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INTRODUCTION: Small cell carcinoma of the bladder (SCCB) is a rare variant of bladder cancer with poor outcomes. We evaluated long-term outcomes of nonmetastatic (M0) and metastatic (M1) SCCB and correlated pathologic response with genomic alterations of patients treated with neoadjuvant chemotherapy (NAC). PATIENTS AND METHODS: Clinical history and pathology samples from SCCB patients diagnosed at our institution were reviewed. RESULTS: One hundred and ninety-nine SCCB patients were identified. (M0: 147 [74%]; M1: 52 [26%]). Among M0 patients, 108 underwent radical cystectomy (RC) (NAC: 71; RC only: 23; adjuvant chemotherapy: 14); 14 received chemoradiotherapy; the rest received chemotherapy alone or no cancer-directed therapy. RC-only patients had a median follow-up of 9.1 years, and median disease-free survival (DFS) and overall survival (OS) were 1.1 and 1.2 years, respectively. NAC patients had pathologic response (

Supplementary Figure 2: Enrichment of biallelic RB1 in SCCBs.

Purpose: Small-cell carcinoma of the bladder (SCCB) is a rare and aggressive neuroendocrine tumor with a dismal prognosis and limited treatment options. As SCCB is histologically indistinguishable from small-cell lung cancer, a shared pathogenesis and cell of origin has been proposed. The aim of this study is to determine whether SCCBs arise from a preexisting urothelial carcinoma or share a molecular pathogenesis in common with small-cell lung cancer.Experimental Design: We performed an integrative analysis of 61 SCCB tumors to identify histology- and organ-specific similarities and differences.Results: SCCB has a high somatic mutational burden driven predominantly by an APOBEC-mediated mutational process. TP53, RB1, and TERT promoter mutations were present in nearly all samples. Although these events appeared to arise early in all affected tumors and likely reflect an evolutionary branch point that may have driven small-cell lineage differentiation, they were unlikely the founding transforming event, as they were often preceded by diverse and less common driver mutations, many of which are common in bladder urothelial cancers, but not small-cell lung tumors. Most patient tumors (72%) also underwent genome doubling (GD). Although arising at different chronologic points in the evolution of the disease, GD was often preceded by biallelic mutations in TP53 with retention of two intact copies.Conclusions: Our findings indicate that small-cell cancers of the bladder and lung have a convergent but distinct pathogenesis, with SCCBs arising from a cell of origin shared with urothelial bladder cancer. Clin Cancer Res; 24(8); 1965–73. ©2017 AACR.See related commentary by Oser and Jänne, p. 1775

Supplementary Table 1: Patients and sequencing platforms utilized. Supplementary Table 2: Clinical annotation of the cohort. Supplementary Table 3: MSK-IMPACT gene content Supplementary Table 4: Gene-level DNA copy number Supplementary Table 5: Cardinal and key lesions in SCCBs Supplementary Table 6: Somatic mutations. Supplementary Table 7: Purity, ploidy, and genome doubling.

To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a genetic-algorithm-improved neural network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover the missing data and perform denoising. In particular, these data were filled using cubic spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed by modifying the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%.

In order to meet the current pavement intelligent detection and feature parameter extraction requirements, a 3D pavement surface data denoising method based on breakpoint interpolation and reference plane filtering (BI-RPF) is proposed and demonstrated using 3D pavement data acquired by a laser-based 3D imaging system. First, the principal structure and major characteristics of 3D pavement data were analyzed. Next, the breakpoint interpolation method considering the relationship between ordinate and slope was proposed to complete the missing values from the original pavement data. Then, reference plane filtering based on the horizontal and longitudinal reference plane was realized so as to overcome the unevenness of the pavement. Finally, the filtering effectiveness of the proposed method was compared with that of the standard deviation filtering and median filtering methods. The results show that the proposed filtering method takes breakpoints and uneven road surface into account, which can provide more accurate 3D road surface data under more complex conditions. The algorithm provided a good denoising effect while retaining valid data describing the road surface morphology, which establishes a foundation of BI-RPF to 3D pavement reconstruction.

Ag and Pd bimetallic systems loaded on m-BiVO4 were obtained by hydrothermal and NaBH4 reduction methods with different loading sequences. During the oxidation of benzyl alcohol to benzaldehyde under visible light, the photocatalytic performance of Ag/Pd/m-BiVO4with Ag loaded on the support after Pd loading is higher than that of the other catalysts (Pd/Ag/m-BiVO4 with Pd loaded on the support after Ag, Ag–Pd/m-BiVO4 with Ag and Pd loaded at the same time, and m-BiVO4). The morphology and optical properties of the prepared samples were investigated using different characterization techniques, including XRD, SEM, TEM, UV–Vis DRS and PL. The Arrhenius equation and kinetic data of photocatalytic reactions at different temperatures were used to estimate the apparent activation energy. The experimental results show that the Ag/Pd/m-BiVO4 (2 wt%, 2:1) catalyst can decrease the activation energy by 22.4 kJ mol−1 during the oxidation of benzyl alcohol to benzaldehyde under visible light irradiation.

In this paper, a new method called the Virtual Cutting Method is proposed to evaluate the angularity index (AI) values of 3D point cloud coarse aggregate images with the aim of characterizing the angularity of aggregates on conveyor belts. The 3D point cloud images of coarse aggregates were first captured, preprocessed, and segmented into single 3D aggregate objects. Based on the processed 3D aggregate images, intersection contours were extracted using a series of intersection planes with an equivalent angle between two adjacent planes. The AI was evaluated by averaging the angularity of the contours using the gradient method, which was used in the AIMS2 system. Statistical analysis was then performed to select the optimum angle between two adjacent planes. It was found that an angle of five degrees was the ideal angle, as it can balance the execution time and effectiveness of the method. Finally, the AI results of the Virtual Cutting Method were compared with those of 2D and 3D Projection Methods. It was found that the AI rankings of the three methods for different aggregate textures are generally consistent. The findings of this study conclude that the Virtual Cutting Method can be employed to quantify the angularity of a single aggregate or aggregates in piles on conveyor belts based on 3D point cloud images.

The cervical cytology smear test is an effective method for cervical cancer early screening, and segmentation accuracy is essential for computer-aided diagnosis. In this study, an improved cervical nucleus and cytoplasm segmentation method based on a deep convolutional network was proposed. This method consisted of a cellular region proposal and pixel-level segmentation network (CRP-PSN). Data were obtained from the 2014 International Symposium on Biomedical Imaging cervical cell segmentation competition open dataset. In CRP networks, online hard example mining and soft-nonmaximum suppression algorithms were incorporated to solve problems, including background noise, impurities, and other interferences in smear images. For PSN networks, a generative adversarial network-generated adversarial network algorithm and least-squares loss function were used to generate cell region segmentation, thereby improving the cytoplasm segmentation results. Finally, the improved CRP-PSN model was analyzed and compared with other typical cervical cell segmentation methods. The experimental results showed that the proposed model could effectively improve the segmentation accuracy of cytoplasm and nuclei in cervical cytology smear images by 92% and 98.6%, respectively. These findings provided strong support for the application of this method for automated interpretation of cervical cytology smear images and improved diagnostic reliability.

Pavement crack detection, classification, and characterization are key components of automatic pavement monitoring systems. Most of the currently acceptable methods used for these purposes ...

Bi(NO3)3&middot, 5H2O and (NH4)6Mo7O24&middot, 4H2O were used as precursors to synthesize flaky &gamma, Bi2MoO6 samples by a hydrothermal method, and Pt/&gamma, Bi2MoO6 samples with different mass fractions were prepared by an NaBH4 reduction method. Alpha alkylation of benzyl alcohol and acetophenone with photocatalysts under visible light irradiation was performed, and the activity of 4 wt % Pt/&gamma, Bi2MoO6 (&gamma, Bi2MoO6 was prepared by a nitric acid method, pH = 9, and reaction temperature 180 &deg, C) was the best. The photocatalytic reaction conditions were optimized by changing various kinds of variables, such as the type of catalyst, solvent, and base, and the amount of base, catalyst, and reactant. The optimal conditions for the organic reaction were 75 mg 4 wt % Pt/&gamma, Bi2MoO6, 6 mL n-heptane, 1.2 mmol NaOH, 1 mmol acetophenone, and 3 mmol benzyl alcohol. Under the optimal reaction conditions, the effects of different light wavelengths and light intensities on the reaction were measured, and the cycling ability of the photocatalyst was tested. After five cycles, the photochemical properties of the catalyst were relatively stable. Finally, the active substances were identified (such as electrons (e&minus, ), holes (h+), hydroxyl radicals (&bull, OH), and superoxide radicals (&bull, O2&minus, ).

To handle upcoming advanced technology nodes, the spec of focus control continues to tighten to satisfy more stringent CD uniformity (CDU). In high volume manufacturing (HVM) nowadays, yield at wafer edge suffers from CDU degradation due to process variation like edge roll-off topography. Advance focus control using diffraction-based focus (DBF) was proposed as one solution; we can apply the focus compensation values, which are determined from DBF measurement. However, the confidence of the focus compensation depends on the accuracy of focus measurement, which is a widely recognized challenge in the industry. In this paper, we have investigated the performance and the accuracy of two different designs of DBF marks by collecting full maps of DBF measurements. The measurement shows different signatures cross wafer from the two designs. For focus accuracy validation, CDU maps of several focus sensitive patterns have been collected and analyzed against the defocus measurement results. Our results demonstrate that with the accuracy improved DBF design and focus correction per exposure (CPE) schematic, the CDU is improved by 20%, at the wafer edge. We find that the dimension of the DBF main pattern (W1) dominates the accuracy of focus measurement. A DBF design guideline is proposed that the main pattern dimension (W1) should be close to device focus sensitive pattern size to capture its defocus signature more effectively. The accuracy of focus measurement benefits from designs that are close to device weak point patterns, which in turn improves the overall CD uniformity.

A series of Ag/Pd/m-BiVO4 (monoclinic) bimetallic photocatalytic materials with different loading amounts and different mass ratios of Ag and Pd were synthesized by a hydrothermal method and an NaBH4 reduction method. The Ag/Pd/m-BiVO4 photocatalyst with a total Ag and Pd loading of 2 wt% and an Ag-to-Pd mass ratio of 2:1 can selectively oxidize benzyl alcohol to benzaldehyde under visible light irradiation, the conversion rate was up to 89.9%, and the selectivity was greater than 99%. The conversion rate on Ag/Pd/m-BiVO4 was higher than those on Ag/m-BiVO4 and Pd/m-BiVO4. The photocatalysts were characterized by X-ray powder diffraction (XRD), ultraviolet-visible diffuse reflection spectroscopy (UV-vis DRS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), photoluminescence (PL) spectroscopy, N2 adsorption-desorption isothermal curves (BET) and other means. The effects of different light wavelengths and light intensities were compared. Then, the effects of different alcohol derivatives on the reactions were explored. The cycle experiments proved that the Ag/Pd/m-BiVO4 photocatalyst had good light stability and thermal stability. In addition, the capturing experiment of active species shows that the selective oxidation of benzyl alcohol is mainly accomplished through the synergistic action of h+, e&minus, &bull, OH and &bull, O2&minus

Aggregate size is usually measured by manual sampling and sieving. Machine vision techniques can provide fast, non-invasive measurement. However, the traditional imaging method using a single size descriptor to discriminate different sieve-size classes of coarse aggregates might not yield high-precision classification results. To determine the optimum supervised machine learning model for coarse aggregates sieve-size measurement, 17 methods were evaluated and compared. To train our model, a new dataset named MFCA27 (Multiple Features of Coarse Aggregate 27) was introduced, which contains 27 features of aggregates based on aggregate three-dimensional (3D) top-surface object. In addition, a feature selection approach for investigating how accuracy varied with the datasets under different feature sets was developed, where feature selection was performed according to the impurity-based feature importance score measured using an extremely randomized tree model. Experiments demonstrated that the Gaussian process classifier (GPC) was the best-performing method on the datasets with two- or three-dimensional (2D/3D) feature sets in terms of accuracy and robustness. The results also showed that, compared with the traditional aggregate sieve-size measurement method, which is based on a single size descriptor, GPC can achieve an accuracy of 95.06% on the test dataset of MFCA27 in the aggregate sieve-size class measurement task.

As semiconductor technology nodes keep shrinking, ever-tightening on-product overlay (OPO) budgets coupled with continuous process development and improvement make it critical to have a robust and accurate metrology setup. Process monitoring and control is becoming increasingly important to achieve high yield production. In recently introduced advanced overlay (OVL) systems, a supercontinuum laser source is applied to facilitate the collection of overlay spectra to increase measurement stability. In this paper, an analysis methodology has been proposed to couple the measured overlay spectra with overlay simulation to extract exact process information from overlay spectra. This paper demonstrates the ability to use overlay spectra to capture and quantify process variation, which in turn can be used to calibrate the simulation stacks used to create the SCOL (scatterometry-based overlay) and AIM overlay metrology targets, and can be fed into the fab for process monitoring and improvement.

Shrinking on-product overlay (OPO) budgets in advanced technology nodes require more accurate overlay measurement and better measurement robustness to process variability. Pupil-based accuracy flags have been introduced to the scatterometry-based overlay (SCOL) system to evaluate the performance of a SCOL measurement setup. Wavelength Homing is a new robustness feature enabled by the continuous tunability of advanced SCOL systems using a supercontinuum laser light source in combination with a flexible bandpass filter. Inline process monitoring using accuracy flags allows for detection, quantification and correction of shifts in the optimal measurement wavelength. This work demonstrates the benefit of Wavelength Homing in overcoming overlay inaccuracy caused by process changes and restoring the OPO and residual levels in the original recipe.

Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.

Due to complexity, the use of conventional single-mass point vibration model to estimate the seismic effect of space shaped bridge towers is relatively effective roughly. In this paper, based on the calculation of the stiffness and mass balance of the cable-stayed bridge of the semi-floating system, and based on the analysis of the single-mass vibration pattern, the two-mass points are refined. The analysis model and the simplified calculation formula of the basic cycle are used to carry out targeted analysis and structural optimization of the structural system. The numerical analysis results show that the optimized structure can effectively improve the seismic capacity of the structure. To provide a more accurate calculation method for the seismic estimation of spatially shaped cable-stayed bridges. The results of the study are presented in the long-span semi-floating system cable-stayed bridge. Seismic concept design has been applied.

In this work, 2D WO3 photocatalysts were successfully prepared by a hydrothermal method with different methods, different pH values and different calcination temperatures. The morphology, crystal structure, composition, and chemical properties of the as-prepared samples were systematically investigated by multiple techniques (XRD, XPS, SEM, TEM, UV–vis DRS, BET, and PL). Imines were synthesized from aniline and benzyl alcohol over WO3 under visible light irradiation. The catalysts and reaction conditions were optimized. The results showed that the catalytic performance of WO3 (hydrochloric acid method, pH=1, and calcination temperature=400 °C) was the best. The conversion reached 75.9 %. After five cycles, the catalyst performance was stable and the morphology did not change. Analysis of the active species showed that the amination reaction was mainly realized by the synergistic action between h+ and OH. Visible light irradiation can lower the activation energy of the amination reaction. A possible reaction mechanism was proposed.

The precise calculation of the aggregate size is crucial for detecting aggregate gradation. Therefore, the aim of this study was to propose a method for calculating aggregate particle size. First, a series of image-processing methods, such as filtering, enhancement, segmentation, and morphology, were implemented to obtain a binary image. Consequently, a method for determining the particle shape and correcting the particle size was devised. The aggregate particles are divided into four categories, i.e., rectangle, ellipse, diamond, and triangle, according to three parameters: compact factor, projected contour, and distance distribution from edge point to centre of mass. The corresponding correction coefficient of each category based on the equivalent elliptical minor axis was then calculated, and the grading accuracy of the corrected particle size was analyzed. The accuracy of the particle size classification obtained by the method of aggregate particle shape determination and particle size correction proposed in this paper is 80.7 %.

The weight of aggregates of different particle sizes is vital for the online monitoring of the gradation of asphalt mixtures. In order to complete the online intelligent detection of asphalt aggregate gradation, an automatic coarse aggregate weight prediction algorithm based on the feature extraction of aggregate two-dimensional images is proposed. First, use OpenCV to extract the two-dimensional morphological features of coarse aggregate and use high precision electronic gram scale to obtain the actual weight of the aggregate. Then analyze the correlation between these characteristics and aggregate weight. Finally, the weight of coarse aggregate particles can be accurately predicted by establishing a BPNN (Back-Propagation Neural Network) model. The results show that the weight prediction accuracy of coarse aggregate can be achieved 89.49%. The manual weighing process is reduced, which greatly improves the efficiency of online intelligent detection.

The aggregate with good shape characteristics are conducive to the formation of the internal structure of asphalt pavement and have an important impact on the pavement performance. In order to quantify the aggregate shape characteristics more comprehensively, the quantitative evaluation method for the shape of aggregate particles based on 3D point cloud data was proposed. Basalt, granite and limestone aggregates with size of 9.5 mm, 13.2 mm and 16 mm were obtained by screening. Each type of aggregates was divided into five different shapes of the tetrahedron, pentahedron, hexahedron, elongate and flake. The image acquisition system based on Gocator 3D intelligent sensor was used to collect 3D point cloud data of the aggregates. The noise in the 3D point cloud data collected was removed by the cascade filtering method and then the greedy triangulation algorithm was used to reconstruct the 3D aggregate digitized surface. Finally, the characteristic parameters such as length, width, height, volume and the maximum diameter of the 3D convex hull were extracted from the reconstructed aggregate particles, and the shape indexes of the aggregate, such as the isometric ratio, flakiness ratio, sphericity and roundness indexes, were calculated based on the extracted characteristics. The experimental results show that the 3D shape index of road aggregates proposed in this paper can be used for the comprehensive quantitative evaluation of the morphological characteristics of aggregates with different lithology, particle sizes and shapes. It is instructive to improve the performance of the asphalt mixture and study the aggregate gradation.

To realize the critical assessment of concrete joint faulting, a laser-based 3D detection system (Laser-based Joint Faulting Meter, LJFM) was designed and a calculation method was proposed. The 3D information of joint faulting was obtained based on laser triangulation method. Automatic threshold determination method and mean joint faulting value calculation method were proposed based on the double peak characteristic of joint faulting 3D height histogram. To verify the precision and accuracy of the LJFM, indoor and field experiments were designed and conducted. Repeated experiments of simulated joint faulting in indoor experiment show that measurement results of the LJFM has lower coefficients of variation, better reproducibility and higher stability than that of the Level. In indoor and field experiments of different joint faulting, the coefficient of determination between the LJFM and the Level are 0.944 and 0.958 separately while the correlation coefficients between them are 0.9761and 0.979 (both significant at p

We regulated the microstructure of BiOBr/BiOI to change its bandgap to be applicable to the synthesis of imines. Multiple characterization methods, including X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET) analysis, UV–vis diffuse reflectance spectroscopy (DRS), photoluminescence (PL) spectroscopy and electrochemical impedance spectroscopy (EIS), were applied to elucidate the origin of the excellent photoactivity. A series of BiOBr/BiOI catalysts were applied to convert anilines into imines under visible light irradiation, and of the tested catalysts, BiOBr/BiOI-1/3 showed the highest activity. The conversion rate was 55.1% when using the physical mixture of BiOBr/BiOI at a molar ratio of 1/3, while the conversion rate reached 68.9% when using BiOBr/BiOI-1/3 heterojunctions, demonstrating that the heterojunction of BiOBr/BiOI is a key factor enhancing the conversion rate. The effects of different light intensities and wavelengths were explored. Moreover, the activity of the catalyst was stable after five cycles. O2−, h+, and e− were the main active substances in the trapping experiment of reactive species.

Flower-like Bi2WO6 microspheres and bulk g-C3N4 were prepared by the hydrothermal method and high-temperature calcining method, respectively. A new method based on the combination of ultrasonic stripping and mechanical stirring was used to obtain uniform composite x-Bi2WO6/g-C3N4 (x is the Bi2WO6 mass ratio) photocatalysts with a Z-scheme heterojunction. In the reaction of benzyl alcohol and aniline to form imine, the optimal composite, 30%-Bi2WO6/g-C3N4, showed a conversion rate of 87.6%, which is much higher than that of pure Bi2WO6 and g-C3N4. Characterization by SEM and TEM showed that the ultrasonically stripped g-C3N4 significantly reduced the radial dimension compared to bulk g-C3N4. A uniformly dispersed photocatalytic material was formed, and it maintained a flower-like microsphere structure. Materials characterized by N2 adsorption–desorption isotherms (BET) showed that the specific surface area of the g-C3N4 nanosheets increased by approximately ten times after ultrasonic stripping. The photostability of the composite was verified by cyclic experiments. Under visible light irradiation, the activation energy of the coupling reaction between benzyl alcohol and aniline was found to be decreased by 29.8 kJ mol−1. A capture experiment was used to verify the active species in the photocatalytic system, with the reaction found to be mainly completed through the synergistic action of h+, e− and •O2−.

Aggregate plays the role of skeleton filling in asphalt pavements. The shape of the aggregate affects the embedded structure between the aggregates, thus affecting the performance of asphalt concrete. In this study, extreme gradient boosting (XGBoost) classification is used to study the automatic shape classification of aggregates. The expression of main and microscopic features of aggregate was improved by transforming aggregate images into data, and a feature importance analysis method based on method fusion is proposed to select the feature parameters of aggregate morphology. Based on cross-validation, the XGBoost classification model was trained by optimizing the super parameter combination to complete the classification of aggregate shapes. Compared with the random forest model, the results show that the proposed method can effectively classify aggregate shapes. It is also proved that the two-dimensional images can reflect the three-dimensional features of the aggregate to some extent. This method provides a certain theoretical basis for the automatic classification of aggregate, and simultaneously it has important practical significance to promote the intelligent production of asphalt mixtures.

We demonstrate high volume manufacturing feasibility of 7 nm technology overlay correction requirement. This stateof- the-art overlay control is achieved by (i) overlay sampling optimization and advanced modeling, (ii) alignment and advanced process control optimization, (iii) multiple target overlay optimization, and (iv) heating control. We will also discuss further improvements in overlay control for 7 nm technology node and beyond including computational metrology, extreme ultraviolet and optic tools overlay matching control, high order alignment correction, tool stability improvement, and advanced heating control.

Breast cancer tissues and adjacent tissues were collected from 32 patients who were treated in The Third Hospital of Chengde City. Reverse transcription‑quantitative polymerase chain reaction results demonstrated that, compared with the adjacent tissues, interleukin (IL)‑23/IL‑23 receptor (R) gene expression levels were notably higher in breast cancer tissues. Furthermore, IL‑23 and IL‑23R expression levels were positively correlated with patients' tumor size, TNM stage and metastasis. Recombinant human (rh) IL‑23 (10 ng/ml) was used for the stimulation of the MCF‑7 cell line. Effects of rh IL‑23 (10 ng/ml) on cell proliferation was detected after MCF‑7 cells were incubated with rh IL‑23 for 48 h. Whether pre‑treatment with polyclonal antibody (PAb) IL‑23p19, a neutralizing antibody specific for IL‑23, may influence the effects of IL‑23 on cell behavior was also investigated. Cell proliferation assay and cell apoptosis assay were evaluated using MTT assay and flow cytometry assay, respectively. Results suggested that PAb IL‑23p19 reduced IL-23-induced cell proliferation whereas induced IL‑23 inhibited cell apoptosis. Western blot analysis was performed for the detection of molecules that may be responsible for the aforementioned changes. Results indicated that PAb IL‑23p19 treatment reduced IL‑23‑induced upregulation of B‑cell lymphoma‑2 protein expression and activation of the janus kinase 2/signal transducer and activator of transcription 3 signaling pathway. The present results suggested that IL‑23 may be a potential prognosis marker and target for the treatment of breast cancer patients.

For the purpose of obtaining the characteristic parameters of the asphalt mixture aggregate particles, a viable method using two-dimensional image was put forward in this paper. The automatic detection system of asphalt mixture aggregate characteristic parameters is developed based on VC++. A series of processing are done on the collected aggregate images, including image edge detection, image segmentation, morphological processing, region-labeling, contouring and so on .The region-labeling algorithm is improved ending with low complexity, high robustness. Database technology is used to deal with the storage, query of the results and several other operations. The whole system is characterized with perfect function, user-friendly interface and stable performance, which provides a practical and accurate method for the characteristic detection of asphalt mixture aggregate particles.

Purpose: Small-cell carcinoma of the bladder (SCCB) is a rare and aggressive neuroendocrine tumor with a dismal prognosis and limited treatment options. As SCCB is histologically indistinguishable from small-cell lung cancer, a shared pathogenesis and cell of origin has been proposed. The aim of this study is to determine whether SCCBs arise from a preexisting urothelial carcinoma or share a molecular pathogenesis in common with small-cell lung cancer. Experimental Design: We performed an integrative analysis of 61 SCCB tumors to identify histology- and organ-specific similarities and differences. Results: SCCB has a high somatic mutational burden driven predominantly by an APOBEC-mediated mutational process. TP53, RB1, and TERT promoter mutations were present in nearly all samples. Although these events appeared to arise early in all affected tumors and likely reflect an evolutionary branch point that may have driven small-cell lineage differentiation, they were unlikely the founding transforming event, as they were often preceded by diverse and less common driver mutations, many of which are common in bladder urothelial cancers, but not small-cell lung tumors. Most patient tumors (72%) also underwent genome doubling (GD). Although arising at different chronologic points in the evolution of the disease, GD was often preceded by biallelic mutations in TP53 with retention of two intact copies. Conclusions: Our findings indicate that small-cell cancers of the bladder and lung have a convergent but distinct pathogenesis, with SCCBs arising from a cell of origin shared with urothelial bladder cancer. Clin Cancer Res; 24(8); 1965–73. ©2017 AACR. See related commentary by Oser and Jänne, p. 1775

Several focus monitoring methods for high volume manufacturing (HVM) have been developed to improve general tool stability maintenance, fleet matching and focus uniformity on product. One technique involving the usage of diffractive gratings requires special targets that need to be designed and optimized at the individual mask level, which will increase the development cycle time.

As the demand of the technology node shrinks from 14nm to 7nm, the reliability of tool monitoring techniques in advanced semiconductor fabs to achieve high yield and quality becomes more critical. Tool health monitoring methods involve periodic sampling of moderately processed test wafers to detect for particles, defects, and tool stability in order to ensure proper tool health. For lithography TWINSCAN scanner tools, the requirements for overlay stability and focus control are very strict. Current scanner tool health monitoring methods include running BaseLiner to ensure proper tool stability on a periodic basis. The focus measurement on YIELDSTAR by real-time or library-based reconstruction of critical dimensions (CD) and side wall angle (SWA) has been demonstrated as an accurate metrology input to the control loop. The high accuracy and repeatability of the YIELDSTAR focus measurement provides a common reference of scanner setup and user process. In order to further improve the metrology and matching performance, Diffraction Based Focus (DBF) metrology enabling accurate, fast, and non-destructive focus acquisition, has been successfully utilized for focus monitoring/control of TWINSCAN NXT immersion scanners. The optimal DBF target was determined to have minimized dose crosstalk, dynamic precision, set-get residual, and lens aberration sensitivity. By exploiting this new measurement target design, ~80% improvement in tool-to-tool matching, >16% improvement in run-to-run mean focus stability, and >32% improvement in focus uniformity have been demonstrated compared to the previous BaseLiner methodology. Matching

According to the request for 3D pavement surface precise reconstruction and feature extraction as well as detection of pavement diseases, 3D data denoising algorithm was put forward for data collected by 3D laser imaging system. The main sources of 3D laser imaging system noise were analyzed. The algorithm based on biphase standard deviation filtering method and composite structural elements of the cascaded morphology filtering method is proposed, considering noise characteristics of pavement 3D data including the discontinuity and zero noise, which is combined with digital image processing technology. Denoising effect was analyzed from three aspects including the whole data, rows and columns. Experimental result shows that the denoising method can achieve good denoising effect in the total noise, rows noise and columns noise.

In this study, the carrier g-C3N4 was prepared by melamine, and the Ag-Pd/g-C3N4 catalyst was synthesized by the NaBH4 reduction method. Different characterization techniques, including SEM, TEM, XRD, UV–vis DRS, XPS, photoluminescence spectra (PL) and BET, were employed to investigate the morphology and optical properties of the as-prepared samples. The Ag-Pd/g-C3N4 catalyst was used for the synthesis of imine from a benzyl alcohol and aniline. The results show that when the total loading of Ag and Pd is 2 wt%, and the mass ratio of Ag and Pd is 1:1, the activity of the catalyst is the highest (The highest conversion of aniline is 86.7% and the product selectivity is >99%.). The reaction is optimized by changing the type of solvent, the type and amount of base, the type of catalyst, and the amount of reactants. The optimal reaction conditions are 6 ml of n-hexane, 1.4 mmol of Cs2CO3, 50 mg of the Ag-Pd/g-C3N4 (2 wt%, 1:1), and 2:1 mol ratio of benzyl alcohol and aniline. Under optimal reaction conditions, alcohol derivatives and amine derivatives were investigated to determine the suitable range of the catalyst for alcohols and amines. Then, the effects of different light intensities and wavelengths on the reaction were explored. Additionally, the catalyst's recycling ability was tested, and it was found to be relatively stable. The effect of reactive groups on the mechanism shows that the reaction is mainly achieved by the synergy between h+, e− and ·O2−.

Shearlet transform contains some useful and many good properties in signal analysis, such as sparsity, multi-scale and multi-direction. Compared to the existing multi-scale geometry analysis, for instance, the Curvelet and Contourlet, Shearlets are theoretically optimal in representing images and particularly have the ability to fully capture directional and geometrical features. Furthermore, it can be easily implemented. Therefore, it is very suitable for the image denoising. A new adaptive thresholding algorithm in Shearlet domain which based on SURE-LET is proposed in this paper. Noisy image is firstly transformed into Shearlet domain; and then a pointwise thresholding is performed on the Shearlet coefficients based on SURE-LET in order to remove additive white Gaussian noise; at last, the image is reconstructed by the inverse Shearlet transform. Experimental results show that the performance of the proposed method is comparable to the state-of-the-art denoising algorithms, such as NLM and BM3D, but with the advantage of its low complexity and better computational efficiency.

In order to measure the texture depth of asphalt pavement automatically and accurately, a data set generated by a real-time laser pavement texture meter (RLPTM) was realized, and a calculat...

With decreasing CDOF (Critical Depth Of Focus) for 20/14nm technology and beyond, focus errors are becoming increasingly critical for on-product performance. Current on product focus control techniques in high volume manufacturing are limited; It is difficult to define measurable focus error and optimize focus response on product with existing methods due to lack of credible focus measurement methodologies. Next to developments in imaging and focus control capability of scanners and general tool stability maintenance, on-product focus control improvements are also required to meet on-product imaging specifications. In this paper, we discuss focus monitoring, wafer (edge) fingerprint correction and on-product focus budget analysis through diffraction based focus (DBF) measurement methodology. Several examples will be presented showing better focus response and control on product wafers. Also, a method will be discussed for a focus interlock automation system on product for a high volume manufacturing (HVM) environment.