Enhancing Single-Frame Supervision for Better Temporal Action Localization.

Temporal action localization aims to identify the boundaries and categories of actions in videos, such as scoring a goal in a football match. Single-frame supervision has emerged as a labor-efficient way to train action localizers as it requires only one annotated frame per action. However, it often suffers from poor performance due to the lack of precise boundary annotations. To address this issue, we propose a visual analysis method that aligns similar actions and then propagates a few user-provided annotations (e.g., boundaries, category labels) to similar actions via the generated alignments. Our method models the alignment between actions as a heaviest path problem and the annotation propagation as a quadratic optimization problem. As the automatically generated alignments may not accurately match the associated actions and could produce inaccurate localization results, we develop a storyline visualization to explain the localization results of actions and their alignments. This visualization facilitates users in correcting wrong localization results and misalignments. The corrections are then used to improve the localization results of other actions. The effectiveness of our method in improving localization performance is demonstrated through quantitative evaluation and a case study.

Quantifying the interfacial triboelectricity in inorganic-organic composite mechanoluminescent materials.

Mechanoluminescence (ML) sensing technologies open up new opportunities for intelligent sensors, self-powered displays and wearable devices. However, the emission efficiency of ML materials reported so far still fails to meet the growing application requirements due to the insufficiently understood mechano-to-photon conversion mechanism. Herein, we propose to quantify the ability of different phases to gain or lose electrons under friction (defined as triboelectric series), and reveal that the inorganic-organic interfacial triboelectricity is a key factor in determining the ML in inorganic-organic composites. A positive correlation between the difference in triboelectric series and the ML intensity is established in a series of composites, and a 20-fold increase in ML intensity is finally obtained by selecting an appropriate inorganic-organic combination. The interfacial triboelectricity-regulated ML is further demonstrated in multi-interface systems that include an inorganic phosphor-organic matrix and organic matrix-force applicator interfaces, and again confirmed by self-oxidization and reduction of emission centers under continuous mechanical stimulus. This work not only gives direct experimental evidences for the underlying mechanism of ML, but also provides guidelines for rationally designing high-efficiency ML materials.

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision.

Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection. In this paper, we develop an open-source visual analysis tool, Uni-Evaluator, to support a unified model evaluation for classification, object detection, and instance segmentation in computer vision. The key idea behind our method is to formulate both discrete and continuous predictions in different tasks as unified probability distributions. Based on these distributions, we develop 1) a matrix-based visualization to provide an overview of model performance; 2) a table visualization to identify the problematic data subsets where the model performs poorly; 3) a grid visualization to display the samples of interest. These visualizations work together to facilitate the model evaluation from a global overview to individual samples. Two case studies demonstrate the effectiveness of Uni-Evaluator in evaluating model performance and making informed improvements.

Cluster-Aware Grid Layout.

Grid visualizations are widely used in many applications to visually explain a set of data and their proximity relationships. However, existing layout methods face difficulties when dealing with the inherent cluster structures within the data. To address this issue, we propose a cluster-aware grid layout method that aims to better preserve cluster structures by simultaneously considering proximity, compactness, and convexity in the optimization process. Our method utilizes a hybrid optimization strategy that consists of two phases. The global phase aims to balance proximity and compactness within each cluster, while the local phase ensures the convexity of cluster shapes. We evaluate the proposed grid layout method through a series of quantitative experiments and two use cases, demonstrating its effectiveness in preserving cluster structures and facilitating analysis tasks.

Establishing and Validating an Aging-Related Prognostic Signature in Osteosarcoma.

Aging is an inevitable process that biological changes accumulate with time and results in increased susceptibility to different tumors. But currently, aging-related genes (ARGs) in osteosarcoma were not clear. We investigated the potential prognostic role of ARGs and established an ARG-based prognostic signature for osteosarcoma. The transcriptome data and corresponding clinicopathological information of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Molecular subtypes were generated based on prognosis-related ARGs obtained from univariate Cox analysis. With ARGs, a risk signature was built by univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Differences in clinicopathological features, immune infiltration, immune checkpoints, responsiveness to immunotherapy and chemotherapy, and biological pathways were assessed according to molecular subtypes and the risk signature. Based on risk signature and clinicopathological variables, a nomogram was established and validated. Three molecular subtypes with distinct clinical outcomes were classified based on 36 prognostic ARGs for osteosarcoma. A nine-ARG-based signature in the TCGA cohort, including BMP8A, CORT, SLC17A9, VEGFA, GAL, SSX1, RASGRP2, SDC3, and EVI2B, has been created and developed and could well perform patient stratification into the high- and low-risk groups. There were significant differences in clinicopathological features, immune checkpoints and infiltration, responsiveness to immunotherapy and chemotherapy, cancer stem cell, and biological pathways among the molecular subtypes. The risk signature and metastatic status were identified as independent prognostic factors for osteosarcoma. A nomogram combining ARG-based risk signature and metastatic status was established, showing great prediction accuracy and clinical benefit for osteosarcoma OS. We characterized three ARG-based molecular subtypes with distinct characteristics and built an ARG-based risk signature for osteosarcoma prognosis, which could facilitate prognosis prediction and making personalized treatment in osteosarcoma.

VideoPro: A Visual Analytics Approach for Interactive Video Programming.

Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection. While data programming shows promise in generating labeled data at scale with user-defined labeling functions, the high dimensional and complex temporal information in videos poses additional challenges for effectively composing and evaluating labeling functions. In this paper, we propose VideoPro, a visual analytics approach to support flexible and scalable video data programming for model steering with reduced human effort. We first extract human-understandable events from videos using computer vision techniques and treat them as atomic components of labeling functions. We further propose a two-stage template mining algorithm that characterizes the sequential patterns of these events to serve as labeling function templates for efficient data labeling. The visual interface of VideoPro facilitates multifaceted exploration, examination, and application of the labeling templates, allowing for effective programming of video data at scale. Moreover, users can monitor the impact of programming on model performance and make informed adjustments during the iterative programming process. We demonstrate the efficiency and effectiveness of our approach with two case studies and expert interviews.

Characterizing the prognostic and therapeutic value of necroptosis in sarcoma based on necroptosis subtypes.

Necroptosis, a type of necrotic cell death independent of caspase regulation, is mainly mediated by receptor interacting serine/threonine kinase 1 (RIPK1), receptor interacting serine/threonine kinase 3 (RIPK3) and mixed lineage kinase domain-like (MLKL). Necroptosis plays an essential role in many tumors. However, the potential roles of necroptosis in tumor microenvironment (TME) of sarcoma (SARC) remain unknown. This study analyzed the expression, prognosis, genetic alterations of necroptosis genes in SARC. We identified two subtypes (cluster A and B) by performing unsupervised consensus clustering. Cluster A and B greatly differed in prognosis and immune infiltration, with cluster A showing more favorable prognosis, higher immune infiltration and higher expression levels of necroptosis genes than cluster B. Based on the differentially expressed genes (DEGs) between two clusters, a necroptosis scoring system was developed for predicting overall survival of SARC patients. Patients with high necroptosis score had worse survival status, with a decreased infiltration level of most immune cells. Our findings demonstrated the potential role of necroptosis in regulating tumor microenvironment and the prognostic value of necroptosis-related genes for SARC patients.

[Comparison of the effectiveness of the long head of the biceps tendon with or without proximal amputation after arthroscopic repair of the rotator cuff].

Objective: To compare the effectiveness of the long head of the biceps tendon (LHBT) with or without proximal amputation after arthroscopic rotator cuff repair in the treatment of repairable rotator cuff tear with LHBT injury. Methods: The clinical data of 68 patients with LHBT injury combined with supraspinatus tendon tear who met the selection criteria between January 2016 and June 2020 were retrospectively analyzed. According to whether the proximal end of LHBT was cut off or not after arthroscopic rotator cuff repair, they were divided into LHBT fixation without cutting group (group A, 32 cases) and LHBT fixation with cutting group (group B, 36 cases). There was no significant difference in gender, age, operating side, preoperative supraspinatus tear width, Constant-Murley shoulder function scale, University of California Los Angeles (UCLA) score, and visual analogue scale (VAS) score between the two groups ( P>0.05). The operation time, elbow flexion muscle strength, and postoperative complications were compared between the two groups. The Constant-Murley shoulder function scale, UCLA score, and VAS score were used to evaluate the recovery of shoulder function before operation and at 3, 6, 12 months after operation. The acromion-humeral distance (AHD) was measured by Y-view X-ray film of the shoulder joint immediately after operation and at last follow-up. AHD and acromion-greater tubercle distance (AGT) were measured by musculoskeletal ultrasound at 0°, 30°, 60°, and 90° of abduction. Results: There was no significant difference in operation time between the two groups ( t=-0.740, P=0.463). Patients in both groups were followed up (13.0±0.7) months in group A and (13.1±0.8) months in group B, with no significant difference ( t=0.127, P=0.899). At last follow-up, the elbow flexor muscle strength of the two groups reached grade Ⅴ. Complications (including shoulder pain, deltoid atrophy, and rotator cuff re-tear) occurred in 6 patients (18.75%) in group A and 9 patients (25.00%) in group B, without neurovascular injury, surgical site infection, joint stiffness, LHBT spasmodic pain, and Popeye deformity. There was no significant difference in the incidence of complications between the two groups ( χ 2=0.385, P=0.535). The Constant-Murley shoulder function scale, UCLA score, and VAS score significantly improved in both groups at 3, 6, and 12 months after operation ( P>0.05). The above scores in group B were significantly better than those in group A at 3 and 6 months after operation ( P<0.05), and there was no significant difference between the two groups at 12 months after operation ( P>0.05). Y-view X-ray film measurement of the shoulder joint showed that the AHD of the two groups at last follow-up was less than that at immediate after operation, but the difference was not significant ( Z=-1.247, P=0.212); the AHD of group A was significantly greater than that of group B at last follow-up ( t=-2.291, P=0.025). During musculoskeletal ultrasound detection of abduction and shoulder lift, there was no significant difference in the reduction degree of AHD and AGT in group A with abduction of 0°-30° compared with group B ( P>0.05). The reduction degree of AHD and AGT in group A with abduction of 30°-60°, and the reduction degree of AGT in group A with abduction of 60°-90° were significantly smaller than those in group B ( P<0.05). Conclusion: In arthroscopic rotator cuff repair, whether the proximal structure of LHBT is cut off or not after LHBT fixation can effectively improve the symptoms of patients and promote the recovery of shoulder joint function. Compared with preserving the proximal structure of LHBT, cutting the proximal structure of LHBT after LHBT fixation has more obvious pain relief within 6 months, and the latter had better stability above the shoulder joint.

Bioinformatics Analysis Reveals an Association between Autophagy, Prognosis, Tumor Microenvironment, and Immunotherapy in Osteosarcoma.

Autophagy is a catabolic pathway involved in the regulation of bone homeostasis. We explore clinical correlation of autophagy-related key molecules to establish risk signature for predicting the prognosis, tumor microenvironment (TME), and immunotherapy response of osteosarcoma. Single cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in osteosarcoma. Autophagy-related genes (ARGs) were extracted from the established risk signature of the Molecular Signatures Database of Gene Set Enrichment Analysis (GSEA) by univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Overall survival (OS), TME score, abundance of infiltrating immune cells, and response to immune-checkpoint blockade (ICB) treatment in patients with different risks were compared based on risk score. Nine ARGs were identified and risk signature was constructed. In all osteosarcoma datasets, the OS was significantly longer in the high-risk patients than low-risk onset. Risk signature significantly stratified clinical outcomes, including OS, metastatic status, and survival status. Risk signature was an independent variable for predicting osteosarcoma OS and showed high accuracy. A nomogram based on risk signature and metastases was developed. The calibration curve confirmed the consistency in 1-year, 3-year, and 5-year predicted OS and the actual OS. The risk score was related to 6 kinds of T cells and macrophages, myeloid-derived suppressor cell, natural killer cell, immune score, and stromal score in TME. The risk signature helped in predicting patients' response to anti-PD1/anti-PD-L1 treatment. The ARGs-led risk signature has important value for survival prediction, risk stratification, tumor microenvironment, and immune response evaluation of osteosarcoma.

Visualizing Dynamic Mechanical Actions with High Sensitivity and High Resolution by Near-Distance Mechanoluminescence Imaging.

Proportionally converting the applied mechanical energy into photons by individual mechanoluminescent (ML) micrometer-sized particles opens a new way to develop intelligent electronic skins as it promises high-resolution stress distribution visualization and fast response. However, a big challenge for ML sensing technology is its low sensitivity in detecting stress. In this work, a novel stress distribution sensor with the detection sensitivity enhanced by two orders of magnitude is developed by combining a proposed near-distance ML imaging scheme with an improved mechano-to-photon convertor. The enhanced sensitivity is the main contributor to the realization of a maximum photon harvesting rate of ≈80% in the near-distance ML imaging scheme. The developed near-distance ML sensor shows a high sensitivity with a detection limit down to ≈kPa level, high spatial resolution of 254 dpi, and fast response with an interval of 3.3 ms, which allows for high-resolution and real-time visualization of complex mechanical actions such as irregular solid contacts or fluid impacts, and thus enables use in intelligent electronic skin, structural health monitoring, and human-computer interaction.

Deep learning with weak annotation from diagnosis reports for detection of multiple head disorders: a prospective, multicentre study.

BACKGROUND: A large training dataset with high-quality annotations is necessary for building an accurate and generalisable deep learning system, which can be difficult and expensive to prepare in medical applications. We present a novel deep-learning-based system, requiring no annotator but weak annotation from a diagnosis report, for accurate and generalisable performance in detecting multiple head disorders from CT scans, including ischaemia, haemorrhage, tumours, and skull fractures. METHODS: Our system was developed on 104 597 head CT scans from the Chinese PLA General Hospital, with associated textual diagnosis reports. Without expert annotation, we used keyword matching on the reports to automatically generate disorder labels for each scan. The labels were inaccurate because of the unreliable annotator-free strategy and inexact because of scan-level annotation. We proposed RoLo, a novel weakly supervised learning algorithm, with a noise-tolerant mechanism and a multi-instance learning strategy to address these issues. RoLo was tested on retrospective (2357 scans from the Chinese PLA General Hospital), prospective (650 scans from the Chinese PLA General Hospital), cross-centre (1525 scans from the Brain Hospital of Hunan Province), cross-equipment (1484 scans from the Chinese PLA General Hospital), and cross-nation (CQ500 public dataset from India) test datasets. Four radiologists were tested on the prospective test dataset before and after viewing system recommendations to assess whether the system could improve diagnostic performance. FINDINGS: The area under the receiver operating characteristic curve for detecting the four disorder types was 0·976 (95% CI 0·976-0·976) for retrospective, 0·975 (0·974-0·976) for prospective, 0·965 (0·964-0·966) for cross-centre, and 0·971 (0·971-0·972) for cross-equipment test datasets, and 0·964 (0·964-0·966) for CQ500 (with only haemorrhage and fracture). The system achieved similar performance to four radiologists and helped to improve sensitivity and specificity by 0·109 (95% CI 0·086-0·131) and 0·022 (0·017-0·026), respectively. INTERPRETATION: Without expert annotated data, our system achieved accurate and generalisable performance for head disorder detection. The system improved the diagnostic performance of radiologists. Because of its accuracy and generalisability, our computer-aided diganostic system could be used in clinical practice to improve the accuracy and efficiency of radiologists in different hospitals. FUNDING: National Key R&D Program of China, National Natural Science Foundation of China, and Beijing Natural Science Foundation.

Primary seminoma of prostate in a patient with Klinefelter syndrome: A case report.

RATIONALE: Klinefelter syndrome (KS) is a sex differentiation syndrome that occurs in men and is characterized by the 47XXY genotype. An association between KS and cancer has also been reported. The occurrence of seminoma of the prostate in KS has not been reported in the literature to date. Primary seminoma should be included in the differential diagnosis of prostate neoplasms in patients with KS. PATIENT CONCERNS: A 39-year-old man presenting with urinary retention was admitted to our hospital. Physical examination revealed sparse pubic hairs, atrophic testes, and an underdeveloped penis. Hormonal examination revealed significantly lowered serum testosterone levels and markedly higher follicle-stimulating hormone levels. A chromosomal examination was performed. Computed tomography and magnetic resonance imaging imaging showed a neoplasm in the left lobe of the prostate, and immunohistochemical examination of a transrectal needle biopsy of the prostate was performed. DIAGNOSES: Chromosomal examination was exhibited a 47 XXY genotype. Histopathology and of Immunohistochemistry of the transrectal needle biopsy specimen confirmed a seminoma. No other neoplasm was found on systemic examination; therefore, the patient was diagnosed with primary prostate seminoma and Klinefelter syndrome. INTERVENTIONS: The patient refused any treatment except catheterization because of religious reason. OUTCOMES: The patient died 2 years later. LESSONS: Primary seminoma should be included in the differential diagnosis of neoplasms of the prostate in patients with KS. Transrectal ultrasound-guided prostate needle biopsy is essential for the diagnosis of prostate neoplasms, and cisplatin-based chemotherapy remains the primary treatment for seminoma.

Towards Better Caption Supervision for Object Detection.

As training high-performance object detectors requires expensive bounding box annotations, recent methods resort to free-available image captions. However, detectors trained on caption supervision perform poorly because captions are usually noisy and cannot provide precise location information. To tackle this issue, we present a visual analysis method, which tightly integrates caption supervision with object detection to mutually enhance each other. In particular, object labels are first extracted from captions, which are utilized to train the detectors. Then, the objects detected from images are fed into caption supervision for further improvement. To effectively loop users into the object detection process, a node-link-based set visualization supported by a multi-type relational co-clustering algorithm is developed to explain the relationships between the extracted labels and the images with detected objects. The co-clustering algorithm clusters labels and images simultaneously by utilizing both their representations and their relationships. Quantitative evaluations and a case study are conducted to demonstrate the efficiency and effectiveness of the developed method in improving the performance of object detectors.

Interactive Graph Construction for Graph-Based Semi-Supervised Learning.

Semi-supervised learning (SSL) provides a way to improve the performance of prediction models (e.g., classifier) via the usage of unlabeled samples. An effective and widely used method is to construct a graph that describes the relationship between labeled and unlabeled samples. Practical experience indicates that graph quality significantly affects the model performance. In this paper, we present a visual analysis method that interactively constructs a high-quality graph for better model performance. In particular, we propose an interactive graph construction method based on the large margin principle. We have developed a river visualization and a hybrid visualization that combines a scatterplot, a node-link diagram, and a bar chart to convey the label propagation of graph-based SSL. Based on the understanding of the propagation, a user can select regions of interest to inspect and modify the graph. We conducted two case studies to showcase how our method facilitates the exploitation of labeled and unlabeled samples for improving model performance.

OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples.

One major cause of performance degradation in predictive models is that the test samples are not well covered by the training data. Such not well-represented samples are called OoD samples. In this article, we propose OoDAnalyzer, a visual analysis approach for interactively identifying OoD samples and explaining them in context. Our approach integrates an ensemble OoD detection method and a grid-based visualization. The detection method is improved from deep ensembles by combining more features with algorithms in the same family. To better analyze and understand the OoD samples in context, we have developed a novel kNN-based grid layout algorithm motivated by Hall's theorem. The algorithm approximates the optimal layout and has O(kN2) time complexity, faster than the grid layout algorithm with overall best performance but O(N3) time complexity. Quantitative evaluation and case studies were performed on several datasets to demonstrate the effectiveness and usefulness of OoDAnalyzer.

Force-induced charge carrier storage: a new route for stress recording.

Stress sensing is the basis of human-machine interface, biomedical engineering, and mechanical structure detection systems. Stress sensing based on mechanoluminescence (ML) shows significant advantages of distributed detection and remote response to mechanical stimuli and is thus expected to be a key technology of next-generation tactile sensors and stress recorders. However, the instantaneous photon emission in ML materials generally requires real-time recording with a photodetector, thus limiting their application fields to real-time stress sensing. In this paper, we report a force-induced charge carrier storage (FICS) effect in deep-trap ML materials, which enables storage of the applied mechanical energy in deep traps and then release of the stored energy as photon emission under thermal stimulation. The FICS effect was confirmed in five ML materials with piezoelectric structures, efficient emission centres and deep trap distributions, and its mechanism was investigated through detailed spectroscopic characterizations. Furthermore, we demonstrated three applications of the FICS effect in electronic signature recording, falling point monitoring and vehicle collision recording, which exhibited outstanding advantages of distributed recording, long-term storage, and no need for a continuous power supply. The FICS effect reported in this paper provides not only a breakthrough for ML materials in the field of stress recording but also a new idea for developing mechanical energy storage and conversion systems.

X-ray recharged long afterglow luminescent nanoparticles MgGeO3:Mn2+,Yb3+,Li+ in the first and second biological windows for long-term bioimaging.

In this paper, we have designed long afterglow luminescent MgGeO3:Mn2+,Yb3+,Li+ (MGO) nanoparticles in the first (NIR-I) and second (NIR-II) biological windows. Yb3+ ions served not only as the trap center to enhance the NIR-I long afterglow emission of Mn2+ at 680 nm, but also as an emitting center to produce a NIR-II long afterglow emission at ∼1000 nm. Furthermore, we have found the addition of Li+ can greatly increase the NIR-II afterglow emission of Yb3+ and the optimal amount of Mn2+, Yb3+ and Li+ was found to be 0.1, 0.5 and 0.5 mol%, respectively. The MGO nanoparticles synthesized using sol-gel methods showed a uniform morphology with a diameter of 50-100 nm, which were suitable for applications in bioimaging. More importantly, we have found MGO nanoparticles can be effectively excited to produce long persistent NIR-I and II luminescence using soft X-rays, suggesting that low dosage soft X-rays can also serve as a more powerful and deep tissue excitation source to recharge MGO nanoparticles. Furthermore, the MGO nanoparticles can also be re-excited to produce photo-stimulated emission under the irradiation of 650 and 808 nm NIR lasers. The in vivo imaging results have shown that MGO nanoparticles modified with folic acid (FA) can effectively realize super long-term targeted in vivo imaging of inflammation with a high sensitivity via recharging using soft X-rays and NIR lasers, which can provide not only an accurate diagnosis of inflammation, but also long-term monitoring of possible changes in the focus of inflammation in real time.

Circular RNA-9119 protects IL-1ß-treated chondrocytes from apoptosis in an osteoarthritis cell model by intercepting the microRNA-26a/PTEN axis.

AIMS: Osteoarthritis (OA) is a common degenerative joint disease characterized by cartilage degeneration and joint inflammation. As its pathogenesis remains unclear, there are no effective treatments established. Circular RNA (circRNA), microRNA (miRNA), and other noncoding RNAs participate in OA development; however, the effects and mechanisms of circRNA and miRNA in OA remain unknown. MAIN METHODS: Cartilage miRNA was examined in patients with and without OA. KEY FINDINGS: CircRNA-9119 and phosphatase and tensin homolog (PTEN) expression decreased in OA-affected cartilage and interleukin (IL)-1ß-induced chondrocytes, and miR-26a expression significantly decreased in normal cells and tissues. CircRNA-9119 overexpression restored chondrocyte growth, whereas IL-1ß treatment impaired chondrocyte growth. Annexin V-FITC & PI flow cytometry and Bcl-2/Bax ratio measurement indicated that the apoptosis of IL-1ß-treated articular chondrocytes was decreased by circRNA-9119 upregulation. Bioinformatic prediction and the dual-luciferase reporter assay indicated that circRNA-9119 served as a miR-26a sponge and that miR-26a targeted the 3'-UTR of PTEN. Transfection of chondrocytes with a circRNA-9119-overexpressing vector revealed downregulation of miR-26a expression. Furthermore, circRNA-9119 overexpression induced PTEN expression. In addition, a miR-26a mimic induced IL-1ß-induced chondrocyte apoptosis, and circRNA-9119 overexpression inhibited IL-1ß-induced chondrocyte apoptosis. SIGNIFICANCE: CircRNA-9119 is an important regulator of IL-1ß-treated chondrocytes through the miR-26a/PTEN axis, possibly contributing to OA development.

Sinomenine Attenuates Cartilage Degeneration by Regulating miR-223-3p/NLRP3 Inflammasome Signaling.

Sinomenine (SIN) has been shown to protect against IL-1ß-induced chondrocyte apoptosis in vitro. However, the role of SIN in the anterior cruciate ligament transection (ACLT)-induced osteoarthritis (OA) mouse model and its underlying molecular mechanisms remain unclear. In the present study, the protective effect of SIN on ACLT-induced articular cartilage degeneration and IL-1ß-induced chondrocyte apoptosis miR-223-3p/NLRP3 signaling regulation was investigated. Safranin O staining was performed to evaluate the pathological changes of articular cartilage. Chondrocyte apoptosis was measured with Annexin V-fluorescein isothiocyanate/polyimide (annexin V-FITC/PI) staining using flow cytometry. Gene and protein expression were detected by RT-qPCR and Western blotting, respectively. SIN administration markedly improved articular cartilage degradation in mice undergoing ACLT surgery. In addition, SIN treatment downregulated the levels of inflammatory cytokines and the protein expression of NLRP3 inflammasome components and upregulated the expression of miR-223-3p in OA mice and IL-1ß-stimulated chondrocytes. In vitro, we found that NLRP3 was a direct target of miR-223-3p, and overexpression of miR-223-3p blocked IL-1ß-induced apoptosis and the inflammatory response in chondrocytes. These findings indicate that miR-223-3p/NLRP3 signaling could be used as a potential target of SIN for the treatment of OA.

An Interactive Method to Improve Crowdsourced Annotations.

In order to effectively infer correct labels from noisy crowdsourced annotations, learning-from-crowds models have introduced expert validation. However, little research has been done on facilitating the validation procedure. In this paper, we propose an interactive method to assist experts in verifying uncertain instance labels and unreliable workers. Given the instance labels and worker reliability inferred from a learning-from-crowds model, candidate instances and workers are selected for expert validation. The influence of verified results is propagated to relevant instances and workers through the learning-from-crowds model. To facilitate the validation of annotations, we have developed a confusion visualization to indicate the confusing classes for further exploration, a constrained projection method to show the uncertain labels in context, and a scatter-plot-based visualization to illustrate worker reliability. The three visualizations are tightly integrated with the learning-from-crowds model to provide an iterative and progressive environment for data validation. Two case studies were conducted that demonstrate our approach offers an efficient method for validating and improving crowdsourced annotations.