Ir(tri-N-heterocyclic carbene)-catalyzed upgrading of glycerol: C-C bond formation for the synthesis of α-hydroxy acids.

Ir(triNHC) complexes catalyzed glycerol and alcohol dehydrogenative coupling, yielding diverse α-hydroxy acids. Unlike conventional conditions, Ir(triNHC) facilitated additional C-C bond formation after lactic acid production from glycerol, exhibiting high TOFs. This protocol successfully converted 1,2-propanediol and sorbitol into α-hydroxy acids, highlighting biomass-derived sources' potential as valuable platform chemicals.

HDPose: Post-Hierarchical Diffusion with Conditioning for 3D Human Pose Estimation.

Recently, monocular 3D human pose estimation (HPE) methods were used to accurately predict 3D pose by solving the ill-pose problem caused by 3D-2D projection. However, monocular 3D HPE still remains challenging owing to the inherent depth ambiguity and occlusions. To address this issue, previous studies have proposed diffusion model-based approaches (DDPM) that learn to reconstruct a correct 3D pose from a noisy initial 3D pose. In addition, these approaches use 2D keypoints or context encoders that encode spatial and temporal information to inform the model. However, they often fall short of achieving peak performance, or require an extended period to converge to the target pose. In this paper, we proposed HDPose, which can converge rapidly and predict 3D poses accurately. Our approach aggregated spatial and temporal information from the condition into a denoising model in a hierarchical structure. We observed that the post-hierarchical structure achieved the best performance among various condition structures. Further, we evaluated our model on the widely used Human3.6M and MPI-INF-3DHP datasets. The proposed model demonstrated competitive performance with state-of-the-art models, achieving high accuracy with faster convergence while being considerably more lightweight.

IS-CAT: Intensity-Spatial Cross-Attention Transformer for LiDAR-Based Place Recognition.

LiDAR place recognition is a crucial component of autonomous navigation, essential for loop closure in simultaneous localization and mapping (SLAM) systems. Notably, while camera-based methods struggle in fluctuating environments, such as weather or light, LiDAR demonstrates robustness against such challenges. This study introduces the intensity and spatial cross-attention transformer, which is a novel approach that utilizes LiDAR to generate global descriptors by fusing spatial and intensity data for enhanced place recognition. The proposed model leveraged a cross attention to a concatenation mechanism to process and integrate multi-layered LiDAR projections. Consequently, the previously unexplored synergy between spatial and intensity data was addressed. We demonstrated the performance of IS-CAT through extensive validation on the NCLT dataset. Additionally, we performed indoor evaluations on our Sejong indoor-5F dataset and demonstrated successful application to a 3D LiDAR SLAM system. Our findings highlight descriptors that demonstrate superior performance in various environments. This performance enhancement is evident in both indoor and outdoor settings, underscoring the practical effectiveness and advancements of our approach.

Advantages of Using 3D Spheroid Culture Systems in Toxicological and Pharmacological Assessment for Osteogenesis Research.

Bone differentiation is crucial for skeletal development and maintenance. Its dysfunction can cause various pathological conditions such as rickets, osteoporosis, osteogenesis imperfecta, or Paget's disease. Although traditional two-dimensional cell culture systems have contributed significantly to our understanding of bone biology, they fail to replicate the intricate biotic environment of bone tissue. Three-dimensional (3D) spheroid cell cultures have gained widespread popularity for addressing bone defects. This review highlights the advantages of employing 3D culture systems to investigate bone differentiation. It highlights their capacity to mimic the complex in vivo environment and crucial cellular interactions pivotal to bone homeostasis. The exploration of 3D culture models in bone research offers enhanced physiological relevance, improved predictive capabilities, and reduced reliance on animal models, which have contributed to the advancement of safer and more effective strategies for drug development. Studies have highlighted the transformative potential of 3D culture systems for expanding our understanding of bone biology and developing targeted therapeutic interventions for bone-related disorders. This review explores how 3D culture systems have demonstrated promise in unraveling the intricate mechanisms governing bone homeostasis and responses to pharmacological agents.

Transparent Rule Enablement Based on Commonization Approach in Heterogeneous IoT Edge Networks.

The paradigm of the Internet of Things (IoT) and edge computing brings a number of heterogeneous devices to the network edge for monitoring and controlling the environment. For reacting to events dynamically and automatically in the environment, rule-enabled IoT edge platforms operate the deployed service scenarios at the network edge, based on filtering events to perform control actions. However, due to the heterogeneity of the IoT edge networks, deploying a consistent rule context for operating a consistent rule scenario on multiple heterogeneous IoT edge platforms is difficult because of the difference in protocols and data formats. In this paper, we propose a transparent rule enablement, based on the commonization approach, for enabling a consistent rule scenario in heterogeneous IoT edge networks. The proposed IoT Edge Rule Agent Platform (IERAP) deploys device proxies to share consistent rules with IoT edge platforms without considering the difference in protocols and data formats. Therefore, each device proxy only considers the translation of the corresponding platform-specific and common formats. Also, the rules are deployed by the corresponding device proxy, which enables rules to be deployed to heterogeneous IoT edge platforms to perform the consistent rule scenario without considering the format and underlying protocols of the destination platform.

TRRAP Enhances Cancer Stem Cell Characteristics by Regulating NANOG Protein Stability in Colon Cancer Cells.

NANOG, a stemness-associated transcription factor, is highly expressed in many cancers and plays a critical role in regulating tumorigenicity. Transformation/transcription domain-associated protein (TRRAP) has been reported to stimulate the tumorigenic potential of cancer cells and induce the gene transcription of NANOG. This study aimed to investigate the role of the TRRAP-NANOG signaling pathway in the tumorigenicity of cancer stem cells. We found that TRRAP overexpression specifically increases NANOG protein stability by interfering with NANOG ubiquitination mediated by FBXW8, an E3 ubiquitin ligase. Mapping of NANOG-binding sites using deletion mutants of TRRAP revealed that a domain of TRRAP (amino acids 1898-2400) is responsible for binding to NANOG and that the overexpression of this TRRAP domain abrogated the FBXW8-mediated ubiquitination of NANOG. TRRAP knockdown decreased the expression of CD44, a cancer stem cell marker, and increased the expression of P53, a tumor suppressor gene, in HCT-15 colon cancer cells. TRRAP depletion attenuated spheroid-forming ability and cisplatin resistance in HCT-15 cells, which could be rescued by NANOG overexpression. Furthermore, TRRAP knockdown significantly reduced tumor growth in a murine xenograft transplantation model, which could be reversed by NANOG overexpression. Together, these results suggest that TRRAP plays a pivotal role in the regulation of the tumorigenic potential of colon cancer cells by modulating NANOG protein stability.

Environment-Adaptive Object Detection Framework for Autonomous Mobile Robots.

Object detection is an essential function for mobile robots, allowing them to carry out missions efficiently. In recent years, various deep learning models based on convolutional neural networks have achieved good performance in object detection. However, in cases in which robots have to carry out missions in a particular environment, utilizing a model that has been trained without considering the environment in which robots must conduct their tasks degrades their object detection performance, leading to failed missions. This poor model accuracy occurs because of the class imbalance problem, in which the occurrence frequencies of the object classes in the training dataset are significantly different. In this study, we propose a systematic solution that can solve the class imbalance problem by training multiple object detection models and using these models effectively for robots that move through various environments to carry out missions. Moreover, we show through experiments that the proposed multi-model-based object detection framework with environment-context awareness can effectively overcome the class imbalance problem. As a result of the experiment, CPU usage decreased by 45.49% and latency decreased by more than 60%, while object detection accuracy increased by 6.6% on average.

Nontrivial, Unconventional Electrochromic Behaviors of Plasmonic Nanocubes.

Plasmonic electrochromism, a change in the localized surface plasmon resonance (LSPR) with an applied electric potential, has been attracting increasing attention for the development of spectroscopic tools or optoelectronic systems. There is a consensus on the mechanism of plasmonic electrochromism based on the classical capacitor and the Drude model. However, the electrochromic behaviors of metallic nanoparticles in narrow optical windows have been demonstrated only with small monotonic LSPR shifts, which limits the use of the electrochromism. Here, we observed three distinct electrochromic behaviors of gold nanocubes with a wide potential range through in situ dark-field electrospectroscopy. Interestingly, the nanocubes show a faster frequency shift under the highly negative potential, and this opens the possibility of largely tunable electrochromic LSPR shifts. The reversibility of the electrochemical switching with these cubes are also shown. We attribute this unexpected change beyond classical understandings to the material-specific quantum mechanical electronic structures of the plasmonic materials.

Inhibitory Effect of Etravirine, a Non-Nucleoside Reverse Transcriptase Inhibitor, via Anterior Gradient Protein 2 Homolog Degradation against Ovarian Cancer Metastasis.

Anterior gradient protein 2 homolog (AGR2), an endoplasmic reticulum protein, is secreted in the tumor microenvironment. AGR2 is a member of the disulfide isomerase family, is highly expressed in multiple cancers, and promotes cancer metastasis. In this study, we found that etravirine, which is a non-nucleoside reverse transcriptase inhibitor, could induce AGR2 degradation via autophagy. Moreover, etravirine diminished proliferation, migration, and invasion in vitro. Moreover, in an orthotopic xenograft mouse model, the combination of etravirine and paclitaxel significantly suppressed cancer progression and metastasis. This drug may be a promising therapeutic agent for the treatment of ovarian cancer.

Logistic Regression Model for a Bivariate Binomial Distribution with Applications in Baseball Data Analysis.

There has been a considerable amount of literature on binomial regression models that utilize well-known link functions, such as logistic, probit, and complementary log-log functions. The conventional binomial model is focused only on a single parameter representing one probability of success. However, we often encounter data for which two different success probabilities are of interest simultaneously. For instance, there are several offensive measures in baseball to predict the future performance of batters. Under these circumstances, it would be meaningful to consider more than one success probability. In this article, we employ a bivariate binomial distribution that possesses two success probabilities to conduct a regression analysis with random effects being incorporated under a Bayesian framework. Major League Baseball data are analyzed to demonstrate our methodologies. Extensive simulation studies are conducted to investigate model performances.

Enhancement of anti-inflammatory and immunomodulatory effects of adipose-derived human mesenchymal stem cells by making uniform spheroid on the new nano-patterned plates.

Human mesenchymal stem cells (MSCs) are known to have anti-inflammatory and immunomodulatory functions; thus, several MSC products have been applied as cell therapy in clinical trials worldwide. Recent studies have demonstrated that MSC spheroids have superior anti-inflammatory and immunomodulatory functions to a single cell suspension. Current methods to prepare MSC spheroids include hanging drop, concave microwell aggregation, spinner flask, and gravity circulation. However, all these methods have limitations such as low scalability, easy cell clumping, low viability, and irregular size distribution. Here, we present a nano-patterned culture plasticware named PAMcell™ 3D plate to overcome these limitations. Nano-sized silica particles (700 nm) coated with RGD peptide were arrayed into fusiform onto the PLGA film. This uniform array enabled the seeded MSCs to grow only on the silica particles, forming uniform-sized semi-spheroids within 48 h. These MSC spheroids have been shown to have enhanced stemness, anti-inflammatory, and immunomodulatory functions, as revealed by the increased expression of stem cell markers (Oct4, Sox2, and Nanog), anti-inflammatory (IL-10, TSG6, and IDO), and immunomodulatory molecules (HGF, VEGF, CXCR4) both at mRNA and protein expression levels. Furthermore, these MSC spheroids demonstrated an increased palliative effect on glycemic control in a multiple low-dose streptozotocin-induced diabetes model compared with the same number of MSC single cell suspensions. Taken together, this study presents a new method to produce uniform-sized MSC spheroids with enhanced anti-inflammatory and immunomodulatory functions in vitro and in vivo.

Lateral Size Effect of Graphene Oxide on Its Surface-Enhanced Raman Scattering Property.

The lateral size effect of graphene oxide (GO) on surfaced-enhanced Raman scattering (SERS) property is systematically investigated by using size-fractionalized GO. For the size fractionalization without changes of chemical structure, large-sized GO (LGO) and small-sized GO (SGO) are separated from the as-synthesized GO (AGO) by centrifugation and membrane filtration, respectively. The size-fractionalized GO sheets are immobilized on a solid substrate for the parallel comparison of their SERS property. As a result, we find that LGO shows considerably higher SERS property than SGO for typical Raman probes such as rhodamine 6G and crystal violet. Furthermore, the lateral size effect of GO derivatives is consistently observed when they are hybridized with plasmonic silver nanoparticles. These results indicate that LGO is superior to AGO and SGO as a SERS platform, and it is also quantitatively confirmed by calculating their enhancement factor.

Multicomponent Plasmonic Nanoparticles: From Heterostructured Nanoparticles to Colloidal Composite Nanostructures.

Plasmonic nanostructures possessing unique and versatile optoelectronic properties have been vastly investigated over the past decade. However, the full potential of plasmonic nanostructure has not yet been fully exploited, particularly with single-component homogeneous structures with monotonic properties, and the addition of new components for making multicomponent nanoparticles may lead to new-yet-unexpected or improved properties. Here we define the term "multi-component nanoparticles" as hybrid structures composed of two or more condensed nanoscale domains with distinctive material compositions, shapes, or sizes. We reviewed and discussed the designing principles and synthetic strategies to efficiently combine multiple components to form hybrid nanoparticles with a new or improved plasmonic functionality. In particular, it has been quite challenging to precisely synthesize widely diverse multicomponent plasmonic structures, limiting realization of the full potential of plasmonic heterostructures. To address this challenge, several synthetic approaches have been reported to form a variety of different multicomponent plasmonic nanoparticles, mainly based on heterogeneous nucleation, atomic replacements, adsorption on supports, and biomolecule-mediated assemblies. In addition, the unique and synergistic features of multicomponent plasmonic nanoparticles, such as combination of pristine material properties, finely tuned plasmon resonance and coupling, enhanced light-matter interactions, geometry-induced polarization, and plasmon-induced energy and charge transfer across the heterointerface, were reported. In this review, we comprehensively summarize the latest advances on state-of-art synthetic strategies, unique properties, and promising applications of multicomponent plasmonic nanoparticles. These plasmonic nanoparticles including heterostructured nanoparticles and composite nanostructures are prepared by direct synthesis and physical force- or biomolecule-mediated assembly, which hold tremendous potential for plasmon-mediated energy transfer, magnetic plasmonics, metamolecules, and nanobiotechnology.

Echinochrome A Treatment Alleviates Fibrosis and Inflammation in Bleomycin-Induced Scleroderma.

Scleroderma is an autoimmune disease caused by the abnormal regulation of extracellular matrix synthesis and is activated by non-regulated inflammatory cells and cytokines. Echinochrome A (EchA), a natural pigment isolated from sea urchins, has been demonstrated to have antioxidant activities and beneficial effects in various disease models. The present study demonstrates for the first time that EchA treatment alleviates bleomycin-induced scleroderma by normalizing dermal thickness and suppressing collagen deposition in vivo. EchA treatment reduces the number of activated myofibroblasts expressing α-SMA, vimentin, and phosphorylated Smad3 in bleomycin-induced scleroderma. In addition, it decreased the number of macrophages, including M1 and M2 types in the affected skin, suggesting the induction of an anti-inflammatory effect. Furthermore, EchA treatment markedly attenuated serum levels of inflammatory cytokines, such as tumor necrosis factor-α and interferon-γ, in a murine scleroderma model. Taken together, these results suggest that EchA is highly useful for the treatment of scleroderma, exerting anti-fibrosis and anti-inflammatory effects.

Strictly Lobar Microbleeds Reflect Amyloid Angiopathy Regardless of Cerebral and Cerebellar Compartments.

BACKGROUND AND PURPOSE: We aimed to determine whether lobar cerebellar microbleeds or concomitant lobar cerebellar and deep microbleeds, in the presence of lobar cerebral microbleeds, attribute to underlying advanced cerebral amyloid angiopathy pathology or hypertensive arteriopathy. METHODS: We categorized 71 patients with suspected cerebral amyloid angiopathy markers (regardless of the presence of deep and cerebellar microbleeds) into 4 groups according to microbleed distribution: L (strictly lobar cerebral, n=33), L/LCbll (strictly lobar cerebral and strictly lobar cerebellar microbleeds, n=13), L/Cbll/D (lobar, cerebellar, and deep microbleeds, n=17), and L/D (lobar and deep, n=8). We additionally categorized patients with cerebellar microbleeds into 2 groups according to dentate nucleus involvement: strictly lobar cerebellar (n=16) and dentate (n=14). We then compared clinical characteristics, Aß (amyloid-ß) positivity on PET (positron emission tomography), magnetic resonance imaging cerebral amyloid angiopathy markers, and cerebral small vessel disease burden among groups. RESULTS: The frequency of Aß positivity was higher in the L and L/LCbll groups (81.8% and 84.6%) than in the L/Cbll/D and L/D groups (37.5% and 29.4%; P<0.001), while lacune numbers were lower in the L and L/LCbll groups (1.7±3.3 and 1.7±2.6) than in the L/Cbll/D and L/D groups (8.0±10.3 and 13.4±17.7, P=0.001). The L/LCbll group had more lobar cerebral microbleeds than the L group (93.2±121.8 versus 38.0±40.8, P=0.047). The lobar cerebellar group had a higher Aß positivity (75% versus 28.6%, P=0.011) and lower lacune number (2.3±3.7 versus 8.6±1.2, P=0.041) than the dentate group. CONCLUSIONS: Strictly lobar cerebral and cerebellar microbleeds are related to cerebral amyloid angiopathy, whereas any combination of concurrent lobar and deep microbleeds suggest hypertensive angiopathy regardless of cerebral or cerebellar compartments.

Cerebrospinal Fluid Biomarkers for the Diagnosis and Classification of Alzheimer's Disease Spectrum.

BACKGROUND: Cerebrospinal fluid (CSF) biomarkers are increasingly used in clinical practice for the diagnosis of Alzheimer's disease (AD). We aimed to 1) determine cutoff values of CSF biomarkers for AD, 2) investigate their clinical utility by estimating a concordance with amyloid positron emission tomography (PET), and 3) apply ATN (amyloid/tau/neurodegeneration) classification based on CSF results. METHODS: We performed CSF analysis in 51 normal controls (NC), 23 mild cognitive impairment (MCI) and 65 AD dementia (ADD) patients at the Samsung Medical Center in Korea. We attempted to develop cutoff of CSF biomarkers for differentiating ADD from NC using receiver operating characteristic analysis. We also investigated a concordance between CSF and amyloid PET results and applied ATN classification scheme based on CSF biomarker abnormalities to characterize our participants. RESULTS: CSF Aß42, total tau (t-tau) and phosphorylated tau (p-tau) significantly differed across the three groups. The area under curve for the differentiation between NC and ADD was highest in t-tau/Aß42 (0.994) followed by p-tau/Aß42 (0.963), Aß42 (0.960), t-tau (0.918), and p-tau (0.684). The concordance rate between CSF Aß42 and amyloid PET results was 92%. Finally, ATN classification based on CSF biomarker abnormalities led to a majority of NC categorized into A-T-N-(73%), MCI as A+T-N-(30%)/A+T+N+(26%), and ADD as A+T+N+(57%). CONCLUSION: CSF biomarkers had high sensitivity and specificity in differentiating ADD from NC and were as accurate as amyloid PET. The ATN subtypes based on CSF biomarkers may further serve to predict the prognosis.

City Data Hub: Implementation of Standard-Based Smart City Data Platform for Interoperability.

Like what happened to the Internet of Things (IoT), smart cities have become abundant in our lives as well. One of the smart city definitions commonly used is that smart cities solve city problems to enhance citizens' life quality and make cities sustainable. From the perspective of information and communication technologies (ICT), we think this can be done by collecting and analyzing data to generate insights. The City Data Hub, which is a standard-based city data platform that has been developed, and a couple of problem-solving examples have been demonstrated. The key elements for smart city platforms have been chosen and they have been included in the core architecture principles and implemented as a platform. It has been proven that standard application programming interfaces (APIs) and common data models with data marketplaces, which are the keys, increase interoperability and guarantee ecosystem extensibility.

Low-Temperature Graphene Growth by Forced Convection of Plasma-Excited Radicals.

We developed the forced convection (FC)-PECVD method for the synthesis of graphene, in which a specially designed blowing plasma source is used at moderate gas pressure (1-10 Torr) and the distribution of reactive radicals reaching the substrate surface can be controlled by forced convection. Self-limiting growth of graphene occurs on copper foil, and monolayer graphene growth with a few defects is achieved even at low temperatures (<400 °C). We also demonstrated the enlargement of the growth area using the scalable blowing plasma source. We expect that the FC-PECVD method overcomes the limitations of conventional low-temperature PECVD and provides a breakthrough for the achievement of industrial applications based on graphene.

Implementation of Sensing and Actuation Capabilities for IoT Devices Using oneM2M Platforms.

In this paper, we present an implementation work of sensing and actuation capabilities for IoT devices using the oneM2M standard-based platforms. We mainly focus on the heterogeneity of the hardware interfaces employed in IoT devices. For IoT devices (i.e., Internet-connected embedded systems) to perform sensing and actuation capabilities in a standardized manner, a well-designed middleware solution will be a crucial part of IoT platform. Accordingly, we propose an oneM2M standard-based IoT platform (called nCube) incorporated with a set of tiny middleware programs (called TAS) responsible for translating sensing values and actuation commands into oneM2M-defined resources accessible in Web-based applications. All the source codes for the oneM2M middleware platform and smartphone application are available for free in the GitHub repositories. The full details on the implementation work and open-source contributions are described.

An Optically Flat Conductive Outcoupler Using Core/Shell Ag/ZnO Nanochurros.

Transparent conductive electrodes (TCEs) featuring a smooth surface are indispensable for preserving pristine electrical characteristics in optoelectronic and transparent electronic devices. For high-efficiency organic light emitting diodes (OLEDs), a high outcoupling efficiency, which is crucial, is only achieved by incorporating a wavelength-scale undulating surface into a TCE layer, but this inevitably degrades device performance. Here, an optically flat, high-conductivity TCE composed of core/shell Ag/ZnO nanochurros (NCs) is reported embedded within a resin film on a polyethylene terephthalate substrate, simultaneously serving as an efficient outcoupler and a flexible substrate. The ZnO NCs are epitaxially grown on the {100} planes of a pentagonal Ag core and the length of ZnO shells is precisely controlled by the exposure time of Xe lamp. Unlike Ag nanowires films, the Ag/ZnO NCs films markedly boost the optical tunneling of light. Green-emitting OLEDs (2.78 × 3.5 mm2 ) fabricated with the Ag/ZnO TCE exhibit an 86% higher power efficiency at 1000 cd m-2 than ones with an Sn-doped indium oxide TCE. A full-vectorial electromagnetic simulation suggests the suppression of plasmonic absorption losses within their Ag cores. These results provide a feasibility of multifunctional TCEs with synthetically controlled core/shell nanomaterials toward the development of high-efficiency LED and solar cell devices.