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Abstract: In recent years, spectral clustering has become quite popular for data analysis because it can be solved efficiently by standard linear algebra tools and do not suffer from the problem of local optima. The clustering effect by using such spectral method, however, depends heavily on the description of similarity between instances of the datasets. In this paper, we defined the adjustable line segment length which can adjust the distance in regions with different density. It squeezes the distances in high density regions while widen them in low density regions. And then a density sensitive distance measure satisfied by symmetric, non-negative, reflexivity and triangle inequality was present, by which we can define a new similarity function for spectral clustering. Experimental results show that compared with conventional Euclidean distance based and Gaussian kernel function based spectral clustering, our proposed algorithm with density sensitive similarity measure can obtain desirable clusters with high performance on both synthetic and real life datasets. [Copyright &y& Elsevier]

Abstract: Detection of outlier from high dimensional dataset have found important applications in many fields, yet the unexpected time consumption is likely to hinder its practical use. Thus, it makes sense to build an efficient method for finding meaningful outliers and analyzing their intentional knowledge. In this paper, we utilize the concept of rough set to construct a method for outlying reduction, based on an outlier detection and analysis system. By defining outlying partition similarity, we can mine outliers on the key attribute subset rather than on the full dimensional attribute set of dataset, as long as the similarity between outlying partitions produced on them is large enough. For this purpose, we propose a novel method for finding the key attribute subset in dataset, which starts by seeking all outliers on the full attribute set, and then searches through all outlying attribute subsets for these points. After that, it turns out to be able to determine the key attribute subset in accordance with the similarity between outlying partitions. By experiments, we show that our method allows more efficient seeking of key attribute subset than the previous methods, thereby improving the feasibility of outlier detection. [ABSTRACT FROM AUTHOR]

The stress relaxation responses of the Sn–3.8Ag–0.7Cu joints following exposure to electrical currents were examined to investigate the effect of electromigration on the reliability of solder joints. It was found that the stress relaxation rate was enhanced for the Sn–3.8Ag–0.7Cu solder joints subjected to a current density of 2 × 104 A/cm2. Sn hillock formation was observed in situ on the surface of the solder joint and the increase of the hillock volume was obtained as a function of the current application time. Analysis of the vacancy flux indicated that the variations of the vacancy concentration with the electromigration time from the calculations agreed with the growth kinetics of the hillocks observed in the experiments. By modeling the stress relaxation as a climb-assisted dislocation glide process, it is shown that the vacancy accumulation induced by electromigration enhanced the dislocation climb rate, resulting in a large increase of the stress relaxation rate. [ABSTRACT FROM AUTHOR]

Abstract: Many semi-supervised learning algorithms only consider the distribution of word frequency, ignoring the semantic and syntactic information underlying the documents. In this paper, we present a new multi-view approach for semi-supervised document classification by incorporating both semantic and syntactic information. For this purpose, a co-training style algorithm, Co-features, is proposed. In the phase of active querying, we assign a weight to each sample document according to its uncertainty factor. Then the most informative samples are selected and labeled by other “teachers”. In contrast to batch training mode, we developed an incremental Naive Bayes update method, which allows for more efficient training even with a large pool of unlabeled data. Experimental results show that our algorithm works successfully on the datasets Reuters-21578 and WebKB, and is superior to Co-testing in the learning efficiency. [Copyright &y& Elsevier]

Abstract: The growth kinetics and interfacial morphologies of intermetallic compounds (IMCs) and tensile properties of single crystal Ag/Sn–3.8Ag–0.7Cu (SAC) solder joint were investigated by solid-state aging at 170°C and liquid-state aging at 250°C. It is found that the growth kinetics of IMCs under both solid- and liquid-state aging conditions follow parabola relationship. Accordingly, the diffusion coefficients under the two conditions were calculated to be about 2.50×10−17 m2 s−1 and 1.60×10−14 m2 s−1, respectively. With increasing aging time, the scallop-like morphology of the IMC gradually evolved into planar type after solid-state aging; but it became rougher after liquid-state aging. Meanwhile, the average grain size of IMC also increases with increasing aging time. The tensile strength of the Sn–Ag–Cu/Ag single crystal joints did not decrease seriously with increasing aging time and the corresponding tensile fracture mechanism was discussed. [Copyright &y& Elsevier]

In 2014, Rodriguez and Laio proposed a famous clustering algorithm based on a fast search and find density peaks dubbed as DPC (Rodriguez and Laio, 2014). DPC has been widely used in many fields because of its simplicity and effectiveness. However, DPC has two obvious drawbacks. First, initial cluster centers must be manually selected from the decision graph, which is extremely difficult and even impossible in some cases. Second, the result of DPC is affected by the so called "chain reaction" problem. To overcome these two drawbacks, we propose a density decay graph-based density peak clustering (DGDPC) algorithm. The key to DGDPC is the concept of density decay graph, which is inspired by the decay phenomenon (a common phenomenon in nature). In our algorithm, we form initial clusters according to density decay graphs and then merge clusters based on a simple method. As a result, there is no need to manually select cluster centers and the chain reaction problem is alleviated. Although DGDPC introduces an additional parameter m, m is robust and easy to determine in advance. Experiments on 10 synthetic datasets and 10 real-world datasets show that our algorithm outperforms DPC, DGB (Wu and Wilamowski, 2016), K-means (MacQueen et al., 1967), DBSCAN (Ester et al., 1996), and Single-link (Murtagh and Contreras, 2012) in most cases. [ABSTRACT FROM AUTHOR]

Learning a classifier from class-imbalance data is an important challenge. Among existing solutions, SMOTE is one of the most successful methods and has an extensive range of practical applications. The performance of SMOTE and its extensions usually degrades owing to noisy and borderline examples. Filtering-based methods have been developed to address this problem but still have the following technical defects: (a) Error detection techniques heavily rely on parameter settings; (b) Examples detected by error detection techniques are directly eliminated, leading to deviation of obtained decision boundary and class imbalance again. To advance the state of the art, a novel filtering-based oversampling method called SMOTE-NaN-DE is proposed in this paper. In SMOTE-NaN-DE, a SMOTE-based method is first used to generate synthetic samples and improve original class-imbalance data. Secondly, an error detection technique based on natural neighbors is used to detect noisy and borderline examples. Thirdly, the differential evolution (DE) is used to optimize and change iteratively the position (attributes) of found examples instead of eliminating them. The main advantages of SMOTE-NaN-DE are that (a) It can improve almost all of SMOTE-based methods in terms of the noise problem; (b) Error detection technique is parameter-free; (c) Examples found by error detection technique are optimized by the differential evolution rather than removed, which keeps imbalance ratio and improve the boundary; (d) It is more suitable for data sets with more noise (especially class noise). The effectiveness of the proposed SMOTE-NaN-DE is validated by intensive comparison experiments on artificial and real data sets. • A novel oversampling method based on natural neighbors and differential evolution. • A novel error detection based on natural neighbors. • The differential evolution is modified to optimize found noisy and borderline samples. • The proposed algorithm is applied to 4 oversampling methods and outperforms 9 SMOTE-based methods. [ABSTRACT FROM AUTHOR]

Semi-supervised self-labeled methods apply unlabeled data to improve the performance of classifiers which are trained by labeled data alone. Nevertheless, applying unlabeled data may deteriorate the prediction accuracy. One of the causes is that there are insufficient labeled data for training an initial classifier in self-labeled methods. However, existing solutions for this problem of lacking sufficient initial labeled data still have technical defects. For example, they fail to deal with non-spherical data and improve insufficient initial labeled data effectively, when initial labeled data are extremely scarce. In this paper, we propose an effective semi-supervised self-labeled framework based on local cores, aiming to solve the problem of lacking adequate initial labeled data in self-labeled methods and overcome existing technical defects above. Main ideas of our framework include two sides: (a) inadequate initial labeled data are improved by adding predicted local cores to them, where local cores are predicted by active labeling or co-labeling; (b) we use any semi-supervised self-labeled method to train a given classifier on improved labeled data and updated unlabeled data. In our framework, local cores roughly reveal the data distribution, which helps the proposed framework work on spherical or non-spherical data sets. In addition, local cores also help our framework improve insufficient initial labeled data effectively, even when initial labeled data are extremely scarce. Experiments show that the proposed framework is compatible with tested self-labeled methods, and can help self-labeled methods train a k nearest neighbor or support vector machine, when initial labeled data are insufficient. [ABSTRACT FROM AUTHOR]

Self-training method is one of the relatively successful methodologies of semi-supervised classification. It can exploit both labeled data and unlabeled data to train a satisfactory supervised classifier. Mislabeling is one of the largest challenges in the self-training method and the most common technique for removing mislabeled samples is the local noise filter. However, existing local noise filters used in self-training methods confront following technical defects: parameter dependence and using only labeled data to remove mislabeled samples. To address these shortcomings, this paper proposes a novel self-training method based on density peaks and an extended parameter-free local noise filter (STDPNF). In STDPNF, the self-training method based on density peaks is redesigned to be more suitable for combination with local noise filters. Moreover, a new local noise filter based on natural neighbors is proposed to filter out mislabeled instances. Compared with existing local noise filters used in self-training methods, the one in STDPNF is parameter-free and can remove mislabeled samples by exploiting the information of both labeled data and unlabeled data. We focus on k nearest neighbor as a base classifier. In experiments, we verify the efficiency of STDPNF in improving the performance of the base classifier of k nearest neighbor and the advantage of STDPNF in having the ability to remove mislabeled instances efficiently even when labeled data are not sufficient. [ABSTRACT FROM AUTHOR]

Background: The treatment for unstable intertrochanteric fractures in the elderly has always been a controversial issue. The aim in this study was to compare the curative effects of proximal femoral nail anti-rotation (PFNA) and cementless bipolar hemiarthroplasty (CPH) on femoral intertrochanteric fracture in the elderly.Methods: From March 2008 to December 2012, 108 elderly patients with femoral intertrochanteric fractures were treated by PFNA or CPH. There were 63 males and 45 females, aged 75.3-99.1 years [(83.7 ± 5.6) years]. The patients' bone mineral density was routinely measured, and the fractures were classified according to Evans-Jensen. The patients were divided into CPH group and PFNA group. The differences in operation time, intraoperative bleeding, immobilization duration, hospitalization time, Harris scores and postoperative complications including deep venous thrombosis, lung and urinary infection were analyzed.Results: All patients were followed for 12.5-36.2 months [(28.0 ± 6.3) months)]. The operation time was (53.7 ± 15.2) min and (77.5 ± 16.8) min in PFNA group and CPH group, respectively (P < 0.05); intraoperative bleeding was (132.5 ± 33.2) mL and (286.3 ± 43.2) mL, respectively (P < 0.05); immobilization duration was (28.2 ± 3.7) days and (3.1 ± 1.2) days, respectively (P < 0.05); hospitalization time was (7.6 ± 1.8) days and (6.9 ± 2.2) days, respectively (P > 0.05); and the Harris scores after 1 year were (87.7 ± 7.9) points and (88.3 ± 9.2) points, respectively (P > 0.05). There was no significant difference in postoperative complications between the two groups (P > 0.05).Conclusion: Both PFNA and CPH are safe and effective treatments for femoral intertrochanteric fracture in elderly patients. Nonetheless, CPH allows faster mobilization and recovery.Trial Registration: Registration Number: ChiCTR1900022846 . Reg Date:2019-04-26 00:27:33 Retrospective registration. [ABSTRACT FROM AUTHOR]

Cloud computing has nowadays become a dominant technology to reduce the computation cost by elastically providing resources to users on a pay-per-use basis. More and more scientific and business applications represented by workflows have been moved or are in active transition to cloud platforms. Therefore, efficient cloud workflow scheduling methods are in high demand. This paper investigates how to simultaneously optimize makespan and economical cost for workflow scheduling in clouds and proposes a multiobjective evolutionary list scheduling (MOELS) algorithm to address it. It embeds the classic list scheduling into a powerful multiobjective evolutionary algorithm (MOEA): a genome is represented by a scheduling sequence and a preference weight and is interpreted to a scheduling solution via a specifically designed list scheduling heuristic, and the genomes in the population are evolved through tailored genetic operators. The simulation experiments with the real-world data show that MOELS outperforms some state-of-the-art methods as it can always achieve a higher hypervolume (HV) value. Note to Practitioners—This paper describes a novel method called MOELS for minimizing both costs and makespan when deploying a workflow into a cloud datacenter. MOELS seamlessly combines a list scheduling heuristic and an evolutionary algorithm to have complementary advantages. It is compared with two state-of-the-art algorithms MOHEFT (multiobjective heterogeneous earliest finish time) and EMS-C (evolutionary multiobjective scheduling for cloud) in the simulation experiments. The results show that the average hypervolume value from MOELS is 3.42% higher than that of MOHEFT, and 2.27% higher than that of EMS-C. The runtime that MOELS requires rises moderately as a workflow size increases. [ABSTRACT FROM AUTHOR]

Self-labeled techniques are semi-supervised classification models that overcome the shortage of labeled samples via an iterative process. Most relevant proposals are inspired by boosting schemes to iteratively enlarge labeled data, but these methods are constrained by the number and distribution of the initial labeled data. Up to the present, the only exceptions which can solve the above problem are SEG-SSC, k -means-SSC and LC-SSC. However, SEG-SSC relies on too many parameters. Besides, it is hard to improve the distribution of the initial labeled data when the initial labeled set can not roughly represent the distribution of the original data. k -means-SSC and LC-SSC fail to significantly improve the number of the initial labeled data by a limited number of representative points. To address the above issues, this paper proposes a framework based on local cores and synthetic examples generation for self-labeled semi-supervised classification (LCSEG-SSC). First, a new method for finding local cores on labeled and unlabeled data is proposed to improve the distribution of the initial labeled data. Second, STOPF or active labeling is used to predict found local cores. Third, a new example generation technique is proposed to create synthetic labeled samples, intending to improve the number of the initial labeled data. After that, any self-labeled with boosting schemes can be executed on the improved labeled data effectively. Intensive experiments prove that LCSEG-SSC outperforms state-of-the-art methods, especially in a relatively low ratio of labeled data. [ABSTRACT FROM AUTHOR]

Abstract: Recommender systems which can automatically match users with their potential favorites usually rely on Collaborative Filtering (CF). Since in real-world applications the data of historical user behavior are ever growing, it is important to study the incremental CF models which can adapt to this data explosion quickly and flexibly. The rating similarity based K-Nearest-Neighborhood (RS-KNN) is a classical but still popular approach to CF; therefore, to investigate the RS-KNN based incremental CF is significant. However, current incremental RS-KNN (I-KNN) models have the drawbacks of high storage complexity and relatively low prediction accuracy. In this work, we intend to boost the RS-KNN based incremental CF. We focus on two points which are respectively (a) reducing the storage complexity while maintaining the prediction accuracy by employing the generalized Dice coefficients, and (b) improving the prediction accuracy by integrating the similarity support and linear biases as well as implementing the corresponding incremental update. The efficiency of our strategies is supported by the positive results of the experiments conducted on two real datasets. [Copyright &y& Elsevier]

Polyphenols are secondary metabolites of plants and used as effective antioxidants in dietary supplements, whose main sources are fruits, vegetables, and grains. To clarify the content and distribution of polyphenols in different fruit species samples accurately, a rapid and sensitive ultrahigh-pressure liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) method combining dispersive liquid-liquid microextraction (DLLME) was developed for quantitative determination of fifteen polyphenol compounds in fruit juice. In this method, the targets were first extracted from 1 g of fruit juice sample using 10 mL of 80% ethanol solution by ultrasonic-assisted extraction (UAE). Then, 1.0 mL of UAE extracted solution, 60 μL of n-octanol and 2.0 mL of H2O were performed in the following DLLME procedure. A C18 reversed-phase column, ZORBAX SB (100 × 4.6 mm, 3.5 μm), was proposed under gradient elution with 0.1% formic acid aqueous solution and methanol mobile phases for the determination of 15 polyphenols, allowing us to obtain polyphenolic profiles in less than 23.0 min. Under the optimum conditions, the enrichment factors ranged from 162 to 194. The results showed that the 15 polyphenols had linear correlation coefficients (R2) more than 0.99. The limits of detection (LODs) were between 18.3 and 103.5 ng/g, and the average recoveries were between 96.9 and 116.3% with interday relative standard deviations (RSDs) ranging from 4.4 to 8.2% in all cases. The method was successfully applied to the analysis of real fruit juice samples and presented itself as a simple, rapid, practical, and environment-friendly technique. [ABSTRACT FROM AUTHOR]

At present, many scholars are experimentally investigating the grinding performance of ceramic matrix composites (C–SiCs). However, accurately reflecting the microscopic mechanisms of crack initiation and extension and the material removal mechanism (MRM) is difficult. To research the micro-MRM of C–SiCs, a theoretical model (TTM) and a numerical simulation model (NSM) were established in this study and were proven to be reliable by experiments. The TTM was established according to the kinematics and dynamics of a single abrasive particle. In the procedure of establishing the NSM, the SiC matrix (SiCM) and carbon fibre reinforcement (CFRT) were each modelled based on the internal structure characteristics of C–SiCs and then combined by an interface layer. The TTM, NSM and verification experiments all showed that fibre pull-out, fibre outcrop, matrix cracking and interfacial debonding were the basic defects in the C–SiCs. As the grinding depth (a p) increased, the grinding performance of the C–SiCs gradually deteriorated. The material removal characteristics of C–SiCs can be directly modelled at the microlevel by the NSM. The NSM showed that the grinding force fluctuated periodically because the CFRT and SiCM have different properties. High stresses occurred mainly in the SiCM. This research can supply a scientific basis for understanding the micro-MRM of C–SiCs and provide important guidance for the high-quality grinding of C–SiCs. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: This study aims to investigate the outcome of a two-stage surgery in the treatment of neglected femoral diaphyseal fractures which are not uncommon in developing nations. Patients and methods: Ten patients with neglected or late-presenting femoral diaphyseal fractures were considered in this study. All patients underwent a two-stage surgery, which consisted of distraction by an external fixator and open reduction by internal plate fixation. All patients received a supervised regimen of physiotherapy. Patients were followed up clinically and with radiographs at 2 months to assess union and at monthly intervals thereafter. Results: All patients achieved bony union in an average of 3.7 months (2–6 months) with no one lost follow-up. Seven patients regained full range of motion, and the mean knee range of motion was 139.5°. No wound related or neurovascular complications were detected. One patient with stiff knee was re-admitted 1 year after surgery for metal removal, arthrolysis and quadricepsplasty for improving knee range of motion to 90° flexion. Conclusions: We conclude that the treating of neglected femoral diaphyseal fractures with a two-stage surgery is a satisfactory therapy showing reliable bony union, however continuing medical education is necessary for physicians in primary medical facilities as well as for patients with traditional views. [Copyright &y& Elsevier]

Abstract: Matrix Factorization (MF) based Collaborative Filtering (CF) have proved to be a highly accurate and scalable approach to recommender systems. In MF based CF, the learning rate is a key factor affecting the recommendation accuracy and convergence rate; however, this essential parameter is difficult to decide, since the recommender has to keep the balance between the recommendation accuracy and convergence rate. In this work, we choose the Regularized Matrix Factorization (RMF) based CF as the base model to discuss the effect of the learning rate in MF based CF, trying to deal with the dilemma of learning rate tuning through learning rate adaptation. First of all, we empirically validate the affection caused by the change of the learning rate on the recommendation performance. Subsequently, we integrate three sophisticated learning rate adapting strategies into RMF, including the Deterministic Step Size Adaption (DSSA), the Incremental Delta Bar Delta (IDBD), and the Stochastic Meta Decent (SMD). Thereafter, by analyzing the characteristics of the parameter update in RMF, we further propose the Gradient Cosine Adaption (GCA). The experimental results on five public large datasets demonstrate that by employing GCA, RMF could maintain good balance between accuracy and convergence rate, especially with small learning rate values. [Copyright &y& Elsevier]

Abstract: The Matrix-Factorization (MF) based models have become popular when building Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability. However, most of the current MF based models are batch models that are incapable of being incrementally updated; while in real world applications users always enjoy receiving quick responses from the system once they have made feedbacks. In this work, we aim to design an incremental CF recommender based on the Regularized Matrix Factorization (RMF). To achieve this objective, we first simplify the training rule of RMF to propose the SI-RMF, which provides a simple mathematic form for further investigation; whereby we design two Incremental RMF models, respectively are the Incremental RMF (IRMF) and the Incremental RMF with linear biases (IRMF-B). The experiments on two large, real datasets suggest positive results, which prove the efficiency of our strategy. [Copyright &y& Elsevier]

Abstract: By a different use of relevance feedback (the order in which the relevant documents are retrieved, the terms of the relevant documents, and the terms of the irrelevant documents) in the design of fitness function, and by introducing three different genetic operators, we have developed a new genetic algorithm-based query optimization method on relevance feedback for Web information retrieval. Based on three benchmark test collections Cranfield, Medline and CACM, experiments have been carried out to compare our method with three well-known query optimization methods on relevance feedback: the traditional Ide Dec-hi method, the Horng and Yeh’s GA-based method and the López-Pujalte et al.’s GA-based method. The experiments show that our method can achieve better results. [Copyright &y& Elsevier]

Metformin, an anti-hyperglycemic agent used for type 2 diabetes, has recently been found to have more effects apart from glucose regulation. We found that, in ultra-high-molecular-weight polyethylene particle-induced osteolysis mouse models, metformin had bone protect property and reduced the negative regulator of bone formation sclerostin (SOST) and Dickkopf-related protein 1 (DKK1), and increased osteoprotegerin (OPG) secretion and the ratio of OPG/Receptor Activator for Nuclear Factor-κB Ligand (RANKL). In vitro, we established a 3D co-culture system in which metformin affects osteoblasts and osteoclasts through mature osteocytes secretion. Metformin (50 μM) significantly decreased SOST and DKK1 mRNA expression, stimulating alkaline phosphatase activity and proliferation of osteoblast, and increased OPG secretion and the ratio of OPG/RANKL, inhibiting osteoclastogenesis. Moreover, the effect on OPG was reversed by adenosine 5'-monophosphate-activated protein kinase inhibitor, Compound C. Our finding suggests that metformin induces differentiation and mineralization of osteoblasts, while inhibits osteoclastogenesis via mature osteocytes secretion. Therefore, the drug might be beneficial for not only diabetes but also in other bone disorders by acting on mature osteocytes. [ABSTRACT FROM AUTHOR]

Background: Use of Ta biomaterials in medicine started in the middle of the last century. The good biocompatibility and chemical stability, and the unique physical characteristics of Ta metal have resulted in many possible developments of Ta biomaterials. Methods: In this study, histopathological observation, histomorphometric analysis, scanning electron microscope (SEM) observation, energy-dispersive X-ray spectroscopy (EDX) analysis, biomechanical testing, and examination of the coating's mechanical strength have been used to evaluate the value of clinical application of Ta-coated prostheses prepared by a plasma-spraying process. Results: Histopathological observation has demonstrated that the periprosthetic new bone tissues tightly and stably adhere to the Ta coating after the implantation, with no signs of loosening. Early after implantation, there is no significant difference in periprosthetic bone volume and ultimate shear strength between Ta-coated and Ti-coated prostheses ( P > 0.05). EDX analysis suggests that the ultimate shear stress does not damage Ta coating. Mechanical strength testing shows that the adhesive strength and Vicker's surface hardness (HV) of the Ta coating are significantly higher than those of the Ti coating ( P < 0.01). Conclusions: Ta coating has good stability and bone biocompatibility; the extraordinary physical characteristics of Ta coating have great significance in maintaining prosthetic stability and surface porosity after implantation. [ABSTRACT FROM AUTHOR]

Background: Implant failure remains a major obstacle to successful treatment via TJA. Periprosthetic osteolysis and aseptic loosening are considered as proof of wear debris-induced disruption of local regulatory mechanisms related to excessive bone resorption associated with osteolysis and the damage at the bone-prosthesis interface. Therefore, there is an immediate need to explore strategies for limiting and curing periprosthetic osteolysis and aseptic loosening. Methods: We analyzed the in vitro cytokine production by primary mouse bone marrow macrophages (BMMs) that were exposed to ultra-high molecular weight polyethylene (UHMWPE) particles and treated with metformin at different concentrations with or without 5-aminoimidazole-4-carboxamide ribonucleoside to activate or inhibit AMPK. A mouse calvarial model was used to examine the in vivo effects of metformin on UHMWPE particle-induced osteolysis. Results: With particles, primary mouse BMMs secreted more pro-inflammatory cytokines tumor necrosis factor-α and interleukin (IL)-6. Treatment with metformin inhibited these variations and promoted the release of cytokine IL-10 with anti-inflammatory capability. In vivo, metformin reduced the production of pro-inflammatory cytokines, osteoclastogenesis, and osteolysis, increasing IL-10 production. Metformin also promoted the polarization of macrophages to an anti-inflammatory phenotype in vivo via AMPK activation. Discussion: A crucial point in limiting and correcting the periprosthetic osteolysis and aseptic loosening is the inhibition of inflammatory factor production and osteoclast activation induced by activated macrophages. The ability of metformin to attenuate osteolysis induced in mouse calvaria by the particles was related to a reduction in osteoclast number and polarization of macrophages to an anti-inflammatory functional phenotype. Conclusions: Metformin could limit the osteolysis induced by implant debris. Therefore, we hypothesized that metformin could be a potential drug for osteolysis induced by implant debris. [ABSTRACT FROM AUTHOR]

Objective: To explore the relationship of serum levels of IL-1β, IL-6, and TNF-α with prosthesis loosening after hip arthroplasty, and to establish a predictive model for prosthesis loosening., Methods: We retrospectively analyzed the data of 501 patients who underwent hip arthroplasty in Xi'an International Medical Center Hospital from January 2020 to August 2022. Based on radiological diagnosis, the patients were divided into a prosthesis loosening group and a non-loosening group. Clinical data including postoperative serum levels of inflammatory cytokines were collected. Univariant analysis, Lasso regression, decision tree, and random forest models were used to screen feature variables. Based on the screening results, a nomogram model for predicting the risk of prosthesis loosening was established and then validated using ROC curve, and calibration curve, and other methods., Results: There were 50 cases in the loosening group and 451 cases in the non-loosening group. Postoperative levels of IL-1β, IL-6, and TNF-α were found to be significantly higher in the loosening group (P<0.0001). Univariant analysis showed that osteoporosis and postoperative infection were risk factors for prosthesis loosening (P<0.001). The machine learning algorithm identified osteoporosis, postoperative infection, IL-1β, IL-6, and TNF-α as 5 relevant variables. The predictive model based on these 5 variables exhibited an area under the ROC curve of 0.763. The calibration curve and DCA curve verified the accuracy and practicality of the model., Conclusion: Serum levels of IL-1β, IL-6, and TNF-α were significantly elevated in patients with postoperative prosthesis loosening. Osteoporosis, postoperative infection, and inflammatory cytokines are independent risk factors for prosthesis loosening. The predictive model we established through machine learning can effectively determine the risk of prosthesis loosening. Monitoring inflammatory cytokines and postoperative infections, combined with prevention of osteoporosis, can help reduce the risk of prosthesis loosening., Competing Interests: None., (AJTR Copyright © 2024.)

Upper confidence bound (UCB)-based contextual bandit algorithms require one to know the tail property of the reward distribution. Unfortunately, such tail property is usually unknown or difficult to specify in real-world applications. Using a tail property heavier than the ground truth leads to a slow learning speed of the contextual bandit algorithm, while using a lighter one may cause the algorithm to diverge. To address this fundamental problem, we develop an estimator (evaluated from historical rewards) for the contextual bandit UCB based on the multiplier bootstrap technique. Our proposed estimator mitigates the problem of specifying a heavier tail property by adaptively converging to the ground truth contextual bandit UCB (i.e., eliminating the impact of the specified heavier tail property) with theoretical guarantees on the convergence. The design and convergence analysis of the proposed estimator is technically nontrivial. The proposed estimator is generic and it can be applied to improve a variety of UCB-based contextual bandit algorithms. To demonstrate the versatility of the proposed estimator, we apply it to improve the linear reward contextual bandit UCB (LinUCB) algorithm resulting in our bootstrapping LinUCB (BootLinUCB) algorithm. We prove that the BootLinUCB has a sublinear regret. We conduct extensive experiments on both synthetic dataset and real-world dataset from Yahoo! to validate the benefits of our proposed estimator in reducing regret and the superior performance of BootLinUCB over the latest baseline.

In this study, we employ coarse-grained molecular dynamics simulations to explore the microstructure of MSA (methanesulfonic acid)-type electroplating solution, containing Sn(MSA) 2 as the primary salt, MSA as the stabilizer, amphiphilic alkylphenol ethoxylate (APEO) as surfactants and cinnamaldehyde (CA) as the brightener agents, as well as water as the solvent. Our simulation indicates that temperature variations can significantly affect the structural properties of the electroplating solution and the adsorption behavior of its key components onto the substrate. Specifically, at low temperatures, the primary salt ions aggregate into ionic clusters, and the amphiphilic APEO surfactants and CA molecules form micelles composed of hydrophobic cores and hydrophilic shells, which reduces the uniformity of the solution and hinders the adsorption of ions, CA and surfactants onto the substrate. Appropriately increasing the temperature can weaken the aggregation of these components in bulk solution due to the accelerated molecular movements and arouse their adsorption. However, on further increasing the temperature, the elevated kinetic energy of the components thoroughly overwhelms the adsorption interactions, and therefore, the ions, surfactants, and CA desorb from the substrate and redissolve into the solution. We systematically analyze the complex interactions between these components at different temperatures and clarify the mechanism of the non-monotonic dependence of adsorption strength on the temperature at the molecular level. Our simulations demonstrate that there is low-temperature scope for reprocessing/recycling and intermediate-temperature scope for substrate-adsorptions of the key components. This study confers insights into a fundamental understanding of the microscopic mechanism for electroplating and can provide guidance for the development of precise electroplatings.

Due to climate change and human activities, ecological and environmental issues have become increasingly prominent and it is crucial to deeply study the coordinated development between human activities and the ecological environment. Combining panel data from 31 provinces in China spanning from 2011 to 2020, we employed a fixed-effects model, a threshold regression model, and a spatial Durbin model to empirically examine the intricate impacts of population agglomeration on ecological resilience. Our findings indicate that population agglomeration can have an impact on ecological resilience and this impact depends on the combined effects of agglomeration and crowding effects. Also, the impact of population agglomeration on ecological resilience exhibits typical dual-threshold traits due to differences in population size. Furthermore, population agglomeration not only directly impacts the ecological resilience of the local area, but also indirectly affects the ecological resilience of surrounding areas. In conclusion, we have found that population agglomeration does not absolutely impede the development of ecological resilience. On the contrary, to a certain extent, reasonable population agglomeration can even facilitate the progress of ecological resilience.

Aseptic loosening after total hip replacement brings adverse health outcomes and increased risk for complications. The resorptive activity of inflammatory cells activated by the presence of wear-generated debris plays a critical role in debris-induced osteolysis. Previous studies indicate that the abnormally expressed LINC01534 plays a critical role in inflammatory responses. In this study, we aimed to elucidate the functional role and underlying mechanism of LINC01534 in debris-induced osteolysis. We first confirmed that LINC01534 was highly expressed in hip cartilage tissues from aseptic loosening patients. By using an IL-1β-induced inflammation model mimicking debris-induced osteolysis, we demonstrated that LINC01534 promoted IL-1β-induced inflammatory response in hip chondrocytes. Knockdown of LINC01534 inhibited the expression of inflammatory IL-6, IL-8, and TNF-α in hip chondrocytes. Our results showed that LINC01534 functioned as a competing endogenous RNA (ceRNA) by sponging miR-135b-5p in hip chondrocytes. Moreover, bioinformatics analysis and luciferase reporter assay demonstrated that CCHC-Type Zinc Finger Nucleic Acid Binding Protein (PTPRT) is a downstream target of miR-135b-5p. Knockdown of PTPRT attenuated the IL-1β-induced inflammatory responses in hip chondrocytes. In addition, we revealed that inhibition of miR-135b-5p or overexpression of PTPRT could antagonize the effects of LINC01534 knockdown on inflammation attenuation in hip chondrocytes. Mechanistically, we demonstrated that LINC01534/miR-135b-5p/PTPRT axis regulated the NF-κB signaling pathway in hip chondrocytes. Taken together, our findings suggest that LINC01534/miR-135b-5p/PTPRT axis might be a valuable therapeutic target for the treatment of debris-induced osteolysis., 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 © 2022. Published by Elsevier Ltd.)

Background: Fibular support for the lateral tibial plateau through the proximal tibiofibular joint (PTFJ) results in nonuniform settlement of the tibial plateau in middle-aged and elderly persons and may lead to medial compartment knee osteoarthritis. However, the inclination angle of the PTFJ surface varies widely and may affect nonuniform settlement. The purpose of this case-control study was to assess the association between the inclination angle of the PTFJ surface and medial compartment knee osteoarthritis., Methods: The fibular inclination angle (FIA) and tibial inclination angle (TIA) of the PTFJ surface were measured using radiographs. Differences of FIA and TIA among groups were assessed with t tests and the odds ratios (ORs) for risk factors of medial compartment knee osteoarthritis were calculated with binary logistic regression analysis., Results: Forty patients and 40 control participants were included in this case-control study. Patients had both a lower FIA (P=0.005) and TIA (P=0.000) than the controls, and logistic regression analysis showed that FIA (OR =7.000) and TIA (OR =17.000) were risk factors for medial compartment knee osteoarthritis., Conclusions: A lower inclination angle of the PTFJ surface is associated with a risk of medial compartment knee osteoarthritis. Clinically, early prevention of medial compartment knee osteoarthritis should be considered for middle-aged and elderly persons with low PTFJ inclination angles.

Quaking (QKI), an RNA-binding protein, has been reported to exhibit numerous biological functions, such as mRNA regulation, cancer suppression, and anti-inflammation. However, little known about the effects of QKI on bone metabolism. In this study, we used a monocyte/macrophage-specific QKI knockout transgenic mouse model to investigate the effects of QKI deficiency on receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis. The loss of QKI promoted the formation of multinucleated tartrate-resistant acid phosphatase (TRAP)-positive osteoclasts (OCs) from bone marrow macrophages, and upregulated the expression of OC-specific markers, including TRAP (Acp5) and cathepsin K (Ctsk). The pro-osteoclastogenesis effect of QKI deficiency was achieved by amplifying the signaling cascades of the NF-κB and mitogen-activated protein kinase (MAPK) pathways; then, signaling upregulated the activation of nuclear factor of activated T cells c1 (NFATc1), which is considered to be the core transcription factor that regulates OC differentiation. In addition, QKI deficiency could inhibit osteoblast (OB) formation through the inflammatory microenvironment. Taken together, our data suggest that QKI deficiency promoted OC differentiation and disrupted bone metabolic balance, and eventually led to osteopenia under physiological conditions and aggravated the degree of osteoporosis under pathological conditions.

It is critical to evaluate the quality of clusters for most cluster analysis. A number of cluster validity indexes have been proposed, such as the Silhouette and Davies-Bouldin indexes. However, these validity indexes cannot be used to process clusters with arbitrary shapes. Some researchers employ graph-based distance to cluster nonspherical data sets, but the computation of graph-based distances between all pairs of points in a data set is time-consuming. A potential solution is to select some representative points. Inspired by this idea, we propose a novel Local Cores-based Cluster Validity (LCCV) index to improve the performance of Silhouette index. Local cores, with local maximum density, are selected as representative points. Since graph-based distance is used to evaluate the dissimilarity between local cores, the LCCV index is effective for obtaining the optimal cluster number for data sets containing clusters with arbitrary shapes. Moreover, a hierarchical clustering algorithm based on the LCCV index is proposed. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease that primarily manifests as persistent synovitis and progressive joint destruction. Imatinib exhibited a therapeutic effect in murine collagen-induced arthritis (CIA) via selective inhibition tyrosine kinases. The second-generation tyrosine kinase inhibitor dasatinib exhibits more durable hematological and cytogenetic effects and more potency compared to imatinib. However, the effect of dasatinib on CIA is poorly understood. The present study investigated the treatment effect of dasatinib on autoimmune arthritis. We demonstrated that dasatinib alleviated arthritis symptoms and histopathological destruction in CIA mice. Dasatinib treatment inhibited the production of proinflammatory cytokines including IL-1β, TNF-α, and IL-6, and promoted the production of the anti-inflammatory cytokine IL-10. Dasatinib treatment also suppressed the expression of anti-mouse CII antibodies including total IgG, IgG1, IgG2, and IgG2b, in CIA mice. We further demonstrated that dasatinib inhibited the migration and proliferation of fibroblast-like synoviocytes (FLS) from RA patients and promoted FLS apoptosis. The mRNA expression of MMP13, VEGF, FGF, and DKK1 was down-regulated in FLS treated with dasatinib. Our findings suggest that dasatinib exhibited treatment effects on CIA mice and that FLS are an important target cell of dasatinib treatment in autoimmune arthritis.

Macrophage surface antigen-1 (Mac-1, CD11b/CD18) has been implicated in the regulation of osteoclastogenesis. In the synovial tissues of patients with aseptic loosening after total hip replacement, CD11b was up-regulated, which indicated that CD11b is closely involved in osteolysis around the prosthesis. We found that CD11b, but not CD18, promoted osteoclast (OC) maturation. Here, we show CD11b up-regulated the levels of spleen tyrosine kinase (Syk), c-Fos and nuclear factor of activated T cells, cytoplasmic-1 (NFATc1), as well as the activity of extracellular-regulated kinase (Erk), and as a result, osteoclast precursors (OCPs) differentiated and became tartrate-resistant acid phosphatase (TRAP)-positive. In addition, increased tumour necrosis factor-α (TNF-α) induced by ultra-high molecular weight polyethylene (UHMWPE) particles up-regulated the level of CD11b. Taken together, these findings suggest that CD11b is a positive regulator of osteoclastogenesis and that it functions by activating the Syk signalling pathway, while CD18 does not have the same effect., (© 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.)

Background: Simultaneous bilateral total knee arthroplasty (TKA) can lead to greater blood loss and higher risk of venous thromboembolism. The effectiveness and safety of tranexamic acid (TXA) in simultaneous bilateral TKAs have not been clearly defined. We presumed that a fixed dose of TXA may be a preferable alternative for ease of administration in patients undergoing simultaneous bilateral TKAs., Methods: We prospectively randomized 120 primary simultaneous bilateral TKAs to a fixed dose of TXA or equivalent volume of normal saline intravenously. The primary outcome measure was total blood loss. The secondary outcome measures were blood transfusion rate, transfusion units, intraoperative blood loss, drainage volumes, hidden blood loss, maximum decline of hemoglobin, and postoperative suprapatellar girth increment., Results: There were statistically significant lower total blood loss, blood transfusion rate, drainage volumes, transfusion units, and maximum decline of hemoglobin in the TXA group than in the control group (P < .05), without increasing incidence of asymptomatic and symptomatic venous thromboembolism. However, TXA did not significantly reduce the hidden blood loss (P = .123). No differences were observed in suprapatellar girth increments between both groups on postoperative day 5 and week 6 (P = .251 and .299)., Conclusion: Fixed dose of TXA for patients undergoing simultaneous bilateral TKAs was effective and safe in reducing total blood loss and allogeneic blood transfusion needs without any additional thromboembolic risk. However, TXA administered intravenously did not significantly reduce the hidden blood loss., (Copyright © 2016 Elsevier Inc. All rights reserved.)

The aim of this study is to investigate the effect of porous tantalum material in repair tibial defects caused by firearm injuries in a rabbit model. A multifunctional biological impact machine was used to establish a rabbit tibial defect model of firearm injury. Porous tantalum rods were processed into a hollow cylinder. Kirschner wires were used for intramedullary fixation. We compared the differences of the bone ingrowth of the porous tantalum material by gross observations, X-rays and histological evaluations. The radiographic observations revealed that fibrous tissue covered the material surface after 4 weeks, and periosteal reactions and new bone callus extending materials appeared after 8 weeks. After 16 weeks, the calluses of the firearm injury group were completely wrapped around a porous tantalum material. The group with the highest Lane-Sandhu X-rays cores was the firearm injury and tantalum implant group, and the blank control group exhibited the lowest scores. The histological evaluations revealed that the presence of new bone around the biomaterial had grown into the porous tantalum. By the 16th week, the areas of bone tissue of the firearm injury group was significant higher than that of non-firearm injury group (P<0.05). The comminuted fractures treated with tantalum cylinders exhibited greater bone ingrowth in the firearm injury group. In conditions of firearm injuries, the porous tantalum biomaterial exhibited bone ingrowth that was beneficial to the treatment of bone defects.

Background: Fibrochondrocytes are involved in entheses repair, but their response to mechanical strain (MS) is ill known., Objective: To determine if parathyroid hormone-related protein (PTHrP) expression in fibrochondrocytes from fibrocartilaginous entheses is modulated by MS, and to further observe the regulatory effects of human (h) PTHrP on fibrochondrocyte differentiation., Methods: Fibrochondrocytes from fibrocartilaginous entheses of Guizhou miniature pig׳s Achilles tendon were submitted or not to MS (4%, 8% or 12% cyclic tensile strain; 1 Hz). Fibrochondrocytes were also exposed to: cyclopamine (Indian hedgehog (Ihh) inhibitor) (10 μM), hPTHrP (10 nM) or cyclopamine/hPTHrP (cyclopamine 10 uM+hPTHrP 10 nM). Types I, II and X collagen and PTHrP expressions were measured by real-time RT-PCR and Western blot., Result: Under 4% strain load for 12 h, types I and II collagen mRNA expressions were increased (+324% and +659%, P<0.001), while type X collagen was decreased (-89%, P<0.001). At 12%, types I and II collagen mRNA expressions were decreased (-62% and -62%, P<0.001), while type X collagen was increased (+375%, P<0.05). Under 4% strain load, PTHrP mRNA expression was increased in relation with strain duration (from 3 to 12 h: +168%, P<0.001), while at 12%, PTHrP expression decreased with time (from 3 to 12 h: -81%, P<0.001). Using cyclopamine for 24 h, PTHrP mRNA expression was significantly decreased (-88%, P<0.05), types I and II collagen were decreased (-90% and -82%, P<0.001), and type X collagen was increased (+261%, P<0.001)., Conclusions: Dynamic MS modulate PTHrP expressions. Thus, PTHrP might play an important role in fibrochondrocyte differentiation, indirectly revealing a role in entheses׳ formation and repair., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Metformin exhibits diverse protective effects against diabetic complications, such as bone loss. Here, we investigated the effect of metformin on vascular calcification, another type 2 diabetes complication. In female rat aortic smooth muscle cells (RASMCs), we observed that metformin significantly alleviated β-glycerophosphate-induced Ca deposition and alkaline phosphatase activity, corresponding with reduced expression of some specific genes in osteoblast-like cells, including Runx2 and bone morphogenetic protein-2, and positive effects on α-actin expression, a specific marker of smooth muscle cells. Mechanistic analysis showed that phosphorylation levels of both AMP-activated protein kinase (AMPK) and endothelial nitric oxide synthase (eNOS) were increased with NO overproduction. After inhibition of either AMPK or eNOS with the pharmacologic inhibitors, compound C or Nω-Nitro-L-arginine methyl ester, NO production was lowered and metformin-meditated vascular protection against β-glycerophosphate-induced Ca deposition was removed. Our results support that metformin prevents vascular calcification via AMPK-eNOS-NO pathway.

Background: Hydroxyapatite (HA) coatings composed with bisphosphonates (BPs) which have high mineral-binding affinities have been confirmed to successfully enhance implant stability. However, few previous studies focused on HA coatings composed with low-affinity BPs or on systemic effects of locally released BPs., Methods: In this long-term study, we developed two kinds of BP-HA composite coatings using either high-affinity BP (alendronate, ALN) or low-affinity BP (risedronate, RIS). Thirty-six rabbits were divided into three groups according to different coating applications (group I: HA, group II: ALN-HA, and group III: RIS-HA). Implants were inserted into the proximal region of the medullary cavity of the left tibiay. At insertion, 2 × 10(8) wear particles were injected around implants to induce a peri-implant high bone turnover environment. Both local (left tibias) and systemic (right tibias and lumbar vertebrae) inhibitory effect on bone resorption were compared, including bone-implant integration, bone architecture, bone mineral density (BMD), implant stability, and serum levels of bone turnover markers., Results: The results indicated that ALN-HA composite coating, which could induce higher bone-implant contact (BIC) ratio, bone mass augmentation, BMD, and implant stability in the peri-implant region, was more potent on peri-implant bone, while RIS-HA composite coating, which had significant systemic effect, was more potent on non-peri-implant bone, especially lumbar vertebrae., Conclusions: It is instructive and meaningful to further clinical studies that we could choose different BP-HA composite coatings according to the patient's condition.