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To solve the problem of poor stability of additive flame retardant waterborne polyurethane， a series of waterborne polyurethane with flame retardant properties was synthesized by prepolymer method using aliphatic polyisocyanate， polyether polyol， and flame retardant phosphorus-containing polyol as raw materials. The flame retardant waterborne polyurethane was applied to the dipping process of microfiber synthetic leather base （microfiber base）. Its effects on the mechanics， quality， flame retardant， and thermal decomposition properties of microfiber base were discussed. The results show that with the increase of phosphorus-containing flame retardant polyol content， the combustion rate decreases from 115.00 mm/min to 60.79 mm/min， and the limiting oxygen index increases from 21.00% to 24.20%. After different concentrations of phosphorus-nitrogen flame retardants were sprayed on the surface of microfiber base， microfiber base appears self-extinguishing phenomenon during combustion， and the limiting oxygen index increases to 27.00%. Compared with the microfiber base without flame retardant treatment，the peak heat release rate and total heat release rate of the microfiber base after flame retardant treatment are reduced by 42.29% and 20.01%， respectively. The test analyses of SEM and Raman spectroscopy reveal that the graphitization degree of the carbon layer is improved and the surface structure becomes more compact after flame retardant finishing， suggesting that the flame retardant plays a crucial role by using the condensed phase flame retardant mechanism.

To address the challenges in tracking complex maneuvering extended targets, an effective maneuvering extended target tracking method was proposed for irregularly shaped star-convex using a Transformer network. Initially, the alpha-shape algorithm was used to model the variations in the star-convex shape. In addition, a recursive approach was proposed to estimate the irregular shape of an extended target by detailed derivation in the Bayesian filtering framework. This approach accurately estimated the shape of a static star convex extended target. Moreover, through the structural redesign of the target state transition matrix and the real-time estimation of the maneuvering extended target’s state transition matrix using a transformer network, the accurate tracking of complex maneuvering targets was achieved. Furthermore, the real-time tracking of star convex maneuvering extended targets was achieved by fusing the estimated shape contours with motion states. This study focused on constructing certain complex maneuvering extended target tracking scenarios to assess the performance of the proposed method and the comprehensive estimation capabilities of the algorithm considering both shapes and motion states using multiple performance indicators.

Fishing vessels are important contributors to global emissions in terms of greenhouse gases and air pollutants. However, few studies have addressed the emissions from fishing vessels on fishing grounds. In this study, a framework for estimating fishing vessel emissions, using a bottom-up dynamic method based on the big data from the Beidou VMS (vessel monitoring system) of fishing vessels, is proposed and applied to a survey of fishing vessel emissions in the East China Sea. The results of the study established a one-year emission inventory of fishing vessels in the East China Sea. This study was the first to use VMS data to estimate fishing vessel emissions in a fishing area, and the results will help to support the management of their carbon emissions.

Abstract Background Coronary artery disease (CAD) is a complex disease that is influenced by environmental and genetic factors. In this study, we aimed to investigate the relationship between coding variants in lipid metabolism-related genes and CAD in a Chinese Han population. Methods A total of 252 individuals were recruited for this study, including 120 CAD patients and 132 healthy control individuals. Rare and common coding variants in 12 lipid metabolism-related genes (ANGPTL3, ANGPTL4, APOA1, APOA5, APOC1, APOC3, CETP, LDLR, LIPC, LPL, PCSK9 and SCARB1) were detected via next-generation sequencing (NGS)-based targeted sequencing. Associations between common variants and CAD were evaluated by Fisher’s exact test. A gene-based association test of rare variants was performed by the sequence kernel association test-optimal (SKAT-O test). Results We found 51 rare variants and 17 common variants in this study. One common missense variant, LIPC rs6083, was significantly associated with CAD after Bonferroni correction (OR = 0.47, 95% CI = 0.29–0.76, p = 1.9 × 10− 3). Thirty-three nonsynonymous rare variants were identified, including two novel variants located in the ANGPTL4 (p.Gly47Glu) and SCARB1 (p.Leu233Phe) genes. We did not find a significant association between rare variants and CAD via gene-based analysis via the SKAT-O test. Conclusions Targeted sequencing is a powerful tool for identifying rare and common variants in CAD. The common missense variant LIPC rs6083 confers protection against CAD. The clinical relevance of rare variants in CAD aetiology needs to be investigated in larger sample sizes in the future.

This study explores the optimal subsidy policy to maximize the benefits associated with the suburbanization of university campuses. A transport accessibility index is introduced, and a model is developed to analyze faculty housing relocation, incorporating factors such as transport accessibility, housing price, relocation subsidy, and the influence of children. The impact of housing relocation is assessed using a regional output model that considers both production and consumption aspects. Subsequently, a decision-making model is established to determine the optimal subsidy level and the number of faculty to relocate, with the overarching goal of maximizing total regional benefits. The findings reveal that an increase in subsidies correlates with a rise in the willingness of faculty to relocate, leading to heightened benefits for the region. However, the rate of benefit increase shows diminishing returns with each increment change in the subsidy. Notably, the study demonstrates that 70% of the additional benefits to the region emanate from the housing market, accurately reflecting the current financial landscape in China. This insight underscores why city governments frequently leverage land markets to actively promote suburbanization.

Abstract Background Patients with developmental dysplasia of the hip (DDH) have complex proximal femoral deformities, and orthopedic surgery lacks objectivity. Expectations for surgical outcomes are often not achieved, and postoperative problems are common. Using 3D-printed technology in orthopedics offers a novel approach to precise and individualized treatment in modern orthopedics. The aim of this study was to investigate the value of the application of 3D-printed osteotomy guide plates in femoral osteotomy. The clinical indices of femoral osteotomy in children with DDH using 3D-printed osteotomy guide plates were compared with those of traditional osteotomy. Methods The clinical data of children with DDH who underwent open reduction and Salter pelvic osteotomy combined with femoral osteotomy from September 2010 to September 2020 were retrospectively collected and analyzed. Based on the inclusion and exclusion criteria, a total of 36 patients were included in the study: 16 in the guide plate group and 20 in the conventional group. Operation time (total), operation time (femoral side), X-ray fluoroscopy times (total), X-ray fluoroscopy times (femoral side) and intraoperative blood loss were analyzed and compared between the two groups. Comparison of treatment-related indicators such as postoperative neck-shaft angle, postoperative anteversion angle, hospitalization time, and hospitalization expenses is made between the two groups. The two groups of patients were evaluated at the last follow-up using the McKay clinical evaluation criteria. Results Between the two groups, there were significant differences in operation time (total), operation time (femoral side), X-ray fluoroscopy times (total), X-ray fluoroscopy times (femoral side) and intraoperative blood loss (P 0.05). The MacKay clinical evaluation did not significantly differ at the most recent follow-up (P > 0.05). Conclusions Children with DDH undergoing proximal femoral osteotomy using 3D-printed osteotomy guide plates benefit from a simpler surgical procedure, shorter operative time, less bleeding and less radiation exposure during surgery. This technique is of great clinical value.

Abstract The regulation of inflammatory response at the site of injury and macrophage immunotherapy is critical for tissue repair. Chiral self‐assemblies are one of the most ubiquitous life cues, which is closely related to biological functions, life processes, and even the pathogenesis of diseases. However, the role of supramolecular chiral self‐assemblies in the regulation of immune functions in the internal environment of tissues has not been fully explored yet. Herein, 3D supramolecular chiral self‐assembling matrixes are prepared to regulate the polarization of macrophages and further enhance the repair of myocardial infarction (MI). Experiments studies show that M‐type (left‐handed) self‐assembling matrixes significantly inhibit inflammation and promote damaged myocardium repair by upregulating M2 macrophage polarization and downstream immune signaling compared with P‐type (right‐handed), and R‐type (non‐chirality) self‐assembling matrixes. Classical molecular dynamics (MD) simulation demonstrates that M‐type self‐assembling matrixes display higher stereo‐affinity to cellular binding, which enhances the clustering of mechanosensitive integrin β1 (Itgβ1) and activates focal adhesion kinase (FAK) and Rho‐associated protein kinase (ROCK), as well as downstream PI3K/Akt1/mTOR signaling axes to promote M2 polarization. This study of designing a 3D chiral self‐assembling matrixes microenvironment suitable for regulating the polarization of macrophages will provide devise basis for immunotherapy with biomimetic materials.

Background: Spontaneous preterm birth (sPTB) stands as a leading cause of neonatal mortality. Consequently, preventing sPTB has emerged as a paramount concern in healthcare. Therefore, our study aimed to develop a nomogram, encompassing patient characteristics and cervical elastography, to predict sPTB in singleton pregnancies. Specifically, we targeted those with a short cervix length (CL), no history of sPTB, and who were receiving vaginal progesterone therapy. Methods: A total of 568 patients were included in this study. Data from 392 patients, collected between January 2016 and October 2019, constituted the training cohort. Meanwhile, records from 176 patients, spanning November 2019 to January 2022, formed the validation cohort. Following the univariate logistic regression analysis, variables exhibiting a P-value less than 0.05 were integrated into a multivariable logistic regression analysis. The primary objective of this subsequent analysis was to identify the independent predictors linked to sPTB in the training cohort. Next, we formulated a nomogram utilizing the identified independent predictors. This tool was designed to estimate the likelihood of sPTB in singleton pregnancies, particularly those with a short CL, devoid of any sPTB history, and undergoing vaginal progesterone therapy. The C-index, Hosmer-Lemeshow (HL) test, calibration curves, decision curve analysis (DCA), and receiver operating characteristic (ROC) were used to validate the performance of the nomogram. Results: Upon finalizing the univariate analysis, we progressed to a multivariable analysis, integrating 8 variables with P

Abstract Background The shortening length of the lower extremity after the proximal femoral osteotomy is an important issue to be considered in preoperative planning of developmental dysplasia of the hip (DDH) in children. There is still a lack of research on shortening the length of the lower extremities in different proximal femoral osteotomy varus styles. We aimed to verify the relationship between the shortening length after “point-to-face” and “face-to-face” varus osteotomy and proposed a formula for calculating the difference in shortening length and verified its feasibility. Methods Fifty-five children with unilateral DDH were enrolled. The preoperative hip CT data were imported into mimics 21, 3-Matic 10 (Materialise, Leuven, Belgium) for femoral reconstruction and simulated osteotomy, and the difference (t) was calculated by directly measuring the length of the proximal femur after osteotomy. d* sinθ was measured in a three-dimensional environment to calculate the difference in femoral shortening length between the two osteotomy methods (t'). Results The results of the direct measurement method and the formula measurement method are shown in the table; the differences in the results of the femoral shortening length difference were not statistically significant (P > 0.05). The limits of agreement (95%) of the difference values using Bland–Altman analysis were between − 0.50 and 0.46 mm, with a mean of − 0.02 mm, indicating a high agreement between the two methods. r = 0.99 (P

Abstract Background Patients with recurrent implantation failure (RIF) may have more uterine contractions. Several observational studies suggested that atosiban administration around embryo transfer resulted in higher pregnancy rates in RIF patients. This study aimed to evaluate the effect of atosiban given before fresh embryo transfer on pregnancy outcomes of women with RIF. Methods A prospective, randomized, double-blind controlled clinical trial was performed in IVF center of Shanghai First Maternity and Infant Hospital. According to a computer-generated randomization list, 194 infertile women with RIF received fresh embryo transfer between July 2017 and December 2019 were randomly allocated into the atosiban (n = 97) and the placebo (n = 97) groups. Women in the treatment group received atosiban intravenously about 30 min before embryo transfer with a bolus dose of 6.75 mg over one minute. Those in the placebo group received only normal saline infusion for the same duration. Results There was no significant difference in the live birth rate between the atosiban and placebo groups (42.3% vs 35.1%, P = 0.302, RR = 1.206 (0.844–1.723)). No significant differences were found between the two groups in the positive pregnancy test, clinical pregnancy, ongoing pregnancy, miscarriage, multiple pregnancy, ectopic pregnancy and implantation rates. Similar results were found when stratified by the number of embryos previously transferred, number of previous failed embryo transfers, frequency of endometrial peristalsis on embryo transfer day (≥ 3 waves/min) or serum estradiol (E2) on the day of hCG above the median level. And, there was no correlation between the serum E2 level on the day of hCG and the frequency of endometrial peristalsis on embryo transfer day. The frequency of endometrial peristalsis on embryo transfer day, total FSH/HMG dosage and duration were the significant factors which independently predicted the likelihood of a live birth. Conclusions These results suggested that atosiban treatment before fresh embryo transfer might not improve the live birth rate in RIF patients. Trial registration The study had been approved by the Institutional Review Board of the hospital (2017 ethics No.43) and was registered under Clinicaltrials.gov with an identifier NCT02893722.

In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, in a typical radar tracking application, the measurement noise is correlated over time as the sampling frequency of a radar is generally much higher than the bandwidth of the measurement noise. In addition, target maneuvers and measurement outliers imply that the process noise and measurement noise are non-Gaussian. To solve this problem, we resort to triplet Markov chain (TMC) models to describe stochastic systems with correlated noise and derive a new filter under the maximum correntropy criterion to deal with non-Gaussian noise. By stacking the state vector, measurement vector, and auxiliary vector into a triplet state vector, the TMC model can capture the complete dynamics of stochastic systems, which may be subjected to potential parameter uncertainty, non-stationarity, or error sources. Correntropy is used to measure the similarity of two random variables; unlike the commonly used minimum mean square error criterion, which uses only second-order statistics, correntropy uses second-order and higher-order information, and is more suitable for systems in the presence of non-Gaussian noise, particularly some heavy-tailed noise disturbances. Furthermore, to reduce the influence of round-off errors, a square-root implementation of the new filter is provided using QR decomposition. Instead of the full covariance matrices, corresponding Cholesky factors are recursively calculated in the square-root filtering algorithm. This is more numerically stable for ill-conditioned problems compared to the conventional filter. Finally, the effectiveness of the proposed algorithms is illustrated via three numerical examples.

Data loss is ubiquitous in practical engineering applications due to communication delay or congestion. Data loss rate is a key metric to evaluate the reliability of state estimation. To jointly estimate system state and data loss rate, we propose a class of Gaussian-Beta filters for linear and moderate nonlinear Gaussian state-space models with unknown probability of measurement loss. In the filters, the arrival of the measurement at each time is formulated as a binary random variable, which is determined by the classical threshold technology. In addition, the hidden state and the unknown probability of measurement loss are modeled as a product of Gaussian and Beta distributions, and the form remains unchanged through recursive operations. Simulation results verify the effectiveness of the proposed Gaussian-Beta filters compared with the existing filtering algorithms.

The classical Kalman filter is a very important state estimation approach, which has been widely used in many engineering applications. The Kalman filter is optimal for linear dynamic systems with independent Gaussian noises. However, the independence and Gaussian assumptions may not be satisfied in practice. On the one hand, modeling physical systems usually results in discrete-time state-space models with correlated process and measurement noises. On the other hand, the noise is non-Gaussian when the system is disturbed by heavy-tailed noise. In this case, the performance of the Kalman filter will deteriorate, or even diverge. This paper is devoted to addressing the state estimation problem of linear dynamic systems with high-order autoregressive moving average (ARMA) non-Gaussian noise. First, a triplet Markov model is introduced to model the system with high-order ARMA noise, since this model relaxes the independence assumption of the hidden Markov model. Then, a new filter is derived based on correntropy, instead of the commonly used minimum mean square error (MMSE), to deal with non-Gaussian noise. Unlike the MMSE, which uses only second-order statistics of error, correntropy can capture second-order and higher-order statistics. Finally, simulation results verify the effectiveness of the proposed algorithm.

A robust generalized labeled multi-Bernoulli (GLMB) filter is presented to perform multitarget tracking (MTT) with unknown non-stationary heavy-tailed measurement noise (HTMN). The HTMN is modeled as a multivariate Student’s t-distribution with unknown and time-varying mean. The proposed filter relaxes the restrictive assumption that the mean of HTMN is zero, and can effectively deal with MTT under the condition that the mean of HTMN is unknown and time-varying. The variational Bayesian (VB) approximation is applied in the GLMB filtering framework with the augmented state. The marginal likelihood function is obtained via minimizing the Kullback-Leibler divergence by the variational lower bound. The simulation results demonstrate that the proposed filter can effectively track multiple targets in both linear and nonlinear scenarios when the mean of HTMN is unknown and time-varying.

Extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs) have emerged as important mediators of intercellular communication in response to cartilage damage. In this study, we sought to characterize the inhibitory role of microRNA (miR)-31 encapsulated in synovial MSC (SMSC)-derived EVs in knee osteoarthritis (OA). The expression of miR-31, lysine demethylase 2A (KDM2A), E2F transcription factor 1 (E2F1), and pituitary tumor transforming gene 1 (PTTG1) was validated in cartilage tissues of knee OA patients. Following SMSC-EV extraction and identification, chondrocytes with the miR-31 inhibitor were added with SMSC-EVs, whereupon the effects of miR-31 on proliferation and migration of chondrocytes were assessed. The interaction among miR-31, KDM2A, E2F1, and PTTG1 in chondrocyte activities was probed in vitro, along with an in vivo mouse knee OA model. We identified downregulated miR-31, E2F1, and PTTG1 and upregulated KDM2A in cartilage tissues of knee OA patients. SMSC-EV-packaged miR-31 potentiated chondrocyte proliferation and migration as well as cartilage formation by targeting KDM2A. Mechanistically, KDM2A bound to the transcription factor E2F1 and inhibited its transcriptional activity. Enrichment of E2F1 in the PTTG1 promoter region activated PTTG1 transcription, accelerating chondrocyte proliferation and migration. SMSC-EVs and EVs from miR-31-overexpressed SMSCs alleviated cartilage damage and inflammation in knee joints in vivo. SMSC-EV-encapsulated miR-31 ameliorates knee OA via the KDM2A/E2F1/PTTG1 axis.

To clarify the impact of new transport services on consumers’ shopping behaviors and shopping-related transport emissions, a back-propagation neural network shopping channel choice model is established to estimate the number of times that consumers engage in online and offline shopping. A brick-and-mortar store choice model and travel mode choice model are developed, and a method to measure the quality of life of consumers is established to evaluate the impact of new transport services on shopping behaviors and the corresponding shopping transport emissions. The findings reveal that a new passenger transport service increases the number of times that consumers shop in brick-and-mortar stores and correspondingly shopping transport emissions; a new commodity transport service reduces the number of times that consumers shop in brick-and-mortar stores and in turn shopping transport emissions. In a scenario with both new commodity services and new passenger transport services, although online shopping is convenient, consumers are still willing to pay travel expenses for offline shopping; in a scenario with the new commodity transport service but without the new passenger transport service, the emissions from shopping-related transport are the lowest.

Superhydrophobicity and superhydrophilicity are extreme properties of solid surface governed by surface morphology and surface chemistry. Switching between superhydrophobicity and superhydrophilicity can be achieved by applying external stimuli and exchanging counterions due to the change in surface morphology and/or surface chemistry. This review mainly gives a concise overview of switchable wettability of surface. Through applying external stimuli such as light, temperature, pH value, solvent, electric potential and exchanging of counterions, the wettability of surface can switch between superhydrophobicity and superhydrophilicity. The material with asymmetric wettability exhibits some novel abilities such as directional liquid transport. Finally, the advances of surface with switchable wettability are prospected. By controlling the micro-nano structure and chemical composition, the switch between superhydrophobicity and superhydrophilicity can be realized on various surfaces.

Background/Aims: Adipose-derived stem cells (ASCs) belong to mesenchymal stem cells and may play a potential role as seeding cells in stem cell transplantation. To be able to exploit stem cells as therapeutic tool, their defects in some important cellular functions, such as low survival rate and cellular activity, should be considered. This is especially the case for stem cells that are intended for transplantation. Of note, stem cell responses to hormones should be considered since estrogen is known to play a critical role in stem cell behavior. However, different impacts of the estrogen receptor (ER) types α and β have not been fully determined in ASC function. In this study, we investigated effects of ERα and ERβ on ASC proliferation, migration, as well as in adipogenesis. Methods: ASCs obtained from mice were cultured with 100nM ERα or ERβ agonist PPT and DPN, respectively. The ERα and ERβ antagonist ICI 182,780 (100nM) was used as control. Results: Compared to ERβ, ERα appears more potent in improving ASC proliferation and migration. Investigation of adipogenesis revealed that ERβ played a significant role in suppressing ASC-mediated brown tissue adipogenesis which is in contrast to ERα. These results correlated with reduced mRNA expression of UCP-1, PGC-1α and PPAR-γ. Conclusions: ERα plays a more critical role in promoting ASC proliferation and migration while ERβ is more potent in suppressing ASC brown adipose tissue differentiation mediated by decreased UCP-1, PGC-1α and PPAR-γ expression.

A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.

Although the bone marrow mononuclear cell (BMMNC) is known as an ideal cell type for cell-based therapy for MI treatment, the effective subpopulation still remains unknown. Our study aimed at identifying the optimal subset of BMMNCs suited for cardiac regeneration. In this study, we observed that MI led to (i) a significant increase of the c-kit+AT2R+ BMMNC subpopulation in mice and (ii) a modest increase of AT2R+ BMMNCs in humans. c-kit+AT2R+ and c-kit+AT2R− BMMNC subpopulations were obtained from mice after MI. Then, we cocultured cardiac H9C2 cells with c-kit+AT2R+, c-kit+AT2R−, and unfractionated BMMNCs; finally, we found that the c-kit+AT2R+ subset is superior to the c-kit+AT2R− subset in improving cardiomyocyte protection in vitro. Of note, c-kit+AT2R+ BMMNCs showed a more robust migration capacity than c-kit+AT2R− and unfractionated BMMNCs in vitro and in vivo. Additionally, compared to c-kit+AT2R− and unfractionated BMMNCs, intravenous transplantation of c-kit+AT2R+ BMMNC resulted in smaller infarct size and lower levels of inflammatory reactions in heart tissue, leading to a higher global heart function improvement. In conclusion, our results indicate that the c-kit+AT2R+ BMMNC subpopulation exerts a protective effect against MI and shows promising therapeutic possibilities with regard to the treatment of ischemic heart disease.

The existing multi-sensor control algorithms for multi-target tracking (MTT) within the random finite set (RFS) framework are all based on the distributed processing architecture, so the rule of generalized covariance intersection (GCI) has to be used to obtain the multi-sensor posterior density. However, there has still been no reliable basis for setting the normalized fusion weight of each sensor in GCI until now. Therefore, to avoid the GCI rule, the paper proposes a new constrained multi-sensor control algorithm based on the centralized processing architecture. A multi-target mean-square error (MSE) bound defined in our paper is served as cost function and the multi-sensor control commands are just the solutions that minimize the bound. In order to derive the bound by using the generalized information inequality to RFS observation, the error between state set and its estimation is measured by the second-order optimal sub-pattern assignment metric while the multi-target Bayes recursion is performed by using a δ-generalized labeled multi-Bernoulli filter. An additional benefit of our method is that the proposed bound can provide an online indication of the achievable limit for MTT precision after the sensor control. Two suboptimal algorithms, which are mixed penalty function (MPF) method and complex method, are used to reduce the computation cost of solving the constrained optimization problem. Simulation results show that for the constrained multi-sensor control system with different observation performance, our method significantly outperforms the GCI-based Cauchy-Schwarz divergence method in MTT precision. Besides, when the number of sensors is relatively large, the computation time of the MPF and complex methods is much shorter than that of the exhaustive search method at the expense of completely acceptable loss of tracking accuracy.

BACKGROUND: Circulating microRNAs (miRNAs) are emerging as promising biomarkers for human cancer. Osteosarcoma is the most common human primary malignant bone tumor in children and young adults. The objective of this study was to investigate whether circulating miRNAs in plasma could be a useful biomarker for detecting osteosarcoma and monitoring tumor removal dynamics. METHODS: Plasma samples were obtained from 90 patients before surgery, 50 patients after one month of surgery, and 90 healthy individuals. The study was divided into three steps: First, initial screening of the profiles of circulating miRNAs in pooled plasma samples from healthy controls and pre-operative osteosarcoma patients using a TaqMan low density array (TLDA). Second, evaluation of miRNA concentration in individual plasma samples from 90 pre-operative osteosarcoma patients and 90 healthy controls by a quantitative real time PCR (qRT-PCR) assay. Third, evaluation of miRNA concentration in paired plasma samples from 50 pre- and post-operative osteosarcoma patients by qRT-PCR assay. RESULTS: Four plasma miRNAs including miR-195-5p, miR-199a-3p, miR-320a, and miR-374a-5p were significantly increased in the osteosarcoma patients. Receiver operating characteristics curve analysis of the combined populations demonstrated that the four-miRNA signature could discriminate cases from controls with an area under the curve of 0.9608 (95% CI 0.9307-0.9912). These 4 miRNAs were markedly decreased in the plasma after operation. In addition, circulating miR-195-5p and miR-199a-3p were correlated with metastasis status, while miR-199a-3p and miR-320a were correlated with histological subtype. CONCLUSIONS: Our data suggest that altered levels of circulating miRNAs might have great potential to serve as novel, non-invasive biomarkers for osteosarcoma.

The error bound is a typical measure of the limiting performance of all filters for the given sensor measurement setting. This is of practical importance in guiding the design and management of sensors to improve target tracking performance. Within the random finite set (RFS) framework, an error bound for joint detection and estimation (JDE) of multiple targets using a single sensor with clutter and missed detection is developed by using multi-Bernoulli or Poisson approximation to multi-target Bayes recursion. Here, JDE refers to jointly estimating the number and states of targets from a sequence of sensor measurements. In order to obtain the results of this paper, all detectors and estimators are restricted to maximum a posteriori (MAP) detectors and unbiased estimators, and the second-order optimal sub-pattern assignment (OSPA) distance is used to measure the error metric between the true and estimated state sets. The simulation results show that clutter density and detection probability have significant impact on the error bound, and the effectiveness of the proposed bound is verified by indicating the performance limitations of the single-sensor probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters for various clutter densities and detection probabilities.

Spin-center shift (SCS) is a radical process involving 1,2-radical translocation along with a two-electron ionic movement, such as elimination of an adjacent leaving group. Such a process was initially observed in some important biochemical transformations, and the unique property has also attracted considerable interest in synthetic chemistry. Experimental, kinetic, as well as computational studies have been performed, and a series of useful radical transformations have been developed and applied in organic synthesis based on SCS processes in the last 20 years. This Perspective is an overview of radical transformations involving the SCS mechanism.