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Abstract Increasing evidence points toward an essential role for complement activation in the pathogenesis of diabetic kidney disease (DKD). However, the precise molecular mechanisms remain unclear, and the pathway predominantly contributing to complement activation in DKD is of particular interest. In this study, the glomerular proteome, especially the profiles of the complement proteins, was analyzed in kidney biopsies from 40 DKD patients and 10 normal controls using laser microdissection-assisted liquid chromatography-tandem mass spectrometry (LMD-LC-MS/MS). The glomerular abundances of three proteins related to classical pathway (CP) (C1q, C1r, C1s), five proteins related to alternative pathway (AP) (CFB, CFH, CFHR1, CFHR3, CFHR5), one common protein related to CP and lectin pathway (LP) (C4), and six proteins related to terminal complement pathway (C3, C5, C6, C7, C8, C9) were significantly increased in DKD. Notably, none of the proteins unique to the lectin complement pathway, including mannose-binding lectin (MBL) and its associated proteins, were detected in DKD glomeruli. Furthermore, the glomerular complement proteins of CP and AP were positively correlated with glomerular pathological grades and proteinuria, and negatively correlated with eGFR in DKD patients. Our results highlight a critical role for complement activation of the CP and AP, rather than the LP, in DKD progression.

Abstract The analysis of people's daily activities has played a crucial role in various applications, such as urban geography, activity prediction, and homogeneous population detection. However, limited studies have explored changes in the residents’ activity patterns in a particular region across various periods. To explore the changes, a methodological framework of sequence visualization analysis based on machine learning that extracts the activity patterns across various periods using sequence analysis, visualizes the activity patterns by calculating the frequency of different activities at time points and categorizes them through graphical similarity, and then compares the activity patterns in terms of activity and demographic characteristics is proposed. Empirical testing on the New York Metropolitan data of the National Household Travel Survey (NHTS) is conducted for 2001, 2009, and 2017. The findings reveal significant intra‐similarities, inter‐differences, and distinct changes in activity patterns across three periods for different social populations in the New York Metropolitan. From the perspective of information analysis, this work is anticipated to enhance the understanding of travel needs for diverse social populations in a particular region, thereby facilitating targeted policy adjustments for the departments concerned.

Abstract Increased proinflammatory cytokines and infiltration of inflammatory cells in the stroma are important pathological features of type IIIA chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS-A), and the interaction between stromal cells and other cells in the inflammatory microenvironment is closely related to the inflammatory process of CP/CPPS-A. However, the interaction between stromal and epithelial cells remains unclear. In this study, inflammatory prostate epithelial cells (PECs) released miR-203a-3p-rich exosomes and facilitated prostate stromal cells (PSCs) inflammation by upregulating MCP-1 expression. Mechanistically, DUSP5 was identified as a novel target gene of miR-203a-3p and regulated PSCs inflammation through the ERK1/2/MCP-1 signaling pathway. Meanwhile, the effect of exosomes derived from prostatic fluids of CP/CPPS-A patients was consistent with that of exosomes derived from inflammatory PECs. Importantly, we demonstrated that miR-203a-3p antagomirs-loaded exosomes derived from PECs targeted the prostate and alleviated prostatitis by inhibiting the DUSP5-ERK1/2 pathway. Collectively, our findings provide new insights into underlying the interaction between PECs and PSCs in CP/CPPS-A, providing a promising therapeutic strategy for CP/CPPS-A.

Abstract Renal involvement is common in monoclonal gammopathy (MG); however, the same patient may have both MG and non-paraprotein-associated renal damage. Accordingly, distinguishing the cause of renal damage is necessary because of the different clinical characteristics and associated treatments. In this multicenter retrospective cohort study, we described the clinicopathological characteristics and prognosis of 703 patients with MG and renal damage in central China. Patients were classified as having MG of renal significance (MGRS), MG of undetermined significance (MGUS), or hematological malignancy. 260 (36.98%), 259 (36.84%), and 184 (26.17%) had MGRS, MGUS, and hematological malignancies, respectively. Amyloidosis was the leading pattern of MGRS (74.23%), followed by thrombotic microangiopathy (8.85%) and monoclonal immunoglobulin deposition disease (8.46%). Membranous nephropathy was the leading diagnosis of MGUS (39.38%). Renal pathological findings of patients with hematological malignancies included paraprotein-associated lesions (84.78%) and non-paraprotein-associated lesions (15.22%). The presence of nephrotic syndrome and an abnormal free light chain (FLC) ratio were independently associated with MGRS. The overall survival was better in patients with MGUS than in those with MGRS or hematological malignancies.

Abstract Background Chronic pelvic pain syndrome (CPPS) is a typical symptom of chronic prostatitis (CP) in males that may cause abnormal urination, sexual dysfunction, or depression and significantly affect the quality of life of the patient. Currently, there is no effective treatment for CPPS due to its recurrence and intractability. For synergistic CPPS therapy, we developed pH/reactive oxygen species (ROS) dual-responsive dexamethasone (Dex) nanoformulations using a ROS-responsive moiety and phytochemical modified α-cyclodextrin (α-CD) as the carrier. Results Dex release from the nanoformulations can be controlled in acidic and/or ROS-rich microenvironments. The fabricated Dex nanoformulations can also be efficiently internalized by lipopolysaccharide (LPS)-stimulated macrophages, prostatic epithelial cells, and stromal cells. Moreover, the levels of proinflammatory factors (e.g., TNF-α, IL-1β, and IL-17 A) in these cells were significantly decreased by Dex nanoformulations treatment through the release of Dex, phytochemical and elimination of ROS. In vivo experiments demonstrated notable accumulation of the Dex nanoformulations in prostate tissue to alleviate the symptoms of CPPS through the downregulation of proinflammatory factors. Interestingly, depression in mice may be relieved due to alleviation of their pelvic pain. Conclusion We fabricated Dex nanoformulations for the effective management of CPPS and alleviation of depression in mice.

AbstractOsteosarcoma is the most common malignant tumor in the skeletal system with high mortality. Phytic acid (PA) is a natural compound extracted from plant seeds, which shows certain antitumor activity and good bone targeting ability. To develop a novel theranostics for magnetic resonance imaging (MRI) and targeting therapy of osteosarcoma, we employed PA to modify manganese dioxide nanoparticles (MnO2@PA NPs) for osteosarcoma treatment. The MnO2 NPs oligomer was formed by PA modification with uniformed size distribution and negative zeta potential. Fourier-transform infrared spectroscopy, X-ray diffraction, energy dispersive spectroscopy, X-ray photoelectron spectroscopy, and thermogravimetric analysis demonstrated that PA has been successfully modified on MnO2 NPs, and the structure of MnO2@PA NPs is amorphous. In vitro experiments demonstrated that MnO2@PA NPs oligomer can be efficiently internalized by tumor cell, and the internalized NPs can react with H2O2 under acid microenvironment to produce Mn2+ and O2. In vivo experiments demonstrated that MnO2@PA NPs oligomer can passively accumulate in tumor tissue, and the accumulated NPs can produce Mn2+ and O2 for MRI and targeting therapy of osteosarcoma. In conclusion, we prepared a novel bone-targeting nano theranostics for MRI and therapy of osteosarcoma.

Abstract Background Early diagnosis and typing are crucial for improving the prognosis of patients with renal amyloidosis. Currently, Untargeted proteomics based precise diagnosis and typing of amyloid deposits are crucial for guiding patient management. Although untargeted proteomics achieve ultra-high-throughput by selecting the most abundant eluting cationic peptide precursors in series for tandem MS events, it lacks in sensitivity and reproducibility, which may not be suitable for early-stage renal amyloidosis with minor damages. Here, we aimed to develop parallel reaction monitoring (PRM)-based targeted proteomics to achieve high sensitivity and specificity by determining absolute abundances and codetecting all transitions of highly repeatable peptides of preselected amyloid signature and typing proteins in identifying early-stage renal immunoglobulin-derived amyloidosis. Methods and results In 10 discovery cohort cases, Congo red-stained FFPE slices were micro-dissected and analyzed by data-dependent acquisition-based untargeted proteomics for preselection of typing specific proteins and peptides. Further, a list of proteolytic peptides from amyloidogenic proteins and internal standard proteins were quantified by PRM-based targeted proteomics to validate performance for diagnosis and typing in 26 validation cohort cases. The diagnosis and typing effectiveness of PRM-based targeted proteomics in 10 early-stage renal amyloid cases was assessed via a comparison with untargeted proteomics. A peptide panel of amyloid signature proteins, immunoglobulin light chain and heave chain in PRM-based targeted proteomics showed significantly distinguishing ability and amyloid typing performance in patients. The diagnostic algorithm of targeted proteomics with a low amount of amyloid deposits in early-stage renal immunoglobulin-derived amyloidosis showed better performance than untargeted proteomics in amyloidosis typing. Conclusions This study demonstrates that the utility of these prioritized peptides in PRM-based targeted proteomics ensure high sensitivity and reliability for identifying early-stage renal amyloidosis. Owing to the development and clinical application of this method, rapid acceleration of the early diagnosis, and typing of renal amyloidosis is expected.

IntroductionWilson's disease is an autosomal recessive disorder caused by ATP7B pathogenic mutations. The hallmark of this disorder mainly consists of liver involvement, neurologic dysfunction and psychiatric features. In addition, the kidneys can also be affected by excessive copper deposition.MethodsA total of 34 patients clinically diagnosed with WD were recruited. They underwent ATP7B gene sequencing and clinical data of symptoms, examination, and treatment were collected. Moreover, renal pathology information was also investigated.ResultsWe identified 25 potentially pathogenic ATP7B variants (16 missense, 5 frameshift, 3 splicing variants and 1 large deletion mutation) in these 34 WD patients, 5 of which were novel. In our cases, the most frequent variant was c.2333G>T (R778L, 39.06%, exon 8), followed by c.2621C>T (A874V, 10.94%, exon 11) and c.3316G>A (V1106I, 7.81%, exon 11). Furthermore, we described the thinning of the glomerular basement membrane as a rare pathologically damaging feature of Wilson's disease for the first time. Additionally, two patients who received liver transplant were observed with good prognosis in present study.DiscussionOur work expanded the spectrum of ATP7B variants and presented rare renal pathological feature in WD patients, which may facilitate the development of early diagnosis, counseling, treatment regimens of WD.

PurposeNeuroblastoma is a solid malignant tumor with high malignancy and high risk for metastasis. The prognosis of neuroblastoma ranges from spontaneous regression to insensitivity to therapies and widespread metastasis. There is a non-invasive, panoramic imaging technique called 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography–computed tomography (PET/CT), which can provide both complete anatomical information via CT and extent of FDG uptake value in tumors via positron emission detection. PET/CT is a powerful approach to estimating tumoral metabolic activities, and PET/CT parameters have been demonstrated to be associated with the prognosis of various tumors. However, the predictive performance of PET/CT for the prognosis of neuroblastoma remains unclear. This meta-analysis aims to assess the predictive values of maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for progression-free survival (PFS), event-free survival (EFS), and overall survival (OS) in neuroblastoma patients.MethodsLiterature in PubMed, Embase, Cochrane Library, and Web of Science from January 1985 to June 2023 was searched for studies evaluating predictive values of PET/CT parameters for the prognosis of neuroblastoma. Search items mainly included “Positron Emission Tomography Computed Tomography” and “Neuroblastoma”. Hazard ratio (HR) was used as a pooled statistic to assess the association of SUVmax, MTV, and TLG with PFS, EFS, and OS in neuroblastoma patients. Heterogeneity test and sensitivity analysis were performed.ResultsThere were eight studies included, with 325 participants. Meta-analysis showed that higher SUVmax was associated with shorter OS [HR = 1.27, 95% CI (1.11, 1.45), p = 0.001], while no association with PFS [HR = 1.03, 95% CI (0.99, 1.07), p = 0.222] and EFS [HR = 2.58, 95% CI (0.37, 18.24), p = 0.341] was presented. MTV showed no association with OS [HR = 2.46, 95% CI (0.34, 18.06), p = 0.376] and PFS [HR = 2.60, 95% CI (0.68, 9.88), p = 0.161]. There was a statistically significant association between TLG and OS [HR = 1.00, 95% CI (1.00, 1.00), p = 0.00], while the HR was 1, so the association could not be concluded, and TLG showed no association with PFS [HR = 1.00, 95% CI (0.99, 1.00), p = 0.974].ConclusionHigh SUVmax indicates poor OS in patients with neuroblastoma. The MTV and TLG are potential prognostic predictors that need to be further validated by more well-designed studies.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier 340729.

Chronic bacterial prostatitis usually occurs in men and seriously affects the quality of life of patients. The efficacy of chronic bacterial prostatitis treatment is limited by the difficulty for free drugs (e.g., antibiotics) to penetrate the prostate epithelium and target inflammatory tissues. The advent of nanotechnology offers the possibility to address this issue, such as the development of targeted nanoparticle delivery strategies that may overcome these important limitations. The physicochemical properties of nanoparticles, such as particle size, shape and surface modification ligands, determine their targeting effectiveness. In this study, nanoparticles with different physicochemical properties were prepared to explore and confirm their targeting capacities to inflammatory prostate tissues of chronic bacterial prostatitis, focusing on the effects of size and different modification ligands on the targeting performance. In vivo and ex vivo imaging results verified that folic acid-modified nanoparticles with a particle size of 180–190 nm via tail intravenous injection had the optimal targeting efficiency to prostate tissues. Our results provide an experimental basis and reference value for targeted therapy of prostate-related diseases with nanotechnology in the future.

This study presents a new semi-supervised action recognition method via adaptive feature analysis. We assume that action videos can be regarded as data points in embedding manifold subspace, and their matching problem can be quantified through a specific Grassmannian kernel function while integrating feature correlation exploration and data similarity measurement into a joint framework. By maximizing the intra-class compactness based on labeled data, our algorithm can learn multiple features and leverage unlabeled data to enhance recognition. We introduce the Grassmannian kernels and the Projected Barzilai–Borwein (PBB) method to train a subspace projection matrix as a classifier. Experiment results show our method has outperformed the compared approaches when a few labeled training samples are available.

Recently a large number of 3D object tracking methods have been extensively investigated and applied in a variety of applications using convolutional neural networks. Although most of them have made great progress in partial occlusion, the intricate interweaving of moving agents (e.g. pedestrians and vehicles) may lead to inferior performance of 3D object tracking in complex traffic scenes. To boost the performance of 3D object tracking in cases of severe occlusions, we present an end-to-end deep learning framework with a driving behavior-aware model that takes full advantage of spatial-temporal details in consecutive frames and learns the driving behavior from object variations in 2D center point, depth, rotation and translation in parallel. In contrast to prior work, our novelty formulates driving behavior that reasons about the possible motion trajectories of the investigated target for autonomous systems. We show in experiments that our method outperforms state-of-the-art approaches on 3D object tracking in the challenging nuScenes dataset.

Chemotherapy-induced senescence promotes immunocyte aggregation in the tumor microenvironment by upregulating the surface expression of activating ligands in cancer cells. However, these senescent tumor cells cannot be completely cleared and can induce tumor recurrence. Previous studiesshowed that soluble natural killer (NK) group 2D (NKG2D) ligands impair the recognition of multiple immune cells. In this study, we established an in vitro senescence model using neuroblastoma cells subjected to low-dose Chemotherapeutic drug doxorubicin or the Aurora A inhibitor MLN8237. The results showed that different neuroblastoma cell lines showed increased secretion of the NKG2D ligand MHC class I polypeptide-related sequence A/B (MICA/B) following proteolysis after treatment, with MICA/B subsequently recruited to exosomes to downregulate NKG2D expression in NK cells. Interestingly, disintegrin and metalloproteinase domain-containing 10 (ADAM10) was upregulated in senescent tumor cells, and combined treatment with the ADAM10 inhibitor GI254023X and chemotherapeutic drugs inhibited MICA/B secretion and enhanced recognition and killing by NK cells. Additionally, we found that expression of the long noncoding RNA MALAT1 was significantly increased in senescent neuroblastoma cells, and that MALAT1 served as a sponge for microRNA (miR)-92a-3p to counteract miR-92a-3p-mediated repression of ADAM10 levels. Furthermore, administration of a MALAT1 inhibitor or an miR-92a-3p mimic reduced the MICA/B shedding and enhanced recognition and killing by NK cells. These results confirmed that low-dose chemotherapy induces senescence in neuroblastoma cells, and that senescent tumor cells promote the shedding of the NKG2D ligand MICA/B through the MALAT1/miR-92a/ADAM10 axis, thereby contributing to the formation of a suppressive immune microenvironment and promoting immune escape.

Under confrontational environment, individual can impersonate others by wearing masks or other skills to conceal real identity, which brings enormous challenges to physical identity recognition. Moreover, massive fake attributes seriously threaten identity management. Aiming at the existence of fake attributes during multimodal identification, we propose a novel method to detect fake attributes and compute real identity for security of systems. Most previous methods focused on the differences about features between fake and normal attributes, but each method generally targeted one type of fake attribute, and the results decreased a lot with unknown attacks. In this paper, we first explore the essential differences of data distribution caused by natural and human behaviors, then with order-of-consensus-calculation based on the differences, fake attributes are detected by analyzing the rank of consensus identity in recognition results, finally maximum-consensus-calculation is applied to compute real identity for evaluating detection performance. Experimental results on face, fingerprint, and voiceprint demonstrate that the proposed method can detect fake attributes effectively, which has a higher accuracy, and the accuracy of identity recognition is increased obviously by about 13.20% with forgery detection. The additional experiments further confirm the feasibility of the proposed method with increase of fake attributes. Furthermore, the proposed method can deal with different kinds of attacks, even with unknown attacks, and it is also significant to improve the security of identification with complex environment.

Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each “descriptor” corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global “feature”, i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5–10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as O(n) for real-time person re-ID applications.

MWCNTs@silk fabric (CSF) with single-side, good conductive property was prepared via a surface micro-dissolution strategy. In this method, the partial dissolution of silk fibers was regulated by the concentration formic acid and the interaction between the protein and its solvent. The dissolved silk protein served as the physical crosslinking agent that connected dispersed carbon nanoparticles to form a continuous conductive network, avoiding complex chemical treatments and the use of physical adhesives. CSF possessed good electrical conductivity (468 Ω cm−1) and durability. Moreover, CSF had a unique single-side loading characteristic and its resistance changed from 98.4% to 102% of the initial value with the bending angle varying from −112.5° to 112.5°, so CSF could be used as sensors to monitor the mechanical activities effectively. Patterned conductive MWCNTs@silk fabric (PCSF) was fabricated by using patterned templates during the deposition process and used for wider applications, such as flexible switches (F-shaped PCSF), soft keyboards (keyboard-like PCSF), electric heating materials (S-shaped PCSF) and microwave-assisted heating materials (butterfly-like PCSF).

With the increasing amount of HD or even UHD video, the video streaming transmission over the mobile network is confronted with various quality of experience issues. To facilitate the delivery of compressed video over the network, it is extremely helpful to further compress the coded video bit stream without any information loss, so-called lossless recompression. However, the existing lossless video/image compression algorithms aim to remove the pixel-domain (e.g., lossless coding mode of HEVC) or the coefficient-domain (e.g., entropy coding) redundancy rather than the bit-stream-domain redundancy. To this end, we propose a novel lossless recompression approach to eliminate statistical redundancy in the coded video bit stream. The core idea is to increase the probability of this repetitive pattern of continuous “0” or “1” symbol strings in the binary symbol sequence by rearranging the sequence, including value mapping and position aggregation. In particular, Fibonacci-based mapping rule is used to convert the original sequence (those binary symbols in Fibonacci positions) into a new one with more continuous “0” or “1.” Besides, we aggregate as many identical symbols as possible together to further increase the probability of the occurrence of repetitive patterns. Finally, the lossless compression algorithm and the asymptotic lossless compression scheme are designed to achieve a compact representation of the rearranged symbol sequence. We also regulate formal syntactic structures for the proposed mapping rule, aggregation algorithm, and recompression scheme so as to deterministically revert from the recoded version to the original bit stream. The experimental results on compressed H.264/AVC and H.265/HEVC video data show that our approach can considerably reduce the bit rate of streaming video.

Recently, context learning networks have shown promise in filling large holes in natural images. These networks can decorate the predicted contents with high-frequency details by borrowing or copying neural information from the known region. However, this operation might introduce undesired content change in the synthesized region, especially when similar neural patterns cannot be found in the known region. To solve this problem, we present a network named Artist-Net to decompose an image into the content code and style code explicitly. The Artist-Net completes a corrupted image following the way an artist restores a damaged picture. It can produce more detailed content by inferring the content code of the corrupted images in the latent space since the dimension of the content space is lower than the original image. The Artist-Net can also keep style consistent over the entire image by decorating the inferred content code with the style code extracted from the known region. The experiments on multiple datasets, including structural and natural images demonstrate that the proposed network out-performs the existing ones in terms of content accuracy as well as texture details.

In response to the demand of wide coverage and massive access,low bandwidth and low power consumption is an important research direction to solve this problem.In smart cities,security monitoring and other application areas,video surveillance based on the region of interest of the face are particularly important.It is a feasible direction to realize the extraction of scene elements,transmission of key information with very low bandwidth and the application of video technology in the Internet of things environment through the strategy of multi-stream differential coding.By designing a face-oriented variable resolution hybrid coding algorithm,the bandwidth could be saved and the power consumption could be reduced greatly,the access requirements of narrowband Internet of things could be met.Through the face detection algorithm based on the deep learning Caffe framework,the face region of interest was acquired in key frames,and the face image was encoded with high resolution.By designing the code rate adaptive allocation algorithm,the bandwidth was utilized rationally,and the encoded face information and the full background content were distinguished.The encoded mixed code stream information was transmitted through the narrowband; the key frame-based face enhancement decoding algorithm was adopted at the receiving end to obtain a partial HD high-definition monitoring picture.Experiments show that when the video encoded by the proposed method is transmitted in a narrow band whose transmission rate is 120~160 kbit/s,the face image can maintain the same definition as the original HD monitoring acquisition end,which has strong practicability.

Background/Aims: Arterial fibrotic intimal thickening and arteriolar hyaline are considered common pathological features in immunoglobulin A nephropathy (IgAN), whereas little is known about the acute pathological manifestations of endothelial cell injury. The aim of this study was to investigate characteristics of intrarenal arterial lesions and to estimate their prognostic values in patients with IgAN. The primary renal endpoint was a 50% reduction in estimated glomerular filtration rate (eGFR) or end-stage renal disease (ESRD). Methods: Various renal arterial lesions (arterial fibrotic intimal thickening, arteriolar hyaline, arteriolar endotheliocyte swelling, arteriolar inflammatory cell infiltration, and arteriolar thrombosis) in 1683 patients with IgAN were reviewed and reclassified using a semi-quantitative scoring system. Their correlations with clinical features, pathological characteristics, and renal outcomes were evaluated. Results: The prevalence of intrarenal arterial lesions was up to 72.2% in IgAN patients. There were 978 patients (58.1%) with arterial fibrotic intimal thickening, 350 patients (20.8%) with arteriolar hyaline, 432 patients (25.7%) with arteriolar endotheliocyte swelling, 356 patients (21.2%) with arteriolar inflammatory cell infiltration and 43 patients (2.6%) with arteriolar thrombosis. Arterial fibrotic intimal thickening and arteriolar hyaline were strongly associated with higher mean arterial pressure (MAP) and reduced eGFR (P < 0.001) but were not related to proteinuria at the time of renal biopsy. In contrast, arteriolar endotheliocyte swelling and arteriolar thrombosis were correlated with heavier proteinuria as well as higher MAP and reduced eGFR. During follow-up, patients with vascular lesions received more renin-angiotensin system (RAS) blockade and less glucocorticoid and showed poorer renal outcomes. Univariate Cox model showed that the presence of renal vascular lesions [hazard ratio (HR) = 25.01, 95% confidence interval (CI): 6.19 to 101.03, P < 0.001] was a risk factor for renal outcomes. However, in multivariable Cox analysis, which included clinical factors and the Oxford-MEST-C, vascular lesions were not significantly associated with an increased risk of renal failure. Remarkably, the impact of vascular lesions on the survival from ESRD or 50% reduction in renal function was eliminated by the use of RAS blockade after adjustment for eGFR, proteinuria, and MAP. Conclusion: Our study demonstrates the high prevalence of vascular lesions, including the chronic and acute arterial pathological changes, in patients with IgAN. The presence of vascular lesions is associated with higher MAP, reduced eGFR and poorer renal outcomes, which could be influenced by the RAS blockade treatment.

Real-time transmission of satellite video data is one of the fundamentals in the applications of video satellite. Making use of the historical information to eliminate the long-term background redundancy (LBR) is considered to be a crucial way to bridge the gap between the compressed data rate and the bandwidth between the satellite and the Earth. The main challenge lies in how to deal with the variant image pixel values caused by the change of shooting conditions while keeping the structure of the same landscape unchanged. In this paper, we propose a representation learning based method to model the complex evolution of the landscape appearance under different conditions by making use of the historical image series. Under this representation model, the image is disentangled into the content part and the style part. The former represents the consistent landscape structure, while the latter represents the conditional parameters of the environment. To utilize the knowledge learned from the historical image series, we generate synthetic reference frames for the compression of video frames through image translation by the representation model. The synthetic reference frames can highly boost the compression efficiency by changing the original intra-frame prediction to inter-frame prediction for the intra-coded picture (I frame). Experimental results show that the proposed representation learning-based compression method can save an average of 44.22% bits over HEVC, which is significantly higher than that using references generated under the same conditions. Bitrate savings reached 18.07% when applied to satellite video data with arbitrarily collected reference images.

In most optical imaging systems and applications, images with high resolution (HR) are desired and often required. However, charged coupled device (CCD) and complementary metal-oxide semiconductor (CMOS) sensors may be not suitable for some imaging applications due to the current resolution level and consumer price. To transcend these limitations, in this paper, we present a novel single image super-resolution method. To simultaneously improve the resolution and perceptual image quality, we present a practical solution that combines manifold learning and sparse representation theory. The main contributions of this paper are twofold. First, a mapping function from low-resolution (LR) patches to HR patches will be learned by a local regression algorithm called sparse support regression, which can be constructed from the support bases of LR-HR dictionary. Second, we propose to preserve the geometrical structure of image patch dictionary, which is critical for reducing artifacts and obtaining better visual quality. Experimental results demonstrate that the proposed method produces high-quality results, both quantitatively and perceptually.

In the task of image captioning, learning the attentive image regions is necessary to adaptively and precisely focus on the object semantics relevant to each decoded word. In this paper, we propose a convolutional attention module that can preserve the spatial structure of the image by performing the convolution operation directly on the 2D feature maps. The proposed attention mechanism contains two components: convolutional spatial attention and cross-channel attention, aiming to determine the intended regions to describe the image along the spatial and channel dimensions, respectively. Both of the two attentions are calculated at each decoding step. In order to preserve the spatial structure, instead of operating on the vector representation of each image grid, the two attention components are both computed directly on the entire feature maps with convolution operations. Experiments on two large-scale datasets (MSCOCO and Flickr30K) demonstrate the outstanding performance of our proposed method.

The protection effectiveness is an important metric to judge whether a security system is good or not. In this paper, a security system deployed in a guard field is regarded abstractly as a security network. A quantitative protection effectiveness evaluation method based on entropy theory is introduced. We propose the protection intensity model, which can be used to calculate the protection intensity of a stationary or moving object provided by a security system or a security network. Using the protection intensity model, an algorithm, specifically for finding the minimal protection intensity paths of a field deployed multiple security system, is also put forward. The minimal protection intensity paths can be considered as the effectiveness measure of security networks. Finally, we present the simulation of the methods and models in this paper.

This article proposes a quantitative risk assessment for security systems which have multiple protected assets and a risk-based benefit-cost analysis for decision makers. The proposed methodology consists of five phases: identification of assets, security unit and intrusion path, security unit effectiveness estimation, intrusion path effectiveness estimation, security system risk assessment and benefit-cost estimation. Key innovations in this methodology include its use of effectiveness entropy to measure the degree of uncertainty of a security system to complete a protection task, and the fact it measures risk like information theory measures the amount of information. A notional example is provided to demonstrate an application of the proposed methodology.

City surveillance enables many innovative applications of smart cities. However, the real-time utilization of remotely sensed surveillance data via unmanned aerial vehicles (UAVs) or video satellites is hindered by the considerable gap between the high data collection rate and the limited transmission bandwidth. High efficiency compression of the data is in high demand. Long-term background redundancy (LBR) (in contrast to local spatial/temporal redundancies in a single video clip) is a new form of redundancy common in Earth observatory video data (EOVD). LBR is induced by the repetition of static landscapes across multiple video clips and becomes significant as the number of video clips shot of the same area increases. Eliminating LBR improves EOVD coding efficiency considerably. First, this study proposes eliminating LBR by creating a long-term background referencing library (LBRL) containing high-definition geographically registered images of an entire area. Then, it analyzes the factors affecting the variations in the image representations of the background. Next, it proposes a method of generating references for encoding current video and develops the encoding and decoding framework for EOVD compression. Experimental results show that encoding UAV video clips with the proposed method saved an average of more than 54% bits using references generated under the same conditions. Bitrate savings reached 25–35% when applied to satellite video data with arbitrarily collected reference images. Applying the proposed coding method to EOVD will facilitate remote surveillance, which can foster the development of online smart city applications.

Growing evidence suggests that there are many common cell biological features shared by neurons and podocytes; however, the mechanism of podocyte foot process formation remains unclear. Comparing the mechanisms of process formation between two cell types should provide useful guidance from the progress of neuron research. Studies have shown that some mature proteins of podocytes, such as podocin, nephrin, and synaptopodin, were also expressed in neurons. In this study, using cell biological experiments and immunohistochemical techniques, we showed that some neuronal iconic molecules, such as Neuron-specific enolase, nestin and Neuron-specific nuclear protein, were also expressed in podocytes. We further inhibited the expression of Neuron-specific enolase, nestin, synaptopodin and Ubiquitin carboxy terminal hydrolase-1 by Small interfering RNA in cultured mouse podocytes and observed the significant morphological changes in treated podocytes. When podocytes were treated with Adriamycin, the protein expression of Neuron-specific enolase, nestin, synaptopodin and Ubiquitin carboxy terminal hydrolase-1 decreased over time. Meanwhile, the morphological changes in the podocytes were consistent with results of the Small interfering RNA treatment of these proteins. The data demonstrated that neuronal iconic proteins play important roles in maintaining and regulating the formation and function of podocyte processes.

To study renalase's expression and distribution in renal tissues and cells, renalase coded DNA vaccine was constructed, and anti-renalase monoclonal antibodies were produced using DNA immunization and hybridoma technique, followed by further investigation with immunological testing and western blotting to detect the expression and distribution of renalase among the renal tissue and cells. Anti-renalase monoclonal antibodies were successfully prepared by using DNA immunization technique. Further studies with anti-renalase monoclonal antibody showed that renalase expressed in glomeruli, tubule, mesangial cells, podocytes, renal tubule epithelial cells and its cells supernatant. Renalase is wildly expressed in kidney, including glomeruli, tubule, mesangial cells, podocytes and tubule epithelial cells, and may be secreted by tubule epithelial cells primarily.

BACKGROUND: OTUB1 is a member of OTUs (Ovarian-tumor-domain-containing proteases), a deubiquitinating enzymes family (DUBs), which was shown as a proteasome-associated DUB to be involved in the proteins Ub-dependent degradation. It has been reported that OTUB1 was expressed in kidney tissue. But its concrete cellular location and function in the kidney remain unclear. Decorin (DCN) in mesangial cells (MC) is considered to be a potentially important factor for antagonizing glomerulonephritides, and its degradation is mediated by ubiquitination. The aim of this study is to investigate the role of OTUB1 expression in MC and its relationship with DCN during glomerulonephritis. METHODOLOGY/PRINCIPAL FINDINGS: Using quantitative RT-PCR and Western blot, we demonstrated that OTUB1 mRNA and protein were constitutively expressed in cultured rat MC and found to be upregulated by the stimulation of IL-1β or ATS. OTUB1 overexpression was detected in the mesangial area of glomeruli in some immunocomplex mediated nephritides such as IgA nephropathy, acute diffuse proliferative glomerulonephritis and lupus nephritis by immunohistochemistry. The immunoprecipitation assay demonstrated that OTUB1 interacted with DCN. The overexpression of OTUB1 enhanced the ubiquitination and degradation of DCN in MC. CONCLUSION/SIGNIFICANCE: These data showed the inflammatory injury could up-regulate OTUB1 expression in MC, which might attribute the promoting effect of OTUB1 on glomerulonephritides to the decrease of DCN level.

Usually multimedia data have to be compressed before transmitting, and higher compression rate, or equivalently lower bitrate, relieves the load of communication channels but impacts negatively the quality. We investigate the bitrate lower bound for perceptually lossless compression of a major type of multimedia—multichannel audio signals. This bound equals to the perceptible information rate of the signals. Traditionally, Perceptual Entropy (PE), based primarily on monaural hearing measures the perceptual information rate of individual channels. But PE cannot measure the spatial information captured by binaural hearing, thus is not suitable for estimating Spatial Audio Coding (SAC) bitrate bound. To measure this spatial information, we build a Binaural Cue Physiological Perception Model (BCPPM) on the ground of binaural hearing, which represents spatial information in the physical and physiological layers. This model enables computing Spatial Perceptual Entropy (SPE), the lower bitrate bound for SAC. For real-world stereo audio signals of various types, our experiments indicate that SPE reliably estimates their spatial information rate. Therefore, “SPE plus PE” gives lower bitrate bounds for communicating multichannel audio signals with transparent quality.