Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation

Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with sufficient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low-and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low-and sufficient-luminance domains.

Self-Adaptive Feature Transformation Networks for Object Detection in low luminance Images

Despite the recent improvement of object detection techniques, many of them fail to detect objects in low-luminance images. The blurry and dimmed nature of low-luminance images results in the extraction of vague features and failure to detect objects. In addition, many existing object detection methods are based on models trained on both sufficient- and low-luminance images, which also negatively affect the feature extraction process and detection results. In this article, we propose a framework called Self-adaptive Feature Transformation Network (SFT-Net) to effectively detect objects in low-luminance conditions. The proposed SFT-Net consists of the following three modules: (1) feature transformation module, (2) self-adaptive module, and (3) object detection module. The purpose of the feature transformation module is to enhance the extracted feature through unsupervisely learning a feature domain projection procedure. The self-adaptive module is utilized as a probabilistic module producing appropriate features either from the transformed or the original features to further boost the performance and generalization ability of the proposed framework. Finally, the object detection module is designed to accurately detect objects in both low- and sufficient- luminance images by using the appropriate features produced by the self-adaptive module. The experimental results demonstrate that the proposed SFT-Net framework significantly outperforms the state-of-the-art object detection techniques, achieving an average precision (AP) of up to 6.35 and 11.89 higher on the sufficient- and low- luminance domain, respectively.

SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25,900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181,164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed.

DSNet: Joint Semantic Learning for Object Detection in Inclement Weather Conditions

In the past half of the decade, object detection approaches based on the convolutional neural network have been widely studied and successfully applied in many computer vision applications. However, detecting objects in inclement weather conditions remains a major challenge because of poor visibility. In this article, we address the object detection problem in the presence of fog by introducing a novel dual-subnet network (DSNet) that can be trained end-to-end and jointly learn three tasks: visibility enhancement, object classification, and object localization. DSNet attains complete performance improvement by including two subnetworks: detection subnet and restoration subnet. We employ RetinaNet as a backbone network (also called detection subnet), which is responsible for learning to classify and locate objects. The restoration subnet is designed by sharing feature extraction layers with the detection subnet and adopting a feature recovery (FR) module for visibility enhancement. Experimental results show that our DSNet achieved 50.84 percent mean average precision (mAP) on a synthetic foggy dataset that we composed and 41.91 percent mAP on a public natural foggy dataset (Foggy Driving dataset), outperforming many state-of-the-art object detectors and combination models between dehazing and detection methods while maintaining a high speed.

Detecting High-Engaging Breaking News Rumors in Social Media

Users from all over the world increasingly adopt social media for newsgathering, especially during breaking news. Breaking news is an unexpected event that is currently developing. Early stages of breaking news are usually associated with lots of unverified information, i.e., rumors. Efficiently detecting and acting upon rumors in a timely fashion is of high importance to minimize their harmful effects. Yet, not all rumors have the potential to spread in social media. High-engaging rumors are those written in a manner that ensures achievement of the highest prevalence among the recipients. They are difficult to detect, spread very fast, and can cause serious damage to society. In this article, we propose a new multi-task Convolutional Neural Network (CNN) attention-based neural network architecture to jointly learn the two tasks of breaking news rumors detection and breaking news rumors popularity prediction in social media. The proposed model learns the salient semantic similarities among important features for detecting high-engaging breaking news rumors and separates them from the rest of the input text. Extensive experiments on five real-life datasets of breaking news suggest that our proposed model outperforms all baselines and is capable of detecting breaking news rumors and predicting their future popularity with high accuracy.

Redox sensor NPGPx restrains ZAP70 activity and modulates T cell homeostasis

Emerging evidences implicate the contribution of ROS to T cell activation and signaling. The tyrosine kinase, ζ-chain-associated protein of 70 kDa (ZAP70), is essential for T cell development and activation. However, it remains elusive whether a direct redox regulation affects ZAP70 activity upon TCR stimulation. Here, we show that deficiency of non-selenocysteine containing phospholipid hydroperoxide glutathione peroxidase (NPGPx), a redox sensor, results in T cell hyperproliferation and elevated cytokine productions. T cell-specific NPGPx-knockout mice reveal enhanced T-dependent humoral responses and are susceptible to experimental autoimmune encephalomyelitis (EAE). Through proteomic approaches, ZAP70 is identified as the key interacting protein of NPGPx through disulfide bonding. NPGPx is activated by ROS generated from TCR stimulation, and modulates ZAP70 activity through redox switching to reduce ZAP70 recruitment to TCR/CD3 complex in membrane lipid raft, therefore subduing TCR responses. These results reveal a delicate redox mechanism that NPGPx serves as a modulator to curb ZAP70 functions in maintaining T cell homeostasis.

Effects of Glycyrrhiza polysaccharide in diet on growth performance, serum antioxidant capacity, and biochemistry of broilers

In the present study, we analyzed the effects of Glycyrrhiza polysaccharide (GCP) on growth performance, serum antioxidant capacity, and biochemistry of broilers. A total of 600, one-day-old AA broilers randomly divided into 5 treatment groups with 6 replicate pens of 20 birds per cage received dietary supplementation with GCP (0, 200, 500, 1,000, and 1,500 mg/kg) for 42 d. The supplementation of GCP linearly decreased (P < 0.05) feed conversion rate on day 22 to 42. Dietary supplementation with GCP reduced (P < 0.05) serum total cholesterol on day 21 and 42 and linearly improved (P < 0.05) albumin and high-density lipoprotein cholesterol. Dietary supplementation with 1,000 or 1,500 mg/kg GCP significantly increased (P < 0.05) serum total superoxide dismutase (T-SOD) activity on day 21 and 42 and reduced (P < 0.05) serum malondialdehyde content on 21 d. Dietary supplementation with 1,000 or 1,500 mg/kg GCP significantly improved (P < 0.05) interleukin-1β (IL-1β) and interferon-γ (IFN-γ) expressions in liver on day 21 and 42. At the end of the experiment, we randomly selected 20 broilers from 3 treatment groups (0, 1,000, and 1,500 mg/kg), respectively, to perform an lipopolysaccharide (LPS)-induced acute stress experiment. The 60 broilers were divided into 6 treatment groups with 10 birds per cage. The experiment was designed as a 3 × 2 factorial arrangement with GCP (0, 1,000, or 1,500 mg/kg) and LPS (injection of saline or 1 mg/kg body weight) levels as treatments. When the grouping was finished, the broilers were immediately intraperitoneally injected with LPS or normal saline. Six hours after challenged, serum antioxidant and liver immunity were analyzed. The results showed that dietary GCP prevented LPS-induced reductions in T-SOD activity and increases in malonaldehyde content (P < 0.05). Also, dietary GCP supplementation mitigated the LPS-induced increase in IL-1β and IFN-γ in the liver. Supplementation with 1,500 mg/kg GCP showed the most optimal effect in broilers. GCP has the potential to be used as feed additive in broilers.

Single Image Snow Removal Using Sparse Representation and Particle Swarm Optimizer

Images are often corrupted by natural obscuration (e.g., snow, rain, and haze) during acquisition in bad weather conditions. The removal of snowflakes from only a single image is a challenging task due to situational variety and has been investigated only rarely. In this article, we propose a novel snow removal framework for a single image, which can be separated into a sparse image approximation module and an adaptive tolerance optimization module. The first proposed module takes the advantage of sparsity-based regularization to reconstruct a potential snow-free image. An auto-tuning mechanism for this framework is then proposed to seek a better reconstruction of a snow-free image via the time-varying inertia weight particle swarm optimizers in the second proposed module. Through collaboration of these two modules iteratively, the number of snowflakes in the reconstructed image is reduced as generations progress. By the experimental results, the proposed method achieves a better efficacy of snow removal than do other state-of-the-art techniques via both objective and subjective evaluations. As a result, the proposed method is able to remove snowflakes successfully from only a single image while preserving most original object structure information.

Pancreatic cancer-derived small extracellular vesical Ezrin regulates macrophage polarization and promotes metastasis

Small extracellular vesicles (sEVs) mediate the interaction between tumor and tumor-associated macrophages (TAMs). This study aims to demonstrate that the pancreatic ductal adenocarcinoma (PDAC)-derived sEV Ezrin (sEV-EZR) could modulate macrophage polarization and promote PDAC metastasis. We isolated PDAC-derived sEVs and plasma sEVs from PDAC patients. Human blood mononuclear cell (PBMC)-derived macrophages were treated with PDAC-derived sEVs or the counterpart depleted Ezrin (EZR) with shRNA-mediated knockdown. We used enzyme-linked immunosorbent assays and flow cytometry to monitor macrophages polarization. NOD/SCID/IL2Rγ^null mice were treated with sEVs to study PDAC liver metastasis. The plasma sEV-EZR levels of 165 PDAC patients and 151 high-risk controls were analyzed. The EZR levels are higher in sEVs derived from PDAC cells and PDAC-patient plasma than that of the normal controls. PDAC-derived sEVs modulate the polarization of macrophages to M2 phenotype, while PDAC-shEZR-derived sEVs polarize macrophages into M1 phenotype. We found an increase in M1 TAMs and a decrease in M2 TAMs in orthotropic tumors treated with PDAC-shEZR-derived sEVs. The amount of liver metastasis in PDAC-shEZR-derived sEVs-treated mice was observed to be smaller than that of controls. The mean plasma sEV-EZR levels from PDAC patients were significantly higher than those from the controls (32.43±20.78 vs. 21.88±11.43 pg/ml; P<0.0001). The overall survival in the high-plasma sEV-EZR patients was significantly shorter than that in the low-EZR group (6.94±15.25 vs. 9.63±15.11 months; P=0.0418). sEV-EZR could modulate macrophage polarization and promote metastasis in PDAC. Targeting sEV-EZR can be considered a promising therapeutic strategy to inhibit PDAC metastasis.

Histidine-Dependent Protein Methylation Is Required for Compartmentalization of CTP Synthase

CTP synthase (CTPS) forms compartmentalized filaments in response to substrate availability and environmental nutrient status. However, the physiological role of filaments and mechanisms for filament assembly are not well understood. Here, we provide evidence that CTPS forms filaments in response to histidine influx during glutamine starvation. Tetramer conformation-based filament formation restricts CTPS enzymatic activity during nutrient deprivation. CTPS protein levels remain stable in the presence of histidine during nutrient deprivation, followed by rapid cell growth after stress relief. We demonstrate that filament formation is controlled by methylation and that histidine promotes re-methylation of homocysteine by donating one-carbon intermediates to the cytosolic folate cycle. Furthermore, we find that starvation stress and glutamine deficiency activate the GCN2/ATF4/MTHFD2 axis, which coordinates CTPS filament formation. CTPS filament formation induced by histidine-mediated methylation may be a strategy used by cancer cells to maintain homeostasis and ensure a growth advantage in adverse environments.

An Evolutionary Multiobjective Carpool Algorithm Using Set-Based Operator Based on Simulated Binary Crossover

Sharing vehicle journeys with other passengers can provide many benefits, such as reducing traffic congestion and making urban transportation more environmentally friendly. For the procedure of sharing empty seats, we need to consider increased ridership and driving distances incurred by carpool detours resulting from matching passengers to drivers, as well as maximizing the number of simultaneous matches. In accordance with these goals, this paper proposes and defines the multiobjective optimization carpool service problem (MOCSP). Previous studies have used evolutionary algorithms by combining multiple objectives into a single objective through a weighted linear or/and nonlinear combination of different objectives, thus turning to a single-objective optimization problem. These single-objective problems are optimized, but there is no guarantee of the performance of the respective objectives. By improving the individual representation and genetic operation, we developed a set-based simulated binary and multiobjective carpool matching algorithm that can more effectively solve MOCSP. Furthermore, the proposed algorithm can provide better driver-passenger matching results than can the binary-coded and set-based nondominated sorting genetic algorithms.

Self-Organizing Neuroevolution for Solving Carpool Service Problem With Dynamic Capacity to Alternate Matches

Traffic congestion often incurs environmental problems. One of the most effective ways to mitigate this is carpooling transportation, which substantially reduces automobile demands. Due to the popularization of smartphones and mobile applications, a carpool service can be conveniently accessed via the intelligent carpool system. In this system, the service optimization required to intelligently and adaptively distribute the carpool participant resources is called the carpool service problem (CSP). Several previous studies have examined viable and preliminary solutions to the CSP by using exact and metaheuristic optimization approaches. For CSP-solving, evolutionary computation (e.g., metaheuristics) is a more promising option in comparison to exact-type approaches. However, all the previous state-of-the-art approaches use pure optimization to solve the CSP. In this paper, we employ the framework of neuroevolution to propose the self-organizing map-based neuroevolution (SOMNE) solver by which the SOM-like network represents the abstract CSP solution and is well-trained by using neural learning and evolutionary mechanism. The experimental section of this paper investigates the comparisons and analyses of two objective functions of the CSP and demonstrates that the proposed SOMNE solver achieves superior results when compared against those the other approaches produce, especially in regard to the optimization of the primary objective functions of the CSP. Finally, the visual results of the SOM are illustrated to show the effectiveness and efficiency of the evolutionary neural learning process.

Haze Removal Using Radial Basis Function Networks for Visibility Restoration Applications

Restoration of visibility in hazy images is the first relevant step of information analysis in many outdoor computer vision applications. To this aim, the restored image must feature clear visibility with sufficient brightness and visible edges, while avoiding the production of noticeable artifacts. In this paper, we propose a haze removal approach based on the radial basis function (RBF) through artificial neural networks dedicated to effectively removing haze formation while retaining not only the visible edges but also the brightness of restored images. Unlike traditional haze-removal methods that consist of single atmospheric veils, the multiatmospheric veil is generated and then dynamically learned by the neurons of the proposed RBF networks according to the scene complexity. Through this process, more visible edges are retained in the restored images. Subsequently, the activation function during the testing process is employed to represent the brightness of the restored image. We compare the proposed method with the other state-of-the-art haze-removal methods and report experimental results in terms of qualitative and quantitative evaluations for benchmark color images captured in typical hazy weather conditions. The experimental results demonstrate that the proposed method is able to produce brighter and more vivid haze-free images with more visible edges than can the other state-of-the-art methods.

TGF-β1 secreted by Tregs in lymph nodes promotes breast cancer malignancy via up-regulation of IL-17RB

Lymph node (LN) metastasis is commonly associated with systemic distant organ metastasis in human breast cancer and is an important prognostic predictor for survival of breast cancer patients. However, whether tumor‐draining LNs (TDLNs) play a significant role in modulating the malignancy of cancer cells for distant metastasis remains controversial. Using a syngeneic mouse mammary tumor model, we found that breast tumor cells derived from TDLN have higher malignancy and removal of TDLNs significantly reduced distant metastasis. Up‐regulation of oncogenic Il‐17rb in cancer cells derived from TDLNs contributes to their malignancy. TGF‐β1 secreted from regulatory T cells (Tregs) in the TDLNs mediated the up‐regulation of Il‐17rb through downstream Smad2/3/4 signaling. These phenotypes can be abolished by TGF‐β1 neutralization or depletion of Tregs. Consistently, clinical data showed that the up‐regulation of IL‐17RB in cancer cells from LN metastases correlated with the increased prevalence of Tregs as well as the aggressive growth of tumors in mouse xenograft assay. Together, these results indicate that Tregs in TDLNs play an important role in modulating the malignancy of breast cancer cells for distant metastasis. Blocking IL‐17RB expression could therefore be a potential approach to curb the process.

Unobtrusive Activity Recognition of Elderly People Living Alone Using Anonymous Binary Sensors and DCNN

Elderly population (over the age of 60) is predicted to be 1.2 billion by 2025. Most of the elderly people would like to stay alone in their own house due to the high eldercare cost and privacy invasion. Unobtrusive activity recognition is the most preferred solution for monitoring daily activities of the elderly people living alone rather than the camera and wearable devices based systems. Thus, we propose an unobtrusive activity recognition classifier using deep convolutional neural network (DCNN) and anonymous binary sensors that are passive infrared motion sensors and door sensors. We employed Aruba annotated open data set that was acquired from a smart home where a voluntary single elderly woman was living inside for eight months. First, ten basic daily activities, namely, Eating, Bed_to_Toilet, Relax, Meal_Preparation, Sleeping, Work, Housekeeping, Wash_Dishes, Enter_Home, and Leave_Home are segmented with different sliding window sizes, and then converted into binary activity images. Next, the activity images are employed as the ground truth for the proposed DCNN model. The 10-fold cross-validation evaluation results indicated that our proposed DCNN model outperforms the existing models with F₁-score of 0.79 and 0.951 for all ten activities and eight activities (excluding Leave_Home and Wash_Dishes), respectively.

DesnowNet: Context-Aware Deep Network for Snow Removal

Existing learning-based atmospheric particle-removal approaches such as those used for rainy and hazy images are designed with strong assumptions regarding spatial frequency, trajectory, and translucency. However, the removal of snow particles is more complicated because they possess additional attributes of particle size and shape, and these attributes may vary within a single image. Currently, hand-crafted features are still the mainstream for snow removal, making significant generalization difficult to achieve. In response, we have designed a multistage network named DesnowNet to in turn deal with the removal of translucent and opaque snow particles. We also differentiate snow attributes of translucency and chromatic aberration for accurate estimation. Moreover, our approach individually estimates residual complements of the snow-free images to recover details obscured by opaque snow. Additionally, a multi-scale design is utilized throughout the entire network to model the diversity of snow. As demonstrated in the qualitative and quantitative experiments, our approach outperforms state-of-the-art learning-based atmospheric phenomena removal methods and one semantic segmentation baseline on the proposed Snow100K dataset. The results indicate our network would benefit applications involving computer vision and graphics.

Molecular characterization of ascaris from Tibetan pigs by three mitochondrial markers of nad1, cox1 and cox2

Ascaris is a helminthic parasite, which infects a wide range of host species causing ascariasis, a predominant disease worldwide. This parasite causes significant economic losses to the pig industry. The current study was designed to determine the Ascaris nematode by the genetic characterization of three mitochondrial (mt) genes namely NADH dehydrogenase subunit 1 (nad1), cytochrome oxidase subunit 1 (cox1) and cytochrome oxidase subunit 2 (cox2). A high infection rate of Ascaris nematode has been found in Tibetan pigs at the slaughter houses in Tibet Autonomous Region of China. The nad1, cox1 and cox2 genes sequences collected from adult Ascaris individuals were amplified by polymerase chain reaction. The cloned-amplicons and the positive products were sequenced and phylogenetic analysis was performed. The results indicated that the Ascaris infecting the Tibetan pigs were Ascaris suum (A. suum). This is the first report on the isolation, identification and genetic characterization of three mitochondrial genomes (nad1, cox1, and cox2) of A. suum originated from Tibetan pigs at high altitudes in Tibet.

Stochastic Set-Based Particle Swarm Optimization Based on Local Exploration for Solving the Carpool Service Problem

The growing ubiquity of vehicles has led to increased concerns about environmental issues. These concerns can be mitigated by implementing an effective carpool service. In an intelligent carpool system, an automated service process assists carpool participants in determining routes and matches. It is a discrete optimization problem that involves a system-wide condition as well as participants' expectations. In this paper, we solve the carpool service problem (CSP) to provide satisfactory ride matches. To this end, we developed a particle swarm carpool algorithm based on stochastic set-based particle swarm optimization (PSO). Our method introduces stochastic coding to augment traditional particles, and uses three terminologies to represent a particle: 1) particle position; 2) particle view; and 3) particle velocity. In this way, the set-based PSO (S-PSO) can be realized by local exploration. In the simulation and experiments, two kind of discrete PSOs-S-PSO and binary PSO (BPSO)-and a genetic algorithm (GA) are compared and examined using tested benchmarks that simulate a real-world metropolis. We observed that the S-PSO outperformed the BPSO and the GA thoroughly. Moreover, our method yielded the best result in a statistical test and successfully obtained numerical results for meeting the optimization objectives of the CSP.

A Hybrid Background Subtraction Method with Background and Foreground Candidates Detection

Background subtraction for motion detection is often used in video surveillance systems. However, difficulties in bootstrapping restrict its development. This article proposes a novel hybrid background subtraction technique to solve this problem. For performance improvement of background subtraction, the proposed technique not only quickly initializes the background model but also eliminates unnecessary regions containing only background pixels in the object detection process. Furthermore, an embodiment based on the proposed technique is also presented. Experimental results verify that the proposed technique allows for reduced execution time as well as improvement of performance as evaluated by Recall, Precision, F1, and Similarity metrics when used with state-of-the-art background subtraction methods.

Hazy Image Restoration by Bi-Histogram Modification

Visibility restoration techniques are widely used for information recovery of hazy images in many computer vision applications. Estimation of haze density is an essential task of visibility restoration techniques. However, conventional visibility restoration techniques often suffer from either the generation of serious artifacts or the loss of object information in the restored images due to uneven haze density, which usually means that the images contain heavy haze formation within their background regions and little haze formation within their foreground regions. This frequently occurs when the images feature real-world scenes with a deep depth of field. How to effectively and accurately estimate the haze density in the transmission map for these images is the most challenging aspect of the traditional state-of-the-art techniques. In response to this problem, this work proposes a novel visibility restoration approach that is based on Bi-Histogram modification, and which integrates a haze density estimation module and a haze formation removal module for effective and accurate estimation of haze density in the transmission map. As our experimental results demonstrate, the proposed approach achieves superior visibility restoration efficacy in comparison with the other state-of-the-art approaches based on both qualitative and quantitative evaluations. The proposed approach proves effective and accurate in terms of both background and foreground restoration of various hazy scenarios.

Transcriptome sequencing and analysis of the coconut leaf beetle, Brontispa longissima

The coconut leaf beetle, Brontispa longissima, is a destructive pest of palm plants. Although its ecological and biological characteristics are well understood, its genetic information remains largely unknown. To advance our understanding of its molecular ba-sis of biology and ecology, we sequenced and analyzed its whole transcriptome by using high-throughput Illumina paired-end sequencing technology. Approximately 8.08 Gb of clean reads were generated in a single run, which were assembled by using Trinity into 41,652 unigenes with an average length of 932 bp. By sequence similarity searches for known proteins, 23,077 (55.4%) unigenes were annotated by BLASTx searches against the NCBI non-redundant protein database. Of the unigenes assembled, 18,153 and 13,733 were assigned to Gene Ontology and Clusters of Orthologous Groups of proteins, respectively. In addition, 10,415 unigenes were mapped onto 247 pathways using the Kyoto Encyclopedia of Genes and Genomes Pathway database. These transcriptomic resources will facilitate gene identification and elucidate the molecular mechanisms of biological and ecological aspects under-lying this palm pest, in order to design a new control strategy.

A new hardware-efficient algorithm and reconfigurable architecture for image contrast enhancement

Contrast enhancement is crucial when generating high quality images for image processing applications, such as digital image or video photography, liquid crystal display processing, and medical image analysis. In order to achieve real-time performance for high-definition video applications, it is necessary to design efficient contrast enhancement hardware architecture to meet the needs of real-time processing. In this paper, we propose a novel hardware-oriented contrast enhancement algorithm which can be implemented effectively for hardware design. In order to be considered for hardware implementation, approximation techniques are proposed to reduce these complex computations during performance of the contrast enhancement algorithm. The proposed hardware-oriented contrast enhancement algorithm achieves good image quality by measuring the results of qualitative and quantitative analyzes. To decrease hardware cost and improve hardware utilization for real-time performance, a reduction in circuit area is proposed through use of parameter-controlled reconfigurable architecture. The experiment results show that the proposed hardware-oriented contrast enhancement algorithm can provide an average frame rate of 48.23 frames/s at high definition resolution 1920 × 1080.

Helminth infection reactivates latent γ-herpesvirus via cytokine competition at a viral promoter

Mammals are coinfected by multiple pathogens that interact through unknown mechanisms. We found that helminth infection, characterized by the induction of the cytokine interleukin-4 (IL-4) and the activation of the transcription factor Stat6, reactivated murine γ-herpesvirus infection in vivo. IL-4 promoted viral replication and blocked the antiviral effects of interferon-γ (IFNγ) by inducing Stat6 binding to the promoter for an important viral transcriptional transactivator. IL-4 also reactivated human Kaposi's sarcoma-associated herpesvirus from latency in cultured cells. Exogenous IL-4 plus blockade of IFNγ reactivated latent murine γ-herpesvirus infection in vivo, suggesting a "two-signal" model for viral reactivation. Thus, chronic herpesvirus infection, a component of the mammalian virome, is regulated by the counterpoised actions of multiple cytokines on viral promoters that have evolved to sense host immune status.

Radial Basis Function Based Neural Network for Motion Detection in Dynamic Scenes

Motion detection, the process which segments moving objects in video streams, is the first critical process and plays an important role in video surveillance systems. Dynamic scenes are commonly encountered in both indoor and outdoor situations and contain objects such as swaying trees, spouting fountains, rippling water, moving curtains, and so on. However, complete and accurate motion detection in dynamic scenes is often a challenging task. This paper presents a novel motion detection approach based on radial basis function artificial neural networks to accurately detect moving objects not only in dynamic scenes but also in static scenes. The proposed method involves two important modules: a multibackground generation module and a moving object detection module. The multibackground generation module effectively generates a flexible probabilistic model through an unsupervised learning process to fulfill the property of either dynamic background or static background. Next, the moving object detection module achieves complete and accurate detection of moving objects by only processing blocks that are highly likely to contain moving objects. This is accomplished by two procedures: the block alarm procedure and the object extraction procedure. The detection results of our method were evaluated by qualitative and quantitative comparisons with other state-of-the-art methods based on a wide range of natural video sequences. The overall results show that the proposed method substantially outperforms existing methods with Similarity and F1 accuracy rates of 69.37% and 65.50%, respectively.

Evaluation of the effects of nanoscale zero-valent iron (nZVI) dispersants on intrinsic biodegradation of trichloroethylene (TCE)

In this study, the biodegradability of nanoscale zero-valent iron (nZVI) dispersants and their effects on the intrinsic biodegradation of trichloroethylene (TCE) were evaluated. Results of a microcosm study show that the biodegradability of three dispersants followed the sequence of: polyvinyl alcohol-co-vinyl acetate-co-itaconic acid (PV3A) > polyoxyethylene (20) sorbitan monolaurate (Tween 20) > polyacrylic acid (PAA) under aerobic conditions, and PV3A > Tween 20 > PAA under anaerobic conditions. Natural biodegradation of TCE was observed under both aerobic and anaerobic conditions. No significant effects were observed on the intrinsic biodegradation of TCE under aerobic conditions with the presence of the dispersants. The addition of PAA seemed to have a slightly adverse impact on anaerobic TCE biodegradation. Higher accumulation of the byproducts of anaerobic TCE biodegradation was detected with the addition of PV3A and Tween 20. The diversity of the microbial community was enhanced under aerobic conditions with the presence of more biodegradable PV3A and Tween 20. The results of this study indicate that it is necessary to select an appropriate dispersant for nZVI to prevent a residual of the dispersant in the subsurface. Additionally, the effects of the dispersant on TCE biodegradation and the accumulation of TCE biodegrading byproducts should also be considered.

Complete response after MAID treatment for advanced primary ovarian angiosarcoma: case report and literature review

The patient presented in this case report was a 45-year-old female, with a Stage IIIA ovarian angiosarcoma combined with mature teratoma, that underwent debulking surgery and achieved complete remission for 11 months after six cycles of MAID chemotherapy (mesna, adriamycin/doxorubicin, ifosfamide, and dacarbazine). Thereafter, she had tumor recurrence with peritoneal seeding and massive pleural effusion; hence she received chemotherapy again. Although she had been undergoing a series of chemotherapies, the tumor continued to progress. Hence, she refused further chemotherapy since September 2012. Unfortunately, she passed away in January 2013 due to severe dyspnea with wide spread tumor progression. She had the longest survival period (31 months) and complete remission period than the other advanced primary ovarian angiosarcoma cases ever reported in the literature.

Müllerian adenosarcoma: a review of cases and literature

OBJECTIVE

Mullerian adenosarcoma usually originates in the endometrium and grows as a polypoid mass in post-menopausal women presenting as abnormal vaginal bleeding. This report reviewed Miillerian adenosarcoma cases to clarify the clinical and pathologic characteristics.

MATERIALS AND METHODS

Fifteen cases ofMiillerian adenosarcoma in two medical centers covering a 15-year period were reviewed. Their clinical characteristics, pathologic findings, treatment, and outcomes were compared.

RESULTS

Of the 15 cases, three originated from the endometrium, six arose from uterine adenomyosis, three from the adnexa, and three from the cervix. There was only one post-menopausal case. One case was of breast cancer with tamoxifen (TMX) therapy. There were four Miillerian adenosarcoma with sarcomatous overgrowth (MASO) cases, three of which died within one year after surgery. Only the focal MASO case survived.

CONCLUSION

The rare variant of MASO is very aggressive and associated with poor prognosis.

Highly accurate moving object detection in variable bit rate video-based traffic monitoring systems

Automated motion detection, which segments moving objects from video streams, is the key technology of intelligent transportation systems for traffic management. Traffic surveillance systems use video communication over real-world networks with limited bandwidth, which frequently suffers because of either network congestion or unstable bandwidth. Evidence supporting these problems abounds in publications about wireless video communication. Thus, to effectively perform the arduous task of motion detection over a network with unstable bandwidth, a process by which bit-rate is allocated to match the available network bandwidth is necessitated. This process is accomplished by the rate control scheme. This paper presents a new motion detection approach that is based on the cerebellar-model-articulation-controller (CMAC) through artificial neural networks to completely and accurately detect moving objects in both high and low bit-rate video streams. The proposed approach is consisted of a probabilistic background generation (PBG) module and a moving object detection (MOD) module. To ensure that the properties of variable bit-rate video streams are accommodated, the proposed PBG module effectively produces a probabilistic background model through an unsupervised learning process over variable bit-rate video streams. Next, the MOD module, which is based on the CMAC network, completely and accurately detects moving objects in both low and high bit-rate video streams by implementing two procedures: 1) a block selection procedure and 2) an object detection procedure. The detection results show that our proposed approach is capable of performing with higher efficacy when compared with the results produced by other state-of-the-art approaches in variable bit-rate video streams over real-world limited bandwidth networks. Both qualitative and quantitative evaluations support this claim; for instance, the proposed approach achieves Similarity and F1 accuracy rates that are 76.40% and 84.37% higher than those of existing approaches, respectively.

Efficient contrast enhancement using adaptive gamma correction with weighting distribution

This paper proposes an efficient method to modify histograms and enhance contrast in digital images. Enhancement plays a significant role in digital image processing, computer vision, and pattern recognition. We present an automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels. To enhance video, the proposed image-enhancement method uses temporal information regarding the differences between each frame to reduce computational complexity. Experimental results demonstrate that the proposed method produces enhanced images of comparable or higher quality than those produced using previous state-of-the-art methods.

Sonographic demonstration of human small intestinal migrating motor complex phase III

BACKGROUND

Migrating motor complex phase III (MMC phase III) of intestine is an important physiological mechanism traditionally recognized by myoelectric recordings or pressure tracings. Direct imaging is difficult and sonographic visualization in human has not been reported.

METHODS

We have demonstrated this unique phenomenon in three patients who underwent abdominal sonographic examinations. Characteristic images were recorded by videotape and both spatial and temporal features were analyzed.

KEY RESULTS

Occurrences of multiple equally spaced, rhythmic intestinal contractions were observed. Parameters including wave frequency, propagation velocity, and duration of the events agreed with those of the well-known phase III. The presence of distinct cyclic patterns observed in two and abolition by meal in the other patient further support our conclusion.

CONCLUSIONS & INFERENCES

We conclude that the migrating waves observed in our study represent the human MMC phase III. This unique finding in human subjects merits further investigation.

Systemic vascular resistance is increased and associated with accelerated arterial stiffening change in patients with chronic cervical spinal cord injury

BACKGROUND

Despite of stiffening change of conduit arteries, how total peripheral resistance (TPR) is adapted to chronic spinal cord injury (SCI) remains unclear.

AIM

To investigate how chronic cervical SCI influences hemodynamic characteristics

DESIGN

Cross-sectional, case-control study.

SETTING

Rehabilitation department in the tertiary medical center.

POPULATION

Twenty-one male patients with traumatic SCI resulting from cervical spine fracture were recruited. The injury occurred three to 289 months (46 months in average) previously. Twenty-one healthy male participants with matched age and body mass index were enrolled as control group.

METHODS

The subjects were asked to maintain supine rest (SR) and then head-up tilt (HUT) at 60 degree for five minutes, respectively. A novel noninvasive bio-reactance device was employed to measure cardiac hemodynamics, whereas heart rate variability was used to determine cardiac autonomic activity. Additionally, the digital volume pulse analysis was applied to calculate arterial stiffness index (SI) and arteriolar reflection index (RI).

RESULTS

SCI patients revealed less stroke volume and cardiac output (CO), as well as, greater total peripheral resistance (TPR) and SI during SR than normal subjects did. Moreover, the positive correlation between TPR and SI was observed in SCI patients rather than normal subjects. In SCI patients, HUT (1) markedly decreased TPR while CO and cardio-acceleration responses remained intact and (2) decreased HF power value but failed to change LF/HF ratio. Furthermore, the degree of orthostatic hypotension was correlated with the TPRHUT/TPRSR ratio but not the COHUT/COSR ratio.

CONCLUSIONS

Chronic cervical SCI leads to a progressively accelerated increase in vascular stiffness, which is associated with increase in systemic vascular resistance. Furthermore, the cervical SCI-related orthostatic hypotension lies in the impairment of vasoconstriction without cardiac dysfunction. Clinical Rehabilitation Impact. SI, rather than blood pressure, reflects not only cardiovascular risk but also TPR in chronic cervical SCI.

Electromagnetic scanning to estimate carcass lean content of Taiwan native broilers

To estimate lean content of Taiwan native broiler carcasses accurately, objectively and rapidly, electrical conductivity measurements of broiler carcasses and other relative factors (carcass weight, length and temperature) were used in multiple linear regression analysis for lean prediction. Forty native broiler carcasses, with average market weight 2477.5±465.5 g, were scanned through a 10 MHz electromagnetic field created by an electromagnetic scanner (SA-3203) to measure the total body electrical conductivity (TOBEC) index. After scanning, each broiler carcass was separated into wing, breast, leg and back. Each primal cut was dissected into lean, fat and bone. The weight, length, temperature and TOBEC index of broiler carcass were significantly correlated with lean weight of broiler carcass (P<0.001). Regression analysis for lean estimation with carcass weight, length, temperature and TOBEC index showed higher coefficient of determination (R(2)=0.968) and lower coefficient of variation (CV=4.178) with an equation using beheaded carcass weight, temperature and TOBEC index as variables.

Bounds on the number of hidden neurons in multilayer perceptrons

Fundamental issues concerning the capability of multilayer perceptrons with one hidden layer are investigated. The studies are focused on realizations of functions which map from a finite subset of E(n) into E(d). Real-valued and binary-valued functions are considered. In particular, a least upper bound is derived for the number of hidden neurons needed to realize an arbitrary function which maps from a finite subset of E(n ) into E(d). A nontrivial lower bound is also obtained for realizations of injective functions. This result can be applied in studies of pattern recognition and database retrieval. An upper bound is given for realizing binary-valued functions that are related to pattern-classification problems.

Heart retransplantation for pediatric primary allograft failure

PURPOSE

Heart transplantation is indicated for children with end-stage heart failure or complex inoperable congenital defects. When the transplanted heart fails, retransplantation is suggested and herein we have presented the prognosis of these pediatric cases.

MATERIALS AND METHODS

From March 1987 to March 2011, we performed 404 heart transplantations including 45 pediatric patients, 6 (13.3%) of whom experienced graft failure requiring retransplantation. Only four of the six patients (66.7%) had a chance for retransplantation.

RESULTS

Six of 45 pediatric heart transplant patients (13.3%) experienced graft failure requiring retransplantation. Four of them (66.7%) underwent retransplantation. Only one of the four died due to severe postoperative sepsis with acute respiratory distress. The other three patients recovered well and remain alive with no neurological sequelae; all are in New York Heart Association functional classification I at present.

CONCLUSION

Pediatric post-heart graft failure require expectations retransplantation, which shows a good prognosis.

Positron emission tomography of brain β-amyloid and τ levels in adults with Down syndrome

OBJECTIVES

To determine the neuropathological load in the living brain of nondemented adults with Down syndrome using positron emission tomography with 2-(1-{6-[(2-fluorine 18-labeled fluoroethyl)methylamino]-2-napthyl}ethylidene) malononitrile ([(18)F]FDDNP) and to assess the influence of age and cognitive and behavioral functioning. For reference, [(18)F]FDDNP binding values and patterns were compared with those from patients with Alzheimer disease and cognitively intact control participants.

DESIGN

Cross-sectional clinical study.

PARTICIPANTS

Volunteer sample of 19 persons with Down syndrome without dementia (mean age, 36.7 years), 10 patients with Alzheimer disease (mean age, 66.5 years), and 10 controls (mean age, 43.8 years).

MAIN OUTCOME MEASURES

Binding of [(18)F]FDDNP in brain regions of interest, including the parietal, medial temporal, lateral temporal, and frontal lobes and posterior cingulate gyrus, and the average of all regions (global binding).

RESULTS

The [(18)F]FDDNP binding values were higher in all brain regions in the Down syndrome group than in controls. Compared with the Alzheimer disease group, the Down syndrome group had higher [(18)F]FDDNP binding values in the parietal and frontal regions, whereas binding levels in other regions were comparable. Within the Down syndrome group, age correlated with [(18)F]FDDNP binding values in all regions except the posterior cingulate, and several measures of behavioral dysfunction showed positive correlations with global, frontal, parietal, and posterior cingulate [(18)F]FDDNP binding.

CONCLUSIONS

Consistent with neuropathological findings from postmortem studies, [(18)F]FDDNP positron emission tomography shows high binding levels in Down syndrome comparable to Alzheimer disease and greater levels than in members of a control group. The positive associations between [(18)F]FDDNP binding levels and age as well as behavioral dysfunction in Down syndrome are consistent with the age-related progression of Alzheimer-type neuropathological findings in this population.

Differential gene expression between the porcine morula and blastocyst

The survival and development of pre-implantation embryos are determinant factors affecting the outcome of animal reproduction. It is essential to transfer the expression of the genetic material from maternal sources, that is the ovum to the zygote before implantation to ensure successful development. Differentiation and transformation of blastomeres initiated during the morula and blastocyst stages is an important step of the embryonic development prior to implantation. We collected morula and early blastocyst samples from pure-bred Landrace pigs in vivo to study the differential gene expression patterns at these two stages. Total RNA was extracted from individual embryos and two rounds of amplification were employed. Two micrograms of antisense RNA, targets, were prepared and hybridized with each of four custom made oligo microarrays representing 24,000 porcine genes. The analyses of replicate hybridizations showed that among the 24,000 genes, 162 genes were expressed fivefold or greater in the morula compared to early blastocysts and 2126 genes were expressed fivefold or greater in early blastocysts compared to the morula. Of these differentially expressed genes, 1429 genes were functionally annotated with related human Gene Ontology terms. In addition to basic metabolic processes, genes related to signal transduction, transportation and cell differentiation were found in both stages and were up-regulated as embryo development proceeded. Real time polymerase chain reaction was utilized to quantify 12 genes differentially expressed in the 2 embryonic stages and validated the reliability of major evidences shown in microarrays. In conclusion, we have obtained a preliminary landscape of genes differentially expressed during the transition from morula to early blastocysts in pigs and showed a generally increased transcriptional activity, perhaps in preparation for implantation. Our results provide an opportunity to study the functions of these genes in relation to the development and survival of pre-implantation porcine embryos.

Lipid raft assembly and Lck recruitment in TRAIL costimulation mediates NF-κB activation and T cell proliferation

The TNF-related apoptosis-inducing ligand was shown to provide a costimulatory signal that cooperates with the TCR/CD3 complex to induce T cell proliferation and cytokine production. Although a number of signaling pathways were linked to the TCR/CD3 complex, it is not known how these two receptors cooperate to induce T cell activation. In this study, we show that TRAIL-induced costimulation of T cells depends on activation of the NF-κB pathway. TRAIL induced the NF-κB pathway by phosphorylation of inhibitor of κB factor kinase and protein kinase C in conjunction with anti-CD3. Furthermore, we demonstrated that TRAIL costimulation induced phosphorylation of the upstream TCR-proximal tyrosine kinases, Lck and ZAP70. Ligation of the TRAIL by its soluble receptor, DR4-Fc, alone was able to induce the phosphorylation of Lck and ZAP70 and to activate the NF-κB pathway; however, it was insufficient to fully activate T cells to support T cell proliferation. In contrast, TRAIL engagement in conjunction with anti-CD3, but not TRAIL ligation alone, induced lipid raft assembly and recruitment of Lck and PKC. These results demonstrate that TRAIL costimulation mediates NF-κB activation and T cell proliferation by lipid raft assembly and recruitment of Lck. Our results suggest that in TRAIL costimulation, lipid raft recruitment of Lck integrates mitogenic NF-κB-dependent signals from the TCR and TRAIL in T lymphocytes.

End-stage renal disease after orthotopic heart transplantation: a single-institute experience

Orthotopic heart transplantation is the treatment of choice for end-stage heart failure, and calcineurin inhibitor agents allow for better allograft survival. However, pretransplantation low cardiac output status and posttransplantation immunosuppressants contribute toward deterioration of renal function. From 1987 to 2008, 350 patients underwent orthotopic heart transplantation in our hospital. Most of them received anti-thymocyte globulin (ATG) as the induction immunosuppressant. The introduction of mycophenolate mofetil (MMF) reduced the maintenance level of cyclosporine. The 26 patients who developed end-stage renal disease required dialysis. We reviewed the patient characteristics, including pretransplantation status, immunosuppressant regimens and drug levels, time and type of dialysis, and mortality rate. The mean age of these 26 patients was 53 years. Three patients underwent peritoneal dialysis. The overall 1-year survival rate was 96%, and the 5-year survival rate was 80%. The duration from heart transplantation to chronic dialysis correlated with the presence of a pretransplantation diagnosis of diabetes (P<.05) and an elevated pretransplantation blood creatinine level (P=.01), but there was no significant effect of the initial level of cyclosporine. In addition, the pretransplantation blood creatinine level was also related to the necessity of immediate postoperative hemodialysis (P=.01). There was no significant risk factor in relation to mortality. Regardless of modification of immunosuppressant regimens and initial drug levels, pretransplantation kidney function played an important inverse role in the duration from transplantation to dialysis: the higher the pretransplantation blood creatinine, the shorter the duration. While awaiting a heart transplant, more effort should be spent on protecting renal function to avoid early chronic dialysis.

Diode-pumped passively mode-locked 1,342 nm Nd:YVO4 laser with an AlGaInAs quantum-well saturable absorber

We demonstrate what we believe to be the first use of AlGaInAs quantum wells (QWs) as a saturable absorber for a diode-pumped passively mode locked Nd:YVO(4) laser at 1342 nm. The QWs are grown on a Fe-doped InP substrate that is transparent at lasing wavelength. At an incident pump power of 13.5 W an average output power of 1.05 W with a continuous mode-locked pulse duration of 26.4 ps at a repetition rate of 152 MHz was generated.

Efficient high-peak-power AlGaInAs eye-safe wavelength disk laser with optical in-well pumping

We have demonstrated an efficient high-peak-power AlGaInAs eye-safe wavelength disk laser at 1555 nm. The quantum defect and the thermal load are significantly reduced by pumping the quantum well directly. The overall conversion efficiency is enhanced over three times compared with the barrier pumping method. With a pump peak power of 3.7 kW, an output peak power of 0.52 kW is generated at a pulse repetition rate of 20 kHz.

TNF-related apoptosis-inducing ligand (TRAIL) induces osteoclast differentiation from monocyte/macrophage lineage precursor cells via TRAF-6 dependent pathway (92.14)

Human osteoclast formation from mononuclear phagocyte precursors involves interactions between tumor necrosis factor (TNF) ligand superfamily members and their receptors. Many of the proinflammatory cytokines and growth factors implicated in inflammatory processes have also been demonstrated to impact osteoclast differentiation and function. Recent evidence indicates that the TNF-related apoptosis-inducing ligand (TRAIL) of the TNF ligand superfamily, which was initially thought to induce apoptosis in many transformed cell lines, can serve as an effector molecule in activated T cells. We show in this work that TRAIL can induce osteoclast formation from human monocytes and murine RAW264.7 macrophages. We demonstrated that both cell models differentiate into osteoclast-like cells in the presence of TRAIL in a dose-dependent manner, as evaluated in terms of tartrate-resistant acid phosphatase (TRAP)-positive multinucleated cells and bone resorption activity. The TRAIL-induced osteoclast differentiation is independent of caspase activation and apoptosis induction activity. However, TRAIL-induced osteoclastogenesis is dependent on activation of NF-£eB, ERK, and p38 MAP kinase. The TRAIL-induced osteoclastogenesis was significantly inhibited by treatment with TRAF-6 siRNA and TRAF-6 decoy peptide, indicating this pathway is TRAF-6 dependent. Thus, our data demonstrate that TRAIL induces osteoclast differentiation via direct engagement with the TRAIL death receptor through a signaling pathway distinct from apoptosis. Our results indicate that in addition to triggering apoptosis, TRAIL induces osteoclast differentiation. It provides a novel role for TRAIL in regulating osteoclast differentiation and in osteoimmunology.

Influence of cognitive status, age, and APOE-4 genetic risk on brain FDDNP positron-emission tomography imaging in persons without dementia

CONTEXT

Amyloid senile plaques and tau neurofibrillary tangles are neuropathological hallmarks of Alzheimer disease that accumulate in the brains of people without dementia years before they develop dementia. Positron emission tomography (PET) scans after intravenous injections of 2-(1-{6-[(2-[F-18]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malononitrile (FDDNP), which binds to plaques and tangles in vitro, demonstrate increased cerebral binding in patients with Alzheimer disease compared with cognitively intact controls. Here we investigated whether known risk factors for Alzheimer disease and dementia are associated with FDDNP-PET binding.

OBJECTIVE

To determine if impaired cognitive status, older age, apolipoprotein E-4 (APOE-4) genetic risk for Alzheimer disease, family history of dementia, and less education are associated with increased regional cerebral FDDNP-PET binding.

DESIGN

Cross-sectional clinical study.

SETTING

A university research institute.

PARTICIPANTS

Volunteer sample of 76 middle-aged and older persons without dementia (mean age, 67 years) including 36 with mild cognitive impairment. Of the 72 subjects with genetic data, 34 were APOE-4 carriers.

MAIN OUTCOME MEASURES

The FDDNP-PET signal in brain regions of interest, including medial and lateral temporal, posterior cingulate, parietal, and frontal.

RESULTS

For all regions studied, cognitive status was associated with increased FDDNP binding (P < .02 to .005). Older age was associated with increased lateral temporal FDDNP binding. Carriers of APOE-4 demonstrated higher frontal FDDNP binding than noncarriers. In the mild cognitive impairment group, age was associated with increased medial and lateral temporal FDDNP binding, and APOE-4 carriers had higher medial temporal binding than noncarriers.

CONCLUSIONS

Impaired cognitive status, older age, and APOE-4 carrier status are associated with increased brain FDDNP-PET binding in persons without dementia, consistent with previous clinical and postmortem studies associating these risk factors with amyloid plaque and tau tangle accumulation. Stratifying subject groups according to APOE-4 carrier status, age, and cognitive status may therefore be an informative strategy in future clinical trials using FDDNP-PET.