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Dendrimers, a type of dendritic molecule, have a well-defined structure with a homogeneous molecular weight and precise multiple terminal groups. These characteristics are favorable for both research and development, which require accuracy and multivalency. This review focuses on various dendrimers as interfacial materials and reviews their biological functionality, as represented by biological recognition, based on our previous research. At the surface of a gold substrate, the immobilized dendrimer with closely packed terminals was inactive to proteins, but the immobilized dendrimer with loosely packed terminals and many signature ligands, such as saccharides, was highly active to specific proteins with multivalent interactions. Additionally, the loosely packed glycodendrimer controlled the biological functionality of proteins by strictly regulated multivalent interactions. Likewise, an amphiphilic glycodendrimer gave a dynamic biointerface by self-assembly in aqueous media and represented biological functionality similar to that on the surface of the substrate. Biointerfaces involving dendrimers have different useful characteristics from linear polymers on the surface or in solution, and promise to provide a new approach for the development of superior biomaterials and procedures to elucidate and control vital phenomena.Dendrimers, a type of dendritic molecule, have a well-defined structure with a homogeneous molecular weight and precise multiple terminal groups. Although the immobilized dendrimer with closely packed terminals was inactive to various proteins due to huge steric hindrance, the immobilized dendrimer with loosely packed terminals and many signature ligands, such as saccharides, was highly active to specific proteins with multivalent interactions at the surface. Additionally, the loosely packed glycodendrimer controlled the biological functionality of proteins by strictly regulated multivalent interactions.

Abstract The integration of augmented reality and drones allows past and future landscapes to be visualized from an aerial perspective. However, these visualizations still suffer from the occlusion problem, where the three-dimensional (3D) virtual model displayed in the real world is in front of a real-world object. Currently, city digital twins are essential for the sustainable development of cities and the development of detailed 3D models of cities. By visualizing the city digital twin, augmented reality can facilitate the participation of nonexpert citizens in the decision-making process of urban design, but research examples are limited. Here, using detailed city 3D models, we develop a digital-twin approach to outdoor augmented reality with occlusion handling for both first-person and bird’s-eye views. In a verification experiment, the occlusion handling accuracy of the prototype system was measured to be about 0.8 using intersection over union. The frame rate of the entire prototype system was about 30 fps, and the delay between the controller and the augmented reality device was about 3 s. The internet-based system architecture was developed to integrate augmented reality and drone systems. Our system allows multiple stakeholders involved in building construction projects to observe aerial perspectives of those projects, both on-site and off-site via an internet browser, using augmented reality with occlusion handling. Graphical Abstract Graphical Abstract

The extraction and integration of building facade data are necessary for the development of information infrastructure for urban environments. However, existing methods for parsing building facades based on semantic segmentation have difficulties in distinguishing individual instances of connected buildings. Manually collecting and annotating instances of building facades in large datasets is time-consuming and labor-intensive. With the recent development and use of city digital twins (CDTs), massive high-quality digital assets of buildings have been created. These assets make it possible to generate high-quality and cost-effective synthetic datasets that can replace real-world ones as training sets for the supervised learning-based instance segmentation of building facades. In this study, we developed a novel framework that can automatically produce synthetic datasets from a CDT. An auto-generation system for synthetic street views was built by rendering city digital assets in a game engine, while the system auto-generated the instance annotations for building facades. The hybrid dataset HSRBFIA, along with various subsets containing different proportions of synthetic and real data, were used to train deep learning models for facade instance segmentation. In our experiments, two types of synthetic data (CDT-based and virtual-based) were compared, and the results showed that the CDT synthetic data were more effective in boosting deep learning training with real-world images compared with the virtual synthetic data (no real-world counterparts). By swapping a certain portion of the real data with the proposed CDT synthetic images, the performance could almost match what is achievable when using the real-world training set.

Precise measuring of urban façade color is necessary for urban color planning. The existing manual methods of measuring building façade color are limited by time and labor costs and hardly carried out on a city scale. These methods also make it challenging to identify the role of the building function in controlling and guiding urban color planning. This paper explores a city-scale approach to façade color measurement with building functional classification using state-of-the-art deep learning techniques and street view images. Firstly, we used semantic segmentation to extract building façades and conducted the color calibration of the photos for pre-processing the collected street view images. Then, we proposed a color chart-based façade color measurement method and a multi-label deep learning-based building classification method. Next, the field survey data were used as the ground truth to verify the accuracy of the façade color measurement and building function classification. Finally, we applied our approach to generate façade color distribution maps with the building classification for three metropolises in China, and the results proved the transferability and effectiveness of the scheme. The proposed approach can provide city managers with an overall perception of urban façade color and building function across city-scale areas in a cost-efficient way, contributing to data-driven decision making for urban analytics and planning.

Background Lighting simulation tools are extending the functionality of Building Information Modeling (BIM) authoring software applications to support the lighting design analysis of buildings. Although such tools enable quantitative and qualitative analysis and visualization of indoor lighting, they do not provide an interactive environment between users and the design context. Moreover, their visualization environments do not allow users to experience visual phenomena such as glare. In addition, lighting energy consumption generated from traditional tools is often separated from the 3D virtual context of the building. Therefore, an incorrect interpretation by designers regarding the relationship between their desirable lighting design and energy feedback may occur. Methods This research proposes a method and develops a BIM-based lighting design feedback (BLDF) prototype system for realistic visualization of lighting condition and the calculation of energy consumption. Results The results of a case study revealed that BLDF supports design stakeholders to better perceive and optimize lighting conditions in order to achieve a higher degree of satisfaction in terms of lighting design and energy savings for future occupants. Conclusions The developed system utilizes an interactive and immersive virtual reality (VR) environment to simulate daylighting and the illumination of artificial lights in buildings and visualizes realistic VR scenes using head mounted displays (HMD). BLDF allows users to interact with design objects, to change them, and to compare multiple design scenarios, and provides real-time lighting quality and energy consumption feedback.

BackgroundThis study evaluated the effectiveness of NUDT15 codon 139 genotyping in optimizing thiopurine treatment for inflammatory bowel disease (IBD) in Japan, using real-world data, and aimed to establish genotype-based treatment strategies.MethodsA retrospective analysis of 4628 IBD patients who underwent NUDT15 codon 139 genotyping was conducted. This study assessed the purpose of the genotyping test and subsequent prescriptions following the obtained results. Outcomes were compared between the Genotyping group (thiopurine with genotyping test) and Non-genotyping group (thiopurine without genotyping test). Risk factors for adverse events (AEs) were analyzed by genotype and prior genotyping status.ResultsGenotyping test for medical purposes showed no significant difference in thiopurine induction rates between Arg/Arg and Arg/Cys genotypes, but nine Arg/Cys patients opted out of thiopurine treatment. In the Genotyping group, Arg/Arg patients received higher initial doses than the Non-genotyping group, while Arg/Cys patients received lower ones (median 25 mg/day). Fewer AEs occurred in the Genotyping group because of their lower incidence in Arg/Cys cases. Starting with < 25 mg/day of AZA reduced AEs in Arg/Cys patients, while Arg/Arg patients had better retention rates when maintaining ≥ 75 mg AZA. Nausea and liver injury correlated with thiopurine formulation but not dosage. pH-dependent mesalamine reduced leukopenia risk in mesalamine users.ConclusionsNUDT15 codon 139 genotyping effectively reduces thiopurine-induced AEs and improves treatment retention rates in IBD patients after genotype-based dose adjustments. This study provides data-driven treatment strategies based on genotype and identifies risk factors for specific AEs, contributing to a refined thiopurine treatment approach.

Real-world big data studies using health insurance claims databases require extraction algorithms to accurately identify target population and outcome. However, no algorithm for Crohn's disease (CD) has yet been validated. In this study we aim to develop an algorithm for identifying CD using the claims data of the insurance system. A single-center retrospective study to develop a CD extraction algorithm from insurance claims data was conducted. Patients visiting the Kitasato University Kitasato Institute Hospital between January 2015-February 2019 were enrolled, and data were extracted according to inclusion criteria combining the Tenth Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) diagnosis codes with or without prescription or surgical codes. Hundred cases that met each inclusion criterion were randomly sampled and positive predictive values (PPVs) were calculated according to the diagnosis in the medical chart. Of all cases, 20% were reviewed in duplicate, and the inter-observer agreement (Kappa) was also calculated. From the 82,898 enrolled, 255 cases were extracted by diagnosis code alone, 197 by the combination of diagnosis and prescription codes, and 197 by the combination of diagnosis codes and prescription or surgical codes. The PPV for confirmed CD cases was 83% by diagnosis codes alone, but improved to 97% by combining with prescription codes. The inter-observer agreement was 0.9903. Single ICD-code alone was insufficient to define CD; however, the algorithm that combined diagnosis codes with prescription codes indicated a sufficiently high PPV and will enable outcome-based research on CD using the Japanese claims database.

BackgroundWe recently reported the efficacy of indigo naturalis (IN) in patients with active ulcerative colitis (UC) in a randomized controlled trial (INDIGO study). However, few studies have been conducted to investigate whether IN is effective even in treatment-refractory cases, such as in those with steroid dependency and anti-TNF refractoriness.MethodsIn the INDIGO study, 86 patients with active UC were randomly assigned to an IN group (0.5–2.0 g daily) or placebo group. The rate of clinical response (CR), mucosal healing (MH), and change in fecal calprotectin (FCP) levels was compared between refractory [patients with steroid-dependent disease, previous use of anti-TNF-α, and concomitant use of immunomodulators (IM)] and non-refractory patients. We also analyzed factors predicting CR and MH at week 8.ResultsThe rates of CR of IN group were significantly higher than placebo group, even in patients with steroid-dependent disease (p < 0.001), previous use of anti-TNF-α (p = 0.002), and concomitant use of IM (p = 0.013). The rates of MH in IN group were significantly higher than in placebo group in patients with steroid-dependent disease (p = 0.009). In the IN group, median FCP levels, at week 8, were significantly lower than baseline in patients with steroid-dependent disease and patients with the previous use of anti-TNF-α (p < 0.001, respectively). Multivariate analysis indicated that the previous use of anti-TNF-α was not a predictive factor for CR and MH at week 8.ConclusionsIn a sub-analysis of data from a randomized placebo-controlled trial, we found that IN may be useful even in patients with steroid-dependent disease and patients with the previous use of anti-TNF-α.

N-succinimidyl monomers such as N-methacryloxysuccinimide (MASI) and N-acryloxysuccinimide (ASI) were utilized as prepolymers to synthesize glycopolymers via postpolymerization modification. Living radical polymerization with RAFT agents succeeded using not only MASI but also ASI. While the polymerization with ATRP initiator did not succeed using ASI, the procedure was successful with MASI. MASI was also applicable for SI-ATRP reactions on substrate surfaces. The introduction rate of NH2-terminated saccharide onto the prepolymer via amine coupling reaction was affected by the reaction temperature. This preparation procedure via postpolymerization modification is expected to provide a facile method for various functional polymers, such as other saccharide and beneficial ligands.

Abstract The objective of this research is to develop a method to detect and virtually remove representations of existing buildings from a video stream in real-time for the purpose of visualizing a future scenario without these buildings. This is done by using semantic segmentation, which eliminates the need to create three-dimensional models of the buildings and the surrounding scenery, and a generative adversarial network (GAN), a deep learning method for generating images. Real-time communication between devices enables users to utilize only portable devices equipped with a camera to visualize the future landscape onsite. As verification of the proposed method’s usefulness, we evaluated the complementation accuracy of the GAN and real-time performance of the entire method. The results indicated that the process is completed accurately when the area to be complemented is less than 15% of the view and that the process runs at 5.71 fps. The proposed method enables users to understand intuitively the future landscape and contributes to reducing the time and cost for building consensus. Graphical Abstract Graphical Abstract