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Molecular dynamics simulation has become a powerful tool to deepen the understanding of the radiation damage mechanism of nuclear materials. Extracting point defects, analyzing their diffusion, and visualizing the defect dynamics in atomic simulation data are important, but challenging tasks to understand irradiation behavior. In the past, irradiation defects have been detected using the so-called Wigner–Seitz cell method and analyzed by the statistics of Frenkel pairs. However, traditional analysis modes blur the fine details of defect dynamics. In this paper, we present a visual analysis pipeline for domain scientists to comfortably explore radiation damage simulation data. We couple defect identification, defect clustering, molecule visualization, and tracking graph to form an integrated visual exploration approach. We describe the application of our approach in practice to study defect clustering in Ni–Fe alloy. With our proposed pipeline, defects can be extracted in a robust way, clusters can be visualized with a favorable representation, in-depth data analysis can be setup, and defect dynamics can be demonstrated in greater detail than previously possible.

Traumatic brain injury is a common and devastating injury that is the leading cause of neurological disability and death worldwide. Patients with cerebral lobe contusion received conservative treatment because of their mild manifestations, but delayed intracranial hematoma may increase and even become life-threatening. We explored the noninvasive method to predict the prognosis of progression and Glasgow Outcome Scale (GOS) by using a quantitative radiomics approach and statistical analysis. Eighty-eight patients who were pathologically diagnosed were retrospectively studied. The radiomics method developed in this work included image segmentation, feature extraction, and feature selection. The nomograms were established based on statistical analysis and a radiomics method. We conducted a comparative study of hematoma progression and GOS between the clinical factor alone and fusion radiomics features. Nineteen clinical factors, 513 radiomics features, and 116 locational features were considered. Among clinical factors, international normalized ratio, prothrombin time, and fibrinogen were enrolled for hematoma progression. As for GOS, treatment strategy, age, Glasgow Coma Scale score, and blood platelet were associated factors. Eight features for GOS and five features for hematoma progression were filtered by using sparse representation and locality preserving projection-combined method. Four nomograms were constructed. After fusion radiomics features, area under the curve of hematoma progression prediction increased from 0.832 to 0.899, whereas GOS prediction went from 0.794 to 0.844. A radiomic-based model that merges radiomics and clinical features is a noninvasive approach to predict hematoma progression and clinical outcomes of cerebral contusions in traumatic brain injury.

Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signature (IS)-based radiomics model to achieve multilayered preoperative diagnosis and prognostic stratification of gliomas. Herein, we established three separate case cohorts, consisting of 655 glioma patients, and carried out a retrospective study. Image and clinical data of three cohorts were used for training (N = 188), cross-validation (N = 411), and independent testing (N = 56) of the IS model. All tumors were segmented from magnetic resonance (MR) images by the 3D U-net, followed by extraction of high-throughput network features, which were referred to as IS. IS was then used to perform noninvasive histopathological diagnosis and molecular subtyping. Moreover, a new IS-based clustering method was applied for prognostic stratification in IDH-wild-type lower-grade glioma (IDHwt LGG) and triple-negative glioblastoma (1p19q retain/IDH wild-type/TERTp-wild-type GBM). The average accuracies of histological diagnosis and molecular subtyping were 89.8 and 86.1% in the cross-validation cohort, while these numbers reached 83.9 and 80.4% in the independent testing cohort. IS-based clustering method was demonstrated to successfully divide IDHwt LGG into two subgroups with distinct median overall survival time (48.63 vs 38.27 months respectively, P = 0.023), and two subgroups in triple-negative GBM with different median OS time (36.8 vs 18.2 months respectively, P = 0.013). Our findings demonstrate that our novel IS-based radiomics model is an effective tool to achieve noninvasive histo-molecular pathological diagnosis and prognostic stratification of gliomas. This IS model shows potential for future routine use in clinical practice.

Purpose Early identification of ischemic stroke lesion regions plays a vital role in its treatments like thrombolytic therapy and patients' recovery. Noncontrast computed tomography (ncCT) is the most widespread imaging modality in emergency departments. Unfortunately, it is extremely hard to distinguish the lesion from healthy tissue during the hyper-acute phase of stroke. In this paper, a two-stage convolutional neural network-based method was proposed to identify the invisible ischemic stroke from ncCT. Methods In order to combine the global and local information of images effectively, a cascaded structure with two coordinated networks was used to detect the suspicious stroke regions on the whole and optimize the detailed localization. In the first stage, an end-to-end U-net with adaptive threshold was proposed to integrate global position, symmetry and gray texture information to detect the suspicious regions. After reducing the interference from most normal regions, a ResNet-based patch classification network was used to eliminate some false positive samples on suspicious regions by mining deeper image features, contributing to a more precise localization of stroke. Finally, a MAP model was used to optimize the result by combining the classification results of each patch with their spatial constraint information. Results Three independent experiments, that is, training and testing on dataset from one hospital, on the combination of two, and on the two respectively, were performed on a total of 277 cases from two hospitals to validate the proposed model, The proposed method achieved identification accuracy of 91.89%, 87.21%, and 85.71% in the three experiments, and the final localization accuracy in terms of precise localization of stroke were 82.35%, 83.02%, and 81.40%, respectively, which indicated the robustness and clinical values of the method. Conclusions There are some deep image feature differences between stroke region and normal region on ncCT images. The proposed two-stage convolutional neural network model can well seize these features and use them to effectively identify and locate stroke.

Insect pest control is considered as a significant factor in the yield of commercial crops. Thus, to avoid economic losses, we need a valid method for insect pest recognition. In this paper, we proposed a feature fusion residual block to perform the insect pest recognition task. Based on the original residual block, we fused the feature from a previous layer between two 1×1 convolution layers in a residual signal branch to improve the capacity of the block. Furthermore, we explored the contribution of each residual group to the model performance. We found that adding the residual blocks of earlier residual groups promotes the model performance significantly, which improves the capacity of generalization of the model. By stacking the feature fusion residual block, we constructed the Deep Feature Fusion Residual Network (DFF-ResNet). To prove the validity and adaptivity of our approach, we constructed it with two common residual networks (Pre-ResNet and Wide Residual Network (WRN)) and validated these models on the Canadian Institute For Advanced Research (CIFAR) and Street View House Number (SVHN) benchmark datasets. The experimental results indicate that our models have a lower test error than those of baseline models. Then, we applied our models to recognize insect pests and obtained validity on the IP102 benchmark dataset. The experimental results show that our models outperform the original ResNet and other state-of-the-art methods.

Earlier insect pest recognition is one of the critical factors for agricultural yield. Thus, an effective method to recognize the category of insect pests has become significant issues in the agricultural field. In this article, we proposed a new residual block to learn multiscale representation. In each block, it contains three branches: one is parameter-free, and the others contain several successive convolution layers. Moreover, we proposed a module and embedded it into the new residual block to recalibrate the channelwise feature response and to model the relationship of the three branches. By stacking this kind of block, we constructed the deep multibranch fusion residual network (DMF-ResNet). For evaluating the model performance, we first test our model on CIFAR-10 and CIFAR-100 benchmark data sets. The experimental results show that DMF-ResNet outperforms the baseline models significantly. Then, we construct DMF-ResNet with different depths for high-resolution image classification tasks and apply it to recognize insect pests. We evaluate the model performance on the IP102 data set, and the experimental results show that DMF-ResNet could achieve the best accuracy performance than the baseline models and other state-of-the-art methods. Based on these empirical experiments, we demonstrate the effectiveness of our approach.

The aerodynamic characteristics of Darrieus vertical axis wind turbines (VAWTs) are affected by several geometrical parameters. Airfoil shape is one of the important factors which have not been received enough attention in the past, compared to other parameters such as solidity, number of blades, chord length, rotor diameter, pitch angle and aspect ratio. In this paper, four airfoils with varying maximum thickness (12%, 15%, 18% and 21%) are investigated by calculating the aerodynamic characteristics of Darrieus VAWTs using computational fluid dynamics technology. The power and torque characteristics, as well as their flow field characteristics, are analyzed. The results indicate that the power coefficient follows a trend of CP_NACA 0018 > CP_NACA 0015 > CP_NACA 0021 > CP_NACA 0012 below an optimum TSR, while it increases with the decrease in airfoil maximum thickness beyond the optimum TSR. The optimum TSR and operational zone also increase with the decrease in airfoil maximum thickness. An optimized airfoil thickness is determined to be between 15 and 18% for a relatively high power coefficient and appropriate optimum TSR, as well as a wider operational zone and a high efficiency band. Moreover, the airfoil maximum thickness influences the strength and region of the vorticity, as well as the interactions between blades and shed vortex, being the main reason for the observed differences in instantaneous torque coefficient. The vorticity of NACA 0015 model is weaker and smaller than that of NACA 0021 model when TSR is 2.0.

Early identification of stroke is critical for the treatment and subsequent recovery. Non-contrast computed tomography (ncCT) is a routinely employed imaging modality for stroke evaluation. However, the early identification of stroke in ncCT images is very difficult, since there are subtle differences between lesion and healthy tissue during the hyperacute phase. In this paper, an image patch classification-based method was developed to detect the early ischemic stroke that is invisible to the radiologist in ncCT. First, we proposed radiomics-based patch classification model to identify whether each patch in ncCT is stroke region or not. To improve the identification accuracy, a symmetry image patch classification was developed, in which an image patch in one brain hemisphere was combined with its contralateral image patch for classification. Second, based on the spatial dependencies between neighboring patches, we built a maximum a posteriori identification model which integrated the spatial constraint information to improve the identification performance. Finally, after performing morphological post-processing, we defined the detected results containing more than 300 voxels as the stroke region. 108 stroke cases that were all invisible to radiologists in ncCT were enrolled in the study and divided into one training cohort and two independent testing cohorts to validate the proposed method. The proposed method has achieved identification accuracies of 76.67% and 75.00% on the two independent testing cohorts, respectively. The results proved the potential of the proposed radiomics model in the task which possessed great clinical values.

Background Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. Results Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. Conclusion We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

Objectives: Brain arteriovenous malformation (AVM) is one of the most common causes of intracranial hemorrhage in young adults, and its expeditious diagnosis on digital subtraction angiography (DSA) is essential for clinical decision-making. This paper firstly proposed a deep learning network to extract vascular time-domain features from DSA videos. Then, the temporal features were combined with spatial radiomics features to build an AVM-assisted diagnosis model.Materials and method: Anteroposterior position (AP) DSA videos from 305 patients, 153 normal and 152 with AVM, were analyzed. A deep learning network based on Faster-RCNN was proposed to track important vascular features in DSA. Then the appearance order of important vascular structures was quantified as the temporal features. The structure distribution and morphological features of vessels were quantified as 1,750 radiomics features. Temporal features and radiomics features were fused in a classifier based on sparse representation and support vector machine. An AVM diagnosis and grading system that combined the temporal and spatial radiomics features of DSA was finally proposed. Accuracy (ACC), sensitivity (SENS), specificity (SPEC), and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the radiomics model.Results: For cerebrovascular structure detection, the average precision (AP) was 0.922, 0.991, 0.769, 0.899, and 0.929 for internal carotid artery, Willis circle, vessels, large veins, and venous sinuses, respectively. The mean average precision (mAP) of five time phases was 0.902. For AVM diagnosis, the models based on temporal features, radiomics features, and combined features achieved AUC of 0.916, 0.918, and 0.942, respectively. In the AVM grading task, the proposed combined model also achieved AUC of 0.871 in the independent testing set.Conclusion: DSA videos provide rich temporal and spatial distribution characteristics of cerebral blood vessels. Clinicians often interpret these features based on subjective experience. This paper proposes a scheme based on deep learning and traditional machine learning, which effectively integrates the complex spatiotemporal features in DSA, and verifies the value of this scheme in the diagnosis of AVM.

Objectives: Delayed neurocognitive recovery (DNR) seriously affects the post-operative recovery of elderly surgical patients, but there is still a lack of effective methods to recognize high-risk patients with DNR. This study proposed a machine learning method based on a multi-order brain functional connectivity (FC) network to recognize DNR.Method: Seventy-four patients who completed assessments were included in this study, in which 16/74 (21.6%) had DNR following surgery. Based on resting-state functional magnetic resonance imaging (rs-fMRI), we first constructed low-order FC networks of 90 brain regions by calculating the correlation of brain region signal changing in the time dimension. Then, we established high-order FC networks by calculating correlations among each pair of brain regions. Afterward, we built sparse representation-based machine learning model to recognize DNR on the extracted multi-order FC network features. Finally, an independent testing was conducted to validate the established recognition model.Results: Three hundred ninety features of FC networks were finally extracted to identify DNR. After performing the independent-sample T test between these features and the categories, 15 features showed statistical differences (P < 0.05) and 3 features had significant statistical differences (P < 0.01). By comparing DNR and non-DNR patients’ brain region connection matrices, it is found that there are more connections among brain regions in DNR patients than in non-DNR patients. For the machine learning recognition model based on multi-feature combination, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the classifier reached 95.61, 92.00, 66.67, and 100.00%, respectively.Conclusion: This study not only reveals the significance of preoperative rs-fMRI in recognizing post-operative DNR in elderly patients but also establishes a promising machine learning method to recognize DNR.

To improve the forming quality of V-shaped clamps, a basis for optimal V-shaped clamp mold surface design is provided. In this paper, the V-shaped clamp stamping process is simulated using Abaqus and the influence of the mold’s friction coefficient on the V-shaped clamp stamping parameters is revealed. The regional friction coefficient of the V-shaped clamp mold surface is optimized using an equal horizontal design scheme and the optimal friction coefficient combination for the mold surface is obtained, along with comparison and analysis of the molding parameters before and after optimization. Results show that the mold’s surface friction properties have significant regional characteristics. Increasing the friction coefficient of the bottom, the rounded section of the punch’s V-shaped groove and the bulge filleted corner of the punch while reducing the friction coefficient of the bulge filleted corner and crimping area of the die head contributes to the material flow. Mold surface optimization makes the material flow velocity more uniform, disperses stress on the part’s surface, reduces strain and thickness reduction of the key parts, and improves overall quality. Measurements show that the V-shaped clamp mold’s surface friction characteristics affect the molding parameters significantly and mold optimization improves the V-shaped clamp’s molding quality.

The quality of magnetic resonance (MR) images obtained with different instruments and imaging parameters varies greatly. A large number of heterogeneous images are collected, and they suffer from acquisition variation. Such imaging quality differences will have a great impact on the radiomics analysis. The main differences in MR images include modality mismatch (M), intensity distribution variance (I), and layer-spacing differences (L), which are referred to as MIL differences in this paper for convenience. An MIL normalization system is proposed to reconstruct uneven MR images into high-quality data with complete modality, a uniform intensity distribution and consistent layer spacing. Three radiomics tasks, including tumor segmentation, pathological grading and genetic diagnosis of glioma, were used to verify the effect of MIL normalization on radiomics analysis. Three retrospective glioma datasets were analyzed in this study: BraTs (285 cases), TCGA (112 cases) and HuaShan (403 cases). They were used to test the effect of MIL on three different radiomics tasks, including tumor segmentation, pathological grading and genetic diagnosis. MIL normalization included three components: multimodal synthesis based on an encoder-decoder network, intensity normalization based on CycleGAN, and layer-spacing unification based on Statistical Parametric Mapping (SPM). The Dice similarity coefficient, areas under the curve (AUC) and six other indicators were calculated and compared after different normalization steps. The MIL normalization system can improved the Dice coefficient of segmentation by 9% (P .001), the AUC of pathological grading by 32% (P .001), and IDH1 status prediction by 25% (P .001) when compared to non-normalization. The proposed MIL normalization system provides high-quality standardized data, which is a prerequisite for accurate radiomics analysis.

Segmenting 3D brain vessels on computed tomographic angiography is critical for early diagnosis of stroke. However, traditional filter and optimization-based methods are ineffective in this challenging task due to imaging quality limits and structural complexity. And learning based methods are difficult to be used in this task due to extremely high time consumption in manually labeling and the lack of labelled open datasets. To address this, in this paper, we develop an asymmetric encoding and decoding-based convolutional neural network for accurate vessel segmentation on computed tomographic angiography. In the network, 3D encoding module is designed to comprehensively extract 3D vascular structure information. And three 2D decoding modules are designed to optimally identify vessels on each 2D plane, so that the network can learn more complex vascular structures, and has stronger ability to distinguish vessels from normal regions. What is more, to improve insufficient fine vessel segmentation caused by pixel-wise loss function, we develop a centerline loss to guide learning model to pay equal attention to small vessels and large vessels, so that the segmentation accuracy of small vessels can be improved. Compared to two state-of-the-art approaches, our model achieved superior performance, demonstrating the effectiveness of both whole vessel segmentation and small vessel maintenance.

Smart grid is willing to make full advantage of distributed clean energy to alleviate energy crisis and environmental problems. However, distributed renewable energy is usually invisible and uncontrollable for the current power system, and there are intermittent problems in its generation. Therefore, how to achieve the power balance, maintain safe operation, and ensure the reliability and quality of power supply when the distributed energy reaches a high penetration in the grid is a huge challenge. Blockchain as one of the research hotspots brings about new solution approach to the dilemma. The two fields have many commons on decentralization, autonomy, marketization and intelligence. In this paper, we discuss the feasible scheme of the integrated system and add fog computing to reduce costs. Considering the realization of system, we choose Hyper Fabric as the basic structure and add verifiable random function to the consensus aimed to improve randomness and security in the encrypted election. Meanwhile, in order to satisfy the business requirements, a flexible adjustment method based on Deep Q Learning algorithm is designed to realize the joint optimization of throughput, latency and storage cost. The proposed scheme provides the advantages including privacy, flexibility, extensibility and implantation simplicity.

Purpose To explore a new approach mainly based on deep learning residual network (ResNet) to detect infarct cores on non-contrast CT images and improve the accuracy of acute ischemic stroke diagnosis. Methods We continuously enrolled magnetic resonance diffusion weighted image (MR-DWI) confirmed first-episode ischemic stroke patients (onset time: less than 9 h) as well as some normal individuals in this study. They all underwent CT plain scan and MR-DWI scan with same scanning range, layer thickness (4 mm) and interlayer spacing (4 mm) (The time interval between two examinations: less than 4 h). Setting MR-DWI as gold standard of infarct core and using deep learning ResNet combined with a maximum a posteriori probability (MAP) model and a post-processing method to detect the infarct core on non-contrast CT images. After that, we use decision curve analysis (DCA) establishing models to analyze the value of this new method in clinical practice. Results 116 ischemic stroke patients and 26 normal people were enrolled. 58 patients were allocated into training dataset and 58 were divided into testing dataset along with 26 normal samples. The identification accuracy of our ResNet based approach in detecting the infarct core on non-contrast CT is 75.9%. The DCA shows that this deep learning method is capable of improving the net benefit of ischemic stroke patients. Conclusions Our deep learning residual network assisted with optimization methods is able to detect early infarct core on non-contrast CT images and has the potential to help physicians improve diagnostic accuracy in acute ischemic stroke patients.

Background The relationship between acute kidney injury (AKI) and stroke needs quantitative summary. Therefore, we analyzed the associations between AKI and stroke including the incidence, risk factors of AKI after stroke, and the influence of AKI after a stroke on prognosis of stroke. Methods Articles published until November 2019 were searched based on the following databases: PubMed, Web of Science, EMBASE, Medline, and Google Scholar. We computed the following results [rates of AKI incidence after a stroke, odds ratios (ORs) or relative risks (RRs) estimates and the 95% confidence intervals (CIs) for the association between risk factors and AKI, ORs or RRs and the CIs for the association between AKI and outcomes after a stroke] by using STATA 13.0 software. Results The study reported an overall incidence of AKI of 12% with a random‐effects model. Additionally, the present study showed that higher National Institutes of Health Stroke Scale (NIHSS) score on admission and history of hypertension were associated with higher risk of AKI after stroke. Moreover, the study showed that AKI after stroke was associated with higher in‐hospital mortality, higher 1‐month mortality, higher long‐term mortality, and poorer functional outcome. Conclusions Acute kidney injury appears to be a common complication after stroke and is related to increased mortality and disability in stroke. Additionally, high NIHSS score on admission and history of hypertension were the critical risk factors for the AKI after stroke. More large‐scale studies should be made to explore AKI after stroke., AKI appears to be a common complication after stroke and is related to increased mortality and disability in stroke. High NIHSS score on admission and history of hypertension were the critical risk factors for the AKI after stroke.

Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach.

Purpose Accurately predicting outcome (i.e., overall survival (OS) time) for higher grade glioma (HGG) has great clinical value and would provide optimized guidelines for treatment planning. Radiomics focuses on revealing underlying pathophysiological information in biomedical images for disease analysis and demonstrates promising prognostic clinical performance. In this paper, we propose a novel sparse representation-based radiomics framework to predict if HGG patients would have long or short OS time. Methods First, taking advantages of the scale invariant feature transform (SIFT) feature in image characterizing, we developed a sparse representation-based method to convert a local SIFT descriptor into a global tumor feature. Next, because preserving sample structure is beneficial for feature selection, we proposed a locality preserving projection and sparse representation-combined feature selection method to select more discriminative features for tumor classification. Finally, we employed a multifeature collaborative sparse representation classification to combine the information of multimodal images to classify OS time. Results Three experiments were performed on the two datasets provided by different institutions. Specifically, the proposed model was trained and independently tested on dataset 1 (135 subjects), on dataset 2 (86 subjects), and on the combination of dataset 1 and dataset 2, respectively. Experimental results demonstrated that the proposed method achieved encouraging prediction performance, exhibiting a testing accuracy of 93.33% on dataset 1 (one modality), 92.31% on dataset 2 (two modalities), and 87.93% on the combined dataset (one modality). Conclusions The sparse representation theory provides reasonable solutions to feature extraction, feature selection, and classification for radiomics. This study provides a promising tool to enhance the prediction performance of HGG patient's outcome.

Multi-modality MRI has been increasingly used for disease diagnosis and prognosis evaluation. However, the acquisitions of multi-modal images always face some practical difficulties such as the increased cost and patient discomfort. In this paper, we proposed an improved 3D UNet model for MRI modality synthesis and use the synthetic modalities to improve the performance of the following image analysis tasks such as tumor segmentation and tumor grading. We developed our model with three improvements on the basic 3D UNet model. First, we constructed a coarse-to-fine resolution fusion network to improve the ability of structural texture preservation. Second, we designed a convolutional fusion-based residual learning framework to learn mapping function, which leads to more detail preservation as well as less artifacts in the background. Third, for many-to-one synthesis, to combine the input multi-modalities more effectively, we introduced a new network module to fuse the UNet output feature maps of each modality. One private and two public datasets including a total of 493 cases were used for synthesis validation. Our proposed method surpasses the best existing method about 2.0 dB and 0.8 dB in PSNRs on the two datasets, respectively. Furthermore, two separate radiomics applications were used to demonstrate the value of our modality synthesis method. In the image segmentation application, integrating the modalities synthesized by our method has improved the segmentation Dice by 1.3%. In gliomas grading diagnosis application, integrating the synthesized modalities improved the diagnostic accuracy by 8.3%.

Metal oxide nanostructure detectors must adsorb both oxygen molecules and incident light to achieve ultrahigh photogain. However, the oxygen adsorption and desorption process can prolong the photoresponse time of the photogain. Therefore, it is a challenge to fabricate such metal oxide nanostructures that have the ability to adsorb both oxygen molecules and incident light simultaneously to generate large amounts of carriers under light illumination, using a simple preparation method. In this work, self-connected core–shell SnO2 microspheres were prepared and used as a photodetector. The interconnected SnO2 device exhibited improved photoresponse properties with photocurrent of 15.4 μA at room temperature, representing a nearly 43-fold enhancement compared with traditional photodetectors. The underlying mechanism for this process was revealed by Hall mobility versus temperature and photocurrent versus power intensity characteristics. We found that conducting channels among the tightly interconnected microspheres are mainly responsible for the improved photocurrent response, providing effective paths for electron transport as well as available sites for charge carrier accumulation.

YAG:Ce transparent ceramics with high luminous efficiency and color render index were prepared via a solid state reaction-vacuum sintering method. Cr 3+ and Pr 3+ were applied to expand the spectrum of YAG:Ce transparent ceramics. As prepared ceramics exhibit luminescence spectrum ranging from 500 nm to 750 nm, which almost covers full range of visible light. After the concentration optimization of Ce 3+ , Pr 3+ and Cr 3+ , high quality white light was obtained by coupling the YAG:Ce,Pr,Cr ceramics with commercial blue LED chips. Color coordinates of the YAG:Ce,Pr,Cr ceramics under 450 nm LED excitation vary from cold white light to warm white light region. The highest luminous efficiency of WLEDs encapsulated by transparent YAG:Ce,Pr,Cr ceramic was 89.3 lm/W, while its color render index can reach nearly 80. Energy transfers between Ce 3+ → Pr 3+ and Ce 3+ → Cr 3+ were proved in co-doped ceramic system. Transparent luminescence ceramics accomplished in this work can be quite prospective for high power WLEDs application.

Purpose To determine whether ACE 2 I/D and BDKRB2 3 + 9/− 9 polymorphism causatively affect diabetic nephropathy progression Results STZ-induced metabolic disorder, as well as inflammatory responses, was significantly aggravated in ACE II-B2R 4 + 9 bp, ACE DD-B2R + 9 bp, or ACE DD-B2R − 9 bp diabetic mice but not ACE II-B2R − 9 bp, indicating the genetic susceptibility of ACE DD or B2R + 9 bp to diabetic nephropathy. Furthermore, ACE II-B2R + 9 bp, ACE DD-B2R + 9 bp, or ACE DD-B2R − 9 bp rather than ACE II-B2R − 9 bp, worsened renal performance and enhanced pathological alterations induced by STZ. Markedly elevated monocyte chemoattractant protein-1(MCP-1), podocin, osteopontin (OPN), transforming growth factor-β1 (TGF-β1), and reduced nephrin, podocin were also detected both in diabetic mice and podocytes under hyperglycemic conditions in response to ACE II-B2R + 9 bp, ACE DD-B2R + 9 bp, or ACE DD-B2R − 9 bp, versus ACE II-B2R − 9 bp. In addition, high glucose-induced mitochondrial oxidative stress and cell apoptosis were observably increased in response to ACE II-B2R + 9 bp, ACE DD-B2R + 9 bp, or ACE DD-B2R − 9 bp but not ACE II-B2R − 9 bp. Conclusions We provide first evidence indicating the causation between ACE DD or B2R + 9 bp genotype and the increased risk for diabetic nephropathy, broadening our horizon about the role of genetic modulators in this disease.

Data broadcasting has been commonly deployed in many emerging mobile applications such as intelligent transportation systems and location-based services, because it is a scalable approach to disseminating information from a mobile support station (MSS) to a large population of mobile hosts (MHs). To provide timely data access and better data availability, MHs can store data items broadcast by the MSS in their local caches and share cached data items cooperatively among neighboring peers via peer-to-peer (P2P) communication. However, if MHs are not neighbors, they cannot cooperate even if they have each others requested data items in their own caches. Network coding is a technique, by which multiple MHs can decode out different requested data items from an encoded packet broadcast by the MSS in one broadcast time unit. In this work, we propose a network coding based solution to enable MHs which are not neighbors to cooperate indirectly. We formulate the Maximum Channel Efficiency Encoding (MCEE) problem by introducing network coding and cooperative caching techniques in on-demand data broadcast environments. We prove that MCEE is NP-hard by constructing a polynomial-time reduction from the Minimum Clique Cover (MCC) problem. Further, we propose two schemes (NCM and NCB) for on-demand data broadcasting using network coding. In each scheme, we propose two algorithms running at the MSS and MHs for making encoding decisions and decoding requested data items, respectively. We build the simulation model for performance evaluation and the simulation results demonstrate that the proposed schemes not only increase the bandwidth efficiency of the limited downlink communication channel, but also enhance the system performance by reducing the data access latency.

Background The determination of molecular subgroups—wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4—of medulloblastomas is very important for prognostication and risk-adaptive treatment strategies. Due to the rare disease characteristics of medulloblastoma, we designed a unique multitask framework for the few-shot scenario to achieve noninvasive molecular subgrouping with high accuracy. Methods We introduced a multitask technique based on mask regional convolutional neural network (Mask-RCNN). By effectively utilizing the comprehensive information including genotyping, tumor mask, and prognosis, multitask technique, on the one hand, realized multi-purpose modeling and simultaneously, on the other hand, promoted the accuracy of the molecular subgrouping. One hundred and thirteen medulloblastoma cases were collected from 4 hospitals during the 8-year period in the retrospective study, which were divided into 3-fold cross-validation cohorts (N = 74) from 2 hospitals and independent testing cohort (N = 39) from the other 2 hospitals. Comparative experiments of different auxiliary tasks were designed to illustrate the effect of multitasking in molecular subgrouping. Results Compared to the single-task framework, the multitask framework that combined 3 tasks increased the average accuracy of molecular subgrouping from 0.84 to 0.93 in cross-validation and from 0.79 to 0.85 in independent testing. The average area under the receiver operating characteristic curves (AUCs) of molecular subgrouping were 0.97 in cross-validation and 0.92 in independent testing. The average AUCs of prognostication also reached to 0.88 in cross-validation and 0.79 in independent testing. The tumor segmentation results achieved the Dice coefficient of 0.90 in both cohorts. Conclusions The multitask Mask-RCNN is an effective method for the molecular subgrouping and prognostication of medulloblastomas with high accuracy in few-shot learning.

This paper numerically investigates the effects of airfoil leading edge radius on the aerodynamic characteristics of H-rotor Darrieus vertical axis wind turbine (VAWT). 10 modified airfoils are generated by changing the leading edge radius of the base NACA 0015 airfoil from 1%c to 9%c, respectively. A 2D unsteady Computational Fluid Dynamics (CFD) model is established and validated with the previously published experimental data. The power, torque, and flow field characteristics of the rotors are analyzed. The results indicate that the maximum and minimum power coefficient at the optimum tip speed ratio (TSR) are obtained for the LE-5%c and LE-1%c model, respectively. The best aerodynamic characteristics are determined by the LE-5%c model below the optimum TSR and the LE-3%c model beyond the optimum TSR. The torque characteristics and pressure distribution for the single blades with different airfoil leading edge radius show an obvious difference in the upwind region and a very small difference in the downwind region. Moreover, the airfoil leading edge radius influences the strength, region, and diffusion rate of the vortices, being the main reason for the observed differences in instantaneous torque coefficient and power coefficient. The vortices of the LE-1%c model are stronger, larger, and diffuse slower than those of the LE-2%c and LE-5%c model at the optimum TSR.

Women with obesity are more likely to have a complicated reproductive life. Insulin resistance and metabolic dysfunction are associated with obesity. Thymoquinone (TQ) is a well-known antioxidant, considered to be an AMPK-activator. The goal of this work was to investigate the ability of TQ to improve fertility and lactation and clarify the possible mechanism. Female C57BL/6 mice were subjected to High Fat Diet (HFD) supplemented with TQ (10% pmm) and TQ (20% pmm). Histopathological examination was conducted on mammary and ovarian samples. Metabolic and oxidant status was evaluated, and qRT-PCR analysis was performed to verify AMPK/PGC1α/SIRT1 metabolic pathway activity. The present study reports positive effects of TQ on ovarian metabolic function in a dose-dependent manner. TQ showed its positive effects on mammary gland metabolic function at lower dose. This is the first study that indicates these dose related impacts of TQ.

// Lili Liu 1, * , Liqin Lu 2, * , Aihong Zheng 2 , Jiansheng Xie 1 , Qian Xue 2 , Fuwei Wang 2 , Xiao Wang 2 , Hongying Zhou 2 , Xiangmin Tong 3 , Yaqing Li 4 , Xiuming Zhu 2 , Guoqing Wu 2 1 Department of Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China 2 Department of Oncology, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China 3 Department of Hematology, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China 4 Department of Respiratory, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China * These authors contributed equally to this work Correspondence to: Xiuming Zhu, email: zhuxm0708@163.com Guoqing Wu, email: gqwuzsu@hotmail.com Keywords: hepatocellular carcinoma, miR-199a-5p, let-7c, MAP4K3, metastasis Received: March 28, 2016 Accepted: January 03, 2017 Published: January 13, 2017 ABSTRACT Hepatocellular carcinoma (HCC) has a high recurrence rate, and patients exhibit poor survival mainly because intrahepatic metastasis is common. We previously reported that let-7c down-regulation is significantly associated with poor differentiation level in HCC. In the present study, we demonstrate that miR-199a-5p and let-7c are frequently down-regulated in HCC cells and tissues, and low expression of miR-199a-5p is correlated with tumor size, liver envelope invasion. Furthermore, miR-199a-5p and let-7c cooperatively inhibit HCC cell migration and invasion in vitro . MAP4K3 is identified as the direct target of miR-199a-5p and let-7c and this regulation is further confirmed by luciferase reporter assays and Western blotting. In addition, MAP4K3 functions as a metastasis promoter since the results demonstrate that MAP4K3 could promote HCC cell migration and invasion. We also find that miR-199a-5p and let-7c increase the sensitivity of HCC cells to sorafenib. Conclusions: We report that miR-199a-5p and let-7c cooperatively and efficiently inhibit HCC cell migration and invasion by targeting the metastasis promoter MAP4K3 and MAP4K3-mediated drug sensitization, suggesting that the use of miRNAs and sorafenib in combination therapy may be a powerful approach to the treatment of HCC.

Image restoration is a crucial problem in image processing and a necessary step before the image segmentation and recognition. A new framework for image restoration in 3D transform domain terms as joint sparse representation (JSR) is proposed in this work. The proposed JSR is able to represent image more sparsely and more precisely in the transform domain by performing 3D transform on each set of similar blocks. In addition to that, in order to overcome the issues of defective block matching and spurious artifact in the 3D sparse representation, JSR introduces a new nonlocal regularization term which characterizes the statistics of the nonlocal image to improve the accuracy of the estimated coefficients. The parameters of regularization terms are calculated based on Bayesian philosophy, and a split Bregman-based technique is developed to obtain the solution in a tractable and robust manner. Extensive experiments on image denoising, image inpainting and image deblurring demonstrate that the proposed JSR algorithm outperforms current state-of-the-art approaches in terms of peak signal-to-noise ratio and visual quality.

Nonlocally centralized sparse representation (CSR) is an effective approach for estimating original image in noise. In order to promote sparse coefficients more accurate than CSR, we present a new framework where another nonlocal sparsity constraint term is introduced to work with the original term alternatively. To gain the two nonlocal sparsity constraint terms, we classify the image into different types according to the statistical characteristics of speckle in SAR image firstly. Then, we choose the appropriate methods to denoise different types of image and utilize the projected coefficients of these denoising results to estimate the nonlocal sparsity constraints. Our method not only modifies the well-salgortudied method of CSR, but also applialgories to despeckle SAR image. Experimental results, carried out on both simulated SAR images and real SAR images, demonstrate that the proposed approach has a competitive despeckling performance in terms of both evaluation indicators and visual quality assessment.

We present a semi-supervised approach for software defect prediction. The proposed method is designed to address the special problematic characteristics of software defect datasets, namely, lack of labeled samples and class-imbalanced data. To alleviate these problems, the proposed method features the following components. Being a semi-supervised approach, it exploits the wealth of unlabeled samples in software systems by evaluating the confidence probability of the predicted labels, for each unlabeled sample. And we propose to jointly optimize the classifier parameters and the dictionary by a task-driven formulation, to ensure that the learned features (sparse code) are optimal for the trained classifier. Finally, during the dictionary learning process we take the different misclassification costs into consideration to improve the prediction performance. Experimental results demonstrate that our method outperforms several representative state-of-the-art defect prediction methods.

A new “E-E-K” (bionic) noise was generated during clutch engagement. By testing the vehicles with the EEK noise, the key parts for EEK noise and the motion modes were pointed out. The module of clutch engagement process was built. The stability of the system was analyzed based on the theory of Hurwitz. Then the multi-body dynamic model of the clutch system was built, and the corresponding EEK noise-vibration model system was created. A system vibration mode corresponding with EEK noise was established in the model, which simulated and reproduced the noise and vibration mechanism of the vehicle testing. All these verified the consistency between vehicle testing and model. Based on the multi-body dynamic simulation, the key component for EEK noise had been clarified, and the further research direction of EEK noise was pointed out.

Sediment and soil samples from the Beitang River (BR) and the Haihe River (HR) in Tianjin were analyzed to investigate the extent of mercury contamination. The results show that total mercury (THg) contents in the BR and HR sediments were 2241 ± 1024 and 653 ± 450 ng g(-1), and THg in rice paddy soils were 907 ± 345 and 328 ± 286 ng g(-1), respectively. Industrial and domestic sewage were regarded as the main sources of mercury in the two river basins. Sediment-bound mercury in the BR and the HR were found to be predominantly associated with the organic-bound fraction (55 %) and residual fraction and (54 %), while soil-bound mercury was mainly in organic-bound fraction in paddy soils (61 % and 57 %, respectively). The availability of this element (soluble and exchangeable and specifically sorbed fraction) seemed restricted, but significantly higher in the paddy soils than in sediments. Higher soluble and exchangeable, specifically sorbed fraction and organic-bound fraction may promote the higher toxic methylmercury and bioavailable fraction formation in the soils during the rice cultivation.

Pathological subtype saved as an important marker in gliomas has considerable diagnostic and prognostic values. However, previous identification of pathological subtype relies on tumor samples, which is invasive. In this paper, we proposed a 3D texture feature learning method which is based on sparse representation (SR) theory to noninvasively estimate the pathological subtype for gliomas. Firstly, we developed a 3D patch-based SR model to extract 3D tumor texture features form magnetic resonance (MR) images. Then, by considering the physical meaning and characteristics of the extracted features, instead of performing feature selection directly, we further extract some deep features describing the statistical difference of the texture features of different tumors for subtype estimation. 213 subjects are divide into cross validation cohort and independent testing cohort to validate the proposed method. The proposed method achieves encouraging performance, with the accuracy of 91.43% and 88.57% by using T1 contrast-enhanced and T2-Flair MR images, respectively.

With the development of wireless communication technology and the lack of spectrum resources, it is very meaningful to study the dynamic spectrum allocation in the cognitive Internet of Things. In this paper, the system model is firstly established. In an underlay mode, considering the interference between primary and secondary users, jointing channel selection and power allocation, aiming to maximize the spectrum efficiency of all secondary users. Different from the traditional heuristic algorithm, the underlay-cognitive-radio-deep-Q-network frame-work (UCRDQN) based on deep reinforcement learning, is proposed to find the optimal solution efficiently. The simulation results show that the UCRDQN algorithm can achieve higher spectrum efficiency and is more stable and efficient than other algorithms.

Background This study aims to establish a radiomics analysis system for the diagnosis and clinical behaviour prediction of hepatocellular carcinoma (HCC) based on multi-parametric ultrasound imaging. Methods A total of 177 patients with focal liver lesions (FLLs) were included in the study. Every patient underwent multi-modal ultrasound examination, including B-mode ultrasound (BMUS), shear wave elastography (SWE), and shear wave viscosity (SWV) imaging. The radiomics analysis system was built on sparse representation theory (SRT) and support vector machine (SVM) for asymmetric data. Through the sparse regulation from the SRT, the proposed radiomics system can effectively avoid over-fitting issues that occur in regular radiomics analysis. The purpose of the proposed system includes differential diagnosis between benign and malignant FLLs, pathologic diagnosis of HCC, and clinical prognostic prediction. Three biomarkers, including programmed cell death protein 1 (PD-1), antigen Ki-67 (Ki-67) and microvascular invasion (MVI), were included and analysed. We calculated the accuracy (ACC), sensitivity (SENS), specificity (SPEC) and area under the receiver operating characteristic curve (AUC) to evaluate the performance of the radiomics models. Results A total of 2560 features were extracted from the multi-modal ultrasound images for each patient. Five radiomics models were built, and leave-one-out cross-validation (LOOCV) was used to evaluate the models. In LOOCV, the AUC was 0.94 for benign and malignant classification (95% confidence interval [CI]: 0.88 to 0.98), 0.97 for malignant subtyping (95% CI: 0.93 to 0.99), 0.97 for PD-1 prediction (95% CI: 0.89 to 0.98), 0.94 for Ki-67 prediction (95% CI: 0.87 to 0.97), and 0.98 for MVI prediction (95% CI: 0.93 to 0.99). The performance of each model improved when the viscosity modality was included. Conclusions Radiomics analysis based on multi-modal ultrasound images could aid in comprehensive liver tumor evaluations, including diagnosis, differential diagnosis, and clinical prognosis.

To evaluate the ability of MRI radiomics to categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer (OEC) patients. A total of 286 patients with pathologically proven adnexal tumor were retrospectively included in this study. We evaluated diagnostic performance of the signatures derived from MRI radiomics in differentiating (1) between benign adnexal tumors and malignancies and (2) between type I and type II OEC. The least absolute shrinkage and selection operator method was used for radiomics feature selection. Risk scores were calculated from the Lasso model and were used for survival analysis. For the classification between benign and malignant masses, the MRI radiomics model achieved a high accuracy of 0.90 in the leave-one-out (LOO) cross-validation cohort and an accuracy of 0.87 in the independent validation cohort. For the classification between type I and type II subtypes, our method made a satisfactory classification in the LOO cross-validation cohort (accuracy = 0.93) and in the independent validation cohort (accuracy = 0.84). Low-high-high short-run high gray-level emphasis and low-low-high variance from coronal T2-weighted imaging (T2WI) and eccentricity from axial T1-weighted imaging (T1WI) images had the best performance in two classification tasks. The patients with higher risk scores were more likely to have poor prognosis (hazard ratio = 4.1694, p = 0.001). Our results suggest radiomics features extracted from MRI are highly correlated with OEC classification and prognosis of patients. MRI radiomics can provide survival estimations with high accuracy. • The MRI radiomics model could achieve a higher accuracy in discriminating benign ovarian diseases from malignancies. • Low-high-high short-run high gray-level emphasis, low-low-high variance from coronal T2WI, and eccentricity from axial T1WI had the best performance outcomes in various classification tasks. • The ovarian cancer patients with high-risk scores had poor prognosis.

Brain tumors are the most common malignant neurologic tumors with the highest mortality and disability rate. Because of the delicate structure of the brain, the clinical use of several commonly used biopsy diagnosis is limited for brain tumors. Radiomics is an emerging technique for noninvasive diagnosis based on quantitative medical image analyses. However, current radiomics techniques are not standardized regarding feature extraction, feature selection, and decision making. In this paper, we propose a sparse representation-based radiomics (SRR) system for the diagnosis of brain tumors. First, we developed a dictionary learning- and sparse representation-based feature extraction method that exploits the statistical characteristics of the lesion area, leading to fine and more effective feature extraction compared with the traditional explicitly calculation-based methods. Then, we set up an iterative sparse representation method to solve the redundancy problem of the extracted features. Finally, we proposed a novel multi-feature collaborative sparse representation classification framework that introduces a new coefficient of regularization term to combine features from multi-modal images at the sparse representation coefficient level. Two clinical problems were used to validate the performance and usefulness of the proposed SRR system. One was the differential diagnosis between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), and the other was isocitrate dehydrogenase 1 estimation for gliomas. The SRR system had superior PCNSL and GBM differentiation performance compared with some advanced imaging techniques and yielded 11% better performance for estimating IDH1 compared with the traditional radiomics methods.

In this paper, we apply the texture conception of natural language to the texture classiflcation, and classify the natural texture into ten classes. Basing on the above, we found a small image library of natural texture. In the thesis, we discuss the common means of texture feature extraction, and bring forward a speciflc algorithm for Gabor fllter. In order to validity of the feature extraction, we adopt the BP network as the classifler to carry out our experiments, which bring us satisfying results.

Accurate prognosis for high grade glioma (HGG) is of great clinical value since it would provide optimized guidelines for treatment planning. Previous imaging-based survival prediction generally relies on some features guided by clinical experiences, which limits the full utilization of biomedical image. In this paper, we propose a sparse representation-based radiomics framework to predict overall survival (OS) time of HGG. Firstly, we develop a patch-based sparse representation method to extract the high-throughput tumor texture features. Then, we propose to combine locality preserving projection and sparse representation to select discriminating features. Finally, we treat the OS time prediction as a classification task and apply sparse representation to classification. Experiment results show that, with 10-fold cross-validation, the proposed method achieves the accuracy of 94.83% and 95.69% by using T1 contrast-enhanced and T2 weighted magnetic resonance images, respectively.

Semiconductors have large optical nonlinear susceptibilities especially in the spectral range above material bandgaps. However, optical frequency conversion encounters large absorption above bandgaps and difficulty using common phase-matching techniques. The frequency conversion bandwidths are thus limited. Here, frequency up-conversion far above the bandgaps using surface emissions from semiconductor nanoribbons is demonstrated, wherein nanoscale waveguiding tightly confines fundamental waves for decreasing pump powers, and above-bandgap absorption is greatly decreased in nanoscale waveguide thickness. By using CdSe nanoribbons, efficient 532- and 404-nm second-harmonic and 459-nm sum-frequency generations are obtained with the continuous-wave pump power less than 100 μW. The normalized efficiency of 532-nm second-harmonic generation is about 2 × 10 -5 mm -1 at pump power of 300 μW. Attractive features such as tunable spatial distribution and highly polarization are observed. A broadband emission with a full width half maximum of ~10 nm is attained by frequency summing a continuous-wave laser and an amplified spontaneous emission source.

By using PdAu nanowires as plasmonic waveguides, hybrid photon-plasmon Mach-Zehnder interferometers by integrating single-crystal PdAu alloy nanowires with silica optical microfibers are demonstrated. Based on an evanescent wave coupling technique using optical fiber tapers, surface plasmon polaritons are efficiently excited and propagated in suspended PdAu nanowires. The interference spectra show attractive properties such as broad and flexible in situ tunability with wavelength spacings ranging from ∼1 to tens of nanometers, and high extinction ratios of over 20 dB. The hybrid Mach-Zehnder interferometers show a higher sensitivity to hydrogen gas than a single-nanowire sensing approach, and the lengths of PdAu nanowires used are less than 20 μm, which are 2 or 3 orders of magnitude shorter than the lengths of Pd coatings in existing fiber-optic hydrogen sensors. Other advantages including good reversibility and low-power operation are also obtained.

Due to the totally different therapeutic regimens needed for primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), accurate differentiation of the two diseases by noninvasive imaging techniques is important for clinical decision-making.Thirty cases of PCNSL and 66 cases of GBM with conventional T1-contrast magnetic resonance imaging (MRI) were analyzed in this study. Convolutional neural networks was used to segment tumor automatically. A modified scale invariant feature transform (SIFT) method was utilized to extract three-dimensional local voxel arrangement information from segmented tumors. Fisher vector was proposed to normalize the dimension of SIFT features. An improved genetic algorithm (GA) was used to extract SIFT features with PCNSL and GBM discrimination ability. The data-set was divided into a cross-validation cohort and an independent validation cohort by the ratio of 2:1. Support vector machine with the leave-one-out cross-validation based on 20 cases of PCNSL and 44 cases of GBM was employed to build and validate the differentiation model.Among 16,384 high-throughput features, 1356 features show significant differences between PCNSL and GBM with p0.05 and 420 features with p0.001. A total of 496 features were finally chosen by improved GA algorithm. The proposed method produces PCNSL vs. GBM differentiation with an area under the curve (AUC) curve of 99.1% (98.2%), accuracy 95.3% (90.6%), sensitivity 85.0% (80.0%) and specificity 100% (95.5%) on the cross-validation cohort (and independent validation cohort).Since the local voxel arrangement characterization provided by SIFT features, proposed method produced more competitive PCNSL and GBM differentiation performance by using conventional MRI than methods based on advanced MRI.

Radiomics can convert digital images to mineable data by extracting a huge number of image features. Because of the high dimensions of radiomics features, feature selection is a very important step which affects the performance of the final prediction or classification. In this paper, we propose a feature selection criterion for radiomics analysis of glioma based on Magnetic Resonance Image (MRI). The proposed method named as minimum Redundancy, Maximum Relevance and Maximum Sparse Representation Coefficient (mRMRMSRC) criterion, which take three factors into consideration at the same time: relevance between features and labels with or without the influence of all other features, and redundancy between each couple of features. Different from traditional feature selection method, the mRMRMSRC manifests the best performance compared with the methods based on sparse representation coefficient (SRC), minimum redundancy maximum relevance (mRMR), F_score and ReliefF. We conducted our methods on glioma Isocitrate Dehydrogenase 1 (IDH1) estimation. The experiment showed that mRMRMSRC produced area under the ROC curve (AUC) of 90% compared with 77%–89% of state-of-art methods.

Dietary methionine restriction (MR) could prevent age-related diseases and extend lifespan. The present study compared the effects of different levels of MR on oxidative stress and behavioral deficits in middle-aged mice fed a diet with various contents of fat. C57BL/6J middle-aged male mice were divided into nine groups with various combinations of dietary fat (low, medium, and high) and MR (0%, 60%, and 80%). MR attenuated dietary fat-induced oxidative stress and anxiety; improved athletic ability, learning, memory, and cognitive ability in conjunction with an improved thyroid function in a dose-dependent manner. Moreover, the beneficial effect of MR60 was prominent in middle-aged mice fed a HF diet relative to the corresponding MR0 group. These results provide more information on the appropriate methionine content to design personalized nutritional diets.

Transverse frequency conversion in the sub-bandgap spectral region is investigated in semiconductor nanowires and nanoribbons using CW lasers with a pump power less than 1 mW. It is found that the properties of the emissions are strongly dependent on the cross-sectional geometries and the surrounding media of nano-waveguides. The polarization is higher in nano-waveguides under single-mode conditions, and the spatial distribution is more tunable in nano-waveguides with higher-order modes involved. Compared with the birefringent approach, transverse frequency conversion shows lower divergence angles, higher polarization, and more tunable spatial distribution.

A balloon-type airfoil, which has the classic airfoil shape but is full of air is designed in this paper. When the angle of attack changes, the pressure distribution on the airfoil surface will correspondingly be changed: the airfoil surface where the pressure is high will be pushed toward the inside of the airfoil resulting in the reduced curvature of this part of the airfoil and even a concave surface formed, but on the other hand, the curvature of the airfoil surface where the pressure is low will be increased. The initial shape of this adaptive reconfigurable airfoil used in this paper is NACA0012. As the forces acting on the airfoil surface should be in balance, a force balance equation is established at first. Then, the surface curvature of the airfoil as well as its shape can thereby be predicted. The flow field and the pressure distribution over the surface of the deformed airfoil can be simulated by use of the variational finite element method. In this paper, the pressure distribution over this adaptive airfoil as well as its corresponding shape under different incoming flows at angles of attack from −14° to 14° has been studied. At last, the XFoil software is used to calculate the coefficient of lift and drag and lift-to-drag ratio of the deformed airfoil. It has been found that this adaptive reconfigurable airfoil can automatically change its shape according to the pressure distribution over its surface and achieve better aerodynamic performance than NACA0012 airfoil in a wide range of application.