Mechanism of RhoA regulating benign prostatic hyperplasia: RhoA-ROCK-ß-catenin signaling axis and static & dynamic dual roles.

BACKGROUND: The pathogenesis of benign prostatic hyperplasia (BPH) has not been fully elucidated. Ras homology family member A (RhoA) plays an important role in regulating cell cytoskeleton, growth and fibrosis. The role of RhoA in BPH remains unclear. METHODS: This study aimed to clarify the expression, functional activity and mechanism of RhoA in BPH. Human prostate tissues, human prostate cell lines, BPH rat model were used. Cell models of RhoA knockdown and overexpression were generated. Immunofluorescence staining, quantitative real time PCR (qRT-PCR), Western blotting, cell counting kit-8 (CCK-8), flow cytometry, phalloidine staining, organ bath study, gel contraction assay, protein stability analysis, isolation and extraction of nuclear protein and cytoplasmic protein were performed. RESULTS: In this study we found that RhoA was localized in prostate stroma and epithelial compartments and was up-regulated in both BPH patients and BPH rats. Functionally, RhoA knockdown induced cell apoptosis and inhibited cell proliferation, fibrosis, epithelial-mesenchymal transformation (EMT) and contraction. Consistently, overexpression of RhoA reversed all aforementioned processes. More importantly, we found that ß-catenin and the downstream of Wnt/ß-catenin signaling, including C-MYC, Survivin and Snail were up-regulated in BPH rats. Downregulation of RhoA significantly reduced the expression of these proteins. Rho kinase inhibitor Y-27632 also down-regulated ß-catenin protein in a concentration-dependent manner. However, overexpression of ß-catenin did not affect RhoA-ROCK levels, suggesting that ß-catenin was the downstream of RhoA-ROCK regulation. Further data suggested that RhoA increased nuclear translocation of ß-catenin and up-regulated ß-catenin expression by inhibiting its proteasomal degradation, thereby activating Wnt/ß-catenin signaling. Overexpression of ß-catenin partially reversed the changes in cell growth, fibrosis and EMT except cell contraction caused by RhoA downregulation. Finally, Y-27632 partially reversed prostatic hyperplasia in vivo, further suggesting the potential of RhoA-ROCK signaling in BPH treatment. CONCLUSION: Our novel data demonstrated that RhoA regulated both static and dynamic factors of BPH, RhoA-ROCK-ß-catenin signaling axis played an important role in the development of BPH and might provide more possibilities for the formulation of subsequent clinical treatment strategies.

Glutathione Peroxidase 3 induced mitochondria-mediated apoptosis via AMPK /ERK1/2 pathway and resisted autophagy-related ferroptosis via AMPK/mTOR pathway in hyperplastic prostate.

BACKGROUND: Benign prostatic hyperplasia (BPH) is a common disease in elderly men, mainly resulted from an imbalance between cell proliferation and death. Glutathione peroxidase 3 (GPX3) was one of the differentially expressed genes in BPH identified by transcriptome sequencing of 5 hyperplastic and 3 normal prostate specimens, which had not been elucidated in the prostate. This study aimed to ascertain the mechanism of GPX3 involved in cell proliferation, apoptosis, autophagy and ferroptosis in BPH. METHODS: Human prostate tissues, GPX3 silencing and overexpression prostate cell (BPH-1 and WPMY-1) models and testosterone-induced rat BPH (T-BPH) model were utilized. The qRT-PCR, CCK8 assay, flow cytometry, Western blotting, immunofluorescence, hematoxylin and eosin, masson's trichrome, immunohistochemical staining and transmission electron microscopy analysis were performed during in vivo and in vitro experiments. RESULTS: Our study indicated that GPX3 was localized both in the stroma and epithelium of prostate, and down-regulated in BPH samples. Overexpression of GPX3 inhibited AMPK and activated ERK1/2 pathway, thereby inducing mitochondria-dependent apoptosis and G0/G1 phase arrest, which could be significantly reversed by MEK1/2 inhibitor U0126 preconditioning. Moreover, overexpression of GPX3 further exerted anti-autophagy by inhibiting AMPK/m-TOR and up-regulated nuclear factor erythroid 2-related factor 2 (Nrf2)/glutathione peroxidase 4 (GPX4, mitochondrial GPX4 and cytoplasmic GPX4) to antagonize autophagy-related ferroptosis. Consistently, GPX3 deficiency generated opposite changes in both cell lines. Finally, T-BPH rat model was treated with GPX3 indirect agonist troglitazone (TRO) or GPX4 inhibitor RAS-selective lethal 3 (RSL3) or TRO plus RSL3. These treatments produced significant atrophy of the prostate and related molecular changes were similar to our in vitro observations. CONCLUSIONS: Our novel data manifested that GPX3, which was capable of inducing apoptosis via AMPK/ERK1/2 pathway and antagonizing autophagy-related ferroptosis through AMPK/m-TOR signalling, was a promising therapeutic target for BPH in the future.

Development and validation of an online dynamic nomogram based on the atherogenic index of plasma to screen nonalcoholic fatty liver disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD), a common liver disease worldwide, can be reversed early in life with lifestyle and medical interventions. This study aimed to develop a noninvasive tool to screen NAFLD accurately. METHODS: Risk factors for NAFLD were identified using multivariate logistic regression analysis, and an online NAFLD screening nomogram was developed. The nomogram was compared with reported models (fatty liver index (FLI), atherogenic index of plasma (AIP), and hepatic steatosis index (HSI)). Nomogram performance was evaluated through internal and external validation (National Health and Nutrition Examination Survey (NHANES) database). RESULTS: The nomogram was developed based on six variables. The diagnostic performance of the present nomogram for NAFLD (area under the receiver operator characteristic curve (AUROC): 0.863, 0.864, and 0.833, respectively) was superior to that of the HSI (AUROC: 0.835, 0.833, and 0.810, respectively) and AIP (AUROC: 0.782, 0.773, and 0.728, respectively) in the training, validation, and NHANES sets. Decision curve analysis and clinical impact curve analysis presented good clinical utility. CONCLUSION: This study establishes a new online dynamic nomogram with excellent diagnostic and clinical performance. It has the potential to be a noninvasive and convenient method for screening individuals at high risk for NAFLD.

The Emerging Role of Cell Adhesion Molecules on Benign Prostatic Hyperplasia.

Benign prostatic hyperplasia (BPH) is a common disease in elderly men. It is characterized by prostatic enlargement and urethral compression and often causes lower urinary tract symptoms (LUTs) such as urinary frequency, urgency, and nocturia. Existing studies have shown that the pathological process of prostate hyperplasia is mainly related to the imbalance of cell proliferation and apoptosis, inflammation, epithelial-mesenchymal transition (EMT), and growth factors. However, the exact molecular mechanisms remain incompletely elucidated. Cell adhesion molecules (CAMs) are a group of cell surface proteins that mediate cell-cell adhesion and cell migration. Modulating adhesion molecule expression can regulate cell proliferation, apoptosis, EMT, and fibrotic processes, engaged in the development of prostatic hyperplasia. In this review, we went over the important roles and molecular mechanisms of cell adhesion molecules (mainly integrins and cadherins) in both physiological and pathological processes. We also analyzed the mechanisms of CAMs in prostate hyperplasia and explored the potential value of targeting CAMs as a therapeutic strategy for BPH.

pHSCNN: CNN-based hyperspectral recovery from a pair of RGB images.

To increase the fidelity of hyperspectral recovery from RGB images, we propose a pairwise-image-based hyperspectral convolutional neural network (pHSCNN) to recover hyperspectral images from a pair of RGB images, obtained by the same color sensor with and without an optical filter in front of the imaging lens. The proposed method avoids the pitfall of requiring multiple color sensors to obtain different RGB images and achieves higher accuracy than recovery from single RGB image. Besides, pHSCNN can also optimize the optical filter to further improve the performance. To experiment on real data, we built a dual-camera hyperspectral imaging system and created a real-captured hyperspectral-RGB dataset. Experimental results demonstrate the superiority of pHSCNN with the highest accuracy of the recovered hyperspectral signature perceptually and numerically.

A High-Precision Method for Segmentation and Recognition of Shopping Mall Plans.

Most studies on map segmentation and recognition are focused on architectural floor plans, while there are very few analyses of shopping mall plans. The objective of the work is to accurately segment and recognize the shopping mall plan, obtaining location and semantic information for each room via segmentation and recognition. This work can be used in other applications such as indoor robot navigation, building area and location analysis, and three-dimensional reconstruction. First, we identify and match the catalog of a mall floor plan to obtain matching text, and then we use the two-stage region growth method we proposed to segment the preprocessed floor plan. The room number is then obtained by sending each segmented room section to an OCR (optical character recognition) system for identification. Finally, the system retrieves the matching text to match the room number in order to obtain the room name, and outputs the needed room location and semantic information. It is considered a successful detection when a room region can be successfully segmented and identified. The proposed method is evaluated on a dataset including 1340 rooms. Experimental results show that the accuracy of room segmentation is 92.54%, and the accuracy of room recognition is 90.56%. The total detection accuracy is 83.81%.

Color polarization demosaicking by a convolutional neural network.

To address color polarization demosaicking problems in polarization imaging with a color polarization camera, we propose a color polarization demosaicking convolutional neural network (CPDCNN), which has a two-branch structure to ensure the fidelity of polarization signatures and enhance image resolution. To train the network, we built a unique dual-camera system and captured a pairwise color polarization image dataset. Experimental results show that CPDCNN outperformances other methods by a large margin in contrast and resolution.

Unsupervised learning for hyperspectral recovery based on a single RGB image.

Hyperspectral imagery often suffers from the degradation of spatial, spectral, or temporal resolution due to the limitations of hyperspectral imaging devices. To address this problem, hyperspectral recovery from a single red-green-blue (RGB) image has recently achieved significant progress via deep learning. However, current deep learning-based methods are all learned in a supervised way under the availability of RGB and correspondingly hyperspectral images, which is unrealistic for practical applications. Hence, we propose to recover hyperspectral images from a single RGB image in an unsupervised way. Moreover, based on the statistical property of hyperspectral images, a customized loss function is proposed to boost the performance. Extensive experiments on the BGU iCVL Hyperspectral Image Dataset demonstrate the effectiveness of the proposed method.

The Overview of Human Localization and Vital Sign Signal Measurement Using Handheld IR-UWB Through-Wall Radar.

Obtaining information (e.g., position, respiration, and heartbeat rates) on humans located behind opaque and non-metallic obstacles (e.g., walls and wood) has prompted the development of non-invasive remote sensing technologies. Due to its excellent features like high penetration ability, short blind area, fine-range resolution, high environment adoption capabilities, low cost and power consumption, and simple hardware design, impulse radio ultra-wideband (IR-UWB) through-wall radar has become the mainstream primary application radar used for the non-invasive remote sensing. IR-UWB through-wall radar has been developed for nearly 40 years, and various hardware compositions, deployment methods, and signal processing algorithms have been introduced by many scholars. The purpose of these proposed approaches is to obtain human information more accurately and quickly. In this paper, we focus on IR-UWB through-wall radar and introduce the key advances in system design and deployment, human detection theory, and signal processing algorithms, such as human vital sign signal measurement methods and moving human localization. Meanwhile, we discuss the engineering pre-processing methods of IR-UWB through-wall radar. The lasts research progress in the field is also presented. Based on this progress, the conclusions and the development directions of the IR-UWB through-wall radar in the future are also preliminarily forecasted.

PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data.

Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

Genome-Wide Identification and Analysis of the Polycomb Group Family in Medicago truncatula.

Polycomb group (PcG) proteins, which are important epigenetic regulators, play essential roles in the regulatory networks involved in plant growth, development, and environmental stress responses. Currently, as far as we know, no comprehensive and systematic study has been carried out on the PcG family in Medicago truncatula. In the present study, we identified 64 PcG genes with distinct gene structures from the M. truncatula genome. All of the PcG genes were distributed unevenly over eight chromosomes, of which 26 genes underwent gene duplication. The prediction of protein interaction network indicated that 34 M. truncatula PcG proteins exhibited protein-protein interactions, and MtMSI1;4 and MtVRN2 had the largest number of protein-protein interactions. Based on phylogenetic analysis, we divided 375 PcG proteins from 27 species into three groups and nine subgroups. Group I and Group III were composed of five components from the PRC1 complex, and Group II was composed of four components from the PRC2 complex. Additionally, we found that seven PcG proteins in M. truncatula were closely related to the corresponding proteins of Cicer arietinum. Syntenic analysis revealed that PcG proteins had evolved more conservatively in dicots than in monocots. M. truncatula had the most collinearity relationships with Glycine max (36 genes), while collinearity with three monocots was rare (eight genes). The analysis of various types of expression data suggested that PcG genes were involved in the regulation and response process of M. truncatula in multiple developmental stages, in different tissues, and for various environmental stimuli. Meanwhile, many differentially expressed genes (DEGs) were identified in the RNA-seq data, which had potential research value in further studies on gene function verification. These findings provide novel and detailed information on the M. truncatula PcG family, and in the future it would be helpful to carry out related research on the PcG family in other legumes.

Deep-learning-based hyperspectral recovery from a single RGB image.

Commercial hyperspectral imaging devices are expensive and tend to suffer from the degradation of spatial, spectral, or temporal resolution. To address these problems, we propose a deep-learning-based method to recover hyperspectral images from a single RGB image. The proposed method learns an end-to-end mapping between an RGB image and corresponding hyperspectral images. Moreover, a customized loss function is proposed to boost the performance. Experimental results on a variety of hyperspectral datasets demonstrate that our proposed method outperforms several state-of-the-art methods in terms of both quantitative measurements and perceptual quality.

PFNet: an unsupervised deep network for polarization image fusion.

Image fusion is the key step to improve the performance of object detection in polarization images. We propose an unsupervised deep network to address the polarization image fusion issue. The network learns end-to-end mapping for fused images from intensity and degree of linear polarization images, without the ground truth of fused images. Customized architecture and loss function are designed to boost performance. Experimental results show that our proposed network outperforms other state-of-the-art methods in terms of visual quality and quantitative measurement.

Weighted-encoding-based image interpolation with the nonlocal linear regression model.

An image interpolation model based on sparse representation is proposed. Two widely used priors including sparsity and nonlocal self-similarity are used as the regularization terms to boost the performance of the interpolation model. Meanwhile, we incorporate nonlocal linear regression into this model, since nonlocal similar patches could provide a better approximation to a given patch. Moreover, we propose a new approach to learn an adaptive sub-dictionary online instead of clustering. For each patch, similar patches are grouped to learn the adaptive sub-dictionary, generating a more sparse and accurate representation. Finally, weighted encoding is introduced to suppress tailing of fitting residuals in data fidelity. Abundant experimental results show that our proposed method achieves better performance compared to several state-of-the-art methods in terms of subjective and objective evaluations.

Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging.

Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.

Construction of the first high-density genetic linkage map and identification of seed yield-related QTLs and candidate genes in Elymus sibiricus, an important forage grass in Qinghai-Tibet Plateau.

BACKGROUND: Elymus sibiricus is an ecologically and economically important perennial, self-pollinated, and allotetraploid (StStHH) grass, widely used for forage production and animal husbandry in Western and Northern China. However, it has low seed yield mainly caused by seed shattering, which makes seed production difficult for this species. The goals of this study were to construct the high-density genetic linkage map, and to identify QTLs and candidate genes for seed-yield related traits. RESULTS: An F2 mapping population of 200 individuals was developed from a cross between single genotype from "Y1005" and "ZhN06". Specific-locus amplified fragment sequencing (SLAF-seq) was applied to construct the first genetic linkage map. The final genetic map included 1971 markers on the 14 linkage groups (LGs) and was 1866.35 cM in total. The length of each linkage group varied from 87.67 cM (LG7) to 183.45 cM (LG1), with an average distance of 1.66 cM between adjacent markers. The marker sequences of E. sibiricus were compared to two grass genomes and showed 1556 (79%) markers mapped to wheat, 1380 (70%) to barley. Phenotypic data of eight seed-related traits (2016-2018) were used for QTL identification. A total of 29 QTLs were detected for eight seed-related traits on 14 linkage groups, of which 16 QTLs could be consistently detected for two or three years. A total of 6 QTLs were associated with seed shattering. Based on annotation with wheat and barley genome and transcriptome data of abscission zone in E. sibiricus, we identified 30 candidate genes for seed shattering, of which 15, 7, 6 and 2 genes were involved in plant hormone signal transcription, transcription factor, hydrolase activity and lignin biosynthetic pathway, respectively. CONCLUSION: This study constructed the first high-density genetic linkage map and identified QTLs and candidate genes for seed-related traits in E. sibiricus. Results of this study will not only serve as genome-wide resources for gene/QTL fine mapping, but also provide a genetic framework for anchoring sequence scaffolds on chromosomes in future genome sequence assembly of E. sibiricus.

Elymus nutans genes for seed shattering and candidate gene-derived EST-SSR markers for germplasm evaluation.

BACKGROUND: Elymus nutans and E. sibiricus are two important forage grasses of the genus Elymus. But they are difficult to grow for commercial seed production due to serious seed shattering. We conducted a comparative transcriptome analysis of abscission zone to find possible transcription changes associated with seed shattering, explore candidate genes involved in seed shattering and identify candidate gene-based EST-SSR markers for germplasm evaluation. RESULTS: cDNA libraries from abscission zone (AZ) and non-abscission zone (NAZ) tissues of E. nutans were constructed and sequenced. A total of 111,667 unigenes were annotated and 7644 differentially expressed transcripts (DETs) were predicted, corresponding to 6936 up-regulated in AZ and 708 down-regulated in NAZ. We identified 489 candidate genes related to transcription factor, cell wall hydrolysis or modification, hydrolase activity, phytohormone signaling and response, lignin biosynthesis, and signal transduction or protein turnover. Eleven similar candidate genes involved in polygalacturonase activity, hydrolase activity, and mitogen-activated protein kinase were up-regulated in the abscission zone of the two Elymus species, suggesting these genes may have specific function for abscission zone development and seed shattering. A total of 67 polymorphic EST-SSR markers were developed and characterized based on the sequences of these candidate genes. Fourteen polymorphic EST-SSR primers were finally used to study genetic diversity in 48 E. nutans genotypes with contrasting seed shattering habit. The dendrogram based on molecular data showed that most accessions with similar seed shattering degree tended to group together. CONCLUSIONS: The expression data generated from this study provides an important resource for future molecular biological research. Many DETs were associated with abscission zone development, and EST-SSR loci related to candidate genes may have potential application in identifying trait-associated markers in E. nutans in the future.

EST-SSR marker development based on RNA-sequencing of E. sibiricus and its application for phylogenetic relationships analysis of seventeen Elymus species.

BACKGROUND: Elymus L. is the largest genus in the tribe Triticeae Dumort., encompassing approximately 150 polyploid perennial species widely distributed in the temperate regions of the world. It is considered to be an important gene pool for improving cereal crops. However, a shortage of molecular marker limits the efficiency and accuracy of genetic breeding for Elymus species. High-throughput transcriptome sequencing data is essential for gene discovery and molecular marker development. RESULTS: We obtained the transcriptome dataset of E. sibiricus, the type species of the genus Elymus, and identified a total of 8871 putative EST-SSRs from 6685 unigenes. Trinucleotides were the dominant repeat motif (4760, 53.66%), followed by dinucleotides (1993, 22.47%) and mononucleotides (1876, 21.15%). The most dominant trinucleotide repeat motif was CCG/CGG (1119, 23.5%). Sequencing of PCR products showed that the sequenced alleles from different Elymus species were homologous to the original SSR locus from which the primer was designed. Different types of tri-repeats as abundant SSR motifs were observed in repeat regions. Two hundred EST-SSR primer pairs were designed and selected to amplify ten DNA samples of Elymus species. Eighty-seven pairs of primer (43.5%) generated clear and reproducible bands with expected size, and showed good transferability across different Elymus species. Finally, thirty primer pairs successfully amplified ninety-five accessions of seventeen Elymus species, and detected significant amounts of polymorphism. In general, hexaploid Elymus species with genomes StStHHYY had a relatively higher level of genetic diversity (H = 0.219, I = 0.330, %P = 63.7), while tetraploid Elymus species with genomes StStYY had low level of genetic diversity (H = 0.182, I = 0.272, %P = 50.4) in the study. The cluster analysis showed that all ninety-five accessions were clustered into three major clusters. The accessions were grouped mainly according to their genomic components and origins. CONCLUSIONS: This study demonstrated that transcriptome sequencing is a fast and cost-effective approach to molecular marker development. These EST-SSR markers developed in this study are valuable tools for genetic diversity, evolutionary, and molecular breeding in E. sibiricus, and other Elymus species.

Fiber bundle imaging resolution enhancement using deep learning.

We propose a deep learning based method to estimate high-resolution images from multiple fiber bundle images. Our approach first aligns raw fiber bundle image sequences with a motion estimation neural network and then applies a 3D convolution neural network to learn a mapping from aligned fiber bundle image sequences to their ground truth images. Evaluations on lens tissue samples and a 1951 USAF resolution target suggest that our proposed method can significantly improve spatial resolution for fiber bundle imaging systems.

Phase unwrapping in optical metrology via denoised and convolutional segmentation networks.

The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.