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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]

Recombinant adeno-associated virus (rAAV) vectors are gene therapy delivery tools that offer a promising platform for the treatment of neurodegenerative diseases. Keeping up with developments in this fast-moving area of research is a challenge. This review was thus written with the intention to introduce this field of study to those who are new to it and direct others who are struggling to stay abreast of the literature towards notable recent studies. In ten sections, we briefly highlight early milestones within this field and its first clinical success stories. We showcase current clinical trials, which focus on gene replacement, gene augmentation, or gene suppression strategies. Next, we discuss ongoing efforts to improve the tropism of rAAV vectors for brain applications and introduce pre-clinical research directed toward harnessing rAAV vectors for gene editing applications. Subsequently, we present common genetic elements coded by the single-stranded DNA of rAAV vectors, their so-called payloads. Our focus is on recent advances that are bound to increase treatment efficacies. As needed, we included studies outside the neurodegenerative disease field that showcased improved pre-clinical designs of all-in-one rAAV vectors for gene editing applications. Finally, we discuss risks associated with off-target effects and inadvertent immunogenicity that these technologies harbor as well as the mitigation strategies available to date to make their application safer. [ABSTRACT FROM AUTHOR]

Through numerical simulation, the blast-resistant performance of spray polyurea elastomer (SPUA) retrofitted concrete masonry unit (CMU) masonry infill walls under far-range blast loading was studied. From an engineering perspective, the effects of boundary conditions and thickness of a SPUA layer on enhancing the blast resistance of masonry infill walls are discussed, and the blast resistance of SPUA-retrofitted and grouted CMU masonry infill walls are compared. It is concluded that the boundary constraint conditions and the anchorage length of SPUA layer have limited improvement on the blast-resistant performance of the wall; the thickness of SPUA layer can significantly improve the blast-resistant performance of the wall as the blast loading increases. In addition, SPUA retrofitting shows relatively better performance to reinforce masonry infill walls. [ABSTRACT FROM AUTHOR]

As a common enclosure structure, masonry walls are widely used in various types of buildings. However, due to the weak out-of-plane resistance of masonry walls and the generally brittle properties of the materials used for blocks, they are highly susceptible to collapse under blast loads and produce high-speed splash fragments, which seriously threatens the safety of personnel and equipment inside buildings. In this paper, based on the existing tests, a refined numerical simulation model was established to carry out numerical studies of clay tile walls and grouted CMU masonry infill walls under far-range blast loads, and the applicability of the finite element model and parameters were verified. Further, the effects of wall boundary configuration, constraints and dimensions on the dynamic response of the walls were carried out. The results show that: the load distribution on the wall is relatively uniform under the far-range explosion and can be considered as uniform load; the blast-resistant performance of the wall can be enhanced by increasing the grouting rate and the uniformity of grout hole distribution; the boundary configuration of the wall has little effect on the blast resistance, while the boundary constraints and the length and width are the main factors affecting the blast resistance of the wall. [ABSTRACT FROM AUTHOR]

In order to study the similarity law of penetration of concrete targets by shaped charges, penetration tests of concrete targets with different sizes of shaped-charge jets were carried out, and the prototype and the model projectiles met the similarity law with a simulation ratio of 1:1.5. LS-DYNA finite element software was used to simulate the tests, and the accuracy of the ALE algorithm, fluid–solid coupling algorithm, material model, equation of state, and corresponding material parameters was verified. Numerical simulations were further conducted for the different types of shaped-charge jets (jets, rod jets, and explosively formed projectiles) formed by different liner angles penetrating into the concrete target, and the results show that the shaped-charge jets basically meet the similarity law when penetrating concrete targets. [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]

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]

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]

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]

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]

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]

This article presents a CMOS image sensor (CIS) with column-parallel single-shot compressive sensing (CS) for always-on Internet-of-Things (IoT) application, which achieves an energy efficiency of 51 pJ/pixel, while maintaining high image quality of PSNR > 33.7 dB and SSIM > 0.89. This is enabled by an energy-efficient encoder, which replaces a densely populated CS encoding matrix with a highly sparse pseudo-diagonal one. Since the proposed column-parallel CS encoder can be implemented directly at pixel outputs with an energy-efficient switched-capacitor matrix multiplier, data compression is achieved prior to the pixel digitization, thereby greatly reducing ADC power, data size, and I/O power. The energy efficiency of the image sensor is further improved by using dynamic single-slope ADCs. A prototype VGA image sensor implemented in a 110-nm CMOS process consumes only 0.7 mW at 45 frames/s. The corresponding energy per pixel (51 pJ/pixel) amounts to more than $5\times $ improvement over the previous low-energy benchmark for CS image sensors. [ABSTRACT FROM AUTHOR]

This article presents an enhanced interstage gain error shaping (GES) technique that adopts a digital error feedback (DEF) method to address the truncation error in the prior implementation, which can extend the interstage gain error tolerance by five times. The proposed DEF technique does not introduce additional errors as it operates purely in the digital domain. This article also proposes a first-order passive quantization noise shaping (NS) technique that reduces the input-pair ratio of the two-input-pair comparator by 2.7 times, thus alleviating the noise penalty caused by using a multiple-input-pair comparator. A prototype analog-to-digital converter (ADC) equipped with the proposed techniques in a 40-nm CMOS technology achieves a 77.1-dB signal-to-noise-and-distortion ratio (SNDR) over 6.25-MHz bandwidth while operating at 100 MS/s and consuming 1.38 mW. It achieves a 173.7-dB Schreier figure of merit (FoM). [ABSTRACT FROM AUTHOR]

As any analog-to-digital converter (ADC) with a front-end sample-and-hold (S/H) circuit, successive approximation register (SAR) ADC suffers from a fundamental signal-to-noise ratio (SNR) challenge: its sampling kT/C noise. To satisfy the SNR requirement, the input capacitor size has to be sufficiently large, leading to a great burden for the design of the ADC input driver and reference buffer. This article presents an SAR ADC with a kT/C noise-cancellation technique. It enables the substantial reduction of ADC input capacitor size but without the large kT/C noise penalty. It greatly relaxes the requirement for ADC input driver and reference buffer. Built in 40-nm CMOS, a prototype 13-bit ADC has only 240-fF input capacitance and occupies a small area of 0.005 mm2. Operating at 40 MS/s, it achieves a 69-dB signal-to-noise-and-distortion ratio (SNDR) across the Nyquist frequency band while consuming 591 $\mu \text{W}$ of power. [ABSTRACT FROM AUTHOR]

This article presents a second-order noise-shaping (NS) successive approximation register (SAR) analog-to-digital converter (ADC) with a process, voltage, and temperature (PVT)-robust closed-loop dynamic amplifier. The proposed closed-loop dynamic amplifier combines the merits of closed-loop architecture and dynamic operation, realizing robustness, high accuracy, and high energy-efficiency simultaneously. It is embedded in the loop filter of an NS SAR design, enabling the first fully dynamic NS-SAR ADC that realizes sharp noise transfer function (NTF) while not requiring any gain calibration. Fabricated in 40-nm CMOS technology, the prototype ADC achieves an SNDR of 83.8 dB over a bandwidth of 625 kHz while consuming only $107~\mu \text{W}$. It results in an SNDR-based Schreier figure-of-merit (FoM) of 181.5 dB. [ABSTRACT FROM AUTHOR]

This article presents an incremental two-step capacitance-to-digital converter (CDC) with a time-domain $\Delta \Sigma $ modulator (TD $\Delta \Sigma \text{M}$). Unlike the classic two-step CDCs, this work replaces the operational transconductance amplifier (OTA)-based active- RC integrator by a voltage-controlled oscillator (VCO)-based integrator, which is mostly digital and low-power. Featuring the infinite dc gain and intrinsic quantization in phase domain, this TD $\Delta \Sigma \text{M}$ enables a CDC design achieving 76-dB SNDR while requiring only a first-order loop, and a low oversampling ratio (OSR) of 15. Fabricated in 40-nm CMOS technology, the prototype CDC achieves a resolution of 0.29 fF while dissipating only 0.083 nJ/conversion, which improves the energy efficiency by over two times comparing to the similar performance designs. [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]

This article presents a reference ripple cancellation technique for high-speed successive approximation register analog-to-digital converters (SAR ADCs) to address the reference voltage settling issue. Unlike prior techniques that aim to minimize the reference ripple, this article proposes a new perspective: it provides an extra path for the full-sized reference ripple to couple to the comparator but with an opposite polarity, so that the effect of the reference ripple is canceled out, thus ensuring an accurate conversion result. To verify the proposed technique, a prototype 10-bit 120-MS/s SAR ADC is fabricated in a 40-nm CMOS process. The proposed ripple cancellation technique improves the signal-to-noise and distortion ratio (SNDR) by 8 dB and reduces the worst case integrated non-linearity (INL)/differential non-linearity (DNL) by ten times. Overall, the ADC achieves an SNDR of 55 dB with only 3-pF reference decoupling capacitor. [ABSTRACT FROM AUTHOR]

This article presents a capacitively coupled voltage-controlled oscillator (VCO)-based sensor readout featuring a hybrid phase-locked loop (PLL)- $\Delta \Sigma $ modulator structure. It leverages phase-locking and phase-frequency detector (PFD) array to concurrently perform quantization and dynamic element matching (DEM), much-reducing hardware/power compared with the existing VCO-based readouts’ counting scheme. A low-cost in-cell data-weighted averaging (DWA) scheme is presented to enable a highly linear tri-level digital-to-analog converter (DAC). Fabricated in 40-nm CMOS, the prototype readout achieves 78-dB SNDR in 10-kHz bandwidth, consuming 4.68 $\mu \text{W}$ and 0.025-mm2 active area. With 172-dB Schreier figure of merit, its efficiency advances the state-of-the-art VCO-based readouts by $50\times $. [ABSTRACT FROM AUTHOR]

This article presents a power-efficient purely voltage-controlled oscillator (VCO)-based second-order continuous-time (CT) $\Delta \Sigma $ analog-to-digital converter (ADC), featuring a modified digital phase-locked loop (DPLL) structure. The proposed ADC combines a VCO with a switched-ring oscillator (SRO)-based time-to-digital converter (TDC), which enables second-order noise shaping without any operational transconductance amplifiers (OTAs). The nonlinearity of the front-end VCO is mitigated by putting it inside a closed loop. An array of phase/frequency detectors (PFDs) is used to relax the requirement on the VCO center frequency and thus reduces the VCO power and noise. The proposed architecture also realizes an intrinsic tri-level data-weighted averaging (DWA). A prototype chip is fabricated in a 40-nm CMOS process. The proposed ADC achieves a peak signal-to-noise-and-distortion ratio (SNDR) of 69.4 dB over 5.2-MHz bandwidth, while operating at the 260 MS/s and consuming 0.86 mW from a 1.1-V supply. [ABSTRACT FROM AUTHOR]

This article presents a two-step analog-to-digital converter (ADC) that operates its first-stage successive approximation register (SAR) ADC in the continuous-time (CT) domain. It avoids the front-end sample-and-hold (S/H) circuit and its associated sampling noise. Hence, the proposed ADC allows the input capacitor size to be substantially reduced without incurring large sampling noise penalty. With input ac coupling, the first-stage CT-SAR can simultaneously perform input tracking and SAR quantization. Its conversion error is minimized by accelerating the SAR speed and providing redundancy. A floating-inverter-based (FIB) dynamic amplifier (DA) is used as the inter-stage amplifier and acts as a low-pass filter for the first-stage residue. To verify the proposed techniques, a 13-bit prototype ADC is built in 40-nm CMOS process. Its input capacitor is only 120 fF, which is over 20 times smaller than what would be needed in a classic Nyquist ADC with the S/H circuit. Operating at 2 MS/s, it achieves 72-dB signal-to-noise-and-distortion-ratio (SNDR) at the Nyquist rate while consuming only 25 $\mu \text{W}$ of power and 0.01 mm2 of area. [ABSTRACT FROM AUTHOR]

It is demonstrated that novel back‐channel‐etch type oxide TFTs (InGaZnSnOx target material for oxide semiconductor) with below 10% threshold voltage uniformity, better stability and above 10cm2/V·s electron mobility in G4.5 factory. From etch‐stop type to back‐channel‐etch type, ultra‐high definition 15.6 inch notebook display with only 1.2mm left and right border has designed and manufactured for the first time by the way of singleside gate driver in panel. [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]

Most existing image fusion methods assume that at least one input image contains high-quality information at any place of an observed scene. Thus, these fusion methods will fail if every input image is degraded. To address this issue, this study proposes a novel fusion framework that integrates image fusion based on spectral total variation (TV) method and image enhancement. For spatially varying multiscale decompositions generated by the spectral TV framework, this study verifies that the decomposition components can be modeled efficiently by tailed $\alpha$-stable-based random variable distribution (TRD) rather than the commonly used Gaussian distribution. Consequently, salience and match measures based on TRD are proposed to fuse each sub-band decomposition. The spatial intensity information is also adopted to fuse the remainder of the image decomposition components. A sub-band adaptive gain function family based on TV spectrum and space variation is constructed for fused multiscale decompositions to enhance fused image simultaneously. Finally, numerous experiments with various multisensor image pairs are conducted to evaluate the proposed method. Experimental results show that even if the input images are degraded, the fused image obtained by the proposed method achieves significant improvement in terms of edge details and contrast while extracting the main features of the input images, thereby achieving better performance compared with the state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

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]

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]

General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention by utilizing elaborately designed loss functions. However, the unsupervised mechanism depends on a well-designed loss function, which cannot guarantee that all vital information of source images is sufficiently extracted. In this work, we propose a novel interactive feature embedding in a self-supervised learning framework for infrared and visible image fusion, attempting to overcome the issue of vital information degradation. With the help of a self-supervised learning framework, hierarchical representations of source images can be efficiently extracted. In particular, interactive feature embedding models are tactfully designed to build a bridge between self-supervised learning and infrared and visible image fusion learning, achieving vital information retention. Qualitative and quantitative evaluations exhibit that the proposed method performs favorably against state-of-the-art methods.

Over the past four decades, prion diseases have received considerable research attention owing to their potential to be transmitted within and across species as well as their consequences for human and animal health. The unprecedented nature of prions has led to the discovery of a paradigm of templated protein misfolding that underlies a diverse range of both disease-related and normal biological processes. Indeed, the "prion-like" misfolding and propagation of protein aggregates is now recognized as a common underlying disease mechanism in human neurodegenerative disorders such as Alzheimer's and Parkinson's disease and the prion principle has led to the development of novel diagnostic and therapeutic strategies for these illnesses. Despite these advances, research into the fundamental biology of prion diseases has declined, likely due to their rarity and the absence of an acute human health crisis. Given the past translational influence, continued research on the etiology, pathogenesis, and transmission of prion disease should remain a priority. In this review we highlight several important "unsolved mysteries" in the prion disease research field and how solving them may be crucial for the development of effective therapeutics, preventing future outbreaks of prion disease, and understanding the pathobiology of more common human neurodegenerative disorders.

Benefiting from deep learning, defocus blur detection (DBD) has made prominent progress. Existing DBD methods generally study multiscale and multilevel features to improve performance. In this article, from a different perspective, we explore to generate confrontational images to attack DBD network. Based on the observation that defocus area and focus region in an image can provide mutual feature reference to help improve the quality of the confrontational image, we propose a novel mutual-referenced attack framework. Firstly, we design a divide-and-conquer perturbation image generation model, where the focus region attack image and defocus area attack image are generated respectively. Then, we integrate mutual-referenced feature transfer (MRFT) models to improve attack performance. Comprehensive experiments are provided to verify the effectiveness of our method. Moreover, related applications of our study are presented, e.g., sample augmentation to improve DBD and paired sample generation to boost defocus deblurring.

Existing works mainly focus on crowd and ignore the confusion regions which contain extremely similar appearance to crowd in the background, while crowd counting needs to face these two sides at the same time. To address this issue, we propose a novel end-to-end trainable confusion region discriminating and erasing network called CDENet. Specifically, CDENet is composed of two modules of confusion region mining module (CRM) and guided erasing module (GEM). CRM consists of basic density estimation (BDE) network, confusion region aware bridge and confusion region discriminating network. The BDE network first generates a primary density map, and then the confusion region aware bridge excavates the confusion regions by comparing the primary prediction result with the ground-truth density map. Finally, the confusion region discriminating network learns the difference of feature representations in confusion regions and crowds. Furthermore, GEM gives the refined density map by erasing the confusion regions. We evaluate the proposed method on four crowd counting benchmarks, including ShanghaiTech Part_A, ShanghaiTech Part_B, UCF_CC_50, and UCF-QNRF, and our CDENet achieves superior performance compared with the state-of-the-arts.

Both salient object detection (SOD) and camouflaged object detection (COD) are typical object segmentation tasks. They are intuitively contradictory, but are intrinsically related. In this paper, we explore the relationship between SOD and COD, and then borrow successful SOD models to detect camouflaged objects to save the design cost of COD models. The core insight is that both SOD and COD leverage two aspects of information: object semantic representations for distinguishing object and background, and context attributes that decide object category. Specifically, we start by decoupling context attributes and object semantic representations from both SOD and COD datasets through designing a novel decoupling framework with triple measure constraints. Then, we transfer saliency context attributes to the camouflaged images through introducing an attribute transfer network. The generated weakly camouflaged images can bridge the context attribute gap between SOD and COD, thereby improving the SOD models' performances on COD datasets. Comprehensive experiments on three widely-used COD datasets verify the ability of the proposed method. Code and model are available at: https://github.com/wdzhao123/SAT.

Existing defocus blur detection (DBD) methods usually explore multi-scale and multi-level features to improve performance. However, defocus blur regions normally have incomplete semantic information, which will reduce DBD's performance if it can't be used properly. In this paper, we address the above problem by exploring deep ensemble networks, where we boost diversity of defocus blur detectors to force the network to generate diverse results that some rely more on high-level semantic information while some ones rely more on low-level information. Then, diverse result ensemble makes detection errors cancel out each other. Specifically, we propose two deep ensemble networks (e.g., adaptive ensemble network (AENet) and encoder-feature ensemble network (EFENet)), which focus on boosting diversity while costing less computation. AENet constructs different light-weight sequential adapters for one backbone network to generate diverse results without introducing too many parameters and computation. AENet is optimized only by the self- negative correlation loss. On the other hand, we propose EFENet by exploring the diversity of multiple encoded features and ensemble strategies of features (e.g., group-channel uniformly weighted average ensemble and self-gate weighted ensemble). Diversity is represented by encoded features with less parameters, and a simple mean squared error loss can achieve the superior performance. Experimental results demonstrate the superiority over the state-of-the-arts in terms of accuracy and speed. Codes and models are available at: https://github.com/wdzhao123/DENets.

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.

Glaciation and mountain orogeny have generated new ecologic opportunities for plants, favoring an increase in the speciation rate. Moreover, they also act as corridors or barriers for plant lineages and populations. High genetic diversity ensures that species are able to survive and adapt. Gene flow is one of the most important determinants of the genetic diversity and structure of out-crossed species, and it is easily affected by biotic and abiotic factors. The aim of this study was to characterize the genetic diversity and structure of an alpine species, Festuca ovina L., in Xinjiang, China. A total of 100 individuals from 10 populations were analyzed using six amplified fragment length polymorphism (AFLP) primer pairs. A total of 583 clear bands were generated, of which 392 were polymorphic; thus, the percentage of polymorphic bands (PPB) was 67.24%. The total and average genetic diversities were 0.2722 and 0.2006 (0.1686-0.2225), respectively. The unweighted group method with arithmetic mean (UPGMA) tree, principal coordinates analysis (PCoA) and Structure analyses revealed that these populations or individuals could be clustered into two groups. The analysis of molecular variance analysis (AMOVA) suggested that most of the genetic variance existed within a population, and the genetic differentiation (Fst) among populations was 20.71%. The Shannon differentiation coefficient (G'st) among populations was 0.2350. Limited gene flow (Nm = 0.9571) was detected across all sampling sites. The Fst and Nm presented at different levels under the genetic barriers due to fragmentation. The population genetic diversity was significant relative to environmental factors such as temperature, altitude and precipitation., Competing Interests: The authors declare no conflict of interest

Infrared image segmentation is a challenging topic since infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow (GVF), have better segmentation performance for clear images. However, the GVF model has the drawbacks of sensitivity to noise and adaptability of the parameters, decreasing the effect of infrared image segmentation significantly. To address these problems, this paper proposes a guide filter-based gradient vector flow module for infrared image segmentation (GFGVF). First, a guide filter is exploited to construct a novel edge map, providing characteristics of the image edge while excluding the effects of noise. This alleviates the possibility of edge leakage caused by using the traditional edge map. Then, a novel weighting function is constructed to effectively handle the extended capture range and preserving the edge even with noise existing. The experimental results demonstrate that the GFGVF model possesses good properties such as large capture range, concavity convergence, noise robustness, and alleviative boundary leakage.