INR-LDDMM: Fluid-based Medical Image Registration Integrating Implicit Neural Representation and Large Deformation Diffeomorphic Metric Mapping

We propose a fluid-based registration framework of medical images based on implicit neural representation. By integrating implicit neural representation and Large Deformable Diffeomorphic Metric Mapping (LDDMM), we employ a Multilayer Perceptron (MLP) as a velocity generator while optimizing velocity and image similarity. Moreover, we adopt a coarse-to-fine approach to address the challenge of deformable-based registration methods dropping into local optimal solutions, thus aiding the management of significant deformations in medical image registration. Our algorithm has been validated on a paired CT-CBCT dataset of 50 patients,taking the Dice coefficient of transferred annotations as an evaluation metric. Compared to existing methods, our approach achieves the state-of-the-art performance

An Economic Analysis of Subscription Sharing of Digital Services

Subscription sharing, where one shares her premium digital services subscription with other users, has become common due to subscription-sharing platforms like Togetherprice, Gowd, and Sharesub. This raises a question: Does it still make economic sense to offer a menu of subscription plans (e.g., an individual plan as well as a discounted family plan)? In this study, we look at a monopolist service provider that offers both plans but faces the potential threat of subscription sharing. We analyze the optimal prices and the impact of sharing on profit, customer surplus, and overall society benefits. Our results indicate that even with subscription sharing, offering both plans is at least as profitable as only offering individual plans. Under certain conditions, subscription sharing can even boost profits. Furthermore, our numerical analysis suggests that subscription sharing can benefit society. These findings suggest that subscription sharing is not necessarily as troublesome as one would have expected

KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions

Adverse drug reaction (ADR) detection is an essential task in the medical field, as ADRs have a gravely detrimental impact on patients' health and the healthcare system. Due to a large number of people sharing information on social media platforms, an increasing number of efforts focus on social media data to carry out effective ADR detection. Despite having achieved impressive performance, the existing methods of ADR detection still suffer from three main challenges. Firstly, researchers have consistently ignored the interaction between domain keywords and other words in the sentence. Secondly, social media datasets suffer from the challenges of low annotated data. Thirdly, the issue of sample imbalance is commonly observed in social media datasets. To solve these challenges, we propose the Knowledge Enhanced Shallow and Deep Transformer(KESDT) model for ADR detection. Specifically, to cope with the first issue, we incorporate the domain keywords into the Transformer model through a shallow fusion manner, which enables the model to fully exploit the interactive relationships between domain keywords and other words in the sentence. To overcome the low annotated data, we integrate the synonym sets into the Transformer model through a deep fusion manner, which expands the size of the samples. To mitigate the impact of sample imbalance, we replace the standard cross entropy loss function with the focal loss function for effective model training. We conduct extensive experiments on three public datasets including TwiMed, Twitter, and CADEC. The proposed KESDT outperforms state-of-the-art baselines on F1 values, with relative improvements of 4.87%, 47.83%, and 5.73% respectively, which demonstrates the effectiveness of our proposed KESDT

Numerical simulation on structural safety and dynamic response of coal mine rescue ball with gas explosion load using Arbitrary Lagrangian-Eulerian (ALE) algorithm

Coal mine rescue devices, which can supply miners underground with fundamental shelters after gas explosion, are essential for safety production of coal mines. In this paper, a novel and composite structure-rescue antiknock ball for coal mine rescue is designed. Further, the structural safety and dynamic response under gas explosion of the antiknock ball is investigated by ALE algorithm. To achieve this goal, the ALE finite element method is described in dynamic form, and governing equations and the finite element expressions of the ALE algorithm are derived. 3 balls with different structures are designed and dynamic response analysis has been conducted in a semi-closed tunnel with explosive load of pre-mixed gas/air mixture by using ALE algorithm based on explicit nonlinear dynamic program LS-DYNA. Displacement field, stress field and energy transmission laws are analyzed and compared via theoretical calculations. Results show that the cabin door, emergency door and spherical shell are important components of the rescue ball. The 3# composite ball is the optimization structure that can delay the shock effect of the gas explosion load on a coal mine rescue system; the simulation results can provide reference data for coal mine rescue system design

TBFormer: Two-Branch Transformer for Image Forgery Localization

Image forgery localization aims to identify forged regions by capturing subtle traces from high-quality discriminative features. In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer). Firstly, two feature extraction branches are elaborately designed, taking advantage of the discriminative stacked Transformer layers, for both RGB and noise domain features. Secondly, an Attention-aware Hierarchical-feature Fusion Module (AHFM) is proposed to effectively fuse hierarchical features from two different domains. Although the two feature extraction branches have the same architecture, their features have significant differences since they are extracted from different domains. We adopt position attention to embed them into a unified feature domain for hierarchical feature investigation. Finally, a Transformer decoder is constructed for feature reconstruction to generate the predicted mask. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed model.Comment: 5 pages, 3 figure

Protective effect of furofuranone against cerebral ischemic stroke via activation of PI3k/Akt/GSK 3β signaling pathway

Purpose: To study the protective effects of furofuranone on oxygen and glucose-deprived damage to brain microvascular endothelial cells (BMECs) in vitro, and in vivo in cerebral ischemic stroke rat model. Methods: BMECs were isolated from the Sprague Dawley rats and deprived of oxygen and glucose. The effect of 10, 20, 30, 40, 50 and 100 µM furofuranone on the oxygen/glucose-deprived BMECs was studied using Transwell chamber method. A rat cerebral ischemic stroke model was established using middle cerebral arterial occlusion method. Caspase-3 and other proteins, inflammatory cytokines, and other parameters of the brain tissue were evaluated by enzyme-linked assay (ELISA), polymerase chain reaction (PCR) and Western blot as appropriate. Further studies on the brain tissues was carried out by immunochemical analysis and hematoxylin and eosin staining. Results: Furofuranone decreased caspase 3 levels in a dose-based manner in rat BMECs, and significantly reduced the release of lactate dehydrogenase (LDH) in ischemic stroke rat model (p < 0.05). It also led to marked increases in the levels of p PI3k, p Akt and p GSK3β in cerebral ischemic stroke rats. Growth-associated protein-43 (GAP-43) and microtubule-associated protein 2 (MAP-2) levels increased in the cerebral ischemic stroke rat brain tissues, in addition to marked increase in ionized calcium-binding adaptor protein (IBA-1) and glial fibrillary acidic protein (GFAP) (p < 0.05). Furofuranone treatment reduced the population of microtubule-associated protein light chain 3 (MAP1LC3A) and Beclin 1-positive cells, and significantly downregulated L selectin, leptin, monocyte chemotactic protein-1 (MCP-1) and tumor necrosis factor (TNF)-α (p < 0.05). The release of tissue inhibitor of metalloproteinases 1 (TIMP-1) was enhanced in the cerebral ischemic stroke rats by furofuranone treatment. Conclusion: Furofuranone treatment prevents cerebral ischemic stroke-induced damage in rats via phosphorylation of PI3k, Akt and GSK3β proteins, and reduction of inflammatory cytokine levels. Therefore, furofuranone may be useful as chemotherapeutic agent for cerebral ischemic stroke