In this paper, we propose a novel
for transformer-based object detection. To tackle the target attribute diffusion challenge of transformer-based object detection, we propose two key components in ...the new target-aware token design mechanism. Firstly, we propose a
, which forces the sampling patterns to converge inside the target region and obtain its representative encoded features. More specifically, a set of four sampling patterns are designed, including small and large patterns, which focus on the detailed and overall characteristics of a target, respectively, as well as the vertical and horizontal patterns, which handle the object's directional structures. Secondly, we propose a
. This is a unified, learnable, feature-embedding matrix which is directly weighted on the feature map to reduce the interference of non-target regions. With such a new design, we propose a new variant of the transformer-based object-detection model, called
, which achieves superior performance over the state-of-the-art transformer-based object-detection models on the COCO object-detection benchmark dataset. Experimental results demonstrate that our Focal DETR achieves a 44.7
in the coco2017 test set, which is 2.7
and 0.9
higher than the DETR and deformable DETR using the same training strategy and the same feature-extraction network.
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Optical microscopy is an indispensable diagnostic tool in modern healthcare. As a prime example, pathologists rely exclusively on light microscopy to investigate tissue morphology in order to make a ...diagnosis. While advances in light microscopy and contrast markers allow pathologists to visualize cells and tissues in unprecedented detail, the interpretation of these images remains largely subjective, leading to inter‐ and intra‐observer discrepancy. Furthermore, conventional microscopy images capture qualitative information which makes it difficult to automate the process, reducing the throughput achievable in the diagnostic workflow. Quantitative Phase Imaging (QPI) techniques have been advanced in recent years to address these two challenges. By quantifying physical parameters of cells and tissues, these systems remove subjectivity from the disease diagnosis process and allow for easier automation to increase throughput. In addition to providing quantitative information, QPI systems are also label‐free and can be easily assimilated into the current diagnostic workflow in the clinic. In this paper we review the advances made in disease diagnosis by QPI techniques. We focus on the areas of hematological diagnosis and cancer pathology, which are the areas where most significant advances have been made to date.
Image adapted from Y. Park, M. Diez‐Silva, G. Popescu, G. Lykotrafitis, W. Choi, M. S. Feld, and S. Suresh, Proc. Natl. Acad. Sci. 105, 13730–13735 (2008).
With the development of more sophisticated optical instruments and greater computing power, disease diagnosis methods are likely to become more quantitative, accurate and objective. In this article, we review the application of Quantitative Phase Imaging (QPI), a label‐free quantitative light microscopy technique, as a tool for clinical diagnosis. By discussing various approaches that target different diseases, we show that QPI provides novel information on disease biology and progression, thus becoming an important addition to the diagnostic toolbox for clinicians.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The triglyceride-glucose (TyG) index, a reliable surrogate indicator of insulin resistance, is independently associated with coronary artery disease of various clinical manifestations. This study ...aimed to investigate the prognostic value of the TyG index in predicting repeat revascularization and in-stent restenosis (ISR) in chronic coronary syndrome (CCS) patients undergoing percutaneous coronary intervention (PCI).
A total of 1414 participants were enrolled and divided into groups according to the tertiles of the TyG index. The primary endpoint was a composite of PCI complications, including repeat revascularization and ISR. The associations between the TyG index and the primary endpoint were assessed by multivariable Cox proportional hazards regression analysis with restricted cubic splines (RCS). The TyG index was calculated as Ln (fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2).
Over a median follow-up of 60 months, 548 (38.76%) patients had experienced at least one primary endpoint event. The follow-up incidence of the primary endpoint increased with the TyG index tertiles. After adjusting for potential confounders, the TyG index was independently associated with the primary endpoint in CCS patients (HR, 1.191; 95% CI 1.038-1.367; P = 0.013). Additionally, the highest tertile of the TyG group was correlated with a 1.319-fold risk of the primary endpoint compared with the lowest tertile of the TyG group (HR, 1.319; 95% CI 1.063-1.637; P = 0.012). Furthermore, a linear and dose-response relationship was observed between the TyG index and the primary endpoint (non-linear P = 0.373, P overall = 0.035).
An increased TyG index was associated with elevated risk for long-term PCI complications, including repeat revascularization and ISR. Our study suggested that the TyG index could be a potent predictor in evaluating the prognosis of CCS patients undergoing PCI.
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Image super-resolution aims to reconstruct a high-resolution image from its low-resolution counterparts. Conventional image super-resolution approaches share the same spatial convolution kernel for ...the whole image in the upscaling modules, which neglect the specificity of content information in different positions of the image. In view of this, this paper proposes a regularized pattern method to represent spatially variant structural features in an image and further exploits a dynamic convolution kernel generation method to match the regularized pattern and improve image reconstruction performance. To be more specific, first, the proposed approach extracts features from low-resolution images using a self-organizing feature mapping network to construct regularized patterns (RP), which describe different contents at different locations. Second, the meta-learning mechanism based on the regularized pattern predicts the weights of the convolution kernels that match the regularized pattern for each different location; therefore, it generates different upscaling functions for images with different content. Extensive experiments are conducted using the benchmark datasets Set5, Set14, B100, Urban100, and Manga109 to demonstrate that the proposed approach outperforms the state-of-the-art super-resolution approaches in terms of both PSNR and SSIM performance.
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Fog computing has recently emerged as an extension of cloud computing in providing high-performance computing services for delay-sensitive Internet of Things (IoT) applications. By offloading tasks ...to a geographically proximal fog computing server instead of a remote cloud, the delay performance can be greatly improved. However, some IoT applications may still experience considerable delays, including queuing and computation delays, when huge amounts of tasks instantaneously feed into a resource-limited fog node. Accordingly, the cooperation among geographically close fog nodes and the cloud center is desired in fog computing with the ever-increasing computational demands from IoT applications. This paper investigates a workload allocation scheme in an IoT-fog-cloud cooperation system for reducing task service delay, aiming at satisfying as many as possible delay-sensitive IoT applications' quality of service (QoS) requirements. To this end, we first formulate the workload allocation problem in an IoT-edge-cloud cooperation system, which suggests optimal workload allocation among local fog node, neighboring fog node, and the cloud center to minimize task service delay. Then, the stability of the IoT-fog-cloud queueing system is theoretically analyzed with Lyapunov drift plus penalty theory. Based on the analytical results, we propose a delay-aware online workload allocation and scheduling (DAOWA) algorithm to achieve the goal of reducing long-term average task serve delay. Theoretical analysis and simulations have been conducted to demonstrate the efficiency of the proposal in task serve delay reduction and IoT-fog-cloud queueing system stability.
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Drug-drug interactions (DDIs) prediction is vital for pharmacology and clinical application to avoid adverse drug reactions on patients. It is challenging because DDIs are related to multiple ...factors, such as genes, drug molecular structure, diseases, biological processes, side effects, etc. It is a crucial technology for Knowledge graph to present multi-relation among entities. Recently some existing graph-based computation models have been proposed for DDIs prediction and get good performance. However, there are still some challenges in the knowledge graph representation, which can extract rich latent features from drug knowledge graph (KG).
In this work, we propose a novel multi-view feature representation and fusion (MuFRF) architecture to realize DDIs prediction. It consists of two views of feature representation and a multi-level latent feature fusion. For the feature representation from the graph view and KG view, we use graph isomorphism network to map drug molecular structures and use RotatE to implement the vector representation on bio-medical knowledge graph, respectively. We design concatenate-level and scalar-level strategies in the multi-level latent feature fusion to capture latent features from drug molecular structure information and semantic features from bio-medical KG. And the multi-head attention mechanism achieves the optimization of features on binary and multi-class classification tasks. We evaluate our proposed method based on two open datasets in the experiments. Experiments indicate that MuFRF outperforms the classic and state-of-the-art models.
Our proposed model can fully exploit and integrate the latent feature from the drug molecular structure graph (graph view) and rich bio-medical knowledge graph (KG view). We find that a multi-view feature representation and fusion model can accurately predict DDIs. It may contribute to providing with some guidance for research and validation for discovering novel DDIs.
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microRNA-146a has been reported to be a regulator in the process of attenuating asthma by inhibiting Toll-like receptor 2 (TLR2) pathway. This study aimed to investigate how miR146a-inhibitor affect ...the symptom of asthma and the underlying mechanisms.
Ovalbumin (OVA)-induced allergic asthma mice model was established by intraperitoneal injection with 20 μg of OVA. Total cells and differential inflammatory cells in bronchoalveolar lavage fluid were counted by flow cytometry. The expression levels of molecules and cytokines in TLR2 signaling pathway were detected by Q-PCR and ELISA.
miR146a-inhibitor attenuated OVA-induced allergic asthma by increasing Th1 cytokines in OVA-induced allergic asthma model, and the treatment of miR146a-inhibitor can reduce the inflammation caused by asthma, followed by the down-regulation of IL-5 and IL-13 in sorted ILC2. The inhibition of miR-146a significantly reduced symptoms of asthma model with TLR2-related molecules being up-regulated.
It was found that miR-146a is an important regulator in OVA-induced allergic asthma model, which can relieve symptoms of asthma through regulating TLR2 pathway. These findings provide a theoretical basis for solving asthma in clinical treatment.
The aim of multi-focus image fusion is to combine multiple images with different focuses for enhancing the perception of a scene. The challenge is to how evaluate the local content (sharp) ...information of the input images. To tackle the above challenge, a new bilateral sharpness criterion is proposed to exploit both the strength and the phase coherence that are evaluated using the gradient information of the images. Then the proposed bilateral sharpness criterion is further exploited to perform weighted aggregation of multi-focus images. Extensive experimental results are provided to demonstrate that the proposed bilateral sharpness criterion outperforms conventional seven sharpness criterions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK
Metabolic syndrome (MetS) could increase the risk of cardiovascular disease (CVD) by 2-fold. Ideal control of modifiable risk factors in Life's Simple 7 (LS7) could reduce the CVD risk among the ...general population. This study aimed to investigate the effects of controlling modifiable risk factors using LS7 in MetS to prevent CVD.
44463 participants in NHANES 1999–2018 were included. The primary endpoint was a composite of CVD, including angina pectoris, coronary artery disease, myocardial infarction, congestive heart failure, and stroke. Multivariable weighted logistic regression analyses estimated the associations. The diagnosis of MetS complied with Harmonized International Diabetes Federation Criteria. Measurement of modifiable risk factors used the 2010 American Heart Association LS7 guideline and was indicated by cardiovascular health (CVH).
14034 individuals were diagnosed with MetS. 4835 participants had CVD. The weighted mean CVH was 8.06 ± 0.03. Intermediate and poor CVH were associated with increased risk for CVD in participants with similar metabolic states compared to ideal CVH. By taking participants with metabolic health and ideal CVH as health control, participants with MetS and poor CVH were demonstrated to have a 3-fold (adjusted odds ratio, 4.00; 95 % confidence interval, 3.21–4.98) greater risk for CVD. Notably, under the condition of ideal CVH, the risk of having CVD was comparable between metabolic health and MetS after fully adjusted.
Ideal control of Life's Simple 7 in metabolic syndrome contributes to a comparable risk of cardiovascular disease with healthy subjects. LS7 could be recognized as a guideline for secondary prevention in MetS.
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The epithelial-mesenchymal transition (EMT) is a vital driver of tumor progression. It is a well-known and complex trans-differentiation process in which epithelial cells undergo morphogenetic ...changes with loss of apical-basal polarity, but acquire spindle-shaped mesenchymal phenotypes. Lysine acetylation is a type of protein modification that favors reversibly altering the structure and function of target molecules via the modulation of lysine acetyltransferases (KATs), as well as lysine deacetylases (KDACs). To date, research has found that histones and non-histone proteins can be acetylated to facilitate EMT. Interestingly, histone acetylation is a type of epigenetic regulation that is capable of modulating the acetylation levels of distinct histones at the promoters of EMT-related markers, EMT-inducing transcription factors (EMT-TFs), and EMT-related long non-coding RNAs to control EMT. However, non-histone acetylation is a post-translational modification, and its effect on EMT mainly relies on modulating the acetylation of EMT marker proteins, EMT-TFs, and EMT-related signal transduction molecules. In addition, several inhibitors against KATs and KDACs have been developed, some of which can suppress the development of different cancers by targeting EMT. In this review, we discuss the complex biological roles and molecular mechanisms underlying histone acetylation and non-histone protein acetylation in the control of EMT, highlighting lysine acetylation as potential strategy for the treatment of cancer through the regulation of EMT. Video Abstract.
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