Functional coupling of Tmem74 and HCN1 channels regulates anxiety-like behavior in BLA neurons.

Anxiety disorders are the most prevalent psychiatric disorders, but their pathogenic mechanism remains poorly understood. Here, we report that transmembrane protein 74 (TMEM74), which contains two putative transmembrane domains and exhibits high levels of mRNA in the brain, is closely associated with the pathogenesis of anxiety disorders. TMEM74 was decreased in the serum of patients with anxiety and the basolateral amygdaloid nucleus (BLA) in chronic stress mice. Furthermore, genetic deletion of Tmem74 or selective knockdown of Tmem74 in BLA pyramidal neurons resulted in anxiety-like behaviors in mice. Whole-cell recordings in BLA pyramidal neurons revealed lower hyperpolarization-activated cation current (Ih) and greater input resistance and excitability in Tmem74-/- neurons than in wild-type neurons. Accordingly, surface expression of hyperpolarization-activated cyclic nucleotide-gated 1 (HCN1) channels was also lower in the BLA of Tmem74-/- mice. The Ih current blocker ZD7288 mimicked these effects in BLA pyramidal neurons in wild-type mice but not in Tmem74-/- mice. Consistent with the improvement in anxiety-like behaviors, Tmem74 overexpression restored HCN1 channel trafficking and pyramidal neuron excitability in the BLA of Tmem74-/- and chronic stress mice. Mechanistically, we demonstrate that interactions between Tmem74 and HCN1 are physiologically relevant and that transmembrane domain 1 (TM1) is essential for the cellular membrane localization of Tmem74 to enhance Ih. Together, our findings suggest that Tmem74 coupling with HCN1 acts as a critical component in the pathophysiology of anxiety and is a potential target for new treatments of anxiety disorders.

Endothelial GPR124 Exaggerates the Pathogenesis of Atherosclerosis by Activating Inflammation.

BACKGROUND/AIMS: Endothelial cell dysfunction is the principal pathological process underlying atherosclerotic cardiovascular disease. G protein-coupled receptor 124 (GPR124), an orphan receptor in the adhesion GPCR subfamily, promotes angiogenesis in the brain. In the present study, we explored the role of endothelial GPR124 in the development and progression of atherosclerosis in adult mice. METHODS: Using tetracycline-inducible transgenic systems, we generated mice expressing GPR124 specifically under control of the Tie-2 promoter. The animal model of atherosclerosis was constructed by intravenously injecting AAV-PCSK9DY into tetracycline-regulated mice and feeding the mice a high-fat diet for 16 consecutive weeks. Biochemical analysis and immunohistochemistry methods were used to address the role and mechanism of GPR124 in the pathological process of atherosclerosis. RESULTS: Higher TC (total cholesterol) and LDL-C (low density lipoprotein cholesterol) levels in serum and greater lipid deposition in the aortic sinus were found in atherosclerotic mice with GPR124 overexpression, coincident with the elevated proliferation of smooth muscle cells. We observed an elevation of ONOO- in the aortic sinus in this model by using immunofluorescence, and the experiments showed that the specific overexpression of GPR124 in the endothelium induced the up-regulation of CD68, NLRP3 and caspase-1 levels in the aortic sinus. CONCLUSION: The above results indicate that manipulating GPR124 in the endothelium may contribute to delayed pathological progression of atherosclerosis.

High Dynamic Micro Vibrator with Integrated Optical Displacement Detector for In-Situ Self-Calibration of MEMS Inertial Sensors.

The scale factor drifts and other long-term instability drifts of Micro-Electro-Mechanical System (MEMS) inertial sensors are the main contributors of the position and orientation errors in high dynamic environments. In this paper, a novel high dynamic micro vibrator, which could provide high acceleration and high angular rate rotation with integrated optical displacement detector, is proposed. Commercial MEMS inertial sensors, including 3-axis accelerometer and 6-axis inertial measurement unit which is about 3 mm * 3 mm * 1 mm with 19 mg, could be bonded on the vibration platform of the micro vibrator to perform in-situ during the self-calibration procedure. The high dynamic micro vibrator is fabricated by a fully-integrated MEMS process, including lead zirconate titanate (PZT) film deposition, PZT and electrodes patterning, and structural ion etching. The optical displacement detector, using vertical-cavity surface-emitting laser (VCSEL) and photoelectric diodes (PD), is integrated on the top of the package to measure the 6-DOF vibrating displacement with the detecting resolution of 150 nm in the range of 500 μm. The maximum out-of-plane acceleration of the z-axis vibrating platform loaded with commercial 3-axis accelerometer (H3LIS331DL) achieves above 16 g and the maximum angular velocity achieves above 720°/s when the driving voltage is ±6 V.

[Discrimination of Plum Browning with Near Infrared Spectroscopy].

Flesh browning mostly happens in plum fruit during the post-harvest storage period, which is an important factor affecting the storage quality of plum fruits. Traditional methods used to discriminate plum browning involve the destruction of the intact fruit, which are highly subjective and error-prone. Therefore, the near-infrared (NIR) spectroscopy technique was applied to achieve rapid and non-destructive identification of plum browning and non-browning in this paper. The near infrared diffuse reflectance spectroscopy of 124 plum samples were collected in the band number of 4 000~12 500 cm-1. These samples were classified into two groups, browning (n=70) and non-browning (n=54). In order to find a new way to effectively discriminate plum fruits with flesh browning, three qualitative identification methods: the qualitative test, Mahalanobis distances discriminate analysis (DA) and Back Propagation-artificial neural networks (BP-ANN) were used to compare their capacity of recognizing browning plums and non-browning oneswhile the last two approaches were based on the principal component analysis (PCA) method. These results showed that DA and BP-ANN could be used to conctruct effective classification models for identifying plum browning, and the first ten principal components extracted from original spectra were applied as input variables to build DA and BP-ANN models. The optimal method was obtained with BP-ANN, which gained an accuracy of 100% for calibration set and 97.56% for prediction set, and the identification accuracy rate reached 100% and 98.57% for non-browning samples and browning ones, respectively. It could be concluded that NIR spectroscopy technique combined with chemometrics methods has great potential to recognize plums of browning and non-browning rapidly, non-destructively and effectively.

Apelin-13's Actions in Controlling Hypertension-Related Cardiac Hypertrophy and the Expressions of Inflammatory Cytokines.

As a key molecule for improving cardiovascular diseases, Apelin-13 was surveyed in this work to explain its actions in controlling inflammation, pyroptosis, and myocardial hypertrophy. First, mouse models with myocardial hypertrophy were established. Then, assessments were made on the pathological variation in the heart of mouse, on the cardiac functions, as well as on the expressions of cardiac hypertrophy markers (ß-MHC, ANP, and BNP), inflammatory factors (TNF-α, COX2, IL-6, ICAM-1, and VCAM-1), myocardial cell pyroptosis markers (NLRP3, ASC, c-caspase-1, and GSDMD-N), and Hippo pathway proteins (p-YAP, YAP, LATS1, and p-LATS1) by HE staining, echocardiography scanning, and western blot tests separately. The expressions of such inflammatory factors as in myocardial tissue were acquired by ELISA. After inducing the phenotype of H9c2 cell hypertrophy by noradrenaline, we used CCK-8 kits to know about the activity of H9c2 cells treated with Apelin-13, and performed ɑ-actinin staining to measure the changes in volumes of such cells. As unraveled through this work, Apelin-13 refrained the activation of the Hippo pathway, which in turn attenuated the hypertrophy, inflammation, and pyroptosis of myocardial tissue and H9c2 cells. Hence, Apelin-13 can be considered as a target for hypertension treatment.

SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring.

The goal of dynamic scene deblurring is to remove the motion blur presented in a given image. To recover the details from the severe blurs, conventional convolutional neural networks (CNNs) based methods typically increase the number of convolution layers, kernel-size, or different scale images to enlarge the receptive field. However, these methods neglect the non-uniform nature of blurs, and cannot extract varied local and global information. Unlike the CNNs-based methods, we propose a Transformer-based model for image deblurring, named SharpFormer, that directly learns long-range dependencies via a novel Transformer module to overcome large blur variations. Transformer is good at learning global information but is poor at capturing local information. To overcome this issue, we design a novel Locality preserving Transformer (LTransformer) block to integrate sufficient local information into global features. In addition, to effectively apply LTransformer to the medium-resolution features, a hybrid block is introduced to capture intermediate mixed features. Furthermore, we use a dynamic convolution (DyConv) block, which aggregates multiple parallel convolution kernels to handle the non-uniform blur of inputs. We leverage a powerful two-stage attentive framework composed of the above blocks to learn the global, hybrid, and local features effectively. Extensive experiments on the GoPro and REDS datasets show that the proposed SharpFormer performs favourably against the state-of-the-art methods in blurred image restoration.

Improved genomic prediction using machine learning with Variational Bayesian sparsity.

BACKGROUND: Genomic prediction has become a powerful modelling tool for assessing line performance in plant and livestock breeding programmes. Among the genomic prediction modelling approaches, linear based models have proven to provide accurate predictions even when the number of genetic markers exceeds the number of data samples. However, breeding programmes are now compiling data from large numbers of lines and test environments for analyses, rendering these approaches computationally prohibitive. Machine learning (ML) now offers a solution to this problem through the construction of fully connected deep learning architectures and high parallelisation of the predictive task. However, the fully connected nature of these architectures immediately generates an over-parameterisation of the network that needs addressing for efficient and accurate predictions. RESULTS: In this research we explore the use of an ML architecture governed by variational Bayesian sparsity in its initial layers that we have called VBS-ML. The use of VBS-ML provides a mechanism for feature selection of important markers linked to the trait, immediately reducing the network over-parameterisation. Selected markers then propagate to the remaining fully connected feed-forward components of the ML network to form the final genomic prediction. We illustrated the approach with four large Australian wheat breeding data sets that range from 2665 lines to 10375 lines genotyped across a large set of markers. For all data sets, the use of the VBS-ML architecture improved genomic prediction accuracy over legacy linear based modelling approaches. CONCLUSIONS: An ML architecture governed under a variational Bayesian paradigm was shown to improve genomic prediction accuracy over legacy modelling approaches. This VBS-ML approach can be used to dramatically decrease the parameter burden on the network and provide a computationally feasible approach for improving genomic prediction conducted with large breeding population numbers and genetic markers.

[Analysis of the clinical effect of percutaneous pedicle screw fixation combined with transpedicular bone grafting in the treatment of thoracolumbar fracture].

OBJECTIVE: To investigate the clinical efficacy of percutaneous screw fixation combined with minimally invasive transpedicular bone grafting and non-bone grafting in the treatment of thoracolumbar fractures. METHODS: From Janury 2021 to June 2022, 40 patients with thoracolumbar fracture were divided into the experimental group and the control group. There were 26 patients in the experimental group, including 21 males and 5 females with an aberage age of (47.3±12.3) years old, who underwent percutaneous pedicle screw fixation combined with transpedicular autogenous bone grafting. In the control group, 14 patients received percutaneous pedicle screw fixation only. including 7 makes and 7 females with an average age of (50.2±11.2) years old. The operative time, intraoperative blood loss, anterior height ratio of injured vertebrae, Cobb angle, visual analogue score (VAS), MacNab scores, loosening or broken of the implants. were compared and analyzed. RESULTS: There was no significant difference in operation time, intraoperative blood loss, VAS and anterior height ratio of injured vertebrae between the two groups. Compared with the preoperative results, VAS and anterior height ratio of injured vertebrae were improved statistically(P<0.05). For Cobb angle of injured vertebra, there was no significant difference between the two groups before surgery (P=0.766). While at 1 week, 3 months and 12 months after surgery, there were statistically differences between the two groups (P values were 0.042, 0.007 and 0.039, respectively). The Cobb angle of injured vertebrae one year after operation was statistically decreased in both groups compared with that before surgery (P<0.001). One year after surgery, the excellent and good rate of Macnab scores was 96.15% in the experimental group and 92.86% in the control group, and there was no statistical differences between the two groups (P=0.648). There was one patient in the control group suffering superficial wound infection on the third day, which was cured by dressing change and anti-infection treatment. There were no postoperative screw loosening and broken in both groups. CONCLUSION: The two surgical methods have the advantages of less trauma, less pain and quicker recovery, which can restore the height of the injured vertebra, reconstruct the spinal sequence and reduce the fracture of the vertebral body. Transpedicular autogenous bone grafting can increase the stability of the fractured vertebra and maintain the height of the vertebra better after surgery, thus reducing the possibility of complications such as kyphosis, screw loosening and broken.

Stored Fas ligand, a mediator of rapid CTL-mediated killing, has a lower threshold for response than degranulation or newly synthesized Fas ligand.

CTL lyse target cells through the release of cytolytic granule mediators and expression of the death receptor ligand Fas ligand (FasL). We previously demonstrated that FasL is stored in vesicles distinct from cytolytic granules and is translocated to the cell surface within 15 min of TCR stimulation, followed by a later wave of newly synthesized FasL cell surface expression at 2 h poststimulation. Initial studies suggested that the two FasL responses had different signaling thresholds. To test this possibility directly, we titrated Ag presented to murine CTL to measure FasL and degranulation response thresholds. Stored FasL translocation to the cell surface required substantially lower concentrations of peptide than was required for de novo expression of FasL and degranulation. Furthermore, a low-affinity agonist peptide stimulated strong stored FasL translocation but only limited de novo FasL expression and degranulation. These data imply that the two FasL populations may have distinct functions. We examined bystander killing and found that the rapidly expressed FasL triggered highly specific lysis of target cells, as did degranulation. In contrast, the newly synthesized later wave of FasL mediated extensive Fas-dependent bystander killing. Our data indicate that stored FasL is mobilized in response to low concentrations of Ag to mediate rapid, highly specific lysis of target cells, whereas the later, newly synthesized FasL requires higher concentrations of Ag and mediates indiscriminate lysis. These findings suggest that early and late FasL and degranulation represent nonredundant lytic mechanisms that have been selected for distinct situations, possibly for optimal pathogen clearance.

HIV Tat protein inhibits hERG K+ channels: a potential mechanism of HIV infection induced LQTs.

HIV-infected patients have a high prevalence of long QT syndrome (LQTs). hERG K(+) channel encoded by human ether-a-go-go related gene contributes to IKr K(+) currents responsible for the repolarization of cardiomyocytes. Inhibition of hERG K(+) channels leads to LQTs. HIV Tat protein, the virus transactivator protein, plays a pivotal role in AIDS. The aim of the present study is to examine the effects of HIV Tat protein on hERG K(+) channels stably expressed in HEK293 cells. The hERG K(+) currents were recorded by whole-cell patch-clamp technique and the hERG channel expression was measured by real-time PCR and Western blot techniques. HIV Tat protein at 200 ng/ml concentration showed no acute effect on hERG currents, but HIV Tat protein (200 ng/ml) incubation for 24 h significantly inhibited hERG currents. In HIV Tat incubated cells, the inactivation and the recovery time from inactivation of hERG channels were significantly changed. HIV Tat protein incubation (200 ng/ml) for 24h had no effect on the hERG mRNA expression, but dose-dependently inhibited hERG protein expression. The MTT assay showed that HIV Tat protein at 50 ng/ml and 200 ng/ml had no effect on the cell viability. HIV Tat protein increased reactive oxygen species (ROS) generation and the inhibition of hERG channel protein expression by HIV Tat protein was prevented by antioxidant tempol. HIV Tat protein in vivo treatment reduced IKr currents and prolonged action potential duration of guinea pig cardiomyocytes. We conclude that HIV Tat protein inhibits hERG K(+) currents through the inhibition of hERG protein expression, which might be the potential mechanism of HIV infection induced LQTs.

Learning to Zoom-In via Learning to Zoom-Out: Real-World Super-Resolution by Generating and Adapting Degradation.

Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs. The SR methods learned on synthetic data do not perform well in real-world, due to the domain gap between the artificially synthesized and real LR images. Some efforts are thus taken to capture real-world image pairs. However, the captured LR-HR image pairs usually suffer from unavoidable misalignment, which hampers the performance of end- to-end learning. Here, focusing on the real-world SR, we ask a different question: since misalignment is unavoidable, can we propose a method that does not need LR-HR image pairing and alignment at all and utilizes real images as they are? Hence we propose a framework to learn SR from an arbitrary set of unpaired LR and HR images and see how far a step can go in such a realistic and "unsupervised" setting. To do so, we firstly train a degradation generation network to generate realistic LR images and, more importantly, to capture their distribution (i.e., learning to zoom out). Instead of assuming the domain gap has been eliminated, we minimize the discrepancy between the generated data and real data while learning a degradation adaptive SR network (i.e., learning to zoom in). The proposed unpaired method achieves state-of- the-art SR results on real-world images, even in the datasets that favour the paired-learning methods more.

M1 Bone Marrow-Derived Macrophage-Derived Extracellular Vesicles Inhibit Angiogenesis and Myocardial Regeneration Following Myocardial Infarction via the MALAT1/MicroRNA-25-3p/CDC42 Axis.

Myocardial infarction (MI) is a severe cardiovascular disease. Some M1 macrophage-derived extracellular vesicles (EVs) are involved in the inhibition of angiogenesis and acceleration dysfunction during MI. However, the potential mechanism of M1 phenotype bone marrow-derived macrophages- (BMMs-) EVs (M1-BMMs-EVs) in MI is largely unknown. This study sought to investigate whether M1-BMMs-EVs increased CDC42 expression and activated the MEK/ERK pathway by carrying lncRNA MALAT1 and competitively binding to miR-25-3p, thus inhibiting angiogenesis and myocardial regeneration after MI. After EV treatment, the cardiac function, infarct size, fibrosis, angiogenesis, and myocardial regeneration of MI mice and the viability, proliferation and angiogenesis of oxygen-glucose deprivation- (OGD-) treated myocardial microvascular endothelial cells (MMECs) were assessed. MALAT1 expression in MI mice, cells, and EVs was detected. MALAT1 downstream microRNAs (miRs), genes, and pathways were predicted and verified. MALAT1 and miR-25-3p were intervened to evaluate EV effects on OGD-treated cells. In MI mice, EV treatment aggravated MI and inhibited angiogenesis and myocardial regeneration. In OGD-treated cells, EV treatment suppressed cell viability, proliferation, and angiogenesis. MALAT1 was highly expressed in MI mice, OGD-treated MMECs, M1-BMMs, and EVs. Silencing MALAT1 weakened the inhibition of EV treatment on OGD-treated cells. MALAT1 sponged miR-25-3p to upregulate CDC42. miR-25-3p overexpression promoted OGD-treated cell viability, proliferation, and angiogenesis. The MEK/ERK pathway was activated after EV treatment. Collectively, M1-BMMs-EVs inhibited angiogenesis and myocardial regeneration following MI via the MALAT1/miR-25-3p/CDC42 axis and the MEK/ERK pathway activation.

COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations.

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019. Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this article, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We first maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. The proposed method achieves state-of-the-art performance. Dice similarity coefficients are 0.987 and 0.726 for lung and COVID-19 segmentation, respectively. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively. The proposed network enhances the segmentation ability of the COVID-19 infection, makes the connection with other techniques and contributes to the development of remedying COVID-19 infection.

Endothelial peroxynitrite causes disturbance of neuronal oscillations by targeting caspase-1 in the arcuate nucleus.

Severe anorexia limits the clinical application of cisplatin, and even leads to the discontinuation of treatment. However, the mechanisms underlying cisplatin-induced anorexia are unknown. Herein, we demonstrated that cisplatin could affect neuronal gamma oscillations and induce abnormal neuronal theta-gamma phase-amplitude coupling in the arcuate nucleus (Arc) of the hypothalamus, and these findings were associated with significantly decreased food intake and weight loss in mice. Chemogenetic activation of AgRP neurons in the Arc reversed the cisplatin-induced food intake reduction in mice. We further demonstrated that endothelial peroxynitrite (ONOO-) formation in the Arc induced nitrosative stress following cisplatin treatment via a previously uncharacterized pathway involving neuronal caspase-1 activation. Strikingly, treatment with the ONOO- scavenger uric acid (UA) reversed the reduced action potential (AP) frequency of AgRP neurons and increased the AP frequency of POMC neurons induced by SIN1, a donor of ONOO-, in the Arc, as determined by whole-cell patch-clamp electrophysiological recording. Consistent with these findings, UA treatment effectively alleviated cisplatin-induced dysfunction of neuronal oscillations and neuronal theta-gamma phase-amplitude coupling in the Arc of mice. Taken together, these results suggest, for the first time, that targeting the overproduction of endothelial ONOO- can regulate cisplatin-induced neurotoxicity through neuronal caspase-1, and thereby serve as a potential therapeutic approach to alleviate chemotherapy-induced anorexia and weight loss.

Learning Deep Gradient Descent Optimization for Image Deconvolution.

As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. The predominant approach is based on optimization subject to regularization functions that are either manually designed or learned from examples. Existing learning-based methods have shown superior restoration quality but are not practical enough due to their restricted and static model design. They solely focus on learning a prior and require to know the noise level for deconvolution. We address the gap between the optimization- and learning-based approaches by learning a universal gradient descent optimizer. We propose a recurrent gradient descent network (RGDN) by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme. A hyperparameter-free update unit shared across steps is used to generate the updates from the current estimates based on a convolutional neural network. By training on diverse examples, the RGDN learns an implicit image prior and a universal update rule through recursive supervision. The learned optimizer can be repeatedly used to improve the quality of diverse degenerated observations. The proposed method possesses strong interpretability and high generalization. Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.

[Partitioning ecosystem respiration of a Platycladus orientalis forest in the west mountainous area of Beijing, China using stable carbon isotope].

Based on stable carbon isotope, we quantitatively partitioned ecosystem respiration in a Platycladus orientalis forest in the west mountainous area of Beijing. Results from this study could lay the foundation for carbon exchange research in forest ecosystems of this region. The spectroscopy technique was used to continuously measure CO2 concentrations and δ13C values at different height of the forest. Soil and branch chambers were used for measuring nighttime δ13C values in underground and aboveground respiration, and then the proportions of respiration components were calculated. Combined with soil respiration efflux measurement, ecosystem respiration was then quantitatively partitioned. The results showed that δ13C values of respiratory components fluctuated, which ranged from -31.74‰ to -23.33‰ in aboveground respiration of plants and from -32.11‰ to -27.74‰ in soil respiration. The δ13C values of ecosystem respiration was at the middle of those ranges. Soil respiration averaged 1.70 µmol·m-2·s-1 at night, accounting for 47%-91% of ecosystem respiration. Aboveground respiration averaged 0.72 µmol·m-2·s-1, contributing less to ecosystem respiration. Daytime respiration based on isotope mixing model calculation had greater variability than that based on temperature response model, with a mean value of 2.31 µmol·m-2·s-1 and 2.28 µmol·m-2·s-1, respectively.

Endothelial Cdk5 deficit leads to the development of spontaneous epilepsy through CXCL1/CXCR2-mediated reactive astrogliosis.

Blood-brain barrier (BBB) dysfunction has been suggested to play an important role in epilepsy. However, the mechanism mediating the transition from cerebrovascular damage to epilepsy remains unknown. Here, we report that endothelial cyclin-dependent kinase 5 (CDK5) is a central regulator of neuronal excitability. Endothelial-specific Cdk5 knockout led to spontaneous seizures in mice. Knockout mice showed increased endothelial chemokine (C-X-C motif) ligand 1 (Cxcl1) expression, decreased astrocytic glutamate reuptake through the glutamate transporter 1 (GLT1), and increased glutamate synaptic function. Ceftriaxone restored astrocytic GLT1 function and inhibited seizures in endothelial Cdk5-deficient mice, and these effects were also reversed after silencing Cxcl1 in endothelial cells and its receptor chemokine (C-X-C motif) receptor 2 (Cxcr2) in astrocytes, respectively, in the CA1 by AAV transfection. These results reveal a previously unknown link between cerebrovascular factors and epileptogenesis and provide a rationale for targeting endothelial signaling as a potential treatment for epilepsy.

Two-Stream Convolutional Networks for Blind Image Quality Assessment.

Traditional image quality assessment (IQA) methods do not perform robustly due to the shallow hand-designed features. It has been demonstrated that deep neural network can learn more effective features than ever. In this paper, we describe a new deep neural network to predict the image quality accurately without relying on the reference image. To learn more effective feature representations for non-reference IQA, we propose a two-stream convolution network that includes two subcomponents for image and gradient image. The motivation for this design is using a two-stream scheme to capture different-level information of inputs and easing the difficulty of extracting features from one steam. The gradient stream focuses on extracting structure features in details, and the image stream pays more attention to the information in intensity. In addition, to consider the locally non-uniform distribution of distortion in images, we add a region-based fully convolutional layer for using the information around the center of the input image patch. The final score of the overall image is calculated by averaging of the patch scores. The proposed network performs in an end-to-end manner in both the training and testing phases. The experimental results on a series of benchmark datasets, e.g., LIVE, CISQ, IVC, TID2013, and Waterloo Exploration Database, show that the proposed algorithm outperforms the state-of-the-art methods, which verifies the effectiveness of our network architecture.

MPTV: Matching Pursuit-Based Total Variation Minimization for Image Deconvolution.

Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing TV-based methods, however, often suffer from the over-smoothing issue and solution bias caused by the homogeneous penalization. In this paper, we consider addressing these issues by applying inhomogeneous regularization on different image components. We formulate the inhomogeneous TV minimization problem as a convex quadratic constrained linear programming problem. Relying on this new model, we propose a matching pursuit-based total variation minimization method (MPTV), specifically for image deconvolution. The proposed MPTV method is essentially a cutting-plane method that iteratively activates a subset of nonzero image gradients and then solves a subproblem focusing on those activated gradients only. Compared with existing methods, the MPTV is less sensitive to the choice of the trade-off parameter between data fitting and regularization. Moreover, the inhomogeneity of MPTV alleviates the over-smoothing and ringing artifacts and improves the robustness to errors in blur kernel. Extensive experiments on different tasks demonstrate the superiority of the proposed method over the current state of the art.

[Experimental study of platelet-rich plasma in treatment of Achilles tendinopathy in rabbits].

OBJECTIVE: To explore the effect of platelet-rich plasma (PRP) in treatment of Achilles tendinopathy in rabbits, and provide experimental evidence for the clinical application of PRP in treatment of Achilles tendinopathy. METHODS: Forty-eight adult New Zealand white rabbits, weighing 2.5-3.0 kg, male or female, were randomly divided into model group (group A), model control group (group B), model+treatment control group (group C), model+treatment group (group D), with 12 in each group. The rabbits were injected with type Ⅰ collagenase to prepare Achilles tendinopathy models in groups A, C, and D, and with an equal dose of normal saline in group B. The blood from the central artery of rabbit ear was taken to preprare PRP by secondary centrifugation in group D. The results of platelet counts showed that PRP platelets reached 3 to 5 times the whole blood. After the model was prepared, the rabbits in groups C and D were injected with physiological saline and autologous PRP at the molding site respectively, once a week, 0.8 mL each time for 4 weeks. At 1 week after PRP injection, the relative hardness (expressed as HRD%) of Achilles tendon was evaluated by ultrasound elastic quantitative imaging detection technique; the maximum breaking load of Achilles tendon was measured by universal electronic tensile testing machine; the contents of collagen type Ⅰ and Ⅲ were determined by ELISA; and the morphology of Achilles tendon collagen fibers was observed by HE and Masson stainings. RESULTS: All animals survived during the experiment. The results of ultrasound elastic quantitative imaging and mechanical tests showed that the HRD% and the maximum breaking load were significantly lower in group A than in group B ( P<0.05) and in group C than in group D ( P<0.05). The results of ELISA showed that the content of collagen type Ⅰ was significantly lower in group A than in group B ( P<0.05) and in group C than in group D ( P<0.05); the content of collagen type Ⅲ was significantly higher in group A than in group B ( P<0.05) and in group D than in group C ( P<0.05). HE and Masson stainings showed that the Achilles tendon collagen fibers were irregularly curled and the structure was severely damaged in group A; the fibers were parallel and ordered, and the structure was complete in group B; the fibers were irregularly curled and structurally disordered in group C; the fibers were slightly curled and the structure was relatively complete in group D. CONCLUSION: A rabbit model of Achilles tendinopathy can be reconstructed by type Ⅰ collagenase injection. PRP treatment can increase the Achilles tendon hardness and maximum breaking load, up-regulate the expression level of collagen type Ⅰ and Ⅲ, improve the structure of Achilles tendon collagen fiber, and promote the repair in rabbit Achilles tendinopathy model.