The spinodal decomposition method emerges as a promising methodology, showcasing its potential in exploring the design space for metamaterial structures. However, spinodal structures design is still ...largely limited to regular structures, due to their relatively easy parameterization and controllability. Efficiently predicting the mechanical properties of 3D spinodal membrane structure remains a challenge, given that the features of the membrane necessitate adaptive mesh through the modelling process. This paper proposes an integrated approach for morphological design with customized mechanical properties, incorporating the spinodal decomposition method and adaptive coarse-grained modeling, which can produce various morphologies such as lamellar, columnar, and cubic structures. Pseudo-periodic parameter
β
and orientational parameter
Θ
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θ
1
,
θ
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,
θ
3
) are identified to achieve the optimal goal of anisotropic mechanical properties. Parametric analysis is conducted to reveal the correlation between the customized spinodal structure and mechanical performance. Our work provides an integrated approach for morphological variation and tuning mechanical properties, paving the way for the design and development of customized functional materials similar to 3D spinodal membrane structures.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The number of patients with Alzheimer's disease (AD) worldwide is increasing yearly, but the existing treatment methods have poor efficacy. Transcranial alternating current stimulation (tACS) is a ...new treatment for AD, but the offline effect of tACS is insufficient. To prolong the offline effect, we designed to combine tACS with sound stimulation to maintain the long-term post-effect.
To explore the safety and effectiveness of tACS combined with sound stimulation and its impact on the cognition of AD patients. This trial will recruit 87 patients with mild to moderate AD. All patients were randomly divided into three groups. The change in Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) scores from the day before treatment to the end of treatment and 3 months after treatment was used as the main evaluation index. We will also explore the changes in the brain structural network, functional network, and metabolic network of AD patients in each group after treatment.
We hope to conclude that tACS combined with sound stimulation is safe and tolerable in 87 patients with mild to moderate AD under three standardized treatment regimens. Compared with tACS alone or sound alone, the combination group had a significant long-term effect on cognitive improvement. To screen out a better treatment plan for AD patients. tACS combined with sound stimulation is a previously unexplored, non-invasive joint intervention to improve patients' cognitive status. This study may also identify the potential mechanism of tACS combined with sound stimulation in treating mild to moderate AD patients.
Clinicaltrials.gov, NCT05251649. Registered on February 22, 2022.
Deep learning models are easily deceived by adversarial examples, and transferable attacks are crucial because of the inaccessibility of model information. Existing SOTA attack approaches tend to ...destroy important features of objects to generate adversarial examples. This paper proposes the split grid mask attack (SGMA), which reduces the intensity of model-specific features by split grid mask transformation, effectively highlighting the important features of the input image. Perturbing these important features can guide the development of adversarial examples in a more transferable direction. Specifically, we introduce the split grid mask transformation into the input image. Due to the vulnerability of model-specific features to image transformations, the intensity of model-specific features decreases after aggregation while the intensities of important features remain. The generated adversarial examples guided by destroying important features have excellent transferability. Extensive experimental results demonstrate the effectiveness of the proposed SGMA. Compared to the SOTA attack approaches, our method improves the black-box attack success rates by an average of 6.4% and 8.2% against the normally trained models and the defense ones respectively.
Transcranial alternating current stimulation (tACS) is a relatively new non-invasive brain electrical stimulation method for the treatment of patients with Alzheimer's disease (AD), but it has poor ...offline effects. Therefore, we applied a new combined stimulation method to observe the offline effect on the cognitive function of patients with AD. Here, we describe the clinical results of a case in which tACS combined with sound stimulation was applied to treat moderate AD. The patient was a 73-year-old woman with a 2-year history of persistent cognitive deterioration despite the administration of Aricept and Sodium Oligomannate. Therefore, the patient received tACS combined with sound stimulation. Her cognitive scale scores improved after 15 sessions and continued to improve at 4 months of follow-up. Although the current report may provide a new alternative therapy for patients with AD, more clinical data are needed to support its efficacy.
Trial registration
Clinicaltrials.gov
, NCT05251649.
Dysbiosis of the gut microbiota is pivotal in Crohn's disease (CD) and modulated by host physiological conditions. Hyperbaric oxygen therapy (HBOT) is a promising treatment for CD that can regulate ...gut microbiota. The relationship between HBOT and the gut microbiota in CD remains unknown.
CD patients were divided into an HBOT group (n = 10) and a control group (n = 10) in this open-label prospective interventional study. The fecal samples before and after HBOT were used for 16 S rRNA gene sequencing and fecal microbiota transplantation (FMT). A colitis mouse model was constructed using dextran sulfate sodium, and intestinal and systematic inflammation was evaluated. The safety and long-term effect of HBOT were observed.
HBOT significantly reduced the level of C-reactive protein (CRP) (80.79 ± 42.05 mg/L vs. 33.32 ± 18.31 mg/L, P = 0.004) and the Crohn's Disease Activity Index (CDAI) (274.87 ± 65.54 vs. 221.54 ± 41.89, P = 0.044). HBOT elevated the declined microbial diversity and ameliorated the altered composition of gut microbiota in patients with CD. The relative abundance of Escherichia decreased, and that of Bifidobacterium and Clostridium XIVa increased after HBOT. Mice receiving FMT from donors after HBOT had significantly less intestinal inflammation and serum CRP than the group before HBOT. HBOT was safe and well-tolerated by patients with CD. Combined with ustekinumab, more patients treated with HBOT achieved clinical response (30%vs.70%, P = 0.089) and remission (20%vs.50%, P = 0.160) at week 4.
HBOT modulates the dysbiosis of gut microbiota in CD and ameliorates intestinal and systematic inflammation. HBOT is a safe option for CD and exhibits a promising auxiliary effect to ustekinumab.
Chinese Clinical Trial Registry, ChiCTR2200061193. Registered 15 June 2022, https://www.chictr.org.cn/showproj.html?proj=171605 .
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Detecting out-of-distribution (OOD) samples is critical for the deployment of deep neural networks (DNN) in real-world scenarios. An appealing direction in which to conduct OOD detection is to ...measure the epistemic uncertainty in DNNs using the Bayesian model, since it is much more explainable. SCOD sketches the curvature of DNN classifiers based on Bayesian posterior estimation and decomposes the OOD measurement into the uncertainty of the model parameters and the influence of input samples on the DNN models. However, since lots of approximation is applied, and the influence of the input samples on DNN models can be hardly measured stably, as demonstrated in adversarial attacks, the detection is not robust. In this paper, we propose a novel AdvSCOD framework that enriches the input sample with a small set of its neighborhoods generated by applying adversarial perturbation, which we believe can better reflect the influence on model predictions, and then we average their uncertainties, measured by SCOD. Extensive experiments with different settings of in-distribution and OOD datasets validate the effectiveness of AdvSCOD in OOD detection and its superiority to state-of-the-art Bayesian-based methods. We also evaluate the influence of different types of perturbation.
Recognizing 3-D objects in cluttered scenes is a challenging task. Common approaches find potential feature correspondences between a scene and candidate models by matching sampled local shape ...descriptors and select a few correspondences with the highest descriptor similarity to identify models that appear in the scene. However, real scans contain various nuisances, such as noise, occlusion, and featureless object regions. This makes selected correspondences have a certain portion of false positives, requiring adopting the time-consuming model verification many times to ensure accurate recognition. This paper proposes a 3-D object recognition approach with three key components. First, we construct a Signature of Geometric Centroids descriptor that is descriptive and robust, and apply it to find high-quality potential feature correspondences. Second, we measure geometric compatibility between a pair of potential correspondences based on isometry and three angle-preserving components. Third, we perform effective correspondence selection by using both descriptor similarity and compatibility with an auxiliary set of "less" potential correspondences. Experiments on publicly available data sets demonstrate the robustness and/or efficiency of the descriptor, selection approach, and recognition framework. Comparisons with the state-of-the-arts validate the superiority of our recognition approach, especially under challenging scenarios.
Dissection puzzles require assembling a common set of pieces into multiple distinct forms. Existing works focus on creating 2D dissection puzzles that form primitive or naturalistic shapes. Unlike 2D ...dissection puzzles that could be supported on a tabletop surface, 3D dissection puzzles are preferable to be steady by themselves for each assembly form. In this work, we aim at computationally designing steady 3D dissection puzzles. We address this challenging problem with three key contributions. First, we take two voxelized shapes as inputs and dissect them into a common set of puzzle pieces, during which we allow slightly modifying the input shapes, preferably on their internal volume, to preserve the external appearance. Second, we formulate a formal model of generalized interlocking for connecting pieces into a steady assembly using both their geometric arrangements and friction. Third, we modify the geometry of each dissected puzzle piece based on the formal model such that each assembly form is steady accordingly. We demonstrate the effectiveness of our approach on a wide variety of shapes, compare it with the state‐of‐the‐art on 2D and 3D examples, and fabricate some of our designed puzzles to validate their steadiness.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
The prevalence of online misinformation, termed “fake news”, has exponentially escalated in recent years. These deceptive information, often rich with multimodal content, can easily deceive ...individuals into spreading them via various social media platforms. This has made it a hot research topic to automatically detect multimodal fake news. Existing works made a great progress on inter-modality feature fusion or semantic interaction yet largely ignore the importance of intra-modality entities and feature aggregation. This imbalance causes them to perform erratically on data with different emphases. In the realm of authentic news, the intra-modality contents and the inter-modality relationship should be in mutually supportive relationships. Inspired by this idea, we propose an innovative approach to multimodal fake news detection (IFIS), incorporating both intra-modality feature aggregation and inter-modality semantic fusion. Specifically, the proposed model implements a entity detection module and utilizes attention mechanisms for intra-modality feature aggregation, whereas inter-modality semantic fusion is accomplished via two concurrent Co-attention blocks. The performance of IFIS is extensively tested on two datasets, namely Weibo and Twitter, and has demonstrated superior performance, surpassing various advanced methods by 0.6 The experimental results validate the capability of our proposed approach in offering the most balanced performance for multimodal fake news detection tasks.
Accurate and high-quality shape generation of individual teeth from cone-beam computerized tomography（CBCT） is essential for computer-aided dentistry. Existing methods need post-process to extract ...isosurfaces and the output meshes cannot be directly used as the input for most subsequent applications(such as finite element analysis（FEA）. In this paper, we propose the network that directly learns the multi-resolution mesh guided by diffeomorphic deformation. Overall, our solution is a classic two-stage schema widely used in tooth reconstruction. Firstly, we adopt a revised anchor-free detector to locate each individual tooth with high precision. Then, we design the top-to-bottom flows from the multi-level features of each individual, referred to as pyramid flows, to predict diffeomorphic deformation from a sphere to a detailed tooth. Finally, we validate the effectiveness and efficiency of the proposed approach by comparing with the previous segmentation methods and other explicit surface learning-based methods
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