A Dataset and Model for the Visual Quality Assessment of Inversely Tone-Mapped HDR Videos.

To enhance the viewer experience of standard dynamic range (SDR) video content on high dynamic range (HDR) displays, inverse tone mapping (ITM) is employed. Objective visual quality assessment (VQA) models are needed for effective evaluation of ITM algorithms. However, there is a lack of specialized VQA models for assessing the visual quality of inversely tone-mapped HDR videos (ITM-HDR-Videos).

Perfusion preparation of the rat bladder decellularized scaffold.

Introduction: Bladder reconstruction is a huge challenge in the field of urology. In recent years, perfusion methods have brought promising results in the field of tissue engineering. We prepared bladder decellularized scaffolds by improved perfusion, which may be suitable for bladder reconstruction.

Bumetanide Rescues Aquaporin-4 Depolarization via Suppressing β-Dystroglycan Cleavage and Provides Neuroprotection in Rat Retinal Ischemia-Reperfusion Injury.

Aquaporin-4 (AQP4) regulates retinal water homeostasis and participates in retinal oedema pathophysiology. β-dystroglycan (β-DG) is responsible for AQP4 polarization and can be cleaved by matrix metalloproteinase-9 (MMP9). Retinal oedema induced by ischemia-reperfusion (I/R) injury is an early complication.

Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning.

Anomaly detection has wide applications in machine intelligence but is still a difficult unsolved problem. Major challenges include the rarity of labeled anomalies and it is a class highly imbalanced problem. Traditional unsupervised anomaly detectors are suboptimal while supervised models can easily make biased predictions towards normal data.

Hypotonic induction of aquaporin5 expression in rat astrocytes through p38 MAPK pathway.

Brain oedema is a common pathological phenomenon following many diseases and may lead to severe secondary damage. Astrocytes are the most numerous cells in the brain. Five aquaporins (AQPs) have been found in mature astrocytes, which play crucial roles in water transportation.

Main Pathological Changes of Benign Ureteral Strictures.

Objective: To identify the pathological classification of benign ureteral strictures according to the histological features and explore the relationship between various pathological types and inflammatory cells, fibroblasts, and collagen.

Patients And Methods: Thirty one specimens from patients diagnosed with ureteral strictures between 2013 and 2021 were included and classified according to the histopathological characteristics. The number of fibroblasts and inflammatory cells was counted, and the proportion of type I and type III collagen in ureteral stricture tissues was detected by picrosirius red staining.

Iopromide and Iodixanol in the Development of Postoperative Contrast Nephropathy in Patients with Renal Insufficiency: A Meta-Analysis.

In order to compare the effects of iopromide and isoxazole on postoperative contrast-induced nephropathy in patients with renal insufficiency, the paper searches for randomized controlled trials and retrospective cohort studies comparing the effects of iopromide and iodixanol on renal function in patients with renal insufficiency after surgery. The data are extracted from eligible studies. We tried to assess the incidence of contrast-agent nephropathy, preoperative and postoperative serum creatinine indicators, and mortality.

Towards Disentangling Latent Space for Unsupervised Semantic Face Editing.

Facial attributes in StyleGAN generated images are entangled in the latent space which makes it very difficult to independently control a specific attribute without affecting the others. Supervised attribute editing requires annotated training data which is difficult to obtain and limits the editable attributes to those with labels. Therefore, unsupervised attribute editing in an disentangled latent space is key to performing neat and versatile semantic face editing.

Silencing Akt1 enhances the resistance of prostate cancer cells to starvation and inhibits starvation-induced lung metastasis through epithelial-mesenchymal transition in prostate cancer.

Nutritional starvation (NST) is the basis of tumor anti-angiogenesis and metabolic therapy strategy. Silencing Akt1 inhibits prostate cancer (PCa) cells growing; slow-growing cells tend to consume less nutrition. It is suggested that Akt1-silenced cancer cells will have a more substantial tolerance to NST.

Neuroglobin expression and function in the temporal cortex of bilirubin encephalopathy rats.

Bilirubin encephalopathy (BE) is a neurological syndrome in newborns, mainly caused by neuronal injury due to excessive oxidative stress produced by unconjugated bilirubin (UCB). Neuroglobin (NGB) can protect the brain by removing oxidative stress species, but its expression and significance in BE are not clear. To address this question, the neonatal BE model was established by injecting UCB into the cerebellomedullary cistern of 7-day-old SD rats.

Class-Imbalanced Deep Learning via a Class-Balanced Ensemble.

Class imbalance is a prevalent phenomenon in various real-world applications and it presents significant challenges to model learning, including deep learning. In this work, we embed ensemble learning into the deep convolutional neural networks (CNNs) to tackle the class-imbalanced learning problem. An ensemble of auxiliary classifiers branching out from various hidden layers of a CNN is trained together with the CNN in an end-to-end manner.

Changes in the prefrontal cortex after the hippocampus was injected with Aβ via the P35/P25-CDK5-Tau hyperphosphorylation signaling pathway.

Alzheimer's disease (AD) is one of the common neurodegenerative illnesses in aging populations around the world. Recently, psychiatric symptoms are becoming increasingly important in recognizing the manifestations of AD in addition to cognitive impairment. Some studies suggest that the prefrontal cortex (PFC) is closely related to apathy/depression, and a network may exist between the CA1 of hippocampus and PFC.

Image Defogging Quality Assessment: Real-World Database and Method.

Fog removal from an image is an active research topic in computer vision. However, current literature is weak in the following two areas which in many ways are hindering progress for developing defogging algorithms. First, there is no true real-world and naturally occurring foggy image datasets suitable for developing defogging models.

End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network.

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion.

[Effect of electroacupuncture of different acupoint groups on learning-memory ability and expression of IL-1β and TNF-α in hippocampus and prefrontal cortex in rats with Alzheimer's disease].

Objective: To compare the effect of electroacupuncture (EA) of acupoint group for "reinforcing the kidney and regulating Governor Vessel" and acopoint group for "reinforcing the kidney and lung and regulating Governor Vessel" on lear-ning-memory ability and expression of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) proteins in the hippocampus and prefrontal cortex (PFC) in Alzheimer's disease （AD） rats, so as to explore the efficacy of the two acupoint groups and mechanisms underlying improvement of AD.

Methods: Forty male SD rats were randomly divided into control, sham operation, model, "Baihui" + "Shenshu" (GV20+BL23, for "reinforcing the kidney and regulating Governor Vessel") EA and GV20+BL23+ "Feishu" (BL13, GV20+BL23+BL13, for "reinforcing the kidney and lung and regulating Governor Vessel") EA groups (=8 rats in each group). The AD model was established by bilateral injection of amyloid β peptide （Aβ，10 μL) into bilateral hippocampus, and rats of the sham operation group received injection of normal saline.

Visual Saliency via Embedding Hierarchical Knowledge in a Deep Neural Network.

Deep neural networks (DNNs) have been extensively applied in image processing, including visual saliency map pre-diction of images. A major difficulty in using a DNN for visual saliency prediction is the lack of labeled ground truth of visual saliency. A powerful DNN usually contains a large number of trainable parameters.

Poldip2 mediates blood-brain barrier disruption and cerebral edema by inducing AQP4 polarity loss in mouse bacterial meningitis model.

Background: Specific highly polarized aquaporin-4 (AQP4) expression is reported to play a crucial role in blood-brain barrier (BBB) integrity and brain water transport balance. The upregulation of polymerase δ-interacting protein 2 (Poldip2) was involved in aggravating BBB disruption following ischemic stroke. This study aimed to investigate whether Poldip2-mediated BBB disruption and cerebral edema formation in mouse bacterial meningitis (BM) model occur via induction of AQP4 polarity loss.

Marker controlled superpixel nuclei segmentation and automatic counting on immunohistochemistry staining images.

Motivation: For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation.

Results: This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method.

Structure and Texture-Aware Image Decomposition via Training a Neural Network.

Structure-texture image decomposition is a funda-mental but challenging topic in computational graphics and image processing. In this paper, we introduce a structure-aware and a texture-aware measures to facilitate the structure-texture de-composition (STD) of images. Edge strengths and spatial scales that have been widely-used in previous STD researches cannot describe the structures and textures of images well.

Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification.

Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the deep learning model is computationally expensive, while resizing the original image to achieve low resolution incurs information loss. Some hard-attention based approaches have emerged to select possible lesion regions from images to avoid processing the original image.

Visual Quality Assessment for Super-Resolved Images: Database and Method.

Image super-resolution (SR) has been an active research problem which has recently received renewed interest due to the introduction of new technologies such as deep learning. However, the lack of suitable criteria to evaluate the SR performance has hindered technology development. In this paper, we fill a gap in the literature by providing the first publicly available database as well as a new image quality assessment (IQA) method specifically designed for assessing the visual quality of super-resolved images (SRIs).

Curcumin Ameliorates Memory Deficits by Enhancing Lactate Content and MCT2 Expression in APP/PS1 Transgenic Mouse Model of Alzheimer's Disease.

Curcumin is a natural product with several anti-Alzheimer's disease (AD) neuroprotective properties. This study aimed to investigate the effects of curcumin on memory deficits, lactate content, and monocarboxylate transporter 2 (MCT2) in APP/PS1 mouse model of AD. APP/PS1 transgenic mice and wild-type (WT) C57BL/6J mice were used in the present study.

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA.

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time-consuming, imprecise, and subjective process, which will lead to inter-observer and intra-observer discrepancies.

Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers.

Background: Colour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy.

Methods: Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images.

Adult-onset Nemaline Myopathy Coexisting With Myasthenia Gravis: A Case Report.

Myasthenia gravis (MG) is an autoimmune neuromuscular junction disorder which is characterized by fluctuating muscle fatigue. However, the association of MG with nemaline myopathy is rarely reported. Here we report a case of MG coexisting with adult-onset nemaline myopathy.