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Abstract Background Dysregulation of iron metabolism is implicated in malignant transformation, cancer progression, and therapeutic resistance. Here, we demonstrate that iron regulatory protein 2 (IRP2) preferentially regulates iron metabolism and promotes tumor growth in colorectal cancer (CRC). Methods IRP2 knockdown and knockout cells were generated using RNA interference and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 methodologies, respectively. Cell viability was evaluated using both CCK-8 assay and cell counting techniques. Furthermore, IRP2 inhibition was determined by surface plasmon resonance (SPR) and RNA immunoprecipitation (IP). The suppressive effects of IRP2 were also corroborated in both organoid and mouse xenograft models, providing a comprehensive validation of IRP2’s role. Results We have elucidated the role of IRP2 as a preferential regulator of iron metabolism, actively promoting tumorigenesis within CRC. Elevated levels of IRP2 expression in patient samples are correlated with diminished overall survival, thereby reinforcing its potential role as a prognostic biomarker. The functional suppression of IRP2 resulted in a pronounced delay in tumor growth. Building on this proof of concept, we have developed IRP2 inhibitors that significantly reduce IRP2 expression and hinder its interaction with iron-responsive elements in key iron-regulating proteins, such as ferritin heavy chain 1 (FTH1) and transferrin receptor (TFRC), culminating in iron depletion and a marked reduction in CRC cell proliferation. Furthermore, these inhibitors are shown to activate the AMPK-ULK1-Beclin1 signaling cascade, leading to cell death in CRC models. Conclusions Collectively, these findings highlight the therapeutic potential of targeting IRP2 to exploit the disruption of iron metabolism in CRC, presenting a strategic advancement in addressing a critical area of unmet clinical need.

Abstract The development of synthetic receptors capable of selectively binding guests with diverse structures and multiple functional groups poses a significant challenge. Here, we present the efficient assembly of foldamer-based receptors for monosaccharides, utilising the principles of complexation-induced equilibrium shifting and adaptive folding. Diimine 4 can be quantitatively assembled from smaller components when d-galactose is added as a guest among monosaccharides we examined. During this assembly, dual complexation-induced equilibrium shifts toward both the formation of diimine 4 and the conversion of d-galactose into α-d-galactofuranose are observed. Diimine 6 is quantitatively assembled in the presence of two different guests, methyl β-d-glucopyranoside and methyl β-d-galactopyranoside, resulting in the formation of two dimeric complexes: (6-MP)2⊃(methyl β-d-glucopyranoside)2 and (6-MM)2⊃(methyl β-d-galactopyranoside∙2H2O)2, respectively. These two complexes exhibit distinct folding structures with domain-swapping cavities depending on the bound guest and temperature. Interestingly, (6-MM)2⊃(methyl β-d-galactopyranoside∙2H2O)2 is exclusively formed at lower temperatures, while (6-MP)2⊃(methyl β-d-glucopyranoside)2 is only formed at higher temperatures.

In the world of real-time computing, there is a demand for platforms that deliver both speed and efficiency while conserving hardware resources. High reliability applications typically use dedicated platforms for their inherent reliability, but they are more expensive. On the other hand, traditional general-purpose platforms are cheaper but suffer from wasted hardware resources when processing messages, hindering distributed processing and process monitoring. To address these limitations, this paper presents the Real-time Message Processing Framework on Multi-service Domains (RMPF), an innovative integrated platform for real-time distributed message processing on multi-service domains. The platform supports a scalable and flexible multi-layer architecture by providing modularity that allows each layer to be designed and updated independently. The interaction between each layer enables real-time distributed message processing and distributed management of multi-service processing in a city-main based, high-speed distributed environment. By accelerating message processing, using a dedicated buffer pool created for each application process, and monitoring the status of all messages in the buffer, the high reliability inherent in dedicated platforms is ensured. In addition, it minimizes the waste of hardware resources by running application processes as daemons in buffer pools. In particular, to provide domain-based multiservice, it supports RMPF grouping by task to distribute tasks to appropriate domains to eliminate system bottlenecks. It is also implemented in a modular way to achieve both scalability and adaptability through various multiservice distributed management schemes such as multiservice API, recovery, monitoring, and multiservice provisioning.

Haze removal in remote sensing images is essential for practical applications in various fields such as weather forecasting, monitoring, mineral exploration and disaster management. The previous deep learning models make use of large convolutional kernel and attention mechanisms for efficient dehazing. However, it has drawbacks such as the loss of image details and low performance. In this paper, we shall present a new dual attention network, called DA-Net, for dehazing remote sensing images which achieves better dehazing performance while reducing model complexity sharply by exploiting a novel dual attention block where two modules, channel-spatial attention and parallel attention are serially connected. We propose a new architecture for parallel attention which achieves better dehazing performance by concatenating three different attention mechanisms in parallel: global channel attention, local channel attention and spatial attention. Moreover, we shall show that the concatenation of channel-spatial attention to parallel attention module enables detecting haze component information more accurately while reducing the model complexity proportional to the number of parameters by combining the channel and spatial information generated respectively from two different channel and spatial branches. Our experimental results show that DA-Net achieves much better performance for both synthetic and real image data sets compared to the other dehazing models in terms of quantitative and qualitative evaluations.

Recently, compression is essential to reduce large traffic generated from video or image-based services such as OTT, IOT, smart city, and self-driving cars. The high data compression reduces data traffic, but causes information loss and data quality deterioration. In this paper, we shall propose a new efficient compression method which increases the compression ratio while maintaining the image quality by partitioning the image into 5 different areas each with the different quantization values according to their priority such as intensity, location, size and correlation. We exploit different compression ratio for each of the areas divided based on saliency map according to their visual priority order in order to fully reflect the visual reaction degree of human visual system. We shall show that our method dramatically increases data preservation and compression efficiency by compressing each area with the different quantization values while using the less memory.

Remote sensing images (RSI) are useful for various tasks such as Earth observation and climate change. However, RSI may suffer from stripe noise due to physical limitations in sensor systems. Therefore, image destriping is essential, since stripe noise may cause serious problems in real applications. In this paper, we shall present a new Alternating Direction method of multipliers (ADMM)-based Optimization Model, called ADOM for stripe noise removal in RSI. First, we formulate an optimization function for finding stripe noise components from the observed image for stripe noise removal, and then optimization process for solving the optimization function in order to extract stripe noise component. In the optimization process, we shall propose a weight-based detection strategy for efficient stripe noise component capture, and an ADMM-based acceleration strategy for fast stripe noise removal. In the weight-based detection strategy, we effectively detect stripe noise similar to the image details by using weighted norm generated by adjusting norm and group norm weights based on the momentum coefficient and residual parameter. In the ADMM-based acceleration strategy, we accelerate optimization process by using two control strategies: evidence-based starting point control and momentum-based step-size control. The former provides a starting point for more accurately finding stripe noise component, and the latter accelerates convergence by using the momentum coefficient while providing optimization stability by exploiting the damping coefficient. Our experimental results show that ADOM achieves better performance for both of simulated and real image data sets compared to the other destriping models.

Abstract Background and aims There are limited comparative data for infliximab and vedolizumab in inflammatory bowel disease patients. Methods We conducted a systematic review and meta-analysis to compare the efficacy and safety of infliximab and vedolizumab in adult patients with moderate-to-severe Crohn’s disease or ulcerative colitis. Results We identified six eligible Crohn’s disease and seven eligible ulcerative colitis trials that randomised over 1900 participants per disease cohort to infliximab or vedolizumab. In the Crohn’s disease and ulcerative colitis cohorts, infliximab yielded better efficacy than vedolizumab for all analysed outcomes (CDAI-70, CDAI-100 responses, and clinical remission for Crohn’s disease and clinical response and clinical remission for ulcerative colitis) during the induction phase, with non-overlapping 95% confidence intervals. In the maintenance phase, similar proportions of infliximab- or vedolizumab-treated patients achieved clinical response, clinical remission, or mucosal healing in both Crohn’s disease and ulcerative colitis. For the safety outcomes, rates of adverse events, serious adverse events, and discontinuations due to adverse events were similar in infliximab- and vedolizumab-treated patients in both diseases. The infection rate was higher in infliximab for Crohn’s disease and higher in vedolizumab when treating patients with ulcerative colitis. There was no difference between the treatments in the proportions of patients who reported serious infections in both indications. Conclusions Indirect comparison of infliximab and vedolizumab trials in adult patients with moderate-to severe Crohn’s disease or ulcerative colitis demonstrated that infliximab has better efficacy in the induction phase and comparable efficacy during the maintenance phase and overall safety profile compared to vedolizumab.

Recently, as the development cycle of applications has been shortened, it is important to develop rapid and accurate application testing technology. Since application testing requires a lot of cost, mobile GUI component detection technology using deep learning is essential to prevent the use of expensive human resources. In this paper, we shall propose a Clickable Object Detection Network (CODNet) for mobile component detection in a wide range of mobile screen resolutions. CODNet consists of three modules: feature extraction, deconvolution and prediction modules in order to provide performance improvement and scalability. Feature extraction module uses squeeze and excitation blocks to efficiently extract features by changing the ratio of the input image to 1:2 most close to that of mobile screen. Deconvolution module provides feature map of various sizes by upsampling feature map through top-down pathway and lateral connections. Prediction module selects an anchor size most suitable for the mobile environment using Anchor Transfer block among the set of anchor candidates obtained through the analysis of mobile dataset. Moreover, we shall show that our model achieves competitive performance in mean average precision on our dataset compared to the other models, and object detection performance is improved by building a new mobile screen dataset which consists of data collected from various resolutions and operating systems.

In this study, we introduce a novel training algorithm specifically designed to overcome the limitations of GPU memory on a single DGX-A100 system. By utilizing the CPU and main memory in the training process and applying a strategy of division and parallelization, our algorithm enhances the size of the trainable language model and the batch size. In addition, we developed a comprehensive management system to effectively manage the execution of the algorithm. This system systematically controls the training process and resource usage, while also enabling the asynchronous deployment of tasks. Finally, we proposed a scheduling technique integrated into the management system, promoting efficient task scheduling in a complex, heterogeneous training environment. These advancements equip researchers with the ability to work with larger models and batch sizes, even when faced with limited GPU memory.

Recently, language models based on the Transformer architecture have been predominantly used in AI natural language processing. These models, which have been proven to perform better with more parameters, have led to a significant increase in model size and computational load. ALBERT solves this problem by significantly reducing the number of parameters it retains by repeatedly reusing parameters. Although ALBERT significantly reduces the parameters it maintains, it requires a computational load similar to the original language model due to the reuse process. In this study, we develop a distillation system that decreases the number of times the ALBERT model reuses parameters and progressively reduces the parameters being reused. We propose a representation in this distillation system that can effectively distill the knowledge of the original model and develop a new architecture with reduced computation. Through this system, F-ALBERT, which had about half the computational load compared to the ALBERT model, restored about 98% of the performance of the original model on the GLUE benchmark.

With the increase in intelligent voice phishing and the increasing reliance on open banking systems, there has been a rise in cases where individuals’ personal information has been exposed, resulting in significant financial losses for the victims. Non-face-to-face transactions in the financial sector face challenges such as customer identification, ensuring transaction integrity and preventing transaction rejection. Blockchain-based distributed ledgers have been proposed as a solution but their adoption is limited due to the difficulty of managing private keys and the burden of gas fees management. This paper proposes a non-face-to-face P2P real-time token payment system that minimizes the risk of key loss by storing private keys in a keystore file and database through a server-based key management module. The proposed system simplifies token creation and management through a server-based token management module and implements an automatic gas-charging function for smooth token transactions. Transaction integrity and non-repudiation are ensured through a transaction confirmation module that uses transaction IDs without exposing personal information. Furthermore, advanced security measures such as blocking foreign IP access and DDoS defense are implemented to securely protect user data. The proposed system aims to provide a convenient, secure and accessible online payment solution to the public by implementing a self-authentication function using a web application that is not limited to smartphones or application platforms.

Abstract The use of kidneys from donation after brain death (DBD) donors with acute kidney injury (AKI) is a strategy to expand the donor pool. The aim of this study was to evaluate how kidney transplantation (KT) from a donor with AKI affects long-term graft survival in various situations. All patients who underwent KT from DBD donors between June 2003 and April 2016 were retrospectively reviewed. The KDIGO (Kidney Disease: Improving Global Outcomes) criteria were used to classify donor AKI. The cohort included 376 donors (no AKI group, n = 117 [31.1%]; AKI group n = 259 [68.9%]). Death-censored graft survival was similar according to the presence of AKI, AKI severity, and the AKI trend (p = 0.929, p = 0.077, and p = 0.658, respectively). Patients whose donors had AKI who received using low dose (1.5 mg/kg for three days) rabbit anti-thymocyte globulin (r-ATG) as the induction agent had significantly superior death-censored graft survival compared with patients in that group who received basiliximab (p = 0.039). AKI in DBD donors did not affect long-term death-censored graft survival. Low-dose r-ATG may be considered as an induction immunosuppression in recipients receiving kidneys with AKI because it showed better graft survival than basiliximab.

Due to the sharp increase in household waste, its separate collection is essential in order to reduce the huge amount of household waste, since it is difficult to recycle trash without separate collection. However, since it is costly and time-consuming to separate trash manually, it is crucial to develop an automatic system for separate collection using deep learning and computer vision. In this paper, we propose two Anchor-free-based Recyclable Trash Detection Networks (ARTD-Net) which can recognize overlapped multiple wastes of different types efficiently by using edgeless modules: ARTD-Net1 and ARTD-Net2. The former is an anchor-free based one-stage deep learning model which consists of three modules: centralized feature extraction, multiscale feature extraction and prediction. The centralized feature extraction module in backbone architecture focuses on extracting features around the center of the input image to improve detection accuracy. The multiscale feature extraction module provides feature maps of different scales through bottom-up and top-down pathways. The prediction module improves classification accuracy of multiple objects based on edge weights adjustments for each instance. The latter is an anchor-free based multi-stage deep learning model which can efficiently finds each of waste regions by additionally exploiting region proposal network and RoIAlign. It sequentially performs classification and regression to improve accuracy. Therefore, ARTD-Net2 is more accurate than ARTD-Net1, while ARTD-Net1 is faster than ARTD-Net2. We shall show that our proposed ARTD-Net1 and ARTD-Net2 methods achieve competitive performance in mean average precision and F1 score compared to other deep learning models. The existing datasets have several problems that do not deal with the important class of wastes produced commonly in the real world, and they also do not consider the complex arrangement of multiple wastes with different types. Moreover, most of the existing datasets have an insufficient number of images with low resolution. We shall present a new recyclables dataset which is composed of a large number of high-resolution waste images with additional essential classes. We shall show that waste detection performance is improved by providing various images with the complex arrangement of overlapped multiple wastes with different types.

Background : Graft survival rate of kidney transplantation recipients improves after induction therapy. However, there is no conclusive evidence on which regimen is superior for deceased donor kidney transplantation (DDKT). This study aims at discussing effective induction therapy in DDKT. Methods : Between 2003 and 2016, 395 DDKT recipients were divided into three groups following induction therapy. Recipients of the basiliximab group (n=184) received basiliximab (20 mg/kg) on days 0 and 4. Recipients of the low-dose rabbit anti-thymocyte globulin (rATG) group (n=113) received rATG (1.5 mg/kg) on days 0, 1, and 2, while those of the high-dose rATG group (n=98) received it for more than 4 days. We retrospectively reviewed and analyzed the clinical outcomes and adverse effects of induction therapy.Results : Compared to other groups, the low-dose rATG group donors were older (P

This study researched the current occurrence status of viral diseases of solanaceous crops in the main production areas of Gangwon region, for four years from 2014 to 2018. From 2014 to 2018, in eight cities and counties including Chuncheon, Cucumber mosaic virus (CMV), Broad bean wilt virus 2 (BBWV2), Pepper mild mottle virus (PMMoV), Tomato spotted wilt virus (TSWV), and Potato virus Y occurred in chilli peppers. In case of tomato, the research was performed in 2017 and 2018 while TSWV and Tomato yellow leaf curl virus (TYLCV) were verified in Chuncheon, Hoengseong, and Cheorwon. Especially, TYLCV occurred in Chuncheon region for the first time in 2017, which was verified in 2018. Lastly, the current occurrence status of viral diseases of bell peppers was researched in seven cities and counties like Gangneung, Hoengseong, and Cheorwon from 2015 to 2018. In the results, CMV, BBWV2, PMMoV, Pepper mottle virus, and TSWV were verified in bell peppers of Gangwon region. During the research period, there were no crops suspected or infected by Beet western yellows virus, so that it is judged that there is no villiferous or infected aphid by this virus in Gangwon-do. Such results of this study could be used as the basic data for the stable production of crops through the early prevention of plant diseases occurring in the solanaceous crops of Gangwon region.

A method of positioning a location on a specific object using a wireless sensor has been developed for a long time. However, due to the error of wavelengths and various interference factors occurring in three-dimensional space, accurate positioning is difficult, and predicting future locations is even more difficult. It uses IoT-based node pattern recognition technology to overcome positioning errors or inaccurate predictions in wireless sensor networks. It developed a method to improve the current positioning accuracy in a sensor network environment and a method to learn a pattern of position data directly from a wavelength receiver. The developed method consists of two steps: The first step is a method of changing location data in 3D space to location data in 2D space in order to reduce the possibility of positioning errors in 3D space. The second step is to reduce the range of the moving direction angle in which the data changed in two dimensions can be changed in the future and to predict future positions through pattern recognition of the position data. It is to calculate the expected position in the future. In conclusion, three-dimensional positioning accuracy was improved through this method, and future positioning accuracy was also improved. The core technology was able to reduce inevitable errors by changing the spatial dimension from 3D to 2D and to improve the accuracy of future location prediction by reducing the range of the movable direction angle of the location data changed to 2D. It was also possible to obtain the result that the prediction accuracy increases in proportion to the amount of data accumulated in the wavelength receiver and the learning time. In the era of the Fourth Industrial Revolution, this method is expected to be utilized in various places, such as smart cities, autonomous vehicles, and disaster prediction.

ABSTRACTThe African swine fever virus (ASFV) was first detected in wild boar in the Demilitarized Zone, a bordered area between South and North Korea, on 2 October 2019. Phylogenetic analyses of ASFV genes encoding p72 and CD2v indicated that the causative strain belongs to genotype II and serogroup 8, respectively, and contained additional tandem repeat sequences between the I73R and the I329L protein genes.

Lymphedema is a common complication associated with cancer itself or with cancer treatment. Lymphedema infrequently occurs after drug therapy. Bee venom is one of the materials used in acupuncture, and it has been used in the treatment of a variety of inflammatory diseases including arthritis. We report a 74-year-old male patient with late-onset post-radiation lymphedema provoked by bee venom therapy. He was free of lymphedema for 5 years after the complete remission of prostate cancer which had been treated with transurethral resection and radiation therapy. The patient developed left leg swelling after undergoing bee venom therapy for left hip pain. Computed tomography and lymphoscintigraphy showed lymphedema without tumor recurrence or infection. The lymphatic system was suspected to be injured by bee venom therapy and lymphedema was provoked. Bee venom therapy should be used cautiously in patients prone to lymphedema.

Abstract As the demand for real-time data processing increases, a high-speed processing platform for large-scale stream data becomes necessary. For fast processing large-scale stream data, it is essential to use multiple distributed nodes. So far, there have been few studies on real-time massive image processing through efficient management and allocation of heterogeneous resources for various user-specified nodes on distributed environments. In this paper, we shall present a new platform called RIDE (Real-time massive Image processing platform on Distributed Environment) which efficiently allocates resources and executes load balancing according to the amount of stream data on distributed environments. It minimizes communication overhead by using a parallel processing strategy which handles the stream data considering both coarse-grained and fine-grained parallelism simultaneously. Coarse-grained parallelism is achieved by the automatic allocation of input streams onto partitions of broker buffer each processed by its corresponding worker node, and maximized by adaptive resource management which adjusts the number of worker nodes in a group according to the frame rate in real time. Fine-grained parallelism is achieved by parallel processing of task on each worker node and maximized by allocating heterogeneous resources such as GPU and embedded machines appropriately. Moreover, it provides a scheme of application topology which has a great advantage for higher performance by configuring the worker nodes of each stage using adaptive heterogeneous resource management. Finally, it supports dynamic fault tolerance for real-time image processing through the coordination between components in our system.

With the recent success of deep learning, the amount of data and computation continues to grow daily. Hence a distributed deep learning system that shares the training workload has been researched extensively. Although a scale-out distributed environment using commodity servers is widely used, not only is there a limit due to synchronous operation and communication traffic but also combining deep neural network (DNN) training with existing clusters often demands additional hardware and migration between different cluster frameworks or libraries, which is highly inefficient. Therefore, we propose TensorLightning which integrates the widely used data pipeline of Apache Spark with powerful deep learning libraries, Caffe and TensorFlow. TensorLightning embraces a brand-new parameter aggregation algorithm and parallel asynchronous parameter managing schemes to relieve communication discrepancies and overhead. We redesign the elastic averaging stochastic gradient descent algorithm with pruned and sparse form parameters. Our approach provides the fast and flexible DNN training with high accessibility. We evaluated our proposed framework with convolutional neural network and recurrent neural network models; the framework reduces network traffic by 67% with faster convergence.

The treatment of neuropathic pain is one of the urgent unmet medical needs and T-type calcium channels are promising therapeutic targets for neuropathic pain. Several potent T-type channel inhibitors showed promising in vivo efficacy in neuropathic pain animal models and are being investigated in clinical trials. Herein we report development of novel pyrrolidine-based T-type calcium channel inhibitors by pharmacophore mapping and structural hybridisation followed by evaluation of their Cav3.1 and Cav3.2 channel inhibitory activities. Among potent inhibitors against both Cav3.1 and Cav3.2 channels, a promising compound 20n based on in vitro ADME properties displayed satisfactory plasma and brain exposure in rats according to in vivo pharmacokinetic studies. We further demonstrated that 20n effectively improved the symptoms of neuropathic pain in both SNL and STZ neuropathic pain animal models, suggesting modulation of T-type calcium channels can be a promising therapeutic strategy for the treatment of neuropathic pain.