes to the nucleus and the plasma membrane when transiently expressed in epidermal cells of Nicotiana benthamiana. Conclusions: This work delivers the first comprehensive characterization of OsPP2C61, establishing a foundation for mechanistic studies and positioning OsPP2C61 as a candidate gene for rice improvement. Full article

Background/Objectives: Type 2 diabetes (T2D) develops gradually over many years through a prolonged preclinical phase, yet traditional static risk scores often fail to capture these dynamic metabolic trajectories. We propose a longitudinal deep learning framework to predict the 10-year risk of Type 2 diabetes onset defined by comprehensive ADA criteria by modeling the physiological acceleration of routine clinical biomarkers. Methods: Utilizing an 18-year longitudinal dataset from the community-based Korean Genome and Epidemiology Study (KoGES) cohort, we selected $N=4354$ participants with complete follow-up records, ensuring high data integrity without requiring synthetic data augmentation. We constructed a 3-dimensional tensor of 21 non-invasive clinical variables spanning a 6-year observation window. To resolve the inherent precision-recall trade-offs of individual models, we developed a stacking ensemble that integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures via a logistic regression meta-learner. To evaluate the added value of longitudinal modeling, we compared this dynamic framework against a static XGBoost baseline that only saw the most recent data. Results: Evaluated on an independent test set ($n=874$), the ensemble significantly outperformed baseline models, achieving an overall accuracy of 0.90 (95% CI: 0.88–0.92) and an AUROC of 0.94 (95% CI: 0.93–0.95). By harmonizing LSTM’s sensitivity and GRU’s precision, the model yielded an exceptional Positive Predictive Value (PPV) of 0.97, a sensitivity of 0.80, and a specificity of 0.98. Conclusions: This framework provides a highly accurate, resource-efficient triage instrument for T2D screening, thereby reducing unnecessary clinical alerts and improving screening efficiency. Full article

In the vehicle electrification sector, the precise and reliable control of e-motors is of the utmost importance for ensuring the efficient and safe operation of the whole electric vehicle drivetrain. Specifically, the assessment of the absolute rotor position of the permanent magnet-based synchronous motors is necessary for precise e-motor control, which is strongly determined by the precision of the sensing device used for the absolute rotor position assessment. Magnetic rotational position sensing devices/encoders are predominantly used in the automotive sector. The accuracy of a magnetic-based rotational position sensing device can be affected by defects/errors which may occur during its manufacturing and/or assembly process. These defects may in turn affect the accuracy of the e-motor’s control and operation. The primary objective of this study was to numerically and experimentally design and investigate the accuracy of a magnetic-based off-axis rotational position sensing device intended for the control of a new permanent magnet e-motor, which was developed for a two-wheeler electric vehicle drivetrain. First, a 3D parametric numerical model of a magnetic rotational position sensing device mounted on the motor shaft was built by virtue of the finite element method (FEM). Based on numerical simulations, the appropriate dimensions of the magnetic ring were determined and the possible errors which may have occurred during its manufacturing process have been numerically imposed and analyzed. Second, the rotor position sensing device was prototyped based on the recommendations obtained with the 3D FEM model. Finally, the accuracy of the designed rotational position device was then experimentally assessed by comparing it to a standardized end-of-shaft rotational position encoder. To evaluate the influence of the possible errors on the e-motor rotor position measurement, the output characteristics of the motor torque as a function of its rotational speed of a real permanent magnet e-motor were experimentally assessed using two different rotational position devices. Based on the numerical end experimental results, we identified the manufacturing errors of the magnetic ring and analyzed their influence on the resulting output characteristics of the e-motor. The results revealed that the magnetic ring eccentricity and its magnetization process could affect the accuracy of the e-motor’s output torque characteristics. Full article

Background: Polygonatum sibiricum (PS), a perennial herbaceous plant belonging to the Liliaceae family, is widely distributed in China and other East Asian countries. PS has been used as food and medicine for thousands of years, and its rhizomes are rich in Polygonatum sibiricum polysaccharides (PSP), which exhibit various bioactivities, yet their structural features and therapeutic mechanisms against ulcerative colitis (UC) remain unclear. Methods: A homogeneous polysaccharide, PSP-1b (57.45 kDa), was isolated from the rhizomes of PS via ion-exchange and gel filtration chromatography and structurally characterized using chromatographic and spectroscopic methods. In vivo, its effects were evaluated in a dextran sulfate sodium (DSS)-induced mouse model of UC, while in vitro mechanisms were explored using macrophages stimulated with lipopolysaccharide (LPS) and neutrophil extracellular traps (NETs). Results: PSP-1b was identified as a neutral polysaccharide with minimal branching. Its primary structural backbone was largely composed of →4)-β-D-Galp-(1→ residues. A portion of these backbone residues was substituted at the O-6 position by side chains primarily composed of β-D-Galp-(1→ units. In vivo, PSP-1b significantly alleviated DSS-induced colitis by reducing inflammatory cytokine secretion, suppressing colonic macrophage infiltration, and reversing neutrophil extracellular traps (NETs) deposition. In vitro, PSP-1b directly interacted with TLR4, inhibited the MAPK/NF-κB signaling pathway, and attenuated LPS- and NET-induced macrophage polarization and inflammation. Conclusions: PSP-1b as a promising candidate for functional foods or therapeutic agents targeting inflammatory bowel disease. Full article

Background: Thrombosis in light chain amyloidosis (LCA) occurs in the context of multiple organ dysfunction and inflammation. Conventional coagulation tests (screening) may not sufficiently capture the procoagulant substrate in the inflammatory/therapeutic dynamics. Methods: A total of 61 consecutive patients with LCA were prospectively included in the study. Clinical data, including organ involvement, time of diagnosis, treatment phase, DOAC exposure and thrombosis history were systematically recorded and subjected to screening. Specialized hemostasis tests such as APTT/PT, fibrinogen, D-dimer, TEG and TGT were performed and conventional times were analyzed in the subgroup without DOAC. Results: The prevalence of documented thrombosis was 32.8%, and thrombosis status was associated with TEG positivity and more strongly with TGT positivity. Hypercoagulability was identified in 50.8% by TEG and 41.0% by TGT, regardless of whether APTT/PT were within the reference values. APTT/PT did not predict thrombosis recurrence (p > 0.05), which was predicted by TEG (p = 0.0027) and TGT (p = 0.0006). An inflammation/fibrin turnover panel (CRP, fibrinogen, D-dimer) predicted TEG positivity (p < 0.0001), but not TGT, and was correlated with assessment at diagnosis, daratumumab-based therapy, and cardiac involvement. Conclusions: Global tests (TEG/TGT) promptly correlate with thrombosis recurrence in our cohort and provide crucial information in addition to clotting times for thrombotic phenotyping. Inflammation can influence TEG, so the decision to recommend the tests and the timing of their performance should be adapted to the clinical, biological, and therapeutic context. Full article

Background/Objectives: The aim of the study was to evaluate the diagnostic accuracy of a commercial artificial intelligence (AI) algorithm originally developed for screening mammography when applied to symptomatic women presenting to a tertiary outpatient clinic. Methods: This single-center, retrospective diagnostic accuracy study included women who presented with breast symptoms to a tertiary outpatient clinic between January and June 2013 and underwent digital mammography. An AI algorithm cleared by the U.S. Food and Drug Administration (FDA)-cleared AI algorithm was applied to all mammograms and generated continuous malignancy scores ranging from 1 to 100. Mammographic breast density was classified according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) by two experienced radiologists. Histopathology, when available, or otherwise a minimum of 2 years of clinical and imaging follow-up served as the reference standard. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis with calculation of the area under the curve (AUC) and 95% confidence intervals (CI) derived by patient level bootstrap resampling (n = 2000). Analyses were performed for the overall cohort and stratified by breast density (non-dense [BI-RADS A–B] vs. dense [BI-RADS C–D]). Results: A total of 78 women (mean age, 55 ± 11 years) were included, of whom 16 had histopathological verification of suspicious lesions with proven breast cancer in 14 patients and 62 were classified based on follow-up alone. In the overall cohort (156 breasts, including 15 breasts with malignancies), the AI algorithm achieved an AUC of 0.96 (95% CI: 0.86–1.00). Performance remained high in non-dense breasts (AUC = 0.96; 95% CI: 0.88–1.00) and dense breasts (AUC = 0.99; 95% CI: 0.93–1.00), with no statistically significant difference observed between density subgroups (DeLong test, p = 0.36), although subgroup comparisons were underpowered. Decision curve analysis suggested a consistent positive net benefit across a wide range of threshold probabilities in both density groups. Conclusions: In this preliminary, single-center retrospective cohort, a screening-trained AI algorithm showed promising diagnostic accuracy when applied to symptomatic mammograms. These findings require validation in larger, contemporary, multicenter cohorts before clinical implementation. Full article

Background: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an autoimmune disorder frequently associated with ovarian teratomas in young women. Although infectious triggers have been proposed to contribute to immune activation, direct evidence linking viral presence within tumor tissue to disease pathogenesis remains limited. Case Presentation: An 18-year-old woman presented with acute neuropsychiatric symptoms, fever, gastrointestinal prodrome, and rapidly progressive behavioral disturbance progressing to encephalopathy. Cerebrospinal fluid and blood test results, together with clinical features, supported the diagnosis of anti-NMDAR encephalitis. Imaging identified an ovarian mass, and surgical resection was performed. Histopathology confirmed a mature teratoma containing neuroglial elements. Molecular analysis detected parvovirus B19 DNA within the resected teratomatous tissue. No systemic viremia or active central nervous system viral infection was identified. The patient received immunotherapy combined with tumor removal, with subsequent clinical improvement. Discussion: Ovarian teratomas remain a critical etiologic factor in anti-NMDAR encephalitis and mandate prompt surgical management. Detection of B19 viral DNA within teratomatous neuroglial tissue raises the hypothesis that viral persistence could enhance local immune activation and autoantibody generation. However, in this case polymerase chain reaction positivity does not indicate active infection, and the biological significance of this finding remains uncertain. Conclusions: This case documents rare detection of B19V DNA within an ovarian teratomatous tissue in anti-NMDAR encephalitis. The observation is hypothesis-generating rather than causal; established management priorities remain immunotherapy and tumor resection, and viral nucleic acid detection should be interpreted within the broader clinical context. Full article

Type 1 diabetes (T1D) is an autoimmune disease with a strong genetic component (~70% heritability). Early identification of individuals at risk is crucial for early intervention or risk assessment. Although polygenic risk scores (PRS) have shown promise in risk assessment, most current approaches remain constrained by linear assumptions and limited generalizability. We aimed to develop a neural network-driven classifier using T1D-associated single nucleotide polymorphisms (SNPs). In addition, we explored the inclusion of an entropy-derived feature as a complementary variable, representing the degree of genetic variability within an individual’s genotype profile across the 67 T1D-associated SNPs, to evaluate its potential additive contribution to the model performance. We analyzed genotype data from 11,909 individuals in the UK BioBank (546 T1D cases and 11,363 controls). Sixty-seven well-known SNPs associated with T1D were utilized as inputs to the model, using two distinct allele-encoding strategies. A feed-forward neural network was evaluated under varying case–control ratios through five-fold cross-validation. Performance was assessed using the area under the receiver operating characteristic curve (AUC) on a held-out test set and on an external European cohort as a validation cohort. Across five-fold cross-validation, the best configuration achieved a median AUC of 0.903. On the held-out UK Biobank test set, the model generalized well, with an AUC of 0.8889 (95% CI: 0.8516–0.9262). A probability-based risk framework, constructed using five risk groups (“very low”, “low”, “intermediate”, “high”, and “very high” risk), yielded a negative predictive value (NPV) of 98.9% for the “very low” risk group and a Positive Predicted Value (PPV) of 61.9% with a specificity of 97.3% for the “very high” risk group, assuming a 10% T1D prevalence. External validation in the German Diabetes Study reproduced clear case–control separation; for individuals with recent onset diabetes and glutamic acid decarboxylase antibodies (GADA+) vs. controls, specificity reached 91.9% in the “high” risk group (PPV of 94.3%) and 97.6% in the “very high” risk group (PPV of 95.7%). The proposed neural network reliably predicts T1D genetic risk using a compact SNP panel of 67 SNPs and maintains accuracy in both internal and external European cohorts. Its probabilistic output enables clinically interpretable risk thresholds, while entropy features contributed modestly to performance. These results demonstrate that a neural network-based approach achieves discriminative performance that is comparable to established T1D genetic risk models, while offering flexible probability-based risk stratification and architectural extensibility for future integration of additional features. Full article

Older adults often need guidance when visiting new buildings for the first time. However, indoor navigation remains challenging due to the lack of Global Positioning System (GPS) availability, visually repetitive corridors, and frequent location failures. This article presents a multimodal indoor navigation assistant that combines graph-based route planning with visual landmark verification to provide step-by-step guidance. The environment is modelled as a directed graph whose nodes are annotated with semantic landmarks, and the graph is constructed primarily from a video of the building, reducing the need for 3D scanners, beacons, or other specialised instruments. Routes are calculated using Dijkstra’s shortest-path algorithm over the semantic graph. During navigation, camera frames are analysed using a restricted vision-language recognition strategy that only considers candidate landmarks from the current and next nodes, reducing false detections and improving interpretability. To increase robustness, a temporary voting mechanism was introduced to confirm node transitions, as well as a hierarchical redirection strategy with local and global recovery. The system is implemented in two modes: handheld mode with visual cues using augmented reality arrows, mini map and voice instructions, and hands-free mode with front camera using voice instructions and keywords. Evaluation involved preliminary technical testing in the United Kingdom followed by formal user validation in Spain. During these trials, participants reported high usability, strong confidence and safety, and increased perceived independence. Full article

This paper introduces D³O-GT, a distributed optimization framework designed to operate under partial, heterogeneous, and delayed information—conditions commonly encountered in large-scale logistics and networked decision support systems. The proposed approach integrates gradient tracking with delay-aware updates to address the steady-state bias and instability that often affect classical distributed gradient methods. We formulate a consensus optimization model that captures decentralized decision variables while preserving global optimality, and we develop an algorithmic structure that balances convergence accuracy, communication efficiency, and robustness to asynchronous updates. Extensive numerical experiments demonstrate that D³O-GT achieves machine precision convergence in synchronous settings and remains stable under bounded communication delays, converging to a small neighborhood of the optimum. In contrast, conventional distributed gradient descent exhibits significant residual error under the same conditions. Scalability analyses further indicate that the proposed method maintains favorable iteration complexity as the number of agents increases. These results position D³O-GT as a practical and scalable solution for distributed decision-making environments, with direct relevance to logistics-oriented applications such as resource allocation, coordination of networked services, and real-time operational planning. Full article