Osaka Prefecture University

Elements have played an essential role in the study of superconductivity, helping us understand this phenomenon. Over the past decade, the progress of experimental and theoretical studies has been significant. The periodic table of superconducting elements added two new superconductors (Rb and Yb). An expanded pressure range of exploration achieved a substantial increase in the highest reported superconducting transition temperatures. Furthermore, we have observed a new direction in searching for new superconducting phases
by examining different temperature-pressure paths on energy landscapes. This paper reviews the state of the art in the superconducting properties of elements, highlighting the knowledge gained to enhance superconducting transition temperatures.

Large-scale radiographic datasets often include errors in labels such as body parts or projection, which can undermine automated image analysis. Therefore, we aimed to develop and externally validate two deep-learning models-one for categorising radiographs by body part, and another for identifying projection and rotation of chest radiographs-using large, diverse datasets. We retrospectively collected radiographs from multiple institutions and public repositories. For the first model (Xp-Bodypart-Checker), we included seven categories (Head, Neck, Chest, Incomplete Chest, Abdomen, Pelvis, Extremities). For the second model (CXp-Projection-Rotation-Checker), we classified chest radiographs by projection (anterior-posterior, posterior-anterior, lateral) and rotation (upright, inverted, left rotation, right rotation). Both models were trained, tuned, and internally tested on separate data, then externally tested on radiographs from different institutions. Model performance was assessed using overall accuracy (micro, macro, and weighted) as well as one-vs.-all area under the receiver operating characteristic curve (AUC). In the Xp-Bodypart-Checker development phase, we included 429,341 radiographs obtained from Institutions A, B, and MURA. In the CXp-Projection-Rotation-Checker development phase, we included 463,728 chest radiographs from CheXpert, PadChest, and Institution A. The Xp-Bodypart-Checker achieved AUC values of 1.00 (99% CI: 1.00-1.00) for all classes other than Incomplete Chest, which had an AUC value of 0.99 (99% CI: 0.98-1.00). The CXp-Projection-Rotation-Checker demonstrated AUC values of 1.00 (99% CI: 1.00-1.00) across all projection and rotation classifications. These models help automatically verify image labels in large radiographic databases, improving quality control across multiple institutions. Question This study examines how deep learning can accurately classify radiograph body parts and detect chest projection/orientation in large, multi-institutional datasets, enhancing metadata consistency for clinical and research workflows. Findings Xp-Bodypart-Checker classified radiographs into seven categories with AUC values of over 0.99 for all classes, while CXp-Projection-Rotation-Checker achieved AUC values of 1.00 across all projection and rotation classifications. Clinical relevance Trained on over 860,000 multi-institutional radiographs, our two deep-learning models classify radiograph body-part and chest radiograph projection/rotation, identifying mislabeled data and enhancing data integrity, thereby improving reliability for both clinical use and deep-learning research.

This paper proposes a simplified mechanical model to estimate transverse shrinkage and angular distortion in multi-pass butt welding. The simplified mechanical model is first derived for an I-groove joint by representing the heated weld region with one-dimensional bar elements and by enforcing force equilibrium to obtain closed-form expressions for pass-by-pass deformation increments and cumulative deformation. For non-I-groove joints, the same simplified mechanical model is applied by updating the layer partition and geometric parameters for each pass based on the pass-wise high-temperature region; the inherent shrinkage of each pass is evaluated from the heat input and an equivalent heated-layer thickness. The simplified mechanical model is validated for V-groove multi-pass joints by comparison with thermo-elastic-plastic finite element (FE) analyses and available experimental data, and for X-groove multi-pass joints by comparison with thermo-elastic-plastic FE analyses. In addition, a parametric study on the V-groove angle (40°–70°) for SUS316L demonstrates that the model captures the increasing trend of final transverse shrinkage with groove angle without a pronounced degradation in prediction accuracy. The results show that the simplified mechanical model reproduces both deformation histories and final values with good accuracy while using only a small set of input parameters and negligible computational cost, making it useful for early-stage welding procedure planning and quick parameter studies.

This paper discussed a frequency-dependent distributed beamforming technique to improve the reliability in implant multiple-input multiple-output (MIMO)-ultra wideband (UWB) communications. To realize distributed beamforming for implant MIMO-UWB systems, we propose an optimization method to theoretically determine the weight coefficients optimized for the implant UWB communication. To evaluate the performance improvement, the propagation characteristics of the implant channel in the low band UWB (3.4-4.8GHz) were analyzed by the finite difference time domain (FDTD) method with a numerical human model. Subsequently, this study conducted computer simulations based on the derived propagation channel model. The evaluation results demonstrate that the proposed beamforming effectively improved the [Formula: see text] performance by 5dB in a capsule endoscopy scenario, compared to the conventional method, which can improve implantable medical devices in the future.

A facultative anaerobic, catalase-negative, lactic acid-producing, non-endospore-forming, Gram-positive coccoid bacterium, strain MT/JULY 2010T, was isolated from the stomach of a Helicobacter pylori-positive Japanese female patient with pre-neoplastic gastric mucosa. This bacterium can form clusters, chains or pairs or occurs singly in liquid media. The growth ranges for temperature, pH and NaCl concentration were 20-45 °C, pH 5.0-7.0 and 1-4%, respectively. Cells exhibited an unusual tumbling motion when observed under the microscope using a hanging drop assay. Observation under an electron microscope showed some unidentified structures and numerous fimbriae-like appendages. The combined results of 16S rRNA gene sequence analysis and the overall genome-related index [average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH)] classified the bacterium as a novel species of the genus Streptococcus. It forms a monophyletic cluster with Streptococcus parasanguinis ATCC 15912T in the phylogenetic tree with 94.4% ANI. Furthermore, the conventional DNA-DNA hybridization assay and dDDH of the two strains identified their relatedness as 62.5% and 58.7% (both below the 70% threshold), respectively. The DNA G+C content, determined by nuclease P1-digested genomic DNA, was 42.6 mol%, while the digital G+C content calculated in silico was 41.8 mol%. Based on the phenotypic and genotypic results, strain MT/JULY 2010T represents a novel species within the genus. The name Streptococcus mobilis sp. nov. is proposed. The type strain is MT/JULY 2010T (=ATCC BAA-2258T=NBRC 107862T=NCBI 948105T=V10/022878T).

Abstract Background Aggregation of α-synuclein is a central pathological feature of Parkinson’s disease (PD), yet reliable and broadly applicable fluid biomarkers reflecting disease-relevant α-synuclein biology remain limited. We aimed to establish acetylated α-synuclein (Ac-αSyn), the predominant proteoform in vivo , as a novel biomarker for PD and to evaluate its diagnostic utility based on a sensitive immunoassay. Methods Using a single molecule array technique capable of quantitatively detecting N-terminally acetylated α-synuclein, plasma Ac-αSyn levels were measured in 110 samples obtained from 52 patients with PD, 24 patients with multiple system atrophy (MSA), and 34 healthy controls (HCs). In a subset of PD patients, plasma Ac-αSyn measurements and 123 I-metaiodobenzylguanidine (MIBG) cardiac scintigraphy were performed in the same individuals, enabling direct comparison between these two testing modalities. Ac-αSyn levels were also quantified in 91 cerebrospinal fluid (CSF) samples obtained from 51 patients with PD, 25 patients with MSA, and 15 non-parkinsonian disease controls (DCs). Results Plasma Ac-αSyn levels robustly differentiated PD from both MSA and HCs ( p < 0.0001). Receiver operating characteristic analysis demonstrated high diagnostic performance (area under the curve [AUC] = 0.89 for PD vs MSA; AUC = 0.94 for PD vs HCs), comparable to established imaging biomarkers. In the same individuals, plasma Ac-αSyn levels correlated with the heart-to-mediastinum ratio derived from MIBG cardiac scintigraphy. CSF Ac-αSyn levels also clearly differentiated PD from both MSA and DCs ( p < 0.0001), with high diagnostic performance (AUC = 0.85 for PD vs MSA; AUC = 0.93 for PD vs DCs), supporting the biological relevance of plasma Ac-αSyn as a biomarker. Conclusion This study identifies Ac-αSyn in plasma as a novel biomarker for PD, enabled by quantitative immunoassay-based detection. Plasma Ac-αSyn represents a practical and minimally invasive biomarker that supports biology-based diagnosis of PD and discrimination from MSA.