Three mutations repurpose a plant karrikin receptor to a strigolactone receptor.

Uncovering the basis of small-molecule hormone receptors' evolution is paramount to a complete understanding of how protein structure drives function. In plants, hormone receptors for strigolactones are well suited to evolutionary inquiries because closely related homologs have different ligand preferences. More importantly, because of facile plant transgenic systems, receptors can be swapped and quickly assessed functionally in vivo. Here, we show that only three mutations are required to turn the nonstrigolactone receptor, KAI2, into a receptor that recognizes the plant hormone strigolactone. This modified receptor still retains its native function to perceive KAI2 ligands. Our directed evolution studies indicate that only a few keystone mutations are required to increase receptor promiscuity of KAI2, which may have implications for strigolactone receptor evolution in parasitic plants.

RAD-seq as an effective strategy for heterogenous variety identification in plants-a case study in Italian Ryegrass (Lolium multiflorum).

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.

Genetic Structure and Eco-Geographical Differentiation of Wild Sheep Fescue (Festuca ovina L.) in Xinjiang, Northwest China.

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.

Guide filter-based gradient vector flow module for infrared image segmentation.

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.

Infrared image detail enhancement based on the gradient field specification.

Human vision is sensitive to the changes of local image details, which are actually image gradients. To enhance faint infrared image details, this article proposes a gradient field specification algorithm. First we define the image gradient field and gradient histogram. Then, by analyzing the characteristics of the gradient histogram, we construct a Gaussian function to obtain the gradient histogram specification and therefore obtain the transform gradient field. In addition, subhistogram equalization is proposed based on the histogram equalization to improve the contrast of infrared images. The experimental results show that the algorithm can effectively improve image contrast and enhance weak infrared image details and edges. As a result, it can give qualified image information for different applications of an infrared image. In addition, it can also be applied to enhance other types of images such as visible, medical, and lunar surface.

Interactive Feature Embedding for Infrared and Visible Image Fusion.

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.

Nowhere to Disguise: Spot Camouflaged Objects via Saliency Attribute Transfer.

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.

Confusion Region Mining for Crowd Counting.

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.

Advances in Recombinant Adeno-Associated Virus Vectors for Neurodegenerative Diseases.

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.

SSPNet: An interpretable 3D-CNN for classification of schizophrenia using phase maps of resting-state complex-valued fMRI data.

Convolutional neural networks (CNNs) have shown promising results in classifying individuals with mental disorders such as schizophrenia using resting-state fMRI data. However, complex-valued fMRI data is rarely used since additional phase data introduces high-level noise though it is potentially useful information for the context of classification. As such, we propose to use spatial source phase (SSP) maps derived from complex-valued fMRI data as the CNN input. The SSP maps are not only less noisy, but also more sensitive to spatial activation changes caused by mental disorders than magnitude maps. We build a 3D-CNN framework with two convolutional layers (named SSPNet) to fully explore the 3D structure and voxel-level relationships from the SSP maps. Two interpretability modules, consisting of saliency map generation and gradient-weighted class activation mapping (Grad-CAM), are incorporated into the well-trained SSPNet to provide additional information helpful for understanding the output. Experimental results from classifying schizophrenia patients (SZs) and healthy controls (HCs) show that the proposed SSPNet significantly improved accuracy and AUC compared to CNN using magnitude maps extracted from either magnitude-only (by 23.4 and 23.6% for DMN) or complex-valued fMRI data (by 10.6 and 5.8% for DMN). SSPNet captured more prominent HC-SZ differences in saliency maps, and Grad-CAM localized all contributing brain regions with opposite strengths for HCs and SZs within SSP maps. These results indicate the potential of SSPNet as a sensitive tool that may be useful for the development of brain-based biomarkers of mental disorders.

Defocus Blur Detection Attack via Mutual-Referenced Feature Transfer.

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.

Cardiac glycoside-mediated turnover of Na, K-ATPases as a rational approach to reducing cell surface levels of the cellular prion protein.

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.

Defocus Blur Detection via Boosting Diversity of Deep Ensemble Networks.

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.

The cellular prion protein interacts with and promotes the activity of Na,K-ATPases.

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.

Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Network.

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.

The effects of physical photostimulable phosphor plate artifacts on the radiologic interpretation of periapical inflammatory disease.

OBJECTIVE: To evaluate how physical photostimulable phosphor (PSP) plate artifacts, such as those created by scratches, phosphor degradation, and surface peeling, affect the radiologic interpretation of periapical inflammatory disease. STUDY DESIGN: A novel technique was developed to digitally superimpose 25 real PSP artifact masks over 100 clinical complementary metal oxide semiconductor (CMOS) periapical images with known radiologic interpretations. These images were presented to 25 general dentists, who were asked to state their radiologic interpretations, their confidence in their interpretations, and their opinions on whether the plates should be discarded. Statistical analyses were conducted by using random intercept mixed models for repeated measures and χ2 tests of the pooled data. RESULTS: No statistically significant adverse effect on interpretation was seen, even at severe artifact levels. There was a statistically significant decrease in the clinicians' confidence and an increase in discard proportions when interpreting images with severe PSP plate artifacts (P < .05). CONCLUSIONS: Although diagnostic efficacy was unaffected, clinicians' confidence decreased and proportionally more clinicians opted to discard sensors when interpreting images with severe artifacts. Future studies on the effects of artifacts on the efficacy of diagnosis of other dental diseases are recommended. Ultimately, these results can guide recommendations for PSP plate quality assurance.

Towards weakly-supervised focus region detection via recurrent constraint network.

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.

Biochemical sensing by nanofluidic crystal in a confined space.

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 Pb(2+) 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.