Formation of a transdisciplinary community of practice in rural areas, with an interactive database of co-created knowledge: A case study in Noto, Japan

Many rural areas suffer from severe depopulation, and the absence of a university is one reason for outmigration. Where research and education are valued, however, such rural areas can attract scholars and students visiting from universities and other external institutions. Scholarly outputs of research, such as research articles and project reports, particularly those from community-based research (CBR), can themselves become an asset for use by local communities. We consider that CBR can contribute to asset-based community development (ABCD) when a transdisciplinary community of practice (TDCOP) emerges and drives the processes of collaborative creation and use of the knowledge. A particularly critical mechanism, which is currently lacking, is to allow the local community to collect knowledge outputs and make them easily available to interested actors within and outside of the community. We assume that a core tool in this mechanism is an interactive database. It can be equipped with a user interface, allowing enjoyable and active searches, and possibly a mechanism by which users themselves can contribute to gradual development of the database. We formed a study group of researchers and practitioners to conduct a case study in the Noto region of Japan. We identified the existing assets in Noto, including the knowledge created through CBR, and then collected and shared our own experiences and practices, as well as lessons learned from other regions in Japan, to explore the principles of designing a database. A CBR database should not only be a static inventory of past research, but also capable of facilitating new cycles of knowledge co-creation. With a comprehensive and easily accessible inventory of knowledge in place, we conclude that there is high potential in enabling CBR itself to be an asset, which can help achieve ABCD in rural communities

Stacked-ring aromaticity from the viewpoint of the effective number of π-electrons

We theoretically examined the effective number of π-electrons in the closely π-stacked 4nπ electron dimers exhibiting stacked-ring aromaticity. Multi-configurational calculations for cyclobutadiene π-dimer models revealed that a double-triplet [1(T1T1)] character bearing 2 x (4n–2)π conjugated electrons + 4 formally unpaired electrons appears in the ground state at small stacking distances. Energy stabilization of the closely π-stacked dimers can be attributed to mixing intermolecular charge-transfer (CT) configurations. However, its energy gain would be insufficient for self-aggregation of the π-dimers with a stacking distance of ~3 Å

Development of Combination Methods for Detecting Malignant Uptakes Based on Physiological Uptake Detection Using Object Detection With PET-CT MIP Images

Deep learning technology is now used for medical imaging. YOLOv2 is an object detection model using deep learning. Here, we applied YOLOv2 to FDG-PET images to detect the physiological uptake on the images. We also investigated the detection precision of abnormal uptake by a combined technique with YOLOv2. Using 3,500 maximum intensity projection (MIP) images of 500 cases of whole-body FDG-PET examinations, we manually drew rectangular regions of interest with the size of each physiological uptake to create a dataset. Using YOLOv2, we performed image training as transfer learning by initial weight. We evaluated YOLOv2's physiological uptake detection by determining the intersection over union (IoU), average precision (AP), mean average precision (mAP), and frames per second (FPS). We also developed a combination method for detecting abnormal uptake by subtracting the YOLOv2-detected physiological uptake. We calculated the coverage rate, false-positive rate, and false-negative rate by comparing the combination method-generated color map with the abnormal findings identified by experienced radiologists. The APs for physiological uptakes were: brain, 0.993; liver, 0.913; and bladder, 0.879. The mAP was 0.831 for all classes with the IoU threshold value 0.5. Each subset's average FPS was 31.60 +/- 4.66. The combination method's coverage rate, false-positive rate, and false-negative rate for detecting abnormal uptake were 0.9205 +/- 0.0312, 0.3704 +/- 0.0213, and 0.1000 +/- 0.0774, respectively. The physiological uptake of FDG-PET on MIP images was quickly and precisely detected using YOLOv2. The combination method, which can be utilized the characteristics of the detector by YOLOv2, detected the radiologist-identified abnormalities with a high coverage rate. The detectability and fast response would thus be useful as a diagnostic tool

Medical Radiation Exposure Reduction in PET via Super-Resolution Deep Learning Model

In positron emission tomography (PET) imaging, image quality correlates with the injected [18F]-fluorodeoxyglucose (FDG) dose and acquisition time. If image quality improves from short-acquisition PET images via the super-resolution (SR) deep learning technique, it is possible to reduce the injected FDG dose. Therefore, the aim of this study was to clarify whether the SR deep learning technique could improve the image quality of the 50%-acquisition-time image to the level of that of the 100%-acquisition-time image. One-hundred-and-eight adult patients were enrolled in this retrospective observational study. The supervised data were divided into nine subsets for nested cross-validation. The mean peak signal-to-noise ratio and structural similarity in the SR-PET image were 31.3 dB and 0.931, respectively. The mean opinion scores of the 50% PET image, SR-PET image, and 100% PET image were 3.41, 3.96, and 4.23 for the lung level, 3.31, 3.80, and 4.27 for the liver level, and 3.08, 3.67, and 3.94 for the bowel level, respectively. Thus, the SR-PET image was more similar to the 100% PET image and subjectively improved the image quality, as compared to the 50% PET image. The use of the SR deep-learning technique can reduce the injected FDG dose and thus lower radiation exposure

Anti-Cyclic Citrullinated Peptide Antibody-Positive Meningoencephalitis in the Preclinical Period of Rheumatoid Arthritis

Rheumatoid meningoencephalitis (RM) is a rare complication of rheumatoid arthritis (RA). This report describes a 63-year-old man with complaints of high-grade fever, headache, and vomiting for several days before admission. Both his serum and cerebrospinal fluid were positive for anti-cyclic citrullinated peptide (CCP) antibody and rheumatoid factor, and contrast-enhanced fluid-attenuated inversion recovery magnetic resonance imaging (MRI) showed abnormal gadolinium enhancement of the meninges and high-intensity lesions in the subarachnoid spaces. The patient was diagnosed with RM despite lack of signs suggesting RA. His symptoms drastically improved with intravenous infusion of high-dose methylprednisolone. Two months later, he developed RA. The findings in this patient suggest that RM could develop prior to the onset of RA. Anti-CCP antibody and MRI findings may be useful for the diagnosis of RM, regardless of RA history