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Gut microbiome composition differs between aortic stenosis and aortic regurgitation patients
Abstract The gut microbiome has emerged as an important modulator of cardiovascular diseases, yet data on aortic valve disorders, particularly aortic stenosis (AS), remain limited. This study aimed to characterize gut microbiome differences in patients with bicuspid (BS) and tricuspid aortic stenosis (TS) using aortic regurgitation (AR) patients as a clinically comparable control group with similar age distribution, comorbidities, and metabolic profiles. A total of 122 patients were included in this prospective cross-sectional study: 33 AR, 22 BS, and 67 TS patients. Microbiome profiling was conducted from anal swabs using 16S rRNA gene sequencing. Beta diversity was assessed via UniFrac distances, and computed redundancy analysis was performed using linear discriminant analysis effect size. The groups were largely homogeneous regarding most clinical characteristics; AR patients showed only marginally worse renal function, while BS patients were slightly younger with fewer cases of diabetes. In beta diversity analyses, both TS and BS patients exhibited clearly distinct microbiome compositions compared with AR controls, independent of clinical parameters used as potential confounders. TS and BS patients differed only minimally from each other. AR patients showed higher abundances of Bacteroides, Faecalibacterium, Lachnoclostridium, and Alistipes, whereas AS patients exhibited increased levels of Corynebacterium, Anaerococcus, Peptoniphilus, and Finegoldia. Co-abundance network analysis revealed that AS patients displayed an extensive and highly interconnected bacterial network, characterized by strong correlations among taxa such as Bacteroides, Alistipes, Parabacteroides, and Faecalibacterium, rather than isolated changes in individual taxa. AR patients provide a clinically suitable control group for AS. AS patients show a distinct microbiome composition and a highly interconnected microbial network with three major hubs, warranting further mechanistic investigation.
RNA molecules with different destinies are processed through overlapping pathways
Decoupling geometry-dependent dimensional errors in FDM assembly elements via a learning-based inverse design approach
Heat tolerance in lactating Holstein dairy cows is associated with oxygen transport and mammary aerobic glucose metabolism under heat stress
Confirmation mechanisms shape order-picking performance in pick-by-light and pick-by-point systems
Abstract Order picking remains one of the most labor-intensive warehouse processes, and the design of human–system interaction may substantially affect both operational efficiency and process stability. This study compares six confirmation mechanisms embedded in Pick-by-Light and Pick-by-Point order-picking systems under controlled experimental conditions. Seventy participants completed standardized manual picking tasks using all tested variants across five repeated trials, resulting in 2,100 observations. The main performance measure was task completion time, while process stability was assessed using standard deviation and the coefficient of variation. The results showed significant differences between confirmation mechanisms, a significant short-term familiarization effect across repeated trials, and a significant confirmation mechanism-by-trial interaction. Pick-by-Light with infrared confirmation achieved the shortest mean completion time (20.74 s) and the lowest coefficient of variation (13.75%), whereas radar-based Pick-by-Point produced the slowest mean completion time (28.20 s) and the highest variability (CV = 27.91%). These findings indicate that performance differences are not determined solely by the general technology family, but also by the specific confirmation logic and interaction design. The study contributes to human-centered warehouse design by showing that low-friction confirmation mechanisms can improve order-picking speed, repeatability, and short-term user adaptation in manual picker-to-parts environments.
Anxiety emotional reactivity social camouflaging and burnout mediate the relationship between autistic traits and mental well-being in medical students
Field-like spin-orbit torque associated with out-of-plane spin polarization in Py/van der Waals heterostructures
Abstract Low-symmetry van der Waals (vdW) materials have recently emerged as a fertile platform for generating unconventional spin-orbit torques (SOTs) beyond the conventional spin Hall effect (SHE) framework. Here, we report the experimental observation of a field-like torque ( $$\:{\tau\:}_{FLT}$$ ) associated with out-of-plane ( z -polarized) spin polarization in Py/WTe 2 and Py/MoTe 2 heterostructures using angle-resolved spin-torque ferromagnetic resonance. Angular symmetry analysis enables the unambiguous separation of vector torque components, revealing that the z -polarized $$\:{\tau\:}_{FLT}$$ reaches a magnitude comparable to that of the conventional in-plane damping-like torque ( $$\:{\tau\:}_{DLT}$$ ) associated with the SHE. Complementary macrospin simulations demonstrate that the inclusion of z -polarized $$\:{\tau\:}_{FLT}$$ enables fast switching of in-plane magnetization, in contrast to conventional heavy-metal-based systems. Our experimental and simulation results establish low-symmetry vdW-based heterostructures as a viable platform for generating sizable out-of-plane SOTs and achieving efficient control of in-plane magnetization.
Party pooper: grandparents’ COVID risk rose after grandchildren’s birthdays
Structural effects of alcohols on H₂S solubility and thermodynamic modeling of binary mixtures with benzyl alcohol, phenol, and ethanol
The effect of green waste as an organic amendment on soil characteristics and the growth performance of sage (Salvia japonica Thunb)
Anatomically constrained hierarchical post-processing for pediatric brain tumor segmentation
Abstract Automated segmentation of pediatric brain tumors from multi-parametric MRI is an intermediate step for clinical workflows such as treatment planning and longitudinal volumetric monitoring, yet remains challenging due to the heterogeneous appearance and complex nested anatomy of tumor sub-regions. Our evaluation of several architectural modifications—class-decoupled networks (CDHNet) and hierarchical region-aware networks (HiRA-Net)—reveals that none consistently outperform the standard nnU-Net on the BraTS-PEDs dataset. We propose Hierarchical Post-Processing (HPP), a model-agnostic pipeline that enforces anatomical constraints specific to pediatric brain tumors through: (1) connected component analysis, (2) anatomical hierarchy enforcement, (3) volume-ratio-based label correction, and (4) morphological boundary smoothing. Applied to nnU-Net, HPP improves the macro-average Dice score from 0.634 to 0.724 (+ 9.0% points), with substantial improvements for cystic component (+ 17.8 pp) and peritumoral edema (+ 13.4 pp); the same gain is reproduced across all five default cross-validation folds (raw 0.617 ± 0.021 → HPP 0.706 ± 0.023 macro Dice; ΔMacro = + 0.089 ± 0.002 per fold; pooled n = 260 paired Wilcoxon p < 10⁻⁶), confirming that the improvement is not specific to a particular data split. HPP improves all five tested backbones, demonstrating its applicability across the convolutional backbones we evaluated. We also show that region-based training, successful for adult gliomas, underperforms on pediatric tumors due to differences in label hierarchy and class distribution.
Childhood trauma, specifically emotional abuse, is associated with psychiatric symptom severity in drug‑naïve first‑episode patients with schizophrenia
Abstract Schizophrenia (SCZ) is a highly heterogeneous disorder. Childhood trauma (CT) is a well‑established risk factor, although the contribution of specific CT dimensions remains incompletely understood. This study systematically investigated which CT dimensions were most strongly associated with symptom severity in drug‑naïve first‑episode SCZ patients. In a cross‑sectional design, 284 patients were classified into a SCZ‑ct group ( n = 234) and a SCZ‑nct group ( n = 50) based on Childhood Trauma Questionnaire. Symptom severity was assessed using the Positive and Negative Syndrome Scale. Hierarchical regression analyses adjusted for demographic and clinical covariates were performed, with rater effects examined via a linear mixed‑effects model. The SCZ‑ct group exhibited significantly higher total and positive symptom scores, whereas negative symptom scores did not differ. Higher total CTQ scores were associated with greater overall symptom severity. Emotional abuse was independently associated with total PANSS scores after adjustment for other CT dimensions and covariates, and this association remained robust after additionally accounting for rater effects. These findings support the potential value of addressing childhood trauma, particularly emotional abuse, in both clinical practice and public health strategies.
Comprehensive assessment of fragmented fiber shedding from recycled cotton textiles part i structural changes in woven fabrics and mass loss quantification during laundering
Multi-scale contextual modeling and fine-grained adaptive fusion for real-time surface defect detection
Abstract Surface defect detection in steel strips requires both accurate recognition and efficient inference under complex industrial backgrounds. Existing methods still encounter difficulties in modeling scale-varied defects, preserving fine-grained defect cues, and aligning multi-level features efficiently. To address these issues, we propose CDF-YOLO, a real-time detection framework that integrates multi-scale contextual modeling and fine-grained adaptive fusion on the YOLOv12 baseline. Specifically, a Dilated Context Pyramid Bottleneck (C2f_DCPB) is introduced to enhance contextual representation through parallel dilated branches; a Fine-grained Adaptive Fusion module (FAN_Block) is embedded into the high-resolution path to strengthen local texture and structural-detail representation; and DySample is adopted to improve content-adaptive upsampling for multi-scale feature fusion. Experiments on the NEU-DET dataset show that CDF-YOLO achieves an mAP50 of 95.4% and 101.3 FPS under the adopted test protocol, while cross-dataset evaluation on DeepPCB further indicates improved generalization performance. These results suggest that CDF-YOLO provides a favorable accuracy-efficiency trade-off on public industrial defect datasets. Nevertheless, validation on larger-scale production-line data and further optimization for edge deployment remain necessary for practical industrial application.
“The feasibility of free septal mucosal grafts in preventing postoperative middle meatus synechia after endoscopic sinus surgery”
Abstract This study aimed to evaluate the feasibility and safety of using a free septal mucosal graft to prevent middle meatal synechiae following endoscopic sinus surgery (ESS). This prospective observational study was conducted at a tertiary referral center between March 2022 and February 2024. Adult patients with chronic rhinosinusitis without nasal polyps (CRSsNP) who were refractory to medical therapy were included. All patients underwent unilateral ESS followed by placement of a free septal mucosal graft over exposed bone in the middle meatus. Postoperative outcomes were assessed using the Sinonasal Outcome Test-22 (SNOT-22), Lund–Kennedy Endoscopic Score (LKES), and Perioperative Sinus Endoscopy (POSE) score. A total of 116 patients completed the study. Significant improvement in SNOT-22 scores was observed at 6 months postoperatively ( p < 0.001). Significant improvement was also noted in endoscopic findings (LKES, p < 0.001). Stable middle turbinate position without lateralization was achieved in 91 (78%) of cases. Free septal mucosal grafting appears to be a feasible, biocompatible, and technically straightforward method to reduce middle meatal synechiae following ESS.