DECENTRALIZED SOCIAL NETWORK SERVICE USING THE WEB HOSTING SERVER FOR PRIVACY PRESERVATION

In recent years, the number of subscribers of the social network services such as Facebook and Twitter has increased rapidly. In accordance with the increasing popularity of social network services, concerns about user privacy are also growing. Existing social network services have a centralized structure that a service provider collects all the user’s profile and logs until the end of the connection. The information collected typically useful for commercial purposes, but may lead to a serious user privacy violation. The user’s profile can be compromised for malicious purposes, and even may be a tool of surveillance extremely. In this paper, we remove a centralized structure to prevent the service provider from collecting all users’ information indiscriminately, and present a decentralized structure using the web hosting server. The service provider provides only the service applications to web hosting companies, and the user should select a web hosting company that he trusts. Thus, the user’s information is distributed, and the user’s privacy is guaranteed from the service provider

SwiFT: Swin 4D fMRI Transformer

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI.Comment: NeurIPS 202

MorphEst: An Automated Toolbox for Measuring Estuarine Planform Geometry from Remotely Sensed Imagery and Its Application to the South Korean Coast

The rapid advance of remote sensing technology during the last few decades provides a new opportunity for measuring detectable estuarine spatial change. Although estuarine surface area and convergence are important hydraulic parameters often used to predict long-term estuarine evolution, the majority of automated analyses of channel plan view dynamics have been specifically written for riverine systems and have limited applicability to most of the estuaries in the world. This study presents MorphEst, a MATLAB-based collection of analysis tools that automatically measure estuarine planform geometry. MorphEst uses channel masks to extract estuarine length, convergence length, estuarine shape, and areal gain and loss of estuarine surface area due to natural or human factors. Comparisons indicated that MorphEst estimates closely matched with independent measurements of estuarine surface area (r = 0.99) and channel width (r = 0.92) of 39 estuaries along the South Korean coast. Overall, this toolbox will help to improve the ability to solve research questions commonly associated with estuarine evolution as it introduces a tool to automatically measure planform geometric features from remotely sensed imagery

Ensemble learning-based radiomics with multi-sequence magnetic resonance imaging for benign and malignant soft tissue tumor differentiation

Many previous studies focused on differentiating between benign and malignant soft tissue tumors using radiomics model based on various magnetic resonance imaging (MRI) sequences, but it is still unclear how to set up the input radiomic features from multiple MRI sequences. Here, we evaluated two types of radiomics models generated using different feature incorporation strategies. In order to differentiate between benign and malignant soft tissue tumors (STTs), we compared the diagnostic performance of an ensemble of random forest (R) models with single-sequence MRI inputs to R models with pooled multi-sequence MRI inputs. One-hundred twenty-five STT patients with preoperative MRI were retrospectively included and consisted of training (n = 100) and test (n = 25) sets. MRI included T1-weighted (T1-WI), T2-weighted (T2-WI), contrast-enhanced (CE)-T1-WI, diffusion-weighted images (DWIs, b = 800 sec/mm2) and apparent diffusion coefficient (ADC) maps. After tumor segmentation on each sequence, 100 original radiomic features were extracted from each sequence image and divided into three-feature sets: T features from T1- and T2-WI, CE features from CE-T1-WI, and D features from DWI and ADC maps. Four radiomics models were built using Lasso and R with four combinations of three-feature sets as inputs: T features (R-T), T+CE features (R-C), T+D features (R-D), and T+CE+D features (R-A) (Type-1 model). An ensemble model was built by soft voting of five, single-sequence-based R models (Type-2 model). AUC, sensitivity, specificity, and accuracy of each model was calculated with five-fold cross validation. In Type-1 model, AUC, sensitivity, specificity, and accuracy were 0.752, 71.8%, 61.1%, and 67.2% in R-T; 0.756, 76.1%, 70.4%, and 73.6% in R-C; 0.750, 77.5%, 63.0%, and 71.2% in R-D; and 0.749, 74.6%, 61.1%, and 68.8% R-A models, respectively. AUC, sensitivity, specificity, and accuracy of Type-2 model were 0.774, 76.1%, 68.5%, and 72.8%. In conclusion, an ensemble method is beneficial to incorporate features from multi-sequence MRI and showed diagnostic robustness for differentiating malignant STTs

Ensemble learning-based radiomics with multi-sequence magnetic resonance imaging for benign and malignant soft tissue tumor differentiation.

Many previous studies focused on differentiating between benign and malignant soft tissue tumors using radiomics model based on various magnetic resonance imaging (MRI) sequences, but it is still unclear how to set up the input radiomic features from multiple MRI sequences. Here, we evaluated two types of radiomics models generated using different feature incorporation strategies. In order to differentiate between benign and malignant soft tissue tumors (STTs), we compared the diagnostic performance of an ensemble of random forest (R) models with single-sequence MRI inputs to R models with pooled multi-sequence MRI inputs. One-hundred twenty-five STT patients with preoperative MRI were retrospectively included and consisted of training (n = 100) and test (n = 25) sets. MRI included T1-weighted (T1-WI), T2-weighted (T2-WI), contrast-enhanced (CE)-T1-WI, diffusion-weighted images (DWIs, b = 800 sec/mm2) and apparent diffusion coefficient (ADC) maps. After tumor segmentation on each sequence, 100 original radiomic features were extracted from each sequence image and divided into three-feature sets: T features from T1- and T2-WI, CE features from CE-T1-WI, and D features from DWI and ADC maps. Four radiomics models were built using Lasso and R with four combinations of three-feature sets as inputs: T features (R-T), T+CE features (R-C), T+D features (R-D), and T+CE+D features (R-A) (Type-1 model). An ensemble model was built by soft voting of five, single-sequence-based R models (Type-2 model). AUC, sensitivity, specificity, and accuracy of each model was calculated with five-fold cross validation. In Type-1 model, AUC, sensitivity, specificity, and accuracy were 0.752, 71.8%, 61.1%, and 67.2% in R-T; 0.756, 76.1%, 70.4%, and 73.6% in R-C; 0.750, 77.5%, 63.0%, and 71.2% in R-D; and 0.749, 74.6%, 61.1%, and 68.8% R-A models, respectively. AUC, sensitivity, specificity, and accuracy of Type-2 model were 0.774, 76.1%, 68.5%, and 72.8%. In conclusion, an ensemble method is beneficial to incorporate features from multi-sequence MRI and showed diagnostic robustness for differentiating malignant STTs

α-Lipoic acid prevents against cisplatin cytotoxicity via activation of the NRF2/HO-1 antioxidant pathway.

The production of reactive oxygen species (ROS) by cisplatin is one of the major mechanisms of cisplatin-induced cytotoxicity. We examined the preventive effect of α-lipoic acid (LA) on cisplatin-induced toxicity via its antioxidant effects on in vitro and ex vivo culture systems. To elucidate the mechanism of the antioxidant activity of LA, NRF2 was inhibited using NRF2 siRNA, and the change in antioxidant activity of LA was characterized. MTT assays showed that LA was safe at concentrations up to 0.5 mM in HEI-OC1 cells and had a protective effect against cisplatin-induced cytotoxicity. Intracellular ROS production in HEI-OC1 cells was rapidly increased by cisplatin for up to 48 h. However, treatment with LA significantly reduced the production of ROS and increased the expression of the antioxidant proteins HO-1 and SOD1. Ex vivo, the organs of Corti of the group pretreated with LA exhibited better preservation than the group that received cisplatin alone. We also confirmed the nuclear translocation of NRF2 after LA administration, and that NRF2 inhibition decreased the antioxidant activity of LA. Together, these results indicate that the antioxidant activity of LA was through the activation of the NRF2/HO-1 antioxidant pathway