Search

Lowering the levels of the cellular prion protein (PrPC) is widely considered a promising strategy for the treatment of prion diseases. Building on work that established immediate spatial proximity of PrPC and Na+, K+-ATPases (NKAs) in the brain, we recently showed that PrPC levels can be reduced by targeting NKAs with their natural cardiac glycoside (CG) inhibitors. We then introduced C4'-dehydro-oleandrin as a CG with improved pharmacological properties for this indication, showing that it reduced PrPC levels by 84% in immortalized human cells that had been differentiated to acquire neural or astrocytic characteristics. Here we report that our lead compound caused cell surface PrPC levels to drop also in other human cell models, even when the analyses of whole cell lysates suggested otherwise. Because mice are refractory to CGs, we explored guinea pigs as an alternative rodent model for the preclinical evaluation of C4'-dehydro-oleandrin. We found that guinea pig cell lines, primary cells, and brain slices were responsive to our lead compound, albeit it at 30-fold higher concentrations than human cells. Of potential significance for other PrPC lowering approaches, we observed that cells attempted to compensate for the loss of cell surface PrPC levels by increasing the expression of the prion gene, requiring daily administration of C4'-dehydro-oleandrin for a sustained PrPC lowering effect. Regrettably, when administered systemically in vivo, the levels of C4'-dehydro-oleandrin that reached the guinea pig brain remained insufficient for the PrPC lowering effect to manifest. A more suitable preclinical model is still needed to determine if C4'-dehydro-oleandrin can offer a cost-effective complementary strategy for pushing PrPC levels below a threshold required for long-term prion disease survival. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) time-difference-of-arrival (TDOA)-based localization has emerged as a promising, low-cost, and scalable indoor localization solution, which is especially suited for multi-robot applications. However, there is a lack of public datasets to study and benchmark UWB TDOA positioning technology in cluttered indoor environments. We fill in this gap by presenting a comprehensive dataset using Decawave's DWM1000 UWB modules. To characterize the UWB TDOA measurement performance under various line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, we collected signal-to-noise ratio (SNR), power difference values, and raw UWB TDOA measurements during the identification experiments. We also conducted a cumulative total of around 150 min of real-world flight experiments on a customized quadrotor platform to benchmark the UWB TDOA localization performance for mobile robots. The quadrotor was commanded to fly with an average speed of 0.45 m/s in both obstacle-free and cluttered environments using four different UWB anchor constellations. Raw sensor data including UWB TDOA, inertial measurement unit (IMU), optical flow, time-of-flight (ToF) laser altitude, and millimeter-accurate ground truth robot poses were collected during the flights. The dataset and development kit are available at https://utiasdsl.github.io/util-uwb-dataset/. [ABSTRACT FROM AUTHOR]

Existing crowd counting approaches predominantly perform well on the training-testing protocol. However, due to large style discrepancies not only among images but also within a single image, they suffer from obvious performance degradation when applied to unseen domains. In this paper, we aim to design a generalizable crowd counting framework which is trained on a source domain but can generalize well on the other domains. To reach this, we propose a gated ensemble learning framework. Specifically, we first propose a diverse fine-grained style attention model to help learn discriminative content feature representations, allowing for exploiting diverse features to improve generalization. We then introduce a channel-level binary gating ensemble model, where diverse feature prior, input-dependent guidance and density grade classification constraint are implemented, to optimally select diverse content features to participate in the ensemble, taking advantage of their complementary while avoiding redundancy. Extensive experiments show that our gating ensemble approach achieves superior generalization performance among four public datasets. Codes are publicly available at https://github.com/wdzhao123/DCSL. [ABSTRACT FROM AUTHOR]

Remote sensing data distribution generally exposes the long-tailed characteristic. This will limit the object recognition performance of existing deep models when they are trained with such unbalanced data. In this article, we propose a novel hierarchical distillation framework (HDF) to address the long-tailed object recognition in aerial images. First, we notice that not only student model should learn feature representations from teachers but also teacher models should learn feature representations from each other. Therefore, we build hierarchical teacher-wise distillation (HTWD) to improve the feature representations of the teacher models trained with middle and tail data, which is achieved by distilling the feature representations of the teacher model trained with head data. Second, we notice that the feature representations of the middle and tail classes cannot be effectively distilled from the teacher to the student since too little middle and tail data can be used to learn. Thus, we propose self-calibrated sampling (SCS) learning that enforces the student to strengthen the learning of the middle and tail data, thereby improving the student’ feature learning ability. Extensive experiments on two widely used DOTA and FGSC-23 datasets demonstrate the superior performance of the proposed method compared with state-of-the-art methods. Model and code are publicly available at https://github.com/wdzhao123/T2FTS. [ABSTRACT FROM AUTHOR]

Fine-grained object classification (FGOC) focuses on identifying subcategories of objects, which is crucial in military and civilian. Existing FGOC methods primarily focus on high-resolution aerial images, limiting their application on low-resolution (LR) FGOC that is a more realistic setting, especially on resource-constrained satellite devices. It is more challenging to deal with LR FGOC since objects’ details are blurred or missing. Addressing this issue, we make the first attempt to explore LR FGOC and propose a novel pipeline based on two technical insights: 1) feature balance strategy discriminatively integrates super-resolution weak and strong detailed presentations into coarse features of LR aerial images, achieving a feature balance to avoid that the weak detailed presentations are inhibited by the strong ones and 2) iterative interaction mechanism alternately refines feature details of the discriminative ship regions and optimizes the performance of FGOC. Moreover, we build a low-resolution fine-grained object (LFS) dataset to promote further study and evaluation. Extensive experiments on the proposed LFS dataset and the other three object datasets of DOTA, FS23, and HRSC2016 demonstrate that our method outperforms state-of-the-art algorithms. Dataset and code are publicly available at https://github.com/wdzhao123/FBNet. [ABSTRACT FROM AUTHOR]

Benefitting from the strong feature extraction capability of deep learning, infrared and visible image fusion has made a great progress. Since infrared and visible images are obtained by different sensors with different imaging mechanisms, there exists domain discrepancy, which becomes stumbling block for effective fusion. In this paper, we propose a novel self-supervised feature adaption framework for infrared and visible image fusion. We implement a self-supervised strategy that facilitates the backbone network to extract features with adaption while retaining the vital information by reconstructing the source images. Specifically, we preliminary adopt an encoder network to extract features with adaption. Then, two decoders with attention mechanism blocks are utilized to reconstruct the source images in a self-supervised way, forcing the adapted features to contain vital information of the source images. Further, considering the case that source images contain low-quality information, we design a novel infrared and visible image fusion and enhancement model, improving the fusion method's robustness. Experiments are constructed to evaluate the proposed method qualitatively and quantitatively, which show that the proposed method achieves the state-of-art performance comparing with existing infrared and visible image fusion methods. Results are available at https://github.com/zhoafan/SFA-Fuse. • Domain discrepancy between infrared and visible images reduces fusion performance. • Self-supervised mechanism is designed for infrared and visible image fusion. • The novel fusion and enhancement model is more suitable for practical application. • Our method performs better than other representative fusion methods. [ABSTRACT FROM AUTHOR]

Defocus blur detection (DBD) is aimed to estimate the probability of each pixel being in-focus or out-of-focus. This process has been paid considerable attention due to its remarkable potential applications. Accurate differentiation of homogeneous regions and detection of low-contrast focal regions, as well as suppression of background clutter, are challenges associated with DBD. To address these issues, we propose a multi-stream bottom-top-bottom fully convolutional network (BTBNet), which is the first attempt to develop an end-to-end deep network to solve the DBD problems. First, we develop a fully convolutional BTBNet to gradually integrate nearby feature levels of bottom to top and top to bottom. Then, considering that the degree of defocus blur is sensitive to scales, we propose multi-stream BTBNets that handle input images with different scales to improve the performance of DBD. Finally, a cascaded DBD map residual learning architecture is designed to gradually restore finer structures from the small scale to the large scale. To promote further study and evaluation of the DBD models, we construct a new database of 1100 challenging images and their pixel-wise defocus blur annotations. Experimental results on the existing and our new datasets demonstrate that the proposed method achieves significantly better performance than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Recent state-of-the-art methods on focus region detection (FRD) rely on deep convolutional networks trained with costly pixel-level annotations. In this study, we propose a FRD method that achieves competitive accuracies but only uses easily obtained bounding box annotations. Box-level tags provide important cues of focus regions but lose the boundary delineation of the transition area. A recurrent constraint network (RCN) is introduced for this challenge. In our static training, RCN is jointly trained with a fully convolutional network (FCN) through box-level supervision. The RCN can generate a detailed focus map to locate the boundary of the transition area effectively. In our dynamic training, we iterate between fine-tuning FCN and RCN with the generated pixel-level tags and generate finer new pixel-level tags. To boost the performance further, a guided conditional random field is developed to improve the quality of the generated pixel-level tags. To promote further study of the weakly supervised FRD methods, we construct a new dataset called FocusBox, which consists of 5000 challenging images with bounding box-level labels. Experimental results on existing datasets demonstrate that our method not only yields comparable results than fully supervised counterparts but also achieves a faster speed. [ABSTRACT FROM AUTHOR]

Common non-focused areas are often present in multi-focus images due to the limitation of the number of focused images. This factor severely degrades the fusion quality of multi-focus images. To address this problem, we propose a novel end-to-end multi-focus image fusion with a natural enhancement method based on deep convolutional neural network (CNN). Several end-to-end CNN architectures that are specifically adapted to this task are first designed and researched. On the basis of the observation that low-level feature extraction can capture low-frequency content, whereas high-level feature extraction effectively captures high-frequency details, we further combine multi-level outputs such that the most visually distinctive features can be extracted, fused, and enhanced. In addition, the multi-level outputs are simultaneously supervised during training to boost the performance of image fusion and enhancement. Extensive experiments show that the proposed method can deliver superior fusion and enhancement performance than the state-of-the-art methods in the presence of multi-focus images with common non-focused areas, anisotropic blur, and misregistration. [ABSTRACT FROM AUTHOR]

Highlights • An efficient multi-focus fusion method based on local binary patterns is proposed. • The frequent loss of low-contrast area information is successfully avoided. • The proposed algorithm outperforms state-of-the-art image fusion algorithms. Abstract Multi-focus image fusion is to integrate the partially focused images into one single image which is focused everywhere. Nowadays, it has become an important research topic due to the applications in more and more scientific fields. However, preserving more information of the low-contrast area in the focus area and maintaining the edge information are two challenges for existing approaches. In this paper, we address these two challenges with presenting a simple yet efficient multi-focus fusion method based on local binary pattern (LBP). In our algorithm, we measure the clarity using the LBP metric and construct the initial weight map. And then we use the connected area judgment strategy (CAJS) to reduce the noise in the initial map. Afterwards, the two source images are fused together by weighted arranging. The experimental results validate that the proposed algorithm outperforms state-of-the-art image fusion algorithms in both qualitative and quantitative evaluations, especially when dealing with low contrast regions and edge information. [ABSTRACT FROM AUTHOR]

Medical image fusion aims at integrating information from multimodality medical images to obtain a more complete and accurate description of the same object, which provides an easy access for image-guided medical diagnostic and treatment. Unfortunately, medical images are often corrupted by noise in acquisition or transmission, and the noise signal is easily mistaken for a useful characterization of the image, making the fusion effect drop significantly. Thus, the existence of noise presents a great challenge for most of traditional image fusion methods. To address this problem, an effective variation model for multimodality medical image fusion and denoising is proposed. First, a multiscale alternating sequential filter is exploited to extract the useful characterizations (e.g., details and edges) from noisy input medical images. Then, a recursive filtering-based weight map is constructed to guide the fusion of main features of input images. Additionally, total variation (TV) constraint is developed by constructing an adaptive fractional order $p$ based on the local contrast of fused image, further effectively suppressing noise while avoiding the staircase effect of the TV. The experimental results indicate that the proposed method performs well with both noisy and normal medical images, outperforming conventional methods in terms of fusion quality and noise reduction. [ABSTRACT FROM PUBLISHER]

Electrokinetics at nanoscale has attracted broad attention as a promising conductivity based biochemical sensing principle with a good selectivity. The nanoparticle crystal, formed by self-assembling nanoparticles inside a microstructure, has been utilized to fulfill a nanoscale electrokinetics based biochemical sensing platform, named nanofluidic crystal in our previous works. This paper introduces a novel nanofluidic crystal scheme by packing nanoparticles inside a well-designed confined space to improve the device-to-device readout consistency. A pair of electrodes was patterned at the bottom of this tunnel-shaped confined space for ionic current recording. The readout from different chips (n = 16) varied within 8.4% under the same conditions, which guaranteed a self-calibration-free biochemical sensing. Biotin and Pb2+ were successfully detected by using nanofluidic crystal devices packed with streptavidin and DNAzyme modified nanoparticles, respectively. The limits of detection (LODs) were both 1 nM. This electrically readable nanofluidic crystal sensing approach may find applications in low cost and fast disease screening in limited resource environments. [ABSTRACT FROM AUTHOR]

Noise is easily mistaken as useful features of input images, and therefore, significantly reducing image fusion quality. In this paper, we propose a novel gradient entropy metric and p -Laplace diffusion constraint-based method. Specifically, the method is based on the matrix of structure tensor to fuse the gradient information. To minimize the negative effects of noise on the selections of image features, the gradient entropy metric is proposed to construct the weight for each gradient of input images. Particularly, the local adaptive p -Laplace diffusion constraint is constructed to further suppress noise when rebuilding the fused image from the fused gradient field. Experimental results show that the proposed method effectively preserves edge detail features of multispectral images while suppressing noise, achieving an optimal visual effect and more comprehensive quantitative assessments compared to other existing methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] Ti-doped ZnO films were deposited on the glass substrate by pulsed laser deposition. [•] The TZO films had the wurtzite structure with a preferred orientation along the c-axis. [•] The optimized deposition conditions were determined. [•] The TZO films showed high optical transmittance and low electrical resistivity. [Copyright &y& Elsevier]

Automated visual data collection using autonomous unmanned aerial vehicles (UAVs) can improve the accessibility and accuracy of the frequent data required for indoor construction inspections and tracking. However, robust localization, as a critical enabler for autonomy, is challenging in ever-changing, cluttered, GPS-denied indoor construction environments. Rapid alterations and repetitive low-texture areas on indoor construction sites jeopardize the reliability of typical vision-based solutions. This research proposes a tag-based visual-inertial localization method for off-the-shelf UAVs with only a camera and an inertial measurement unit (IMU). Given that tag locations are known in the BIM, the proposed method estimates the UAV's global pose by fusing inertial data and tag measurements using an on-manifold extended Kalman filter (EKF). The root-mean-square error (RMSE) achieved in our experiments in laboratory and simulation, being as low as 2 − 5 cm , indicates the potential of deploying the proposed method for autonomous navigation of low-cost UAVs in indoor construction environments. • Automated indoor data collection using autonomous UAVs requires reliable localization. • Existing localization solutions are costly and unreliable in indoor construction settings. • Planar tags (e.g., AprilTags) can be helpful in visually degraded areas on indoor sites. • A low-cost, lightweight tag-based global localization method for UAVs is proposed. • An on-manifold EKF formulation for tag-based visual-inertial localization is presented. • The RMSE in our experiments in the laboratory and simulation was as low as 2–5 cm. [ABSTRACT FROM AUTHOR]

Abstract: The liquid–solid fluidized bed has been widely applied in the separation process of coarse coal particles. The coal slurry should be classified before feeding into the liquid–solid fluidized bed. The most of fine coal particles, with the particle size less than 0.2mm or 0.3mm, should be removed from the coal slurry in the feeding. However, there are still amounts of fine coal particles which cannot be removed due to low classification efficiency. The separation process may be affected by these fine coal particles which always enter in a part of the clean coal without effective separation process. This investigation was carried out to determine the effect of fine coal particles on the separation process of liquid–solid fluidized bed. A liquid–solid fluidized bed was designed and eight sampling points were fixed on the column axis. The changes in size, density and ash content of coal particles were observed on the column axis and the partition coefficients of +0.25mm and −0.25mm fractions were obtained. [Copyright &y& Elsevier]

Defocus blur detection (DBD) for natural images is a challenging vision task especially in the presence of homogeneous regions and gradual boundaries. In this paper, we propose a novel image-scale-symmetric cooperative network (IS2CNet) for DBD. On one hand, in the process of image scales from large to small, IS2CNet gradually spreads the recept of image content. Thus, the homogeneous region detection map can be optimized gradually. On the other hand, in the process of image scales from small to large, IS2CNet gradually feels the high-resolution image content, thereby gradually refining transition region detection. In addition, we propose a hierarchical feature integration and bi-directional delivering mechanism to transfer the hierarchical feature of previous image scale network to the input and tail of the current image scale network for guiding the current image scale network to better learn the residual. The proposed approach achieves state-of-the-art performance on existing datasets. Codes and results are available at: https://github.com/wdzhao123/IS2CNet. [ABSTRACT FROM AUTHOR]

It is difficult to segment medical image with intensity inhomogeneity and complex composition, because most region-based modules relay on the intensity distributions. In this paper, we propose a novel method which uses local region statistics and multi-parameter intensity fitting as well. By replacing the original local region statistics with the novel local region statistics after bias field correction, the effect of intensity inhomogeneity can be eliminated. Then we devise a maximum likelihood energy function based on the distribution of each local region. Segmentation and bias field estimation can be jointly obtained by minimizing the proposed energy function. Furthermore, in order to characterize the features of each local region effectively, two parameters are used to fit the average intensity inside and outside of the counter, respectively. This can well handle the medical images with complex composition, such as larger gray difference even in the same region. Comparisons with several representative methods on synthetic and medical images demonstrate the superiority of the proposed method over other representative algorithms. [ABSTRACT FROM AUTHOR]

Infrared images are characterized by low signal-to-noise ratio and low contrast. Therefore, the edge details are easily immerged in the background and noise, making it much difficult to achieve infrared image edge detail enhancement and denoising. This article proposes a novel method of Gaussian mixture model-based gradient field reconstruction, which enhances image edge details while suppressing noise. First, by analyzing the gradient histogram of noisy infrared image, Gaussian mixture model is adopted to simulate the distribution of the gradient histogram, and divides the image information into three parts corresponding to faint details, noise and the edges of clear targets, respectively. Then, the piecewise function is constructed based on the characteristics of the image to increase gradients of faint details and suppress gradients of noise. Finally, anisotropic diffusion constraint is added while visualizing enhanced image from the transformed gradient field to further suppress noise. The experimental results show that the method possesses unique advantage of effectively enhancing infrared image edge details and suppressing noise as well, compared with the existing methods. In addition, it can be used to effectively enhance other types of images such as the visible and medical images. [ABSTRACT FROM AUTHOR]

It is widely anticipated that a reduction of brain levels of the cellular prion protein (PrPC) can prolong survival in a group of neurodegenerative diseases known as prion diseases. To date, efforts to decrease steady-state PrPC levels by targeting this protein directly with small molecule drug-like compounds have largely been unsuccessful. Recently, we reported Na,K-ATPases to reside in immediate proximity to PrPC in the brain, unlocking an opportunity for an indirect PrPC targeting approach that capitalizes on the availability of potent cardiac glycosides (CGs). Here, we report that exposure of human co-cultures of neurons and astrocytes to non-toxic nanomolar levels of CGs causes profound reductions in PrPC levels. The mechanism of action underpinning this outcome relies primarily on a subset of CGs engaging the ATP1A1 isoform, one of three α subunits of Na,K-ATPases expressed in brain cells. Upon CG docking to ATP1A1, the ligand receptor complex, and PrPC along with it, is internalized by the cell. Subsequently, PrPC is channeled to the lysosomal compartment where it is digested in a manner that can be rescued by silencing the cysteine protease cathepsin B. These data signify that the repurposing of CGs may be beneficial for the treatment of prion disorders. [ABSTRACT FROM AUTHOR]

The primary approach for variety distinction in Italian ryegrass is currently the DUS (distinctness, uniformity and stability) test based on phenotypic traits. Considering the diverse genetic background within the population and the complexity of the environment, however, it is challenging to accurately distinguish varieties based on DUS criteria alone. In this study, we proposed the application of high-throughput RAD-seq to distinguish 11 Italian ryegrass varieties with three bulks of 50 individuals per variety. Our findings revealed significant differences among the 11 tested varieties. The PCA, DAPC and STRUCTURE analysis indicated a heterogeneous genetic background for all of them, and the AMOVA analysis also showed large genetic variance among these varieties (ΦST = 0.373), which were clearly distinguished based on phylogenetic analysis. Further nucleotide diversity (Pi) analysis showed that the variety 'Changjiang No.2' had the best intra-variety consistency among 11 tested varieties. Our findings suggest that the RAD-seq could be an effectively alternative method for the variety distinction of Italian ryegrass, as well as a potential tool for open-pollinated varieties (OPVs) of other allogamous species. [ABSTRACT FROM AUTHOR]

The prion protein (PrP) is best known for its ability to cause fatal neurodegenerative diseases in humans and animals. Here, we revisited its molecular environment in the brain using a well-developed affinity-capture mass spectrometry workflow that offers robust relative quantitation. The analysis confirmed many previously reported interactions. It also pointed toward a profound enrichment of Na,K-ATPases (NKAs) in proximity to cellular PrP (PrPC). Follow-on work validated the interaction, demonstrated partial co-localization of the ATP1A1 and PrPC, and revealed that cells exposed to cardiac glycoside (CG) inhibitors of NKAs exhibit correlated changes to the steady-state levels of both proteins. Moreover, the presence of PrPC was observed to promote the ion uptake activity of NKAs in a human co-culture paradigm of differentiated neurons and glia cells, and in mouse neuroblastoma cells. Consistent with this finding, changes in the expression of 5'-nucleotidase that manifest in wild-type cells in response to CG exposure can also be observed in untreated PrPC-deficient cells. Finally, the endoproteolytic cleavage of the glial fibrillary acidic protein, a hallmark of late-stage prion disease, can also be induced by CGs, raising the prospect that a loss of NKA activity may contribute to the pathobiology of prion diseases. [ABSTRACT FROM AUTHOR]