Methyl orange (MO) is a common anionic azo dye that is harmful to the environment and biology, so it must be treated innocuously before it can be discharged. Adsorption is an effective method to ...remove anionic dyes. Nanotube mineral is a natural one-dimensional adsorption material, and its unique morphology and structure endow it with good adsorption capacity. Although there are many related studies, there is a lack of in-depth discussions on the influence of nanotube's composition and structure on the adsorption of dyes and other pollutants. In this paper, two kinds of natural one-dimensional silicate minerals halloysite nanotubes (HNTs) and chrysotile nanotubes (ChNTs) with similar morphology but slightly different compositions and crystal structures were used as adsorbents, and MO solution was used as simulate pollutants. It is the first time to discuss in depth the influence of the composition and structure of nanotube minerals on their charge properties and the adsorption performance of methyl orange dyes. It is found that HNTs and ChNTs have different adsorption capacity due to the difference of electronegativity between Al
and Mg
in the crystal, so they possess negative and positive charges respectively in near-neutral solution, which leads to the adsorption capacity of MO by ChNTs with positive charges which is greater than that of HNTs.
Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core,and edema from normal brain tissues of White Matter（WM）, Gray Matter（GM）, and Cerebrospinal ...Fluid（CSF）. MRIbased brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging（MRI） images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.
For decades, a variety of predictive approaches have been proposed and evaluated in terms of their prediction capability for Alzheimer's Disease (AD) and its precursor - mild cognitive impairment ...(MCI). Most of them focused on prediction or identification of statistical differences among different clinical groups or phases, especially in the context of binary or multi-class classification. The continuous nature of AD development and transition states between successive AD related stages have been typically overlooked. Though a few progression models of AD have been studied recently, they were mainly designed to determine and compare the order of specific biomarkers. How to effectively predict the individual patient's status within a wide spectrum of continuous AD progression has been largely understudied. In this work, we developed a novel learning-based embedding framework to encode the intrinsic relations among AD related clinical stages by a set of meaningful embedding vectors in the latent space (Disease2Vec). We named this process as disease embedding. By Disease2Vec, our framework generates a disease embedding tree (DETree) which effectively represents different clinical stages as a tree trajectory reflecting AD progression and thus can be used to predict clinical status by projecting individuals onto this continuous trajectory. Through this model, DETree can not only perform efficient and accurate prediction for patients at any stages of AD development (across five fine-grained clinical groups instead of typical two groups), but also provide richer status information by examining the projecting locations within a wide and continuous AD progression process. (Code will be available: https://github.com/qidianzl/Disease2Vec.).
Functional brain networks (FBNs) are spatial patterns of brain function that play a critical role in understanding human brain function. There are many proposed methods for mapping the spatial ...patterns of brain function, however they oversimplify the underlying assumptions of brain function and have various limitations such as linearity and independence. Additionally, current methods fail to account for the dynamic nature of FBNs, which limits their effectiveness in accurately characterizing these networks. To address these limitations, we present a novel deep learning and spatial-wise attention based model called Spatial-Temporal Convolutional Attention (STCA) to accurately model dynamic FBNs. Specifically, we train STCA in a self-supervised manner by utilizing a Convolutional Autoencoder to guide the STCA module in assigning higher attention weights to regions of functional activity. To validate the reliability of the results, we evaluate our approach on the HCP-task motor behavior dataset, the experimental results demonstrate that the STCA derived FBNs have higher spatial similarity with the templates and that the spatial similarity between the templates and the FBNs derived by STCA fluctuates with the task design over time, suggesting that STCA can reflect the dynamic changes of brain function, providing a powerful tool to better understand human brain function. Code is available at https://github.com/SNNUBIAI/STCAE.
•A novel paradigm for mapping the spatial patterns of brain function.•STCA-derived FBNs have higher spatial similarity with templates and can reflect dynamic changes of brain function.•A fast and efficient tool for studying dynamic functional brain networks.
Current functional magnetic resonance imaging (fMRI) data modeling techniques, such as independent component analysis and sparse coding methods, can effectively reconstruct dozens or hundreds of ...concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective, and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications. However, this task is still a challenging and open problem due to the tremendous variability of various types of functional brain networks and the presence of various sources of noises. In recognition of the fact that convolutional neural networks (CNN) has superior capability of representing spatial patterns with huge variability and dealing with large noises, in this paper, we design, apply, and evaluate a deep 3-D CNN framework for automatic, effective, and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fMRI signals. Our extensive experimental results based on the Human Connectome Project fMRI data showed that the proposed deep 3-D CNN can effectively and robustly perform functional networks classification and recognition tasks, while maintaining a high tolerance for mistakenly labeled training instances. This study provides a new deep learning approach for modeling functional connectomes based on fMRI data.
Saliency detection aims at quantitatively predicting attended locations in an image. It may mimic the selection mechanism of the human vision system, which processes a small subset of a massive ...amount of visual input while the redundant information is ignored. Motivated by the biological evidence that the receptive fields of simple cells in V1 of the vision system are similar to sparse codes learned from natural images, this paper proposes a novel framework for saliency detection by using image sparse coding representations as features. Unlike many previous approaches dedicated to examining the local or global contrast of each individual location, this paper develops a probabilistic computational algorithm by integrating objectness likelihood with appearance rarity. In the proposed framework, image sparse coding representations are yielded through learning on a large amount of eye-fixation patches from an eye-tracking dataset. The objectness likelihood is measured by three generic cues called compactness, continuity, and center bias. The appearance rarity is inferred by using a Gaussian mixture model. The proposed paper can serve as a basis for many techniques such as image/video segmentation, retrieval, retargeting, and compression. Extensive evaluations on benchmark databases and comparisons with a number of up-to-date algorithms demonstrate its effectiveness.
The global COVID-19 pandemic has put everyone in an urgent need of accessing and comprehending health information online. Meanwhile, there has been vast amount of ...information/misinformation/disinformation generated over the Internet, particularly social media platforms, resulting in an infodemic. This public health crisis of COVID-19 pandemic has put each individual and the entire society in a test: what is the level of eHealth literacy is needed to seek accurate health information from online resources and to combat infodemic during a pandemic? This article aims to summarize the significances and challenges of improving eHealth literacy in both communicable (e.g., COVID-19) and non-communicable diseases e.g., cancer, Alzheimer's disease, and cardiovascular diseases (CVDs). Also, this article will make our recommendations of a general framework of AI-based approaches to improving eHealth literacy and combating infodemic, including AI-augmented lifelong learning, AI-assisted translation, simplification, and summarization, and AI-based content filtering. This general framework of AI-based approaches to improving eHealth literacy and combating infodemic has the general advantage of matching the right online health information to the right people.
•We used naturalistic paradigm fMRI to investigate the effective connectivities between the amygdala and insula and the modulatory effect of the input arousal on the effective connections.•Amygdala ...was the site of driving arousal input.•Arousal had a modulatory effect on the reciprocal connections between amygdala and insula.
Emotional arousal is a complex state recruiting distributed cortical and subcortical structures, in which the amygdala and insula play an important role. Although previous neuroimaging studies have showed that the amygdala and insula manifest reciprocal connectivity, the effective connectivities and modulatory patterns on the amygdala-insula interactions underpinning arousal are still largely unknown. One of the reasons may be attributed to static and discrete laboratory brain imaging paradigms used in most existing studies. In this study, by integrating naturalistic-paradigm (i.e., movie watching) functional magnetic resonance imaging (fMRI) with a computational affective model that predicts dynamic arousal for the movie stimuli, we investigated the effective amygdala-insula interactions and the modulatory effect of the input arousal on the effective connections. Specifically, the predicted dynamic arousal of the movie served as regressors in general linear model (GLM) analysis and brain activations were identified accordingly. The regions of interest (i.e., the bilateral amygdala and insula) were localized according to the GLM activation map. The effective connectivity and modulatory effect were then inferred by using dynamic causal modeling (DCM). Our experimental results demonstrated that amygdala was the site of driving arousal input and arousal had a modulatory effect on the reciprocal connections between amygdala and insula. Our study provides novel evidence to the underlying neural mechanisms of arousal in a dynamical naturalistic setting.
Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal ...of effort has been devoted to the extraction of low-level visual features, such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed dense individualized and common connectivity-based cortical landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their regions of interests (ROIs) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain's comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian process regression (GPR) algorithm, and a manifold structure is then inferred, in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods.
In recent years, the vaccinia oncolytic virus has entered the clinical trial stage of examination and shown good progress. It has many advantages, such as good safety, high oncolytic efficiency, and ...the regulation ability of the tumor microenvironment, and is expected to be successfully used in the clinical treatment of tumors in the future. However, no bibliometric analysis has so far been performed that generalizes horizontally across this field. Therefore, this study aims to assess the research status and trends in this field from a global perspective to help guide future research priorities.
In this study, the literature related to vaccinia oncolytic virus published in English on Web of Science from 2002 to 2022 was retrieved, and the bibliometric indicators were analyzed using the Histcite. Pro 2.0 tool, while VOSviewer was used to visualize the research trends and hotspots in this field.
In total, 408 related studies were included. In the past 20 years, the number of related publications in this field has increased year by year, and breakthroughs were made in this field in 2008 and 2013. The research has grown rapidly since 2008, and will likely continue to expand in the years to come. The United States plays a leading role in this area. "MOLECULAR THERAPY-ONCOLYTICS", "MOLECULAR THERAPY" and "JOURNAL OF TRANSLATIONAL MEDICINE" are core journals that publish high-quality literature on the latest advances in the field. Some authors with numerous high-quality publications include Bell JC and Szalay AA. At present, the research hotspot in this field focus on the clinical application of vaccinia oncolytic virus.
Overall, the number of vaccinia oncolytic virus-related studies is growing rapidly, in relation to which the United States is the most influential country. The clinical application of vaccinia oncolytic virus will affect the crucial development of future research.
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