Hypoxia activates SREBP2 through Golgi disassembly in bone marrow-derived monocytes for enhanced tumor growth.

Bone marrow-derived cells (BMDCs) infiltrate hypoxic tumors at a pre-angiogenic state and differentiate into mature macrophages, thereby inducing pro-tumorigenic immunity. A critical factor regulating this differentiation is activation of SREBP2-a well-known transcription factor participating in tumorigenesis progression-through unknown cellular mechanisms. Here, we show that hypoxia-induced Golgi disassembly and Golgi-ER fusion in monocytic myeloid cells result in nuclear translocation and activation of SREBP2 in a SCAP-independent manner. Notably, hypoxia-induced SREBP2 activation was only observed in an immature lineage of bone marrow-derived cells. Single-cell RNA-seq analysis revealed that SREBP2-mediated cholesterol biosynthesis was upregulated in HSCs and monocytes but not in macrophages in the hypoxic bone marrow niche. Moreover, inhibition of cholesterol biosynthesis impaired tumor growth through suppression of pro-tumorigenic immunity and angiogenesis. Thus, our findings indicate that Golgi-ER fusion regulates SREBP2-mediated metabolic alteration in lineage-specific BMDCs under hypoxia for tumor progression.

Association between Circulating Levels of CXCL9 and CXCL10 and Physical Frailty in Older Adults.

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.

Association between DNA methylation levels of thioredoxin-interacting protein (TXNIP) and changes in glycemic traits: a longitudinal population-based study.

Thioredoxin-interacting protein (TXNIP) plays an important role in glucose metabolism, and its expression is regulated by DNA methylation (DNAm). Although the association between TXNIP DNAm and type 2 diabetes mellitus has been demonstrated in studies with a cross-sectional design, prospective studies are needed. We therefore examined the association between TXNIP DNAm levels and longitudinal changes in glycemic traits by conducting a longitudinal study involving 169 subjects who underwent two health checkups in 2015 and 2019. We used a pyrosequencing assay to determine TXNIP DNAm levels in leukocytes (cg19693031). Logistic regression analyses were performed to assess the associations between dichotomized TXNIP DNAm levels and marked increases in glycemic traits. At four years, the TXNIP DNA hypomethylation group had a higher percentage of changes in fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) compared to those in the hypermethylation group. The adjusted odds ratios for FPG and HbA1c levels were significantly higher in the TXNIP DNA hypomethylation group than in the hypermethylation group. We found that TXNIP DNA hypomethylation at baseline was associated with a marked increase in glycemic traits. Leukocyte TXNIP DNAm status could potentially be used as an early biomarker for impaired glucose homeostasis.

Calcineurin regulates the stability and activity of estrogen receptor α.

Estrogen receptor α (ER-α) mediates estrogen-dependent cancer progression and is expressed in most breast cancer cells. However, the molecular mechanisms underlying the regulation of the cellular abundance and activity of ER-α remain unclear. We here show that the protein phosphatase calcineurin regulates both ER-α stability and activity in human breast cancer cells. Calcineurin depletion or inhibition down-regulated the abundance of ER-α by promoting its polyubiquitination and degradation. Calcineurin inhibition also promoted the binding of ER-α to the E3 ubiquitin ligase E6AP, and calcineurin mediated the dephosphorylation of ER-α at Ser294 in vitro. Moreover, the ER-α (S294A) mutant was more stable and activated the expression of ER-α target genes to a greater extent compared with the wild-type protein, whereas the extents of its interaction with E6AP and polyubiquitination were attenuated. These results suggest that the phosphorylation of ER-α at Ser294 promotes its binding to E6AP and consequent degradation. Calcineurin was also found to be required for the phosphorylation of ER-α at Ser118 by mechanistic target of rapamycin complex 1 and the consequent activation of ER-α in response to ß-estradiol treatment. Our study thus indicates that calcineurin controls both the stability and activity of ER-α by regulating its phosphorylation at Ser294 and Ser118 Finally, the expression of the calcineurin A-α gene (PPP3CA) was associated with poor prognosis in ER-α-positive breast cancer patients treated with tamoxifen or other endocrine therapeutic agents. Calcineurin is thus a promising target for the development of therapies for ER-α-positive breast cancer.

Text-Guided Image Editing Based on Post Score for Gaining Attention on Social Media.

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.

Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions.

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.

A Novel Frame-Selection Metric for Video Inpainting to Enhance Urban Feature Extraction.

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.

Analysis of Continual Learning Techniques for Image Generative Models with Learned Class Information Management.

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.

Nutritional Care for Adults in Nursing Homes: A Scoping Review.

The need for improved nutrition in older adults requiring care has been acknowledged, but, to the best of our knowledge, there is a lack of systematic review and integration of nutritional care studies with older adults in nursing homes. This scoping review aimed to examine the scope and nature of nutritional care research for older adults in nursing homes and to identify research gaps, following the guidelines of the Joanna Briggs Institute. We found varied nutritional care for older adults living in nursing homes, including individualized sessions, such as nutrition counseling, the addition of foods and preparations for increased nutritional intake, and the maintenance of an eating environment, such as feeding assistance and calling. The nutritional care identified in this scoping review also included studies that have improved the nutritional status of older adults in nursing homes by implementing educational programs for care staff. For future research on effective nutritional care for older adults in nursing homes, we suggest evaluating both short- and long-term intervention effects with an adequate sample size.

Metabolic adaptations of cancer in extreme tumor microenvironments.

Cancer cells are highly heterogeneous to adapt to extreme tumor microenvironments (TMEs). TMEs challenge cancer cells via hypoxia, nutrition starvation, and acidic pH, promoting invasion and metastasis concomitant with genetic, epigenetic, and metabolic alterations. Metabolic adaptation to an extreme TME could allow cancer cells to evade cell death and immune responses, as well as resulting in drug resistance, recurrence, and poor patient prognosis. Therefore, elucidation of the metabolic adaptation of malignant cancer cells within TMEs is necessary, however, most are still elusive. Recently, adaptation of cancer cells within the TME can be analyzed via cell-cell interactions at the single-cell level. In addition, information into organelle-organelle interactions has recently been obtained. These cell-cell, and organelle-organelle interactions demonstrate the potential as new cancer therapy targets, as they play essential roles in the metabolic adaptation of cancer cells to the TME. In this manuscript, we review (1) metabolic adaptations within tumor microenvironments through (2) cell-to-cell, and (3) organelle-organelle metabolic interactions.

Early physical rehabilitation effectiveness in frail older patients hospitalized for community-acquired pneumonia: analysis of a nationwide database in Japan.

BACKGROUND: Frailty is a significant predictor of prognosis in older patients with community-acquired pneumonia (CAP). No effective therapy has been reported in frail patients with CAP, with frailty determined using the Hospital Frailty Risk Score (HFRS). AIMS: To investigate whether early physical rehabilitation intervention would effectively minimize adverse outcomes in frail older patients (determined using the HFRS) hospitalized for CAP. METHODS: This retrospective cohort analysis involved patients with CAP aged ≥ 65 years enrolled in the Japanese Diagnostic Procedure Combination Database between 2014 and 2020 and assessed as being frail. We compared 30-day mortality and readmission rates for patients who did and who did not receive physical rehabilitation within three days of admission and evaluated the association between outcomes and receiving early physical rehabilitation using Cox regression models and inverse probability weighting (IPW) for sensitivity analysis. RESULTS: The analysis involved 31,133 frail older patients hospitalized for CAP (mean age 84.3 ± 6.3 years; females, 49.1%), including 11,515 (37.0%) who received early physical rehabilitation. Cox regression analysis showed that early physical rehabilitation intervention was inversely associated with 30-day mortality and readmission rates. The IPW model also showed similar results. DISCUSSION: Early physical rehabilitation was associated with reduced risks of 30-day mortality, overall in-hospital mortality, and 30-day readmission rates in frail older patients with CAP. CONCLUSIONS: Early physical rehabilitation in frail older patients hospitalized for CAP may improve outcomes. This finding highlights the importance of simultaneously introducing the HFRS and early physical rehabilitation intervention into clinical practice for frail older patients with CAP.

Serum carotenoid levels are positively associated with DNA methylation of thioredoxin-interacting protein.

Background: Carotenoids have been reported to exert protective effects against age-related diseases via changes in DNA methylation. Although lower thioredoxin-interacting protein (TXNIP) DNA methylation is associated with age-related diseases, only a few studies have investigated the factors influencing TXNIP DNA methylation. Carotenoids may be a factor linking TXNIP to specific pathophysiological functions. The aim of this study was to examine whether serum carotenoid levels are associated with TXNIP DNA methylation levels. Methods: We conducted a cross-sectional study using 376 health examination participants (169 men). DNA methylation levels were determined using a pyrosequencing assay. Serum carotenoid levels were determined by high-performance liquid chromatography. Multivariable regression analyses were performed to examine the associations between TXNIP DNA methylation levels and serum carotenoid levels with adjustment for age, BMI, HbA1c, CRP, smoking habits, alcohol consumption, exercise habits, and percentage of neutrophils. Results: Multiple linear regression analyses showed that TXNIP DNA methylation levels were positively associated with serum levels of zeaxanthin/lutein (ß [95%CI]: 1.935 [0.184, 3.685]), ß-cryptoxanthin (1.447 [0.324, 2.570]), α-carotene (1.061 [0.044, 2.077]), ß-carotene (1.272 [0.319, 2.226]), total carotenes (1.255 [0.040, 2.469]), total xanthophylls (2.133 [0.315, 3.951]), provitamin A (1.460 [0.402, 2.519]), and total carotenoids (1.972 [0.261, 3.683]) in men (all p<0.05). Of these, provitamin A showed the stronger association (standardized ß=0.216). No significant association of TXNIP DNA methylation and serum carotenoid was observed in women. Conclusions: The findings of this study suggest that carotenoid intake may protect against age-related diseases by altering TXNIP DNA methylation status in men.

Associations between oral health status and short-term functional outcomes in hospitalized patients aged over 65 years with acute ischemic stroke: a retrospective observational study.

OBJECTIVE: To assess the associations between oral health status and short-term functional outcomes in hospitalized patients aged over 65 years with acute ischemic stroke. MATERIALS AND METHODS: This retrospective observational analysis included older adult patients (age, ≥ 65 years) admitted for acute ischemic stroke. The oral health status at admission was evaluated using the Oral Health Assessment Tool (OHAT). Patients were categorized into the normal oral health (OHAT score, 0-2) or poor oral health (OHAT score, ≥ 3) group. Stroke severity, Functional Oral Intake Scale (FOIS), and medical history were compared. Multivariate analysis was used to determine the association between the OHAT score and modified Rankin Scale (mRS) score at discharge, FOIS score at discharge, and length of hospital stay. RESULTS: The study comprised 129 patients (mean age: 78.8 ± 7.7 years). The poor oral health group (n = 22) had a higher stroke severity and lower FOIS scores than the normal oral health group (n = 107). The poor oral health group exhibited significantly higher rates of moderate to severe disability at discharge (odds ratio = 9.18, 95% confidence interval [CI]: 1.74-48.30, P = 0.009), lower FOIS scores at discharge (ß = -0.96, 95% CI: -1.71 to -0.20, P = 0.014), and longer hospital stays (ß = 10.70, 95% CI: 0.80-20.61, P = 0.034) than the other group. CONCLUSION: In older patients with acute ischemic stroke, poor oral health status at admission was associated with worse short-term functional outcomes, including increased disability, dysphagia, and longer hospital stay. CLINICAL RELEVANCE: Assessing and addressing the oral health status of this population can potentially improve short-term functional outcomes and enhance comprehensive stroke care.

Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships.

In this paper, we propose a hierarchical multi-modal multi-label attribute classification model for anime illustrations using a graph convolutional network (GCN). Our focus is on the challenging task of multi-label attribute classification, which requires capturing subtle features intentionally highlighted by creators of anime illustrations. To address the hierarchical nature of these attributes, we leverage hierarchical clustering and hierarchical label assignments to organize the attribute information into a hierarchical feature. The proposed GCN-based model effectively utilizes this hierarchical feature to achieve high accuracy in multi-label attribute classification. The contributions of the proposed method are as follows. Firstly, we introduce GCN to the multi-label attribute classification task of anime illustrations, enabling the capturing of more comprehensive relationships between attributes from their co-occurrence. Secondly, we capture subordinate relationships among the attributes by adopting hierarchical clustering and hierarchical label assignment. Lastly, we construct a hierarchical structure of attributes that appear more frequently in anime illustrations based on certain rules derived from previous studies, which helps to reflect the relationships between different attributes. The experimental results on multiple datasets show that the proposed method is effective and extensible by comparing it with some existing methods, including the state-of-the-art method.

Zero-Shot Neural Decoding with Semi-Supervised Multi-View Embedding.

Zero-shot neural decoding aims to decode image categories, which were not previously trained, from functional magnetic resonance imaging (fMRI) activity evoked when a person views images. However, having insufficient training data due to the difficulty in collecting fMRI data causes poor generalization capability. Thus, models suffer from the projection domain shift problem when novel target categories are decoded. In this paper, we propose a zero-shot neural decoding approach with semi-supervised multi-view embedding. We introduce the semi-supervised approach that utilizes additional images related to the target categories without fMRI activity patterns. Furthermore, we project fMRI activity patterns into a multi-view embedding space, i.e., visual and semantic feature spaces of viewed images to effectively exploit the complementary information. We define several source and target groups whose image categories are very different and verify the zero-shot neural decoding performance. The experimental results demonstrate that the proposed approach rectifies the projection domain shift problem and outperforms existing methods.

Prediction of Shooting Events in Soccer Videos Using Complete Bipartite Graphs and Players' Spatial-Temporal Relations.

In soccer, quantitatively evaluating the performance of players and teams is essential to improve tactical coaching and players' decision-making abilities. To achieve this, some methods use predicted probabilities of shoot event occurrences to quantify player performances, but conventional shoot prediction models have not performed well and have failed to consider the reliability of the event probability. This paper proposes a novel method that effectively utilizes players' spatio-temporal relations and prediction uncertainty to predict shoot event occurrences with greater accuracy and robustness. Specifically, we represent players' relations as a complete bipartite graph, which effectively incorporates soccer domain knowledge, and capture latent features by applying a graph convolutional recurrent neural network (GCRNN) to the constructed graph. Our model utilizes a Bayesian neural network to predict the probability of shoot event occurrence, considering spatio-temporal relations between players and prediction uncertainty. In our experiments, we confirmed that the proposed method outperformed several other methods in terms of prediction performance, and we found that considering players' distances significantly affects the prediction accuracy.

Zero-Shot Traffic Sign Recognition Based on Midlevel Feature Matching.

Traffic sign recognition is a complex and challenging yet popular problem that can assist drivers on the road and reduce traffic accidents. Most existing methods for traffic sign recognition use convolutional neural networks (CNNs) and can achieve high recognition accuracy. However, these methods first require a large number of carefully crafted traffic sign datasets for the training process. Moreover, since traffic signs differ in each country and there is a variety of traffic signs, these methods need to be fine-tuned when recognizing new traffic sign categories. To address these issues, we propose a traffic sign matching method for zero-shot recognition. Our proposed method can perform traffic sign recognition without training data by directly matching the similarity of target and template traffic sign images. Our method uses the midlevel features of CNNs to obtain robust feature representations of traffic signs without additional training or fine-tuning. We discovered that midlevel features improve the accuracy of zero-shot traffic sign recognition. The proposed method achieves promising recognition results on the German Traffic Sign Recognition Benchmark open dataset and a real-world dataset taken from Sapporo City, Japan.

Manipulation Direction: Evaluating Text-Guided Image Manipulation Based on Similarity between Changes in Image and Text Modalities.

At present, text-guided image manipulation is a notable subject of study in the vision and language field. Given an image and text as inputs, these methods aim to manipulate the image according to the text, while preserving text-irrelevant regions. Although there has been extensive research to improve the versatility and performance of text-guided image manipulation, research on its performance evaluation is inadequate. This study proposes Manipulation Direction (MD), a logical and robust metric, which evaluates the performance of text-guided image manipulation by focusing on changes between image and text modalities. Specifically, we define MD as the consistency of changes between images and texts occurring before and after manipulation. By using MD to evaluate the performance of text-guided image manipulation, we can comprehensively evaluate how an image has changed before and after the image manipulation and whether this change agrees with the text. Extensive experiments on Multi-Modal-CelebA-HQ and Caltech-UCSD Birds confirmed that there was an impressive correlation between our calculated MD scores and subjective scores for the manipulated images compared to the existing metrics.

Estimation of Degradation Degree in Road Infrastructure Based on Multi-Modal ABN Using Contrastive Learning.

This study presents a method for distress image classification in road infrastructures introducing self-supervised learning. Self-supervised learning is an unsupervised learning method that does not require class labels. This learning method can reduce annotation efforts and allow the application of machine learning to a large number of unlabeled images. We propose a novel distress image classification method using contrastive learning, which is a type of self-supervised learning. Contrastive learning provides image domain-specific representation, constraining such that similar images are embedded nearby in the latent space. We augment the single input distress image into multiple images by image transformations and construct the latent space, in which the augmented images are embedded close to each other. This provides a domain-specific representation of the damage in road infrastructure using a large number of unlabeled distress images. Finally, the representation obtained by contrastive learning is used to improve the distress image classification performance. The obtained contrastive learning model parameters are used for the distress image classification model. We realize the successful distress image representation by utilizing unlabeled distress images, which have been difficult to use in the past. In the experiments, we use the distress images obtained from the real world to verify the effectiveness of the proposed method for various distress types and confirm the performance improvement.

A Basic Survey on the Learning Needs of Nurses Caring for Patients with Intractable Cancer in Japan Based on Conceptual Education Integrating Oncology and Palliative Care.

This small-sample pilot study sought to uncover the role of palliative care education for general nurses providing care to patients with intractable cancer. While nurses serve as total care coordinators in patient recuperation, most general nurses in Japan dealing with such patients must independently update their knowledge. A questionnaire was developed comprising 28 items from the five supportive care need categories according to the Integrating Oncology and Palliative Care (IOP) model and 22 items from the Nurses' Difficulties in Cancer Care (NDCC) scale. General nurses who had worked in cancer care for over 5 years were recruited using snowball sampling. Based on the results, we planned a lecture and free study session on IOP using information and communication technology (ICT). Four lectures were delivered to 108 nurses from Hokkaido to Okinawa and remote islands. Overall, 90% of the participants were female nurses. They were categorized into two groups based on the number of times they attended the lectures (Group 1 [G1, attended once or twice]: 45; Group 2 [G2, attended three or four times]: 63). Comparing G1 and G2 showed that the practical ability of the participants in G2 increased for the items "Patient/Family Communication" and "Knowledge and Skills." Continued education using ICT may improve the practical skills of general nurses caring for patients with intractable cancer.