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Waterborne polyurethane, renowned for its lightweight properties, excellent insulation capabilities, and corrosion resistance, has found extensive application in fields such as construction, automotive, leather, and thermal insulation. Nevertheless, during operational usage, waterborne polyurethane materials, akin to other polymeric substances, are susceptible to oxidative aging manifestations like yellowing, cracking, and diminished mechanical performance, significantly curtailing their utility. Consequently, the synthesis of yellowing-resistant polyurethane assumes pivotal significance. This study integrates dynamic reversible reactions into the synthesis process of polyurethane by introducing the dynamic reversible compound 2-hydroxyethyl disulfide as a chain extender, alongside the incorporation of a UV absorber to enhance the polyurethane's resistance to yellowing. When the disulfide bonds absorb heat, they undergo cleavage, yielding thiols that spontaneously recombine into disulfide bonds at ambient temperatures, allowing for the continuous breaking and reformation of disulfide bonds to absorb heat. Concurrently, in collaboration with the UV absorber, the detrimental effects of ultraviolet radiation on the polyurethane material are mitigated, thereby augmenting its resistance to yellowing. This study scrutinizes the positioning of UV absorber addition, the quantity of UV absorber, and the molar ratio of 1,4-butanediol to 2-hydroxyethyl disulfide, characterizing the functional groups of polyurethane through infrared and Raman spectroscopy. It is observed that the successful preparation of yellowing-resistant polyurethane is achieved, and evaluations on the modified polyurethane through color difference, tensile, and centrifugal tests reveal that the optimal yellowing resistance is attained by adding a UV absorber at a mass fraction of 1% to 3% prior to chain extension, resulting in a color change grade of 2, denoting slight discoloration. Simultaneously, the other properties of polyurethane exhibit relative stability. Notably, when the molar ratio of 1,4-butanediol to 2-hydroxyethyl disulfide is 3:2, the overall performance of the polyurethane remains stable, with exceptional yellowing resistance capabilities attaining a color change grade of 2. [ABSTRACT FROM AUTHOR]

Age and elevated intraocular pressure (IOP) are the two primary risk factors for glaucoma, an optic neuropathy that is the leading cause of irreversible blindness. In most people, IOP is tightly regulated over a lifetime by the conventional outflow tissues. However, the mechanistic contributions of age to conventional outflow dysregulation, elevated IOP and glaucoma are unknown. To address this gap in knowledge, we studied how age affects the morphology, biomechanical properties and function of conventional outflow tissues in C57BL/6 mice, which have an outflow system similar to humans. As reported in humans, we observed that IOP in mice was maintained within a tight range over their lifespan. Remarkably, despite a constellation of age‐related changes to the conventional outflow tissues that would be expected to hinder aqueous drainage and impair homeostatic function (decreased cellularity, increased pigment accumulation, increased cellular senescence and increased stiffness), outflow facility, a measure of conventional outflow tissue fluid conductivity, was stable with age. We conclude that the murine conventional outflow system has significant functional reserve in healthy eyes. However, these age‐related changes, when combined with other underlying factors, such as genetic susceptibility, are expected to increase risk for ocular hypertension and glaucoma. [ABSTRACT FROM AUTHOR]

Traditional methods for estimating output property of the photovoltaic (PV) modules are strongly influenced by the selection of reference condition and transforming equations, which determine the calculated physical parameters under real operating conditions. The differences in the carrier transport properties of PV cells under varying operating conditions, such as the number and velocity of minority carriers at the junction edge and their recombination speed, lead to large deviations in the estimation of the output characteristics, especially under low irradiance conditions. To enhance the accuracy of performance estimation, we propose an improved method that is independent of reference condition. This method eliminates the impact of reference conditions and improves the transformation equations under all irradiance levels. Transformation equations of single diode model are established in different irradiance intervals based on the dependence of physical parameter on irradiance and temperature. Especially in the low irradiance range, all effects of irradiance and temperature are considered for each physical parameter in improved transformation equations. To optimize the unknown parameters in the transformation equations, the artificial hummingbird algorithm is used to fit experimental I–V data. The experimental results of six different types PV modules under a wide range of operating conditions are used to verify the effectiveness of the proposed method. The proposed method offers immediate benefits, including independence from reference condition and a more precise relationship between physical parameters and environmental factors in the estimation of PV output properties. Comparing the results to the traditional method by Laudani, the proposed method demonstrates superior capability in estimating I–V characteristics and accurately identifies the maximum power point under various operating conditions, which is of significant value for engineering applications. [ABSTRACT FROM AUTHOR]

In this work, we developed experimental approaches on indium co‐doped ZnO‐based ceramics from the synthesis to the characterizations and analysis of physical features in the aim to achieve the formation of highly conducting ceramics with controlled nonlinearity. Different doping molar ratios of indium were used and the synthesis of the ceramics was realized by the conventional solid‐state reaction method which was then followed by a sintering under air (A), nitrogen (N), or a mixture of nitrogen and carbon monoxide (NC) atmosphere with different reduction capabilities. The structure, morphology, electrical investigations, and the electronic transport analysis were conducted on the different ZnO co‐doped ceramics as functions of the indium incorporation and the used sintering atmosphere, A, N, or NC. Suitable indium doping of 0.3 mol% and the use of highly reducing sintering atmosphere (NC) ensure the formation of ZnO‐based ceramics with an electrical conductivity up to (103 S/cm) and a quasi‐linear behavior on the electrical characteristic I–V. We discussed the obtained performances by correlating the charge transport with the microstructure of the ceramics, the composition, and the structure of grain boundaries as well as the solubility of indium in the host ZnO ceramic grains. [ABSTRACT FROM AUTHOR]

The underreporting issue on shipping accident data has plagued the researchers focused on maritime safety analysis for many years. For improving the quality of shipping accident records, this study proposes a novel methodology comprising three steps to complement the underreported maritime accident records. The first step is to investigate the underreporting rates under various conditions through questionnaire survey. Based on the survey results, the second step is to build a Cluster-Specific Random Effects (CSRE) model to estimate the underreporting rates under various scenarios. Then, the third step is to replicate the underreported accident records using the Monte Carlo simulation technique. Model results show that the occurrence probability of missing accident records involving liquid cargo ships is lower than other ship categories while fishing ships are more likely to have a higher underreporting rate. Non-serious accidents are more likely to be underreported than serious accidents. The case study confirms the effectiveness of the proposed three-step method for complementing the maritime accident databases suffering underreporting problems. [ABSTRACT FROM AUTHOR]

Ship anomaly detection is a vital aspect for monitoring navigational safety in specific water areas. Considering the effect of water channel boundaries, we propose the detection of an abnormal ship trajectory based on the complex polygon (DATCP) method to detect ship anomalies in this study. With the automatic identification systems (AIS) data from the Yangtze River estuary, a case study is created to verify the effectiveness of the proposed DATCP method. The case study results reveal that the proposed DATCP method can provide higher detection accuracy than the conventional A* algorithm. The feature analysis results indicate that ship anomalies are significantly influenced by ship type, time period, weather conditions and ship traffic characteristics. [ABSTRACT FROM AUTHOR]

The traditional methods for performance estimation mainly include two steps: determine the model parameters under reference condition and calculate the model parameters at varying operating conditions through transformation equations. These methods have the disadvantages of being influenced by the selection of reference condition. A novel reference condition independent method is proposed based on double-diode model (DDM) for parameter calculation and performance estimation at varying operating conditions for photovoltaic (PV) modules. In the proposed method, a new set of transformation equations is reconstructed according to the dependence of physical parameters in DDM on irradiance and temperature. All coefficients in the proposed transformation equations are independent of the information under reference condition and applicable to all varying operating conditions, which eliminates the influence of reference condition. The coefficients are optimized by fitting measured I–V data at varying operating conditions using guaranteed convergence particle swarm optimization technology, and used for model parameters calculation at varying operating conditions. Then the output property of PV module under any operating condition can be determined independently, first against the reference condition and then against the model parameters under reference condition. The accuracy and applicability of proposed method is validated by massive experimental data for six different types of PV modules under wide range of operating conditions. Moreover, three traditional methods are selected to compare with the proposed method. The proposed method shows better accuracy in I–V, P –V curve and maximum power point estimation under different irradiance and temperature conditions, which can be further used to predict the output power of PV system. [ABSTRACT FROM AUTHOR]

Dq impedance measurement is of great importance as a part of the impedance-based analysis. At present, impedance of objects in single-phase vehicle-grid system are mainly obtained by injecting the voltage or current disturbance of one single frequency into the system circularly. However, as the number of measurement frequency points increases, there will produce much time-consuming of measurement. In this paper, a quick dq impedance measurement algorithm for single-phase vehicle-grid system based on multi-frequency sweep is proposed. First, pseudo-random binary sequences (PRBS) and ternary sequences are modified as the driving signals rather than directly injecting into the control link. Then three types of disturbance with specific frequency characteristics are introduced into the system and their parameters setting principle are given for the first time. According to the different characteristics of measurement objects, the proposed algorithms are also different to ensure the maximum specificity and sensitivity. Finally, for different objects in vehicle-grid system like traction network, single-phase converter and dual-interleaved converter, simulation and experimental results based on the hardware in the loop real-time simulation platform both show the correctness of the proposed algorithm, and the possible causes of errors are also analyzed in detail. Moreover, the proposed algorithm makes it possible to quickly measure the dq impedance of the single-phase vehicle-grid system, and the correctness of the theoretical and measured value can be judged and used for the stability analysis of electrical systems. [ABSTRACT FROM AUTHOR]

The article discusses a study conducted by Kaneko et al. on the role of protein Z (PZ) in urolithiasis, or kidney stone formation. The study aimed to assess the levels of PZ in the plasma and urine of patients with urolithiasis and healthy individuals. The authors found no relationship between PZ and urolithiasis, but suggested a relationship between urinary PZ levels and its circulating levels. The study raises questions about the control group, the choice of anticoagulant, and the statistical analysis used. Further research is needed to determine the role of PZ in urolithiasis and its incorporation into kidney stones. [Extracted from the article]

A facile strategy was introduced for room-temperature controllable synthesis of hierarchically flower-like hollow COFs (FHF-COFs). Furthermore, the universality for synthesis of the HFH-COFs was validated by altering the building units. Inspired by the unique morphology, extremely large surface area and good chemical stability, HFH-COFs could serve as an attractive adsorption probe by loading with gold nanoparticles and be applied to enrichment of brain natriuretic peptide from human serum. This work opens up a whole new approach for controllable synthesis of the HFH-COFs at room temperature and expands the application of COFs as a promising enrichment probe for complex biological samples. [ABSTRACT FROM AUTHOR]

Deleted in liver cancer 1 (DLC1) is a recognized tumor suppressor gene that negatively regulates Rho family proteins by hydrolyzing the active GTP-bound state to its inactive GDP-bound state. Active Rho proteins play a positive role in tumorigenesis. Numerous in vitro and in vivo experiments have shown that DLC1 is downregulated or inactivated in various solid tumors, which may be due to the following five reasons: genomic deletion, epigenetic modification and ubiquitin-dependent proteasomal degradation may cause DLC1 underexpression; phosphorylation at the post-translation level may cause DLC1 inactivation; and failure to localize at focal adhesions (FAs) may prevent DLC1 from exerting full activity. All of the causes could be attributed to molecular binding. Experimental evidence suggests that direct or indirect targeting of DLC1 is feasible for cancer treatment. Therefore, elucidating the interaction of DLC1 with its binding partners might provide novel targeted therapies for cancer. In this review, we summarized the binding partners of DLC1 at both the gene and protein levels and expounded a variety of anticancer drugs targeting DLC1 to provide information about DLC1 as a cancer diagnostic indicator or therapeutic target. [ABSTRACT FROM AUTHOR]

The crowd counting is challenging for deep networks due to several factors. For instance, the networks can not efficiently analyze the perspective information of arbitrary scenes, and they are naturally inefficient to handle the scale variations. In this work, we deliver a simple yet efficient multi-column network, which integrates the perspective analysis method with the counting network. The proposed method explicitly excavates the perspective information and drives the counting network to analyze the scenes. More concretely, we explore the perspective information from the estimated density maps and quantify the perspective space into several separate scenes. We then embed the perspective analysis into the multi-column framework with a recurrent connection. Therefore, the proposed network matches various scales with the different receptive fields efficiently. Secondly, we share the parameters of the branches with various receptive fields. This strategy drives the convolutional kernels to be sensitive to the instances with various scales. Furthermore, to improve the evaluation accuracy of the column with a large receptive field, we propose a transform dilated convolution. The transform dilated convolution breaks the fixed sampling structure of the deep network. Moreover, it needs no extra parameters and training, and the offsets are constrained in a local region, which is designed for the congested scenes. The proposed method achieves state-of-the-art performance on five datasets (ShanghaiTech, UCF CC 50, WorldEXPO’10, UCSD, and TRANCOS). [ABSTRACT FROM AUTHOR]

DFT calculations show that the Ni-catalyzed decarbonylative cross-coupling of cyclic anhydrides with alkyl bromides undergoes a radical pathway with the cyclic anhydride being activated first, in which the Ni center undergoes a valence change of Ni(0)-Ni(II)-Ni(III)-Ni(I)-Ni(II)-Ni(0). The Ni-catalyzed non-decarbonylative cross-coupling of cyclic anhydrides with aryl triflates undergoes a double oxidative addition pathway with the aryl triflate being activated first, in which the Ni center undergoes a valence change of Ni(0)-Ni(II)-Ni(I)-Ni(III)-Ni(I)-Ni(0). The reasons that affect the selective decarbonylation of the coupling products of cyclic anhydrides are explained from the perspective of substrate activation priority and key intermediate geometric structure. [Display omitted] The mechanisms of the nickel-catalyzed decarbonylative cross-coupling of cyclic anhydrides with alkyl bromides, and the nickel-catalyzed non-decarbonylative cross-coupling of cyclic anhydrides with aryl triflates are investigated using density functional theory (DFT) calculations. The results show that the decarbonylative reaction has a radical-involved mechanism with cyclic anhydrides being activated first by the nickel catalyst. The Ni center undergoes a valence change of Ni(0)-Ni(II)-Ni(III)-Ni(I)-Ni(II)-Ni(0). While the non-decarbonylative reaction has a double oxidative addition mechanism with aryl triflates being activated first by the nickel catalyst. The Ni center undergoes a valence change of Ni(0)-Ni(II)-Ni(I)-Ni(III)-Ni(I)-Ni(0). The reasons that affect the selective decarbonylation of the coupling products of cyclic anhydrides are explained from the perspective of substrate activation priority and key intermediate geometric structure. These theoretical insights may shed light on the understanding and development of transition-metal-catalyzed selective decarbonylative C O bond activation reactions. [ABSTRACT FROM AUTHOR]

Fine particles with different sizes of (Al, Ti, Mg) oxide co-doped ZnO ceramics were realized by a suitable grinding and a mechanical ball milling, respectively. The investigations were devoted to understand the origin of the electrical conductivity evolution with the microstructure by analyzing the local order and defect involvement in the crystalline sites of ZnO ceramics. Experimental investigations of particles were conducted to probe the local order and electronic defects with an emphasis on the electrical behavior of ceramics. Particularly, Al doping is intimately correlated with the high conductivity induced from the stabilization of particular Al Zn -Zn i complexes. The conduction electrons are probed through the induced Knight Shift on NMR spectra and also from the particular relaxation mechanisms of paramagnetic centers revealed by EPR studies. The correlation between the electronic active defects, the microstructure in small sized particles of ZnO based ceramics and the electrical behavior are pointed out and discussed. [ABSTRACT FROM AUTHOR]

Considering unobserved spatial-temporal interaction effects, this study proposes a Bayesian spatial-temporal interaction model for predicting economic loss from fishing boat collisions using 10-year (2004–2014) collision records from six different areas in the waters of Fujian, China. Results show strong spatial heterogeneity and correlation effects in fishing boat collisions, while the economic loss from boat collisions gradually decreases with the time trend. Collision time, collision location, visibility and the involvement of LNG/LPG/chemical-carrying ships show similar marginal effects on the economic loss for the two collision types: fishing boat collisions and collisions involving no fishing boats. Navigational status and the involvement of cargo ships exhibit much bigger effects in fishing boat collisions compared with collisions involving no fishing boat. Unlike collisions involving no fishing boat, fishing boat collisions are associated with reduced economic loss in poor weather conditions characterised by strong wind/waves because in Fujian waters additional safety measures are adopted for fishing boats in such conditions. The proposed model is useful for policymakers in adopting safety enhancement strategies to decrease the economic loss resulting from fishing boat collisions. [ABSTRACT FROM AUTHOR]

Lysyl oxidase‐like‐1 (LOXL1), a vital crosslinking enzyme in elastin fiber maintenance, is essential for the stability and strength of elastic vessels and tissues. Variants in the LOXL1 locus associate with a dramatic increase in risk of exfoliation syndrome (XFS), a systemic fibrillopathy, which often presents with ocular hypertension and exfoliation glaucoma (XFG). We examined the role of LOXL1 in conventional outflow function, the prime regulator of intraocular pressure (IOP). Using Loxl1−/−, Loxl1+/−, and Loxl1+/+ mice, we observed an inverse relationship between LOXL1 expression and IOP, which worsened with age. Elevated IOP in Loxl1−/− mice was associated with a larger globe, decreased ocular compliance, increased outflow facility, extracellular matrix (ECM) abnormalities, and dilated intrascleral veins, yet, no dilation of arteries or capillaries. Interestingly, in living Loxl1−/− mouse eyes, Schlemm's canal (SC) was less susceptible to collapse when challenged with acute elevations in IOP, suggesting elevated episcleral venous pressure (EVP). Thus, LOXL1 expression is required for normal IOP control, while ablation results in altered ECM repair/homeostasis and conventional outflow physiology. Dilation of SC and distal veins, but not arteries, is consistent with key structural and functional roles for elastin in low‐pressure vessels subjected to cyclical mechanical stress. [ABSTRACT FROM AUTHOR]

Deleted in liver cancer‐1 (DLC1), a potential tumor suppressor, acts as a GTPase‐activating protein for Rho family members. In many human cancers, the DLC1 expression is frequently downregulated or inactivated, which allows cancer cells to proliferate and disseminate. In this review, we describe the characteristics and other members of the DLC1 family and delineate the signal pathways DLC1 involved in regulating cancer cell growth, colony formation, apoptosis, senescence, autophagy, migration and invasion. In addition, we explore the clinical data of DLC1 and the mechanisms that natural products upregulate the DLC1 expression to inhibit cancer. Despite these insights, many important unanswered questions remain about the exact mechanisms of DLC1‐mediated cancer suppression. [ABSTRACT FROM AUTHOR]

Co‐doped ZnO‐based ceramics using Al, Ti, and Mg ions in different ratios were synthesized with the objective to investigate the doping effects on the crystalline features, microstructure and the electrical behavior. For Al and Ti doping, a coexistence of crystalline phases was shown with a major wurtzite ZnO structure and secondary spinel phases (ZnAl2O4, Zn2TiO4, or ZnaTibAlcOd), while Mg doping did not alter significantly the structural features of the wurtzite ZnO phase. The electrical behavior induced by Al, Ti, and Mg co‐doping in different ratios was investigated using Raman, electron paramagnetic resonance (EPR) and 27Al and 67Zn solid‐state nuclear magnetic resonance (NMR). Al doping induces a high electrical conductivity compared to other doping elements. In particular, shallow donors from Zni‐AlZn defect structures are inferred from the characteristic NMR signal at about 185 ppm; that is, quite far from the usual oxygen coordinated Al. The Knight shift effect emanating from a highly conducting Al‐doped ZnO ceramics was considered as the origin of this observation. Oppositely, as Ti doping leads to the formation of secondary spinel phases, EPR analysis shows a high concentration of Ti3+ ions which limit the electrical conductivity. The correlation between the structural features at the local order, the involved defects and the electrical behavior as function of the doping process are discussed. [ABSTRACT FROM AUTHOR]

With the increasing popularity of Unmanned Aerial Vehicles (UAVs) in computer vision-related applications, intelligent UAV video analysis has recently attracted the attention of an increasing number of researchers. To facilitate research in the UAV field, this paper presents a UAV dataset with 100 videos featuring approximately 2700 vehicles recorded under unconstrained conditions and 840k manually annotated bounding boxes. These UAV videos were recorded in complex real-world scenarios and pose significant new challenges, such as complex scenes, high density, small objects, and large camera motion, to the existing object detection and tracking methods. These challenges have encouraged us to define a benchmark for three fundamental computer vision tasks, namely, object detection, single object tracking (SOT) and multiple object tracking (MOT), on our UAV dataset. Specifically, our UAV benchmark facilitates evaluation and detailed analysis of state-of-the-art detection and tracking methods on the proposed UAV dataset. Furthermore, we propose a novel approach based on the so-called Context-aware Multi-task Siamese Network (CMSN) model that explores new cues in UAV videos by judging the consistency degree between objects and contexts and that can be used for SOT and MOT. The experimental results demonstrate that our model could make tracking results more robust in both SOT and MOT, showing that the current tracking and detection methods have limitations in dealing with the proposed UAV benchmark and that further research is indeed needed. [ABSTRACT FROM AUTHOR]

Structural and electrical behavior of Al 2 O 3 doped ZnO-based ceramics were investigated as function of the aluminum doping ratios under reducing sintering atmosphere (N 2 +CO). With Al 2 O 3 doping from 0.1 mol% to 0.55 mol%, the electrical conductivity increases firstly to a maximum (1.52 × 105 S·m−1) at 0.25 mol%, and then decreases gradually. The increased conductivity is explained by the formation of shallow donors as Al Zn -Zn i complexes with doping to 0.25 mol%. As Al 2 O 3 doping further increasing to 0.55 mol%, ZnAl 2 O 4 spinel phase and more ZnO-ZnO grain boundaries are formed, hindering charge carriers transport, to decrease charge carrier mobility, thus to decrease the conductivity of ZnO ceramics. Therefore, the Al Zn -Zn i complexes, grain boundaries and ZnAl 2 O 4 spinel can be adjusted by doping different Al 2 O 3 amount, thus the carriers' concentration and their mobility are optimized to increase the conductivity. Our work, as a fundamental research, is of great significance to control conductivity by regulating Al 2 O 3 doping. [ABSTRACT FROM AUTHOR]

Transparent ceramics of La-doped Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT) x/75/25 are prepared, and the ferroelectric domains, dielectric, and ferroelectric properties of x/75/25 are investigated. A double hysteresis loop and small fingerprint domains with uniform distribution observed for 3/75/25 indicate an intermediate ferroelectric state. The dielectric behavior under dc bias and the evolution of the hysteresis loop with increasing electric field for 3/75/25 indicate an electric field-induced phase transition, and it is proposed that the unstable intermediate ferroelectric state which would experience obvious field-induced phase transition is the origin of the giant electro-optic Kerr effect observed in La-doped PMN-PT 3/75/25. [ABSTRACT FROM AUTHOR]

Recently, a series of prolific fracture-vug reservoirs have been discovered in the lower Ordovician dolostone successions of the northern Tarim Basin. However, the genesis of these reservoirs remains unclear. In this study, observations on drilling cores and thin sections identify the pore space characterized by dissolved fractures, fissures, and vugs. Petrology, cathodoluminescence, and homogenization temperatures of fluid inclusions aid in establishing the diagenetic paragenetic sequence. Dissolving enlargement occurred after chemical compaction of overlying limestone and before the Permian volcanic activity. Breccia pores containing unique fillings of terrestrial materials (quartz sand and allogenic kaolinite) and calcite cements with negative δ18OPDB values (−18.4‰) along with 87Sr/86Sr ratios (up to 0.71026) indicate that the dissolving fluid originated from meteoric freshwater at the surface. The δ18OSMOW values of the calcite precipitating fluid (−2.1‰ to −8.7‰) further suggest freshwater as the source of the dissolving fluid, buffered by the Ordovician wall rocks or formation water. As the distance from the unconformity surface increases, both the homogenization temperature and δ18OPDB values of the breccia pore-filling calcite in the southern study area gradually elevate and deplete, respectively, indicating a rise in temperature during the infiltration of meteoric freshwater with increasing subsurface temperatures. The abnormal reflection bodies identified as reservoirs in seismic profiles along deep-seated strike-slip faults delineate these faults as the channel for the infiltration of meteoric freshwater. The penetrating strata of these faults and the high 87Sr/86Sr values of breccia pore-filling calcite suggest that karstification occurred during the Devonian period. Accordingly, we establish a deep karst model in which the Devonian meteoric freshwater penetrated along the strike-slip faults and dissolved the Ordovician dolostones, resulting in the development of deep buried karstic fault reservoirs in the southern region of the northern Tarim Basin. [ABSTRACT FROM AUTHOR]

Novel ZnO–Cr 2 O 3 -based varistor ceramics have superior current-voltage (I – V) characteristics and, do not contain volatile, rare or toxic dopants. The results showed that doping with Co 3 O 4 greatly affected the nonlinearity of the ZnO–Cr 2 O 3 -based ceramics, with the highest coefficient of nonlinearity α equal to 73, while the leakage current was lower than 0.2 μA/cm2. Such a varistor performance enhancement was explained by the substitution of Co2+ into the Zn sites leading to the formation of Zn interstitials and free charge carriers of ZnO grains as well as an increased concentration of electron-trapping interface states like adsorbed oxygen and consequently substantially increased electrostatic barriers at the grain boundaries. The optimal amount of added Co 3 O 4 for high-performance ZnO–Cr 2 O 3 -based varistor ceramics was about 0.5 mol.%. [ABSTRACT FROM AUTHOR]

Abstract In this work, photoactivated oxidase mimetics was prepared by copolymerizing dicyandiamide with barbituric acid (BA) and characterized by X-ray diffraction pattern, Fourier transformed infrared spectrum, X-ray photoelectron spectroscopy, transmission electron microscopy, photoluminescence spectrum, diffuse reflectance spectrum. Experimental results and density functional theory calculation indicated that the substitution of nitrogen atoms by carbon atoms in tri- s -triazine structure due to the copolymerization of BA enhanced visible light absorption and weakened the barrier of photocarrier transfer. In the presence of visible light and oxygen, 3, 3′, 5, 5′-tetramethylbenzidine was oxidized under the catalysis of photoactivated oxidase mimetics to produce a green colored product, which could be reduced by glutathione (GSH). Therefore, a facile method based on the photoactivated oxidase mimetic has been developed for colorimetric detection of GSH. The linear range for GSH was ranged from 2.0 to 50.0 μmol L−1 (R2 = 0.998) with the detection limit of 1.4 μmol L−1. The proposed method was applied to detect the cellular GSH with satisfactory results. Graphical abstract Unlabelled Image Highlights • Metal-free carbon nitride polymer as photoactivated oxidase mimetics was first reported. • Oxidase mimetics catalyzed the oxidation of TMB by oxygen under visible light. • H 2 O 2 was not involved for colorimetric detection of GSH. • The detection of cellular GSH was agreed well with the commercial assay kit. [ABSTRACT FROM AUTHOR]

The investigation of contributory factors in ship traffic accidents is a high-priority issue in maritime safety analysis. In this study, a method based on association rules is proposed to analyze these contributory factors. Using ten years of ship traffic accidents that occurred in Fujian water areas, a total of 66 and 228 association rules is generated for nonserious and serious accidents, respectively. Univariate analysis results show that both severity levels of accidents are probably associated with small ships, good visibility, no strong wind/wave, and the season of spring. The frequency of nonserious accidents is about 33% higher than that of serious accidents in coastal/harbor/port areas. However, the association analysis results show that serious accidents are associated with more influencing factors than are nonserious accidents. Lookout failure is an additional decisive factor in serious accidents but not in nonserious accidents. The association analysis results also reveal that many influencing factors (e.g., navigational status) exhibit effects only under some specific circumstances. [ABSTRACT FROM AUTHOR]

This study develops an ordered probit model to evaluate the factors influencing two-ship collision severity using ten years’ ship collision accident data from Fujian sea areas. The model results show that the involvement of big ships has the largest impact in increasing the probability of a serious or very serious accident, followed by the involvement of fishing vessels. There will be a bigger probability of a serious accident if both ships involved in the collision are cargo ships. We found that the season of spring, poor visibility and night time periods are more likely to be factors in high severity levels of ship collision. The results also reveal that lookout failure plays a decisive role in increasing serious accident risk compared with other types of human errors. The results of this study may be beneficial for policy-makers in proposing efficient strategies to reduce the likelihood of serious ship collisions. [ABSTRACT FROM AUTHOR]

Two-dimensional Cr-group MXene (denoted M 2 CT 2 , where M represents a Cr group transition metal, and T means none or O/F functionalization) materials sometimes have intrinsic magnetism and metallicity. Using first-principles calculations, the properties of Cr-group MXenes are explored using atom-scale analyses. It is concluded that Cr 2 CT 2 compounds are more stable when spin polarized depending on the case, while Mo 2 CT 2 and W 2 CT 2 are always most stable without magnetism. Besides structural stability, our simulations also predict charge transfer, metallicities, battery performances (capacity, charge-discharge velocity), and thermoelectric properties of Cr-group MXenes. Surprisingly, Cr-group MXenes have superior natures to other popular energy storage materials. Meanwhile, Cr-based MXene (Cr 2 CT 2 ) has unique advantages such as excellent performance with lightest weight. Our results provide insight into MXene cognate research. [ABSTRACT FROM AUTHOR]

Visible and near-infrared spectroscopy (Vis–NIR, 350–1100 nm) has great potential for predicting soil properties. However, current research on the hyperspectral prediction of soil parameters in agricultural areas of alpine regions and the types of parameters included is limited, and optimal spectral treatments and predictive models applicable to different parameters have not been sufficiently investigated. Therefore, we evaluated the accuracy of predicting total nitrogen (TN), phosphorus pentoxide (TP2O5), total potassium oxide (TK2O), alkali-hydrolyzable nitrogen (AHN), effective phosphorus (AP), effective potassium (AK), soil organic matter (SOM), and pH in the Qinghai–Tibet Plateau using the Vis–NIR technique in combination with spectral transformations, correlation analysis, feature selection, and machine learning. The results show that spectral transformations improve the correlation between spectra and parameters but are dependent on the parameter type and the method used. Continuum removal (CR), logarithmic first-order differential (FDL), and inverse first-order differential (FDR) had the most significant effects. The feature bands were extracted using the SPA and modeled using partial least squares (PLSR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and backpropagation neural networks (BPNNs). The accuracy was evaluated based on R2, RMSE, RPD, and RPIQ. We found that the PLSR model only enables the prediction of SOM and pH with lower accuracy than the remaining models. XGBoost can predict all of the parameters but only for AHN; the prediction performance is better than other methods (R2 = 0.776, RMSE = 0.043 g/kg, and RPIQ = 2.88). The RF, SVM, and BPNN models cannot predict AK, AP, and AHN, respectively. In addition, TP2O5, AP, and pH are best suited for modeling using RF (RPIQ = 2.776, 3.011, and 3.198); TN, AK, and SOM are best suited for modeling using BPNN (RPIQ = 2.851, 2.394, and 3.085); and AHN and TK2O are best suited for XGBoost and SVM, respectively (RPIQ = 2.880 and 3.217). Therefore, this study can provide technical and data support for the accurate and efficient acquisition of soil parameters in alpine agriculture. [ABSTRACT FROM AUTHOR]

Different kinds of features describe different aspects of image data, and each feature can be treated as a view when we take it as a particular understanding of images. Leveraging multiple views provides a richer and comprehensive description than using only a single view. However, multiview data are often represented by high-dimensional heterogeneous features, so it is meaningful to find a low-dimensional consensus representation from multiple views. In this paper, we propose an unsupervised multiview dimensionality reduction method for images based on bilevel latent space learning. As different views have different physical meanings and statistical properties, they are not directly comparable. Therefore, we learn the comparable representation for each view in the first level. The shared and the private nature of multiview data are exploited to accurately preserve the information of each view. Then, we fuse different views into a low-dimensional representation by conducting joint matrix factorization in the second level. To guarantee the low-dimensional representation to be compact and discriminative, the intrinsic geometric structure of data is utilized. Besides, our method considers resisting the outliers and noise contained in multiview data, which may influence the learned representation and deteriorate its semantic consistency. We design appropriate optimization objectives to learn the latent spaces in different levels. Compared with the existing methods, our method could provide a more flexible multiview learning strategy that not only accurately captures the information of each view but also is robust to outliers and noise, which can obtain a more discriminative and compact low-dimensional representation. Experiments on two real-world image data sets demonstrate the advantages of our method over the existing multiview dimensionality reduction methods. [ABSTRACT FROM AUTHOR]

In multi-label learning, each example is represented by a single instance and associated with multiple class labels. Existing multi-label learning algorithms mainly exploit label correlations globally, by assuming that the label correlations are shared by all the examples. Moreover, these multi-label learning algorithms exploit the positive label correlations among different class labels. In practical applications, however, different examples may share different label correlations, and the labels are not only positive correlated, but also mutually exclusive with each other. In this paper, we propose a simple and effective Bayesian model for multi-label classification by exploiting Local positive and negative Pairwise Label Correlations, named LPLC. In the training stage, the positive and negative label correlations of each ground truth label for all the training examples are discovered. In the test stage, the k nearest neighbors and their corresponding positive and negative pairwise label correlations for each test example are first identified, then we make prediction through maximizing the posterior probability, which is estimated on the label distribution, the local positive and negative pairwise label correlations embodied in the k nearest neighbors. A comparative study with the state-of-the-art approaches manifests a competitive performance of our proposed method. [ABSTRACT FROM AUTHOR]

This study investigates the drivers’ merging behavior in work zone merging areas during the entire merging implementation period from the time of starting a merging maneuver to that of completing the maneuver. With the actual work zone merging traffic data, we propose a time-dependent logistic regression model considering the possible time-varying effects of influencing factors, and a standard logistic regression model for the purpose of model comparison. Model comparison results show that the time-dependent model performs better than the standard model because the former can provide higher prediction accuracy. The time-dependent model results show that seven factors exhibit time-varying effects on the drivers’ merging behavior, including merging vehicle speed, through lane lead vehicle speed and through lane lag vehicle speed, longitudinal gap between the merging and lead vehicles, longitudinal gap between the merging and through lane lead vehicles, types of through lane lead and through lane lag vehicles. Interestingly, both the through lane lead vehicle speed and the through lane lag vehicle speed are found to exhibit heterogeneous effects at different times of the merging implementation period. One important finding from this study is that the merging vehicle has a decreasing willingness to take the choice of “complete a merging maneuver” as the elapsed time increases if the through lane lead vehicle is a heavy vehicle. [ABSTRACT FROM AUTHOR]

• Synthesis of a robust and highly crystalline anionic COF membrane. • The TpPa-SO 3 Na membrane is suitable for filtration of various organic solvents. • The TpPa-SO 3 Na membrane exhibits ultrahigh water permeability. • The TpPa-SO 3 Na membrane shows excellent rejection rate of positively charged dyes. • The TpPa-SO 3 Na membrane can selectively separate different dye molecules. Covalent organic frameworks (COFs) hold great promise in constructing a new-generation of molecular separation membranes. Significantly, ionic COF membranes endow novel functions distinct from those of neutral membranes. However, ionic COF membranes generally suffer from extremely slow reaction rates and low yields, limiting their widespread application. In this work, a self-standing anionic COF membrane was prepared by a dual-activation interfacial polymerization strategy. And the obtained anionic COF membrane possessed a unique ordered pore structure, superhydrophilicity, as well as good chemical stability. Meanwhile, the as-prepared membrane provided the high-efficiency water transport pathways with effective rejection for dye molecules. As expected, the anionic COF membrane exhibited an excellent water permeability of about 270 L m−2 h−1 bar−1, which was much higher than those of commercial nanofiltration membranes, and also approximately 2–15 times significantly higher than those reported for polymer- or neutral COF-based nanofiltration membranes with similar rejection. Owing to the electrostatic attraction between positively charged dyes and anionic COF channels, the rejection rates of cationic dyes were above 96 %. In addition, the size-exclusion effect dominated rejection efficiency of dyes with molecular sizes larger than COF channels. These findings provided a new candidate membrane for efficient dye wastewater treatment. [ABSTRACT FROM AUTHOR]

Stresses are generated in plates under initial loads, which couple with the subsequent transverse deformation to affect the buckling and transverse vibration characteristics of plates. There exists no exact solution method for the stress field of a rectangular plate under arbitrary in-plane loads. In this paper, the stress field of a rectangular plate under arbitrary in-plane loads is solved based on the principle of minimum potential energy. The stress function is decomposed into homogeneous and special solutions. The homogeneous solution is represented by the Chebyshev polynomial while the special solution is expanded by the Fourier series. Then, the Ritz method is used to analyze the transverse vibration and buckling characteristics of the rectangular plate. The product of the boundary function and Chebyshev polynomial is used to build the vibration mode function. The present results are verified by comparing to existing studies and those obtained from finite element method (FEM). The effects of the magnitude and distribution of in-plane boundary stresses and boundary conditions on the dynamic characteristics and stability of the rectangular plate are analyzed. The method used in the paper has demonstrated improved convergence and accuracy with good universality. [ABSTRACT FROM AUTHOR]

Background: Recent findings show that cell-free miRNAs are stable in biological fluid. Urine provides an alternative to blood serum or plasma as a potential source of tumor biomarkers. MiR-210 is proven to be overexpressed in patients with a clear cell renal cell carcinoma (ccRCC). The purpose of this pilot study was to examine the urinary cell-free miR-210 as a potential tool of liquid biopsy for ccRCC.Methods: Overall, 75 patients with a ccRCC and 45 control subjects without a cancer were included in this study. Urine samples were centrifuged twice and the cell-free urine supernanants were stored in -80°C until use. A total of 350µl cell-free urine was used for the extraction of total RNA. The expression levels of these miRNAs were performed by using quantitative RT-PCR.Results: The level of urinary cell-free miR-210 was significantly higher in patients with ccRCC than in control subjects (P<0.001). The urinary cell-free miR-210 yielded the areas under the receiver operating characteristics curve of 0.76 in discriminating the patients with ccRCC from the control subjects with a sensitivity of 57.8% and a specificity of 80.0%. Moreover, the expression level of urinary cell-free miRNA-210 was significantly decreased in the patients a week after surgery (P<0.001).Conclusion: Urinary cell-free miR-210 may be used as a potential tool of liquid biopsy for ccRCC diagnosis. Further studies are needed to confirm our results. [ABSTRACT FROM AUTHOR]

Purpose:The purpose of our study was to test the utility of CA9 expression in preoperative biopsy samples to identify the ccRCC. Materials and methods:A total of 55 patients with a small solid renal mass (≤4 cm) entered into this study. The immunohistochemical staining (51 samples) and RT-PCR (33 samples) were performed to detect CA9 expression. Results:For immunohistochemistry detection, CA9 was positive in 31/34 of biopsy samples of ccRCCs. CA9 was also positive in five suspected diagnosis of ccRCC. For RT-PCR detection, CA9 was positive in 25/25 biopsy samples of ccRCCs. The diagnostic accuracy of CA9 expression for ccRCC was 100%. RT-PCR was performed in four biopsies where immunohistochemistry could not be performed because of limited tissue materials or necrosis. Two ccRCCs with a negative staining by immunohistochemistry were CA9 positive by RT-PCR. Conclusions:CA9 may be potentially useful biomarker in help making a diagnosis of ccRCC in the biopsy of renal mass. RT-PCR might be a preferred method to immunohistochemistry for the detection of CA9 in renal biopsy samples. [ABSTRACT FROM AUTHOR]

The goal of the study was to examine secreted protein response and withdrawal profiles from cultured human trabecular meshwork (HTM) cells following short- and long-term glucocorticoid treatment. Primary cultures of five human HTM cell strains isolated from 5 different individual donor eyes were tested. Confluent HTM cells were differentiated in culture media containing 1% FBS for at least one week, and then treated with Dexamethasone (Dex, 100 nM) 3 times/week for 1 or 4 weeks. Cell culture supernatants were collected 3 times per week for 8 weeks. Secretion profiles of myocilin (MYOC), matrix metalloproteinase-2 (MMP2) and fibronectin (FN) were determined by Western blot analysis and MMP2 activity by zymography. Dex treatment reduced MMP2 expression and activity, returning to normal levels shortly after Dex withdrawal in 5 HTM cell strains. All five cell strains significantly upregulated MYOC in response to Dex treatment by an average of 17-fold, but recovery to basal levels after Dex withdrawal took vastly different periods of time depending on cell strain and treatment duration. Dex treatment significantly increased FN secretion in all strains but one, which decreased FN secretion in the presence of Dex. Interestingly, secretion of FN and MYOC negatively correlated during a 4 week recovery period following 4 weeks of Dex treatment. Taken together, the time course and magnitude of response and recovery for three different secreted, extracellular matrix-associated proteins varied greatly between HTM cell strains, which may underlie susceptibility to glucocorticoid-induced ocular hypertension. [ABSTRACT FROM AUTHOR]

Binary Relevance is a well-known framework for multi-label classification, which considers each class label as a binary classification problem. Many existing multi-label algorithms are constructed within this framework, and utilize identical data representation in the discrimination of all the class labels. In multi-label classification, however, each class label might be determined by some specific characteristics of its own. In this paper, we seek to learn label-specific data representation for each class label, which is composed of label-specific features. Our proposed method LLSF can not only be utilized for multi-label classification directly, but also be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Inspired by the research works on modeling high-order label correlations, we further extend LLSF to learn class-Dependent Labels in a sparse stacking way, denoted as LLSF-DL. It incorporates both second-order and high-order label correlations. A comparative study with the state-of-the-art approaches manifests the effectiveness and efficiency of our proposed methods. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel appearance variation prediction model which can be embedded into the existing generative appearance model based tracking framework. Different from the existing works, which online learn appearance model with obtained tracking results, we propose to predict appearance reconstruction error. We notice that although the learned appearance model can precisely describe the target in the previous frames, the tracking result is still not accurate if in the following frame, the patch that is most similar to appearance model is assumed to be the target. We first investigate the above phenomenon by conducting experiments on two public sequences and discover that in most cases the best target is not the one with minimal reconstruction error. Then we design three kinds of features which can encode motion, appearance, appearance reconstruction error information of target's surrounding image patches, and capture potential factors that may cause variations of target's appearance as well as its reconstruction error. Finally, with these features, we learn an effective random forest for predicting reconstruction error of the target during tracking. Experiments on various datasets demonstrate that the proposed method can be combined with many existing trackers and improve their performances significantly. [ABSTRACT FROM AUTHOR]

Visual impairment due to glaucoma currently impacts 70 million people worldwide. While disease progression can be slowed or stopped with effective lowering of intraocular pressure, current medical treatments are often inadequate. Fortunately, three new classes of therapeutics that target the diseased conventional outflow tissue responsible for ocular hypertension are in the final stages of human testing. The rho kinase inhibitors have proven particularly efficacious and additive to current therapies. Unfortunately, non-contact technology that monitors the health of outflow tissue and its response to conventional outflow therapy is not available clinically. Using optical coherence tomographic (OCT) imaging and novel segmentation software, we present the first demonstration of drug effects on conventional outflow tissues in living eyes. Topical netarsudil (formerly AR-13324), a rho kinase/ norepinephrine transporter inhibitor, affected both proximal (trabecular meshwork and Schlemm’s Canal) and distal portions (intrascleral vessels) of the mouse conventional outflow tract. Hence, increased perfusion of outflow tissues was reliably resolved by OCT as widening of the trabecular meshwork and significant increases in cross-sectional area of Schlemm’s canal following netarsudil treatment. These changes occurred in conjunction with increased outflow facility, increased speckle variance intensity of outflow vessels, increased tracer deposition in conventional outflow tissues and decreased intraocular pressure. This is the first report using live imaging to show real-time drug effects on conventional outflow tissues and specifically the mechanism of action of netarsudil in mouse eyes. Advancements here pave the way for development of a clinic-friendly OCT platform for monitoring glaucoma therapy. [ABSTRACT FROM AUTHOR]

Three positive carbon isotope excursions are reported from Middle-Upper Ordovician in Tahe oil-gas field, northern Tarim Basin. Based on conodont biostratigraphy, the Middle Darriwilian Isotope Carbon Excursion (MDICE) and the Guttenberg Carbon Isotope Excursion (GICE) are identified from Darriwilian to Early Katian by the aid of whole-rock carbon isotope data from two well cores. Positive excursion within conodont Pygodus anserinus zone is developed in Early Sandbian, and the fluctuation range is no less than MDICE. Because the range of this excursion in the generalized global carbon isotope curve is short, previous studies paid little attention to it, and named Early Sandbian Isotope Carbon Excursion (ESICE) in this paper. Furthermore, these positive excursions are not directly related to sea level fluctuations and the MDICE and GICE identified in northern Tarim can be globally correlated to that in southern China, North America, South America, and Europe. The Saergan Fm. source rocks of Middle-Upper Ordovician in Kalpin Dawangou outcrop are in accord with the geologic time of MDICE and ESICE, and GICE have strong ties to the source rock of Lianglitag Fm. in basin. Abundant organic carbon burial is an important factor in genesis of positive isotope carbon excursions. Positive oxygen isotope excursion, conodont fauna turnover, decreased conodont total diversity, and the change of sedimentary facies indicated that dramatic changes of paleoceanographic environment of Early-Middle Ordovician in Tarim Basin started from the end of Darriwillian, and an obvious icehouse climate of Late Ordovician occurred in ESICE. [ABSTRACT FROM AUTHOR]

With the pervasiveness of online social media and rapid growth of web data, a large amount of multi-media data is available online. However, how to organize them for facilitating users' experience and government supervision remains a problem yet to be seriously investigated. Topic detection and tracking, which has been a hot research topic for decades, could cluster web videos into different topics according to their semantic content. However, how to online discover topic and track them from web videos and images has not been fully discussed. In this paper, we formulate topic detection and tracking as an online tracking, detection and learning problem. First, by learning from historical data including labeled data and plenty of unlabeled data using semi-supervised multi-class multi-feature method, we obtain a topic tracker which could also discover novel topics from the new stream data. Second, when new data arrives, an online updating method is developed to make topic tracker adaptable to the evolution of the stream data. We conduct experiments on public dataset to evaluate the performance of the proposed method and the results demonstrate its effectiveness for topic detection and tracking. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new online learning method for measuring affinity between tracklets in multi-target tracking. As targets and background usually keep changing in the video, fixed affinity measurement could not adapt to their variations. Most existing affinity learning methods construct labeled samples based on the obtained tracklets, and then minimize a predefined loss function to get an optimal affinity measurement. However, those methods simply assume that the training error equals to testing error which is not true in many of real time tracking scenarios. Differently, we propose to learn affinity measurement through CovBoosting, which considers the evolution of the tracklets and could obtain affinity measurement with more discriminative ability. To deal with targets׳ disappearance and new targets׳ appearance, we combine tracklet affinity with contextual information to do an optimal inference. Moreover, an online updating algorithm is developed to guarantee that the learned tracklet affinity is always optimal for tracking targets in current sliding window. Experimental results on benchmark datasets demonstrate that tracklet affinity learned with our method is more discriminative and could greatly improve the performance of the multi-target tracker. [ABSTRACT FROM AUTHOR]

Introduction: A disintegrin and metalloproteinase-17 (ADAM17) plays an important role in biological activity in different cancers. Its expression and prognostic value have not been studied in clear cell renal cell carcinoma (cRCC). The objective of this study was to explore the prognostic value of ADAM17 in patients with cRCC. Materials and Methods: A total of 131 patients with cRCC were studied. There were 90 men and 41 women, with an average age of 67 years (range: 34-93 y). There were 110 patients with localized disease and 21 patients with metastatic disease. The expression of ADAM17 was evaluated by immunohistochemistry with a monoclonal antibody. The follow-up varied from 4.2 to 184 months, with a median of 72 months for patients with localized disease. Kaplan-Meier with a log-rank test was performed to compare the progression-free survival after surgery. The univariate and multivariate analyses were performed to examine the significance of ADAM17 expression for the patient's progression-free survival. Results: The ADAM17 expression was found in 109/131 tumors. The ADAM17 expression was found in 20/21 metastatic tumors and in 89/110 localized tumors. Regarding patients with localized tumors, 31 patients experienced a recurrence or death during follow-up. The Kaplan-Meier analysis revealed that the high expression of ADAM17 was associated with a reduced progression-free survival (P = 0.005). The univariate logistic regression analysis indicated that the high expression of ADAM17 was associated with the disease progression (hazard ratio = 2.826; 95% CI: 1.324-6.034; P = 0.007). The high expression of ADAM17 remained a significant factor for decreased progression-free survival in multivariate analysis. Conclusion: ADAM17 was frequently expressed in cRCC. The high expression of ADAM17 was correlated with a worse outcome for patients with cRCC. ADAM17 is a new biomarker for the management of patients with cRCC. [ABSTRACT FROM AUTHOR]

Introduction A disintegrin and metalloproteinase-17 (ADAM17) plays an important role in biological activity in different cancers. Its expression and prognostic value have not been studied in clear cell renal cell carcinoma (cRCC). The objective of this study was to explore the prognostic value of ADAM17 in patients with cRCC. Materials and methods A total of 131 patients with cRCC were studied. There were 90 men and 41 women, with an average age of 67 years (range: 34–93 y). There were 110 patients with localized disease and 21 patients with metastatic disease. The expression of ADAM17 was evaluated by immunohistochemistry with a monoclonal antibody. The follow-up varied from 4.2 to 184 months, with a median of 72 months for patients with localized disease. Kaplan-Meier with a log-rank test was performed to compare the progression-free survival after surgery. The univariate and multivariate analyses were performed to examine the significance of ADAM17 expression for the patient’s progression-free survival. Results The ADAM17 expression was found in 109/131 tumors. The ADAM17 expression was found in 20/21 metastatic tumors and in 89/110 localized tumors. Regarding patients with localized tumors, 31 patients experienced a recurrence or death during follow-up. The Kaplan-Meier analysis revealed that the high expression of ADAM17 was associated with a reduced progression-free survival ( P = 0.005). The univariate logistic regression analysis indicated that the high expression of ADAM17 was associated with the disease progression (hazard ratio = 2.826; 95% CI: 1.324–6.034; P = 0.007). The high expression of ADAM17 remained a significant factor for decreased progression-free survival in multivariate analysis. Conclusion ADAM17 was frequently expressed in cRCC. The high expression of ADAM17 was correlated with a worse outcome for patients with cRCC. ADAM17 is a new biomarker for the management of patients with cRCC. [ABSTRACT FROM AUTHOR]

This study attempts to incorporate multi-scenario underreporting rates into multivariate imputation using chain equation (MICE) to address underreporting issues in maritime accident records. In addition, the Tobit regression model is utilized to evaluate the economic loss of maritime accidents for both the original and supplemented data. With ten years' maritime accident data in Fujian waters, one empirical study is created to testify the effectiveness of the proposed method. The marginal effect results from the proposed method significantly differ from the conventional Tobit regression model. The proposed method could significantly correct the over- or underestimated impacts related to various variables (e.g., serious/non-serious accident types, fishing vessels, other ships, and liquid cargo ships). This reveals that the proposed method is capable of correcting the systematic deviation of the analysis results to a satisfactory extent. • This study presents a reliable solution to deal with underreporting issues in maritime accident records. • The underreporting issues are transformed into missing data problem through multi-scenario underreporting rates. • The underreporting issues may cause biased maritime accident analysis results. • The systematic deviations of the model results are effectively corrected by the proposed MSUR-MICE method. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop an automatic deep learning-based approach and corresponding free, open-source software to perform segmentation of the Schlemm's canal (SC) lumen in optical coherence tomography (OCT) scans of living mouse eyes. A novel convolutional neural network (CNN) for semantic segmentation grounded in a U-Net architecture was developed by incorporating a late fusion scheme, multi-scale input image pyramid, dilated residual convolution blocks, and attention-gating. 163 pairs of intensity and speckle variance (SV) OCT B-scans acquired from 32 living mouse eyes were used for training, validation, and testing of this CNN model for segmentation of the SC lumen. The proposed model achieved a mean Dice Similarity Coefficient (DSC) of 0.694 ± 0.256 and median DSC of 0.791, while manual segmentation performed by a second expert grader achieved a mean and median DSC of 0.713 ± 0.209 and 0.763, respectively. This work presents the first automatic method for segmentation of the SC lumen in OCT images of living mouse eyes. The performance of the proposed model is comparable to the performance of a second human grader. Open-source automatic software for segmentation of the SC lumen is expected to accelerate experiments for studying treatment efficacy of new drugs affecting intraocular pressure and related diseases such as glaucoma, which present as changes in the SC area. [ABSTRACT FROM AUTHOR]

Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role. We analyze the reasons for the performance differences between various sampling strategies under the same framework and loss function. We suggest that deteriorated over-fitting is an important factor causing poor performance, and enhancing statistical stability can rectify this problem. Inspired by that, a simple yet effective approach is proposed, termed group sampling, which gathers samples from the same class into groups. The model is thereby trained using normalized group samples, which helps alleviate the negative impact of individual samples. Group sampling updates the pipeline of pseudo-label generation by guaranteeing that samples are more efficiently classified into the correct classes. It regulates the representation learning process, enhancing statistical stability for feature representation in a progressive fashion. Extensive experiments on Market-1501, DukeMTMC-reID and MSMT17 show that group sampling achieves performance comparable to state-of-the-art methods and outperforms the current techniques under purely camera-agnostic settings. Code has been available at https://github.com/ucas-vg/GroupSampling. [ABSTRACT FROM AUTHOR]

Most existing feature selection methods for object tracking assume that the samples in the previous frames are governed by the same distribution of the labeled samples obtained in the current frame and unlabeled samples collected in the next frame. However, according to our statistical analysis on very common videos, this assumption is not true in many scenarios. As a result, the selected features are not suitable for discriminating between the target from the background in the next frame. A tracking error accumulates and finally the drift problem happens. In this paper, we consider data distribution in tracking from a new perspective to adapt to target's and background's changes. We classify the samples into three categories: auxiliary samples (samples in the previous frames), target samples (samples collected in the current frame), and unlabeled samples (samples obtained in the next frame). To make the best use of them for tracking, we propose a novel semisupervised transfer learning approach that treats samples differently. Specifically, we assume that only target samples follow the same distribution as the unlabeled samples that we want to classify. Then, a novel and interesting semisupervised CovBoost method is developed utilizing the information provided by the three kinds of samples effectively when training the best strong classifier for tracking. Furthermore, we develop a new online updating algorithm for semisupervised CovBoost, making our tracker handle with significant variations of the tracked target and background successfully. Our experimental results demonstrate the advantages of treating samples differently during tracking. Our tracker outperforms state-of-the-art trackers on the benchmark datasets. [ABSTRACT FROM PUBLISHER]