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Background: The purpose of this study was to clarify (1) the differences in cortical bone thickness (CBT) of the tibial diaphysis between healthy and osteoarthritic knees and (2) the influences of the femorotibial angle (FTA) and inclination of the medial compartment of the proximal tibia (MCT) on tibial CBT. Methods: The study assessed 60 subjects with varus knee osteoarthritis (OA) (22 males and 38 females; mean age, 74 ± 7 years) and 53 healthy elderly subjects (28 males and 25 females; mean age, 70 ± 6 years). Three-dimensional estimated CBT of the tibial diaphysis was automatically calculated for 2752–11,296 points using high-resolution measurements from CT. The standardized CBT was assessed in 24 regions by combining six heights and four areas. Additionally, the association between the CBT, each FTA, and MCT inclination was investigated. Results: The OA group showed a thicker CBT in the medial areas than in the lateral areas of the proximal tibia, while the healthy group had a thicker lateral CBT. The medial-to-lateral ratio of the proximal tibia was significantly higher in the OA group than in the healthy group. The proximal-medial CBT correlated with FTA and MCT inclinations in the OA group. Conclusions: This study demonstrated that varus osteoarthritic knees showed a different trend of proximal-medial CBT with associations in FTA and MCT inclination from healthy knees, possibly due to medial load concentration. [ABSTRACT FROM AUTHOR]

Traffic accidents occur frequently in cold and snow‐ or ice‐covered regions due to weather changes that occur during the winter season. To detect the snow‐ or ice‐covered roads in road surface conditions, road surface images captured using fixed‐point cameras installed along the route are sufficient. This paper proposes a snow‐ or ice‐covered road detection method that uses the deep convolutional autoencoding Gaussian mixture model (DCAGMM) with structural similarity (SSIM). The DCAGMM method, which is an unsupervised anomaly detection method, is unaffected by imbalance in the training data. In addition, the end‐to‐end convolutional neural network implemented in the DCAGMM enables the capture of the unique characteristics of the road surface images. Finally, by reconstructing the input images as normal images, the comparison of the input and reconstructed images enables identification of snow‐ or ice‐covered road areas without requiring pixel‐level annotations. Furthermore, the road surface images include complex characteristics for reconstruction, and the SSIM‐based reconstruction error allows us to preserve the image quality of the reconstructed image. Experimental results obtained on real‐world road surface images demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a multimodal Transformer model that uses time-series data to detect and predict winter road surface conditions. For detecting or predicting road surface conditions, the previous approach focuses on the cooperative use of multiple modalities as inputs, e.g., images captured by fixed-point cameras (road surface images) and auxiliary data related to road surface conditions under simple modality integration. Although such an approach achieves performance improvement compared to the method using only images or auxiliary data, there is a demand for further consideration of the way to integrate heterogeneous modalities. The proposed method realizes a more effective modality integration using a cross-attention mechanism and time-series processing. Concretely, when integrating multiple modalities, feature compensation through mutual complementation between modalities is realized through a feature integration technique based on a cross-attention mechanism, and the representational ability of the integrated features is enhanced. In addition, by introducing time-series processing for the input data across several timesteps, it is possible to consider the temporal changes in the road surface conditions. Experiments are conducted for both detection and prediction tasks using data corresponding to the current winter condition and data corresponding to a few hours after the current winter condition, respectively. The experimental results verify the effectiveness of the proposed method for both tasks. In addition to the construction of the classification model for winter road surface conditions, we first attempt to visualize the classification results, especially the prediction results, through the image style transfer model as supplemental extended experiments on image generation at the end of the paper. [ABSTRACT FROM AUTHOR]

• This study examined oral functional decline using the OF-5 and OHF criteria. • This study examined the diagnostic concordance and characteristics of OF-5 and OHF. • The prevalence rates of OF-5 and OHF were 46.0 % and 46.8 %, respectively. • The concordance rate of OF-5 and OHF diagnoses was 70.2 %. • OF-5 showed a potential association with nutritional status and fall risk. This study examined the diagnostic concordance and characteristics of the oral frailty five-item checklist (OF-5) and oral hypofunction (OHF). In this extensive cross-sectional study, older adults were enrolled at a research hospital frailty clinic between July 2021 and July 2023. The diagnosis of OF-5 and OHF was conducted using the proposed assessment methods. The concordance rate of these criteria was determined by evaluating each patient and calculating the number of patients meeting each diagnosis's criteria. Patients who tested negative for the OF-5 and OHF criteria and those who met only one criterion were included in the OF-5 and OHF characteristic analysis. A total of 248 patients were included in the analysis (mean age 77.6 ± 6.8 years; 36.7 % men). Among the patients, 114 (46.0 %) and 116 (46.8 %) met the OF-5 and OHF criteria, respectively. Furthermore, 78 (31.5 %) participants met both the OF-5 and OHF criteria, whereas 96 (38.7 %) did not meet either criterion. Moreover, 36 (14.5 %) and 38 (15.3 %) patients tested exclusively positive for OF-5 and OHF, respectively. The OF-5-positive and OHF-negative group showed an older age, lower Mini Nutritional Assessment Short Form score, and higher fall risk than the OF-5-negative and OHF-negative group. However, no significant differences were observed between the OF-5-negative and OHF-positive and OF-5-negative and OHF-negative groups. The concordance rate of OF-5 and OHF diagnoses was 70.2 %. OF-5 showed a potential association with nutritional status and fall risk. Further longitudinal analyses are warranted to explore our study results. [ABSTRACT FROM AUTHOR]

The advancements in deep learning have significantly enhanced the capability of image generation models to produce images aligned with human intentions. However, training and adapting these models to new data and tasks remain challenging because of their complexity and the risk of catastrophic forgetting. This study proposes a method for addressing these challenges involving the application of class-replacement techniques within a continual learning framework. This method utilizes selective amnesia (SA) to efficiently replace existing classes with new ones while retaining crucial information. This approach improves the model's adaptability to evolving data environments while preventing the loss of past information. We conducted a detailed evaluation of class-replacement techniques, examining their impact on the "class incremental learning" performance of models and exploring their applicability in various scenarios. The experimental results demonstrated that our proposed method could enhance the learning efficiency and long-term performance of image generation models. This study broadens the application scope of image generation technology and supports the continual improvement and adaptability of corresponding models. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel method for algal bed region segmentation using aerial images. Accurately determining the carbon dioxide absorption capacity of coastal algae requires measurements of algal bed regions. However, conventional manual measurement methods are resource-intensive and time-consuming, which hinders the advancement of the field. To solve these problems, we propose a novel method for automatic algal bed region segmentation using aerial images. In our method, we use an advanced semantic segmentation model, a ViT adapter, and adapt it to aerial images for algal bed region segmentation. Our method demonstrates high accuracy in identifying algal bed regions in an aerial image dataset collected from Hokkaido, Japan. The experimental results for five different ecological regions show that the mean intersection over union (mIoU) and mean F-score of our method in the validation set reach 0.787 and 0.870, the IoU and F-score for the background region are 0.957 and 0.978, and the IoU and F-score for the algal bed region are 0.616 and 0.762, respectively. In particular, the mean recognition area compared with the ground truth area annotated manually is 0.861. Our study contributes to the advancement of blue carbon assessment by introducing a novel semantic segmentation-based method for identifying algal bed regions using aerial images. [ABSTRACT FROM AUTHOR]

This study proposes an automatic findings generation method that performs in-context few-shot learning of a visual language model. The automatic generation of findings can reduce the burden of creating inspection records for infrastructure facilities. However, the findings must include the opinions and judgments of engineers, in addition to what is recognized from the image; therefore, the direct generation of findings is still challenging. With this background, we introduce in-context few-short learning that focuses on image similarity and text diversity in the visual language model, which enables text output with a highly accurate understanding of both vision and language. Based on a novel in-context few-shot learning strategy, the proposed method comprehensively considers the characteristics of the distress image and diverse findings and can achieve high accuracy in generating findings. In the experiments, the proposed method outperformed the comparative methods in generating findings for distress images captured during bridge inspections. [ABSTRACT FROM AUTHOR]

Text-guided image editing has been highlighted in the fields of computer vision and natural language processing in recent years. The approach takes an image and text prompt as input and aims to edit the image in accordance with the text prompt while preserving text-unrelated regions. The results of text-guided image editing differ depending on the way the text prompt is represented, even if it has the same meaning. It is up to the user to decide which result best matches the intended use of the edited image. This paper assumes a situation in which edited images are posted to social media and proposes a novel text-guided image editing method to help the edited images gain attention from a greater audience. In the proposed method, we apply the pre-trained text-guided image editing method and obtain multiple edited images from the multiple text prompts generated from a large language model. The proposed method leverages the novel model that predicts post scores representing engagement rates and selects one image that will gain the most attention from the audience on social media among these edited images. Subject experiments on a dataset of real Instagram posts demonstrate that the edited images of the proposed method accurately reflect the content of the text prompts and provide a positive impression to the audience on social media compared to those of previous text-guided image editing methods. [ABSTRACT FROM AUTHOR]

In our digitally driven society, advances in software and hardware to capture video data allow extensive gathering and analysis of large datasets. This has stimulated interest in extracting information from video data, such as buildings and urban streets, to enhance understanding of the environment. Urban buildings and streets, as essential parts of cities, carry valuable information relevant to daily life. Extracting features from these elements and integrating them with technologies such as VR and AR can contribute to more intelligent and personalized urban public services. Despite its potential benefits, collecting videos of urban environments introduces challenges because of the presence of dynamic objects. The varying shape of the target building in each frame necessitates careful selection to ensure the extraction of quality features. To address this problem, we propose a novel evaluation metric that considers the video-inpainting-restoration quality and the relevance of the target object, considering minimizing areas with cars, maximizing areas with the target building, and minimizing overlapping areas. This metric extends existing video-inpainting-evaluation metrics by considering the relevance of the target object and interconnectivity between objects. We conducted experiment to validate the proposed metrics using real-world datasets from Japanese cities Sapporo and Yokohama. The experiment results demonstrate feasibility of selecting video frames conducive to building feature extraction. [ABSTRACT FROM AUTHOR]

Introduction: Dysregulation of pro-inflammatory chemokines is considered a potential mechanism for the development of age-related medical conditions such as frailty. However, evidence linking circulating chemokines with frailty remains lacking. Materials and Methods: We performed a case-control study including 48 cases and 48 controls aged 65–90 years, using the National Center for Geriatrics and Gerontology outpatient registry data. Cases were outpatients with physical frailty and low habitual daily activity. Controls were robust outpatients who performed habitual daily activities. The Japanese version of the Cardiovascular Health Study criteria was used to diagnose physical frailty, and the modified Baecke questionnaire was used to evaluate habitual daily activities. Serum CXCL9 and CXCL10 levels were measured using enzyme-linked immunosorbent assay. Results: The median age (interquartile range) in cases and controls was 78 (73–83) and 76 (72–80) years, with the proportions of men were 47.9% and 43.8%, respectively. In the logistic regression model with adjustment for age, sex, and other confounding factors, the multivariable odds ratios (95% confidence intervals) for the highest versus lowest tertile of CXCL9 and CXCL10 levels were 7.90 (1.61–49.80) and 1.61 (0.42–6.30), respectively. However, we did not observe a linear association between CXCL9 levels and physical frailty components. Discussion/Conclusion: Our preliminary data exhibit that circulating CXCL9 levels were positively associated with the odds of physical frailty. However, these findings lack evidence of a dose-response relationship between CXCL9 levels and physical frailty components. Further research with a larger sample size is required to confirm these findings. [ABSTRACT FROM AUTHOR]

• We investigated the prevalence of postoperative dysphagia in older orthopedic patients. • The prevalence of dysphagia after orthopedic surgery was 16–32 %. • Factors related to postoperative dysphagia included being older, and sarcopenia. • Non-neurogenic dysphagia present in orthopedic patients, and sarcopenia may contribute. Oropharyngeal dysphagia is one of the most prevalent health complications in older adults. The prevalence of postoperative dysphagia is expected to rise with the increasing number of older patients undergoing orthopedic surgery; however, the specific prevalence and contributing factors remain unclear. This scoping review aimed to identify the prevalence and factors related to postoperative dysphagia in older orthopedic patients. This review included studies published up to September 2022 on postoperative patients aged ≥ 60 years who underwent orthopedic surgery. We searched MEDLINE, EMBASE, CINAHL, CENTRAL, Web of Science, and Ichushi-Web. In total, 21 of the 2158 identified studies were reviewed. The studies were classified into the three categories according to the surgical site: cervical spine disease (n = 12), hip fracture (n = 7), and others (n = 2). The estimated dysphagia prevalence rates [95 % confidence interval] of cervical spine disease, hip fractures, and others were 16 % [8–27], 32 % [15–54], and 6 % [4–8], respectively. Factors related to postoperative dysphagia included cervical alignment in cervical spine disease, being older within the cohort, preoperative health status, malnutrition, and sarcopenia in hip fractures. The prevalence of postoperative dysphagia after orthopedic surgery was highest for hip fractures, followed by cervical spine and others. These results suggest non-neurogenic dysphagia in older patients undergoing orthopedic surgery and indicate that sarcopenia may contribute to postoperative dysphagia in this population. Therefore, further research should clarify the trajectory of postoperative dysphagia and the effectiveness of rehabilitation for postoperative dysphagia after orthopedic surgery. [ABSTRACT FROM AUTHOR]