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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 FROM AUTHOR]

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 FROM AUTHOR]

In this paper, the work in refid="ref1"/ is analyzed. An error in its theoretical description part is pointed out and illustrated by a simple example. A modification suggestion is proposed to make the theoretical description of the work refid="ref1"/ more deliberate and thus being used appropriately. [ABSTRACT FROM PUBLISHER]

Person search, which aims to find the target person in the whole scene images, has become a crucial application in security surveillance with various types of noise. Existing methods solve this problem by addressing two subtasks simultaneously: detection, distinguishing people from background noise; and identification, separating target from others noise. However, these two tasks are fundamentally contradictory, as detection is designed to be robust to intra-class variations to capture general human features, while identification is designed to be sensitive to inter-class variations to focus only on the target. Inspired by the observation that the police first narrowed the scope to a small group of people similar to the suspect, and then located the target, we propose a Dual-Focus framework consisting of Coarse-Grained Focus (search for similar people) and Fine-Grained Focus (find the target). Extensive experiments on datasets such as CUHK-SYSU and PRW are conducted to evaluate the effectiveness of our proposed methods. The evaluations demonstrate that our method delivers superior results compared with the state-of-art methods. [ABSTRACT FROM AUTHOR]

Face recognition systems are widely used for target recognition and identity authentication, such as automated teller machines, mobile phones, and entrance guard systems. However, face recognition systems are vulnerable to presentation attacks, such as photo, replay, and 3D mask attacks. In particular, silicone mask attacks pose a greater threat to face recognition systems because high-quality silicone masks do living properties. To promote the development of face anti-spoofing detection algorithms for silicone mask attacks, this paper constructs a Silicone Mask Face Motion Video Dataset (SMFMVD) containing 200 real face videos and 200 silicone mask face videos. These videos include different facial motions collected from 20 subjects. Moreover, inspired by the observation that the silicone mask face's facial movement is not so natural as the real face, we propose a novel silicone mask face anti-spoofing detection method based on visual saliency and facial motion characteristics. Specifically, we compute the visual saliency map of a given face image by simulating two kinds of eye movement behaviors, namely "gaze" and "saccade". Then, we propose a saliency-weighted histogram of local binary pattern operator to extract facial texture features in spatial domain and a saliency-guided histogram of oriented optical flow operator to extract facial motion features in temporal domain. Finally, the support vector machine is used to fuse two groups of facial features to distinguish real and spoof faces. Extensive experiments on public and self-built datasets show its superiority over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background Malignant nephrosclerosis, defined as renal microangiopathy in the setting of severe hypertension, remains a critical renal emergency leading to end-stage renal disease despite aggressive anti-hypertensive treatment. Recently, activation of the complement alternative pathway (AP) has been reported to play a prominent role in the pathogenesis of malignant nephrosclerosis. However, subsequent study failed to recapitulate the findings of genetic complement abnormalities in the disease. This study aimed to determine the presence of AP activation and genetic complement defects and establish their correlations to renal microangiopathy lesions, clinical features and prognosis in patients with malignant nephrosclerosis. Methods Fifty patients with malignant hypertension and concomitant thrombotic microangiopathy (TMA) proven by renal biopsy were investigated; 25 cases of kidney donors who received zero-hour allograft biopsies were used as normal controls. Various renal TMA lesions in patients with malignant nephrosclerosis were reviewed and evaluated using a semi-quantitative scoring system. Deposition of C5b-9, C3a, C5a, C4d and mannose-binding lectin was assessed by immunohistochemistry. Co-localization of C5b-9 and CD34 was detected by confocal microscopy. Complement factor B (FB), factor P (FP; properdin), factor D (FD), factor H (FH), C3a and C5a levels were quantified by enzyme-linked immonosorbent assay in plasma and urine samples of patients with malignant nephrosclerosis and controls. Genetic abnormalities of complement components were analysed by whole-exome sequencing. Results Renal biopsies of malignant nephrosclerosis showed identical histopathological and ultrastructural features to atypical haemolytic uraemic syndrome. C5b-9, C3a and C5a deposits were found along the walls of arteries/arterioles and glomerular capillaries and localized in the endothelial cells. Elevated plasma and urinary levels of FB, FP, FD, C3a and C5a as well as decreased FH levels were observed in patients with malignant nephrosclerosis compared with normal controls. The urinary levels of complement AP components, but not the plasma levels, were correlated with renal functions, prognosis and active TMA lesions except for arteriolar thrombi. Finally, mutations of the MCP , CFB , CFH and CFHR5 genes were identified in 8 of 20 patients with malignant nephrosclerosis. Conclusions Aberrant complement AP dysregulation was demonstrated and associated with the activity, severity and renal outcomes of malignant nephrosclerosis. This observation warrants screening for complement defects in patients with malignant nephrosclerosis for the potential use of complement regulators and also highlights the need for further investigation of the precise role of AP in the pathogenesis of the disease. [ABSTRACT FROM AUTHOR]

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). Unlabelled Image • MWCNTs@silk fabric exhibited good electrical conductivity (468 Ω•cm-1 and good conductive durability. • The resistance of MWCNTs@silk fabric changed with its deformation, making the fabric could be used as mechanical sensors. • MWCNTs@silk fabric was customizable and had potential applications in flexible electronics and heating materials. [ABSTRACT FROM AUTHOR]

Correlation filter (CF) has achieved great success in the tracking community due to its high efficiency and effectiveness. However, in most existing CF trackers: (i) they mainly focus on object localization and cannot handle scale variations well; (ii) the learned CF is unavoidably deteriorated by the boundary effects, especially the background pixels inside the bounding box; (iii) there exists a localization gap between the original image and feature map. To handle these issues, we propose an online scale-adaptive correlation tracker by collaboratively learning a target-insight model (TIM) for object localization and a scale-insight model (SIM) for progressive refinement. In TIM, we introduce a target likelihood map to impose discriminative weights on the image, and then formulate a target-insight correlation filter for encoding the target as well as its surrounding context, which can handle boundary effect and distractors. Furthermore, we developed a SIM based on multiple instance learning and structured-labelled samples, which realizes the scale estimation and position refinement over time. Specifically, a hard negatives mining strategy is introduced to update the SIM rather than random sampling, which helps realize long-term tracking with re-capturing the missing target even tracking failure occurs. Extensive experiments on four benchmarks demonstrate that our method outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Present an overview of ASC methods covering earlier works and recent advances. • Review core techniques including data processing, feature acquisition, and modeling. • Summarize available resources for ASC and analyze ASC tasks in DCASE challenges. • Discuss limitations and open challenges of current ASC algorithms. • Provide suggestions for future research. Acoustic scene classification (ASC) has gained significant interest recently due to its diverse applications. Various audio signal processing and machine learning methods have been proposed for ASC. The volume and scope of ASC publications covering theories, algorithms, and applications have also been expanded. However, no recent comprehensive surveys exist to collect and organize the knowledge, impeding the ability of researchers and its applications. To fill this gap, we present an up-to-date overview of ASC methods, covering earlier works and recent advances. In this work, we first define a general framework for ASC, starting with a historical review of previous research in the ASC field. Then, we review core techniques for ASC that have achieved good performance. Focus on machine learning based ASC systems, this work summarizes and groups the existing techniques in terms of data processing, feature acquisition, and modeling. Furthermore, we provide a summary of the available resources for ASC research and analyze ASC tasks in Detection and Classification of Acoustic Scenes and Events (DCASE) challenges. Finally, we discuss limitations of the current ASC algorithms and open challenges to possible future developments toward practical applications of ASC systems. [ABSTRACT FROM AUTHOR]

Surveillance video analysis plays a vital role in the daily operations of smart cities, which increasingly relies on person re-identification technology to sustain smart security applications. However, research challenges of re-identification remain especially in terms of recognizing the different appearances of the same person in a harsh real-world environment: (1) the adaptability of the selected features to the dynamic environment cannot be guaranteed, and (2) existing methods rooted from metric learning aim to find a single metric function, and they lack the ability to measure the different appearances of the same person. To address these problems, this study proposes a multiple deep metric learning method empowered by the functionality of person similarity probability measurement. The proposed method exploits multiple stacked auto-encoder networks and classification networks to quantify pedestrians' similarity relations. The stacked auto-encoder networks directly recognize persons from surveillance images at the pixel level. The classification networks are equipped with the Softmax regression models and produce multiple similarity probabilities to characterize different appearances belonging to the same person. An Adaboost-like model is designed to fuse the probabilities corresponding to multiple metrics, which ensures a high accuracy of recognition. Experimental results on two public datasets (VIPeR and CUHK-01) indicate that the proposed method outperforms existing algorithms by 2%–10% at rank 1. Based on the similarity probabilities learned by the proposed model, the algorithm for matching the person pair can achieve a time complexity as low as O (n) , which can be deployed at a large scale on the distributed intelligent surveillance network, with each node maintaining limited computing capabilities. • This study developed a person re-identification method well adapting to harsh environment. • The method is suited for measuring the different appearances of the same person. • The algorithm has a low time complexity (O (n)) while it supports a high accuracy. [ABSTRACT FROM AUTHOR]

• A novel multi-attention network simulating the visual attention of humans is designed to realize highly efficient alignment and feature embedding globally and locally for vehicle re-ID, improving the performance of re-ID both contextually and spatially. • We propose a dual self-attention mechanism where the static self-attention and the dynamicself-attention (named cross-region attention) are designed to capture long-range dependenciesglobally and the position-related-range dependencies locally respectively, so as to enhance thespatial/positional awareness of our model. • We conduct extensive experiments to validate the effectiveness of our model and show that the proposed method achieves the best experimental results on most metrics compared withmany other state-of-the-art methods, e.g., Rank-1 accuracy of 80.6%, 78.1%, 75.0% on three testing subsets of VehicleID and 92.9%, 91.8%, 90.4% on those of Vehicle-1M. Our model also achieves 76.3% mAP and 94.8% on Veri776. Vehicle re-identification has attracted tremendous attention from computer vision communities for its extensive applications in intelligent transportation and public security, while the high inter-class similarity and the large intra-class difference between vehicles bring out great challenges for re-identification (re-ID). To tackle these challenges, we learn from the self-attention mechanism in Natural Language Processing and propose a dual self-attention module to learn different regional dependencies: static self-attention for selectively refining semantic features and dynamic self-attention (called cross-region attention) for enhancing the spatial awareness of local feature. The static self-attention refines attended pixels within the entire image and salient regions, while the cross-region attention creatively captures the position-related regional dependencies for pixels within the windscreen area. These attention modules capture long-range dependencies and relative position information between different pixels or regions for vehicle feature learning globally and locally, realizing an efficient vehicle feature embedding by concatenating these augmented features for vehicle re-ID. Extensive experiments demonstrate the effectiveness and promising performance of our approach against the state-of-the-arts. [ABSTRACT FROM AUTHOR]

• We decouple the difficulties affecting the person re-identification task into the Camera-Camera (CC) problem and the Camera-Person (CP) problem. • We propose a bi-stream generative model for solving the CC and CP problems separately, with promising results. • We design a part-weighted loss based on the unbalanced number of human body parts in the dataset to guide the model to focus on the more important parts. Generalizable person re-identification (re-ID) has attracted growing attention due to its powerful adaptation capability in the unseen data domain. However, existing solutions often neglect either crossing cameras (e.g., illumination and resolution differences) or pedestrian misalignments (e.g., viewpoint and pose discrepancies), which easily leads to poor generalization capability when adapted to the new domain. In this paper, we formulate these difficulties as: 1) Camera-Camera (CC) problem, which denotes the various human appearance changes caused by different cameras; 2) Camera-Person (CP) problem, which indicates the pedestrian misalignments caused by the same identity person under different camera viewpoints or changing pose. To solve the above issues, we propose a Bi-stream Generative Model (BGM) to learn the fine-grained representations fused with camera-invariant global feature and pedestrian-aligned local feature, which contains an encoding network and two stream decoding sub-network. Guided by original pedestrian images, one stream is employed to learn a camera-invariant global feature for the CC problem via filtering cross-camera interference factors. For the CP problem, another stream learns a pedestrian-aligned local feature for pedestrian alignment using information-complete densely semantically aligned part maps. Moreover, a part-weighted loss function is presented to reduce the influence of missing parts on pedestrian alignment. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on the large-scale generalizable re-ID benchmarks, involving domain generalization setting and cross-domain setting. [ABSTRACT FROM AUTHOR]

• We address a novel task named "group activity trajectory recovery" and screen suspected high-risk patients in the population by combining GPS data and video data to track COVID-19 contacts in an epidemiological survey. It combines efficiency and accuracy compared to existing methods, and the actual operation is more reliable and efficient. • To address the problem of the inaccuracy of existing GPS positioning for tracking confirmed patients, this paper combines GPS data and video data to achieve tracking of close contact patients and proposes a combined optimization method (CO-SC). • A high-quality GATR-GPS dataset is constructed to simulate the tracking of close contact patients in realistic scenarios, and it is verified that the proposed method can significantly improve the performance of close contact patient tracking. • Randomly take the GPS data and video data at the same moment, calculate their errors, traverse them 10 times, and average them to get the average error of the two kinds of data. The average error of GPS data is over 200m, while the error of video data is within 1m. • In the self-built dataset and all using GPS data, our method obtains 89.80% for Rank 1 and 80.15% for mAP, which is a 7.35% improvement on Rank 1 and a 5.81% improvement on mAP compared to the CADL method. Contact tracking plays an important role in the epidemiological investigation of COVID-19, which can effectively reduce the spread of the epidemic. As an excellent alternative method for contact tracking, mobile phone location-based methods are widely used for locating and tracking contacts. However, current inaccurate positioning algorithms that are widely used in contact tracking lead to the inaccurate follow-up of contacts. Aiming to achieve accurate contact tracking for the COVID-19 contact group, we extend the analysis of the GPS data to combine GPS data with video surveillance data and address a novel task named group activity trajectory recovery. Meanwhile, a new dataset called GATR-GPS is constructed to simulate a realistic scenario of COVID-19 contact tracking, and a coordinated optimization algorithm with a spatio-temporal constraint table is further proposed to realize efficient trajectory recovery of pedestrian trajectories. Extensive experiments on the novel collected dataset and commonly used two existing person re-identification datasets are performed, and the results evidently demonstrate that our method achieves competitive results compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• To address the data collection problem in person reID, we present a novel active redundancy reduction framework to alleviate the data redundancy problem in public re-ID datasets. • To minimize the annotation workload while maximizing the performance of the re-ID model, a simple baseline is presented to select informative and diverse samples for annotation by estimating their uncertainty and intra-diversity. • A computer-assisted Identity Recommendation Module is proposed to help the human annotators to rapidly and accurately label the selected samples. Annotating a large-scale image dataset is very tedious, yet necessary for training person re-identification (re-ID) models. To alleviate such a problem, we present an active redundancy reduction (ARR) framework via training an effective re-ID model with the least labeling efforts. The proposed ARR framework actively selects informative and diverse samples for annotation by estimating their uncertainty and intra-diversity, thus it can significantly reduce the annotation workload. Moreover, we propose a computer-assisted identity recommendation module embedded in the ARR framework to help human annotators to rapidly and accurately label the selected samples. Extensive experiments were carried out on several public re-ID datasets to demonstrate the existence of data redundancy. Experimental results indicate that our method can reduce 57%, 63%, and 49% annotation efforts on the Market1501, MSMT17, and CUHK03, respectively, while maximizing the performance of the re-ID model. [ABSTRACT FROM AUTHOR]

Aims: Light chain proximal tubulopathy (LCPT) is a rare complication of paraprotein-related diseases. We report a case series to present the clinicopathological characteristics and outcomes of LCPT., Methods: A multicentre retrospective case series of 47 patients with LCPT, consisting of 36 crystalline, three non-crystalline, and eight mixed LCPTs, was studied between January 2007 and December 2023., Results: The median age at diagnosis was 57 years. Presentations included proteinuria (100%), renal insufficiency (62%) and Fanconi syndrome (68%). The underlying haematological diagnoses were monoclonal gammopathy of renal significance in 81% and multiple myeloma in 19%. Monoclonal light chain (LC) was detected in all cases using serum/urine-free LC assays or immunofixation electrophoresis. Among 36 crystalline LCPTs, 34 were κ-restricted and 2 λ-restricted. Three non-crystalline LCPTs were all λ-restricted. In mixed LCPTs, seven were κ-restricted and one was λ-restricted. Notably, 66% frozen-section immunofluorescence failed to reveal restricted LC, requiring paraffin-immunofluorescence or immunoelectron microscopy. The appearance of inclusions displayed intraindividual homogeneity but interindividual heterogeneity in 42 patients and notable intraindividual heterogeneity in the remaining 5 patients. Haematological complete response, very good partial response and partial response occurred in 61%. Kidney function improved or remained stable in 84%, worsened in 8% and progressed to end-stage renal disease in 8%., Conclusions: Proteinuria and kidney dysfunction are the most common but less-specific renal manifestations of LCPTs, with most featuring Fanconi syndrome. Crystalline LCPT, primarily associated with κ-LC, is the predominant form. Most inclusions displayed intraindividual homogeneity and interindividual heterogeneity by electron microscopy. Most achieved haematological responses and favourable renal outcomes., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Rationale & Objective: Light and heavy chain deposition disease (LHCDD) is a rare form of monoclonal immunoglobulin (Ig) deposition disease, and limited clinical data are available characterizing this condition. Here we describe the clinicopathological characteristics and outcomes of LHCDD., Study Design: Case series., Setting & Participants: 13 patients with biopsy-proven LHCDD diagnosed between January 2008 and December 2022 at one of 2 Chinese medical centers., Findings: Among the 13 patients described, 6 were men and 7 were women, with a mean age of 52.6±8.0 years. Patients presented with hypertension (76.9%), anemia (84.6%), increased serum creatinine concentrations (84.6%; median, 1.7mg/dL), proteinuria (100%; average urine protein, 3.0g/24h), nephrotic syndrome (30.8%), and microscopic hematuria (76.9%). Serum immunofixation electrophoresis showed monoclonal Ig for 11 patients (84.6%). Serum free light chain ratios were abnormal in 11 patients (84.6%), and heavy/light chain ratios were abnormal in 9 of 10 patients (90%) with available data. Five patients were diagnosed with multiple myeloma. A histological diagnosis of nodular mesangial sclerosis was made in 10 patients (76.9%). Immunofluorescence demonstrated deposits of IgG subclass in 7 patients (γ-κ, n=4; γ-λ, n=3) and IgA in 5 patients (α-κ, n=2; α-λ, n=3). Six patients underwent IgG subclass staining (γ1, n=3; γ2, n=2; γ3, n=1). The deposits of IgD-κ were confirmed by mass spectrometry in 1 patient. Among 12 patients for whom data were available during a median of 26.5 months, 11 received chemotherapy and 1 received conservative treatment. One patient died, and disease progressed to kidney failure in 3 (25%). Among the 9 patients evaluable for hematological and kidney disease progression, 5 (56%) had a hematologic response and 1 (11%) exhibited improvement in kidney disease., Limitations: Retrospective descriptive study, limited number of patients, urine protein electrophoresis or immunofixation electrophoresis test results missing for most patients., Conclusions: In this case series of LHCDD, light and heavy chain deposition in kidney tissues were most frequent with monoclonal IgG1-κ. Among patients with evaluable data, more than half had a hematologic response, but a kidney response was uncommon., (Copyright © 2024 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

The development of telecom technology not only facilitates social interactions but also inevitably provides the breeding ground for telecom fraud crimes. However, telecom fraud detection is a challenging task as fraudsters tend to commit co-fraud and disguise themselves within the mass of benign ones. Previous approaches work by unearthing differences in calling sequential patterns between independent fraudsters, but they may ignore synergic fraud patterns and oversimplify fraudulent behaviors. Fortunately, graph-like data formed by traceable telecom interaction provides opportunities for graph neural network (GNN)-based telecom fraud detection methods. Therefore, we develop a latent synergy graph (LSG) learning-based telecom fraud detector, named LSG-FD, to model both sequential and interactive fraudulent behaviors. Specifically, LSG-FD introduces (1) a multi-view LSG extractor to reconstruct synergy relationship-oriented graphs from the meta-interaction graph based on second-order proximity assumption; (2) an LSTM-based calling behavior encoder to capture the sequential patterns from the perspective of local individuals; (3) a dual-channel based graph learning module to alleviate the disassortativity issue (caused by the camouflages of fraudsters) by incorporating the dual-channel frequency filters and the learnable controller to adaptively aggregate high- and low-frequency information from their neighbors; (4) an imbalance-resistant model trainer to remedy the graph imbalance issue by developing a label-aware sampler. Experiment results on the telecom fraud dataset and another two widely used fraud datasets have verified the effectiveness of our model., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate high-quality videos with higher resolution (HR) and higher frame rate (HFR). Quite intuitively, pioneering two-stage based methods complete ST-VSR by directly combining two sub-tasks: Spatial Video Super-Resolution (S-VSR) and Temporal Video Super-Resolution (T-VSR) but ignore the reciprocal relations among them. 1) T-VSR to S-VSR: temporal correlations help accurate spatial detail representation; 2) S-VSR to T-VSR: abundant spatial information contributes to the refinement of temporal prediction. To this end, we propose a one-stage based Cycle-projected Mutual learning network (CycMuNet) for ST-VSR, which makes full use of spatial-temporal correlations via the mutual learning between S-VSR and T-VSR. Specifically, we propose to exploit the mutual information among them via iterative up- and down projections, where spatial and temporal features are fully fused and distilled, helping high-quality video reconstruction. In addition, we also show interesting extensions for efficient network design (CycMuNet+), such as parameter sharing and dense connection on projection units and feedback mechanism in CycMuNet. Besides extensive experiments on benchmark datasets, we also compare our proposed CycMuNet (+) with S-VSR and T-VSR tasks, demonstrating that our method significantly outperforms the state-of-the-art methods.

Introduction: Wilson'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., Methods: A 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., Results: We 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., Discussion: Our 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Xu, Lv, Chen, Lian, Xing, Wang and Hu.)

Purpose: Neuroblastoma 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 18 F-fluorodeoxyglucose ( 18 F-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., Methods: Literature 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., Results: There 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]., Conclusion: High 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 Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier 340729., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Hu, Zhang, Liu, Lee, Liu and Liu.)

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., (© 2023. The Author(s).)

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., (© 2023. The Author(s).)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hu, Yang, Song, Zhao, Chen, Zhou, Zheng and Shen.)

In order to solve the problem that the accuracy of face recognition is not good due to the influence of jitter and environmental factors in the mobile shooting environment, this paper proposes the application of an artificial neural network algorithm in the information scanning and recognition of face biological images. The spatial neighborhood information is integrated into the amplitude detection of multipose face images, and the dynamic corner features of multipose face images are extracted. The structural texture information of multipose face images is compared to a global moving RGB three-dimensional bit plane random field, and the multipose face images are detected and fused. At different scales, appropriate feature registration functions are selected to describe the feature points of multipose face images. The parallax analysis of target pixels and key feature detection of multipose face images are carried out. Image stabilization and automatic recognition are realized by combining artificial neural network learning and feature registration methods. The experimental results show that the experiment is designed with MATLAB, the frame frequency of dynamic face image acquisition is 1200 kHz, the number of collected samples of the multipose face image is 2000, the training sample set is 200, the noise coefficient = 0.24, the number of multipose face image blocks is 120, and the structural similarity is 0.12. It is found that the output signal-to-noise ratio of multipose face image recognition using the method in this paper is high. Conclusion . This method has good performance in feature point registration and high recognition accuracy for multipose face image recognition., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Zexin Zhang et al.)

Chronic bacterial prostatitis (CBP) occurs frequently in the male population and significantly influences quality of life. Antibiotics are the main strategy for managing chronic bacterial prostatitis; however, most antibiotics have low efficacy due to their poor penetration of prostate tissues. To overcome this challenge, we fabricated cefpodoxime proxetil (CPD)-loaded reactive oxygen species (ROS)-responsive nanoparticles (NPs) for targeted treatment of CBP. These NPs were modified with folic acid (FA) and could be effectively internalized by bacteria-infected macrophages and prostatic epithelial cells because of the high expression of folate receptors (FRs) in these cells. In vitro cellular assays demonstrated that the CPD-loaded nanomedicine can obviously reduce proinflammatory cytokine expression in cells since the nanomedicine can efficiently eradicate cellular bacteria. In vivo imaging results verified that FA-modified nanomedicines can penetrate the prostatic epithelium and accumulate in the glandular lumen because FRs overexpression was also observed in the prostate tissues of CBP mice. Animal experiments demonstrated that FA-modified nanomedicine can remarkably relieve pelvic pain in CBP mice and dramatically decrease proinflammatory cytokine expression in prostate tissues via eradication of bacteria and scavenging of ROS. Our results provide a new strategy to deliver antibiotics for targeted therapy of CBP. STATEMENT OF SIGNIFICANCE: To overcome poor penetration of antibiotics in prostatic tissues, we developed an antibiotics-loaded ROS-responsive NPs for targeted treatment of CBP. We demonstrated that both bacteria-infected macrophages and prostatic epithelial cells have FRs overexpression, thus FA-modified NPs can be efficiently internalized by these cells. FA-modified NPs can penetrate the prostatic epithelium and accumulate in the glandular lumen via FRs-mediated endocytosis, and the accumulated NPs can smartly release their payload under high ROS microenvironment. A distinguished therapy outcome was obtained in murine CBP model since CPD-loaded NPs can efficiently eradicate the resident bacteria in prostate tissues and downregulate proinflammatory cytokine expression. Our work provides a practicable strategy to expand the application of antibiotics for management of CBP., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Hu, Xi, Nie, Xu and Liu.)

Purpose: Uveal melanoma (UM) is one of the primary intraocular malignancies. Emerging studies have confirmed dysregulation of microRNA (miRNA/miR) in UM. The present study focused on the biofunctions of miR-137 in UM., Methods: MiR-137 expressions in tissue samples were analyzed by qRT-PCR. MTT and transwell assays were applied to investigate the impacts of miR-137 on UM cell proliferation, invasion and migration. Luciferase assay was carried out to explore the downstream target of miR-137. Western blot was used to analyze the roles of miR-137 in UM cells, Wnt/β-catenin pathway and epithelial-mesenchymal transition (EMT)., Results: qRT-PCR showed that miR-137 expressions were lower in UM tissue samples than para-carcinoma tissues, whereas EZH2 was simultaneously upregulated. MiR-137 overexpression evidently suppressed UM cell proliferation, invasion and migration. The findings also indicated that miR-137 restoration could block Wnt/β-catenin pathway and EMT in UM cells thus resulting in downregulation of malignant behaviors. EZH2 was a downstream target of miR-137 as demonstrated by luciferase assay., Conclusion: The present study indicated that EZH2 participated in the anti-UM functions of miR-137. Taken together, the data in our study established miR-137/EZH2 axis in regulating UM progression, suggesting that miR-137 may function as a novel therapeutic biomarker for UM patients.

By developing sophisticated image priors or designing deep(er) architectures, a variety of image super-resolution (SR) approaches have been proposed recently and achieved very promising performance. A natural question that arises is whether these methods can be reformulated into a unifying framework and whether this framework assists in SR reconstruction? In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers). Based on the assumption that better component super-resolver should have larger ensemble weight when performing SR reconstruction, we present a maximum a posteriori (MAP) estimation framework for the inference of optimal ensemble weights. Especially, we introduce a reference dataset, which is composed of high-resolution (HR) and low-resolution (LR) image pairs, to measure the SR abilities (prior knowledge) of different component super-resolvers. To obtain the optimal ensemble weights, we propose to incorporate the reconstruction constraint, which states that the degenerated HR estimation should be equal to the LR observation one, as well as the prior knowledge of ensemble weights into the MAP estimation framework. Moreover, the proposed optimization problem can be solved by an analytical solution. We study the performance of the proposed method by comparing with different competitive approaches, including four state-of-the-art nondeep learning-based methods, four latest deep learning-based methods, and one ensemble learning-based method, and prove its effectiveness and superiority on some general image datasets and face image datasets.

Chronic kidney diseases have become a major issue worldwide. The spectrum of biopsy proven renal diseases differs between locations and changes over time. It is therefore essential to describe the local epidemiological trends and the prevalence of renal biopsy in various regions to shine new light on the pathogenesis of various renal diseases and provide a basis for further hypothesis-driven research. We retrospectively analyzed 34,630 hospitalized patients undergoing native renal biopsy between January 1, 2009 and December 31, 2018. Indications for renal biopsy and histological diagnosis were analyzed to describe the prevalence of renal biopsy, and changing prevalence between period 1 (2009-2013) and period 2 (2014-2018) were further analyzed. Nephrotic syndrome (NS) was the most common indication for biopsy. Membranous nephropathy (MN, 24.96%) and IgA nephropathy (IgAN, 24.09%) were the most common primary glomerulonephritis (PGN). MN was most common in adults, with IgAN more prevalent in children. Lupus nephritis (LN) was the most common secondary glomerulonephritis (SGN) in adults, while Henöch-Schönlein purpura nephritis (HSPN) in children. The prevalence of MN increased significantly and nearly doubled from period 1 (15.98%) to period 2 (30.81%) (P = 0.0004). The same trend appeared with membranoproliferative glomerulonephritis (MPGN), diabetic nephropathy (DN) and obesity-related glomerulopathy (ORG), while the frequencies of minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), LN and hepatitis B associated glomerulonephritis (HBV-GN) significantly decreased between the two intervals. NS was the most common indication for biopsy across all age groups and genders. MN has overtaken IgAN to become the most common PGN in adults, while IgAN was the most common PGN in children. LN was the most common SGN in adults, and HSPN the most common in children.

Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods.

Although recent semisupervised approaches have proven their effectiveness when there are limited training data, they assume that the samples from different actions lie on a single data manifold in the feature space and try to uncover a common subspace for all samples. However, this assumption ignores the intraclass compactness and the interclass separability simultaneously. We believe that human actions should occupy multimanifold subspace and, therefore, model the samples of the same action as the same manifold and those of different actions as different manifolds. In order to obtain the optimum subspace projection matrix, the current approaches may be mathematically imprecise owe to the badly scaled matrix and improper convergence. To address these issues in unconstrained convex optimization, we introduce a nontrivial spectral projected gradient method and Karush-Kuhn-Tucker conditions without matrix inversion. Through maximizing the separability between different classes by using labeled data points and estimating the intrinsic geometric structure of the data distributions by exploring unlabeled data points, the proposed algorithm can learn global and local consistency and boost the recognition performance. Extensive experiments conducted on the realistic video data sets, including JHMDB, HMDB51, UCF50, and UCF101, have demonstrated that our algorithm outperforms the compared algorithms, including deep learning approach when there are only a few labeled samples.

In this paper, a simple and environment friendly approach was used to prepare the multifunctional composite fabrics via coating commercial TiO 2 nanoparticles (NPs) on the surface of cotton fibers by surface micro-dissolution process in 55%wt ZnCl 2 aqueous solution under the aid of ultrasound without any adhesive. The TiO 2 /cotton composite fabrics were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR) and thermal gravity (TG) methods. The treated fabric had photocatalytic property in Rhodamine B (RhB) degradation, and had good antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) cultures. The results of contamination degradation and antibacterial experiments confirmed that ultrasonic vibration had a significant effect on the tightly coating of nanoparticles on cotton fiber surface in the process of micro-dissolution. Even after intense ultrasonic washing, the contamination degradation rate and antibacterial efficiency of S3 (with ultrasonic) still reach 95% and 99% respectively, while S4 (without ultrasonic) only remained 72% and 59% of S2, which was attributed that ultrasonic facilitated the entry and load of nanoparticles onto the fabrics. The mechanical property of treated cotton fabrics kept well., (Copyright © 2019. Published by Elsevier B.V.)