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Objective: Our study aims to investigate post-abortion needs-based education via the WeChat platform for women who had intended abortion in the first trimester, whether they are using effective contraception or becoming pregnant again. Design: This single hospital intervention-controlled trial used a nearly 1:1 allocation ratio. Women who had intended abortions were randomly assigned to a Wechat group (needs-based education) and a control group (Traditional education). The women's ability to use effective contraception was the main result. Whether they unknowingly became pregnant again was the second result. Another result was patient anxiousness. Before and after education, women filled out questionnaires to assess their contraception methods and anxiety. Methods: Based on the theoretical framework of contraceptions of IBL (inquiry-based learning), post-abortion women were included in WeChat groups. We use WeChat Group Announcement, regularly sending health education information, one-on-one answers to questions, and consultation methods to explore the possibilities and advantages of WeChat health education for women after abortion. A knowledge paradigm for post-abortion health education was established: From November 2021 until December 2021, 180 women who had an unintended pregnancy and undergone an induced or medical abortion were recruited, their progress was tracked for four months, and the PAC service team monitored the women's speech, discussed and classified the speech entries and summarized the common post-abortion needs in 8 aspects. At least 2 research group members routinely extracted records and categorized the outcomes. Results: Before education, there were no appreciable variations between the two groups regarding sociodemographic characteristics, obstetrical conditions, abortion rates, or methods of contraception (P > 0.05). Following education, the WeChat group had a greater rate of effective contraception (63.0%) than the control group (28.6%), and their SAS score dropped statistically more than that of the control group (P < 0.05). Following the education, there were no unwanted pregnancies in the WeChat group, whereas there were 2 in the traditional PAC group. Only 5 participants in the WeChat group and 32 in the conventional PAC group reported mild anxiety after the education. [ABSTRACT FROM AUTHOR]

The current state of the practice in probabilistic seismic hazard analysis (PSHA) employs ergodic ground motion models (GMMs), which assume that the ground motion variability observed in a global database is the same as the variability in ground motion at a single site-source combination. However, the fast-growing empirical ground motion databases indicate significant regional differences in ground motions due to repeatable and systematic source, path, and site effects. These systematic effects, which are spatially correlated, are not consistent with the ergodic assumption, promoting the transition to non-ergodic GMMs for PSHA. In this study, we use Gaussian processes with different covariance functions to model the spatial correlation structures of systematic source, path, and site effects for the Ridgecrest area. We compare the proposed correlation models for Ridgecrest with those previously developed for the ANZA array in terms of predictive performance. We also evaluate the effects of the cell-specific attenuation approach on the spatial correlation structures of path effects. We find that the spatial correlation of systematic source and path effects is best characterized by anisotropic non-stationary covariance functions in Gaussian processes. We also find that the cell-specific attenuation approach with squared grids has limitations in predicting path effects and does not affect the correlation structures significantly for the Ridgecrest database. Finally, comparisons with the ANZA array suggest that the spatial correlation structures of path effects derived from the ANZA array may be transferable to the Ridgecrest area, potentially due to their similarity in crustal heterogeneity. [ABSTRACT FROM AUTHOR]

The current state of the practice in performance-based earthquake engineering relies on using ergodic ground motion models (GMMs), which assume that the ground motion variability observed in a global database is the same as the variability in ground motion at a single site-source combination. However, as empirical ground motion databases have grown, it has become clear that there are significant systematic differences, which depend on repeatable effects for a combination of sites and seismic sources in a particular region. These systematic differences do not support the ergodic assumption, which is prompting the transition from ergodic to nonergodic GMMs. In this study, we use the Ridgecrest ground motion database, which has 20,000 + ground motion recordings, to evaluate the performance of ergodic and nonergodic GMMs. The large number of ground motions in the Ridgecrest database allows us to quantify repeatable effects for a relatively small region, considering earthquakes with a range of magnitudes, which has been seldom attempted in previous efforts. In developing the nonergodic GMMs, we propose a novel approach based on computer graphics to quantify the cell-specific attenuation terms to constrain the path effects. We use the developed ergodic and nonergodic GMMs to evaluate their performance in earthquake scenarios that occurred in the Ridgecrest area by creating GMM-based maps of ground shaking (MGSs) and comparing them with actual MGSs from recorded ground motions. We use the results from MGSs to assess the assumption in nonergodic GMMs, namely that repeatable effects observed in small magnitude earthquakes are correlated with repeatable effects observed in large magnitude earthquakes. Our results show that the nonergodic GMMs provide information on the spatial variation of repeatable effects induced by complex physical processes; they have a lower aleatory variability, consistent with previous studies; they have a better performance for the considered earthquake scenarios in the Ridgecrest area; and the repeatable effects quantified in small magnitude earthquakes improve the estimation of intensity measures (IMs) at larger magnitudes. [ABSTRACT FROM AUTHOR]

Background: Lymph node (LN) metastasis confers gastric cancer (GC) progression, poor survival and cancer-related death. Aberrant activation of Wnt/β-catenin promotes epithelial-mesenchymal transition (EMT) and LN metastasis, whereas the constitutive activation mutation of Wnt/β-catenin is rare in GC, suggesting that the underlying mechanisms enhancing Wnt/β-catenin activation need to be further investigated and understood. Methods: Bioinformatics analyses and immunohistochemistry (IHC) were used to identify and detect LN metastasis-related genes in GC. Cellular functional assays and footpad inoculation mouse model illustrate the biological function of CCT5. Co-immunoprecipitation assays, western blot and qPCR elucidate the interaction between CCT5 and E-cadherin, and the regulation on β-catenin activity. Results: CCT5 is upregulated in LN metastatic GCs and correlates with poor prognosis. In vitro assays prove that CCT5 markedly promotes GC cell proliferation, anti-anoikis, invasion and lymphatic tube formation. Moreover, CCT5 enhances xenograft GC growth and popliteal lymph node metastasis in vivo. Furthermore, CCT5 binds the cytoplasmic domain of E-cadherin and abrogates the interaction between E-cadherin and β-catenin, thereby releasing β-catenin to the nucleus and enhancing Wnt/β-catenin signalling activity and EMT. Conclusion: CCT5 promotes GC progression and LN metastasis by enhancing wnt/β-catenin activation, suggesting a great potential of CCT5 as a biomarker for GC diagnosis and therapy. [ABSTRACT FROM AUTHOR]

Background: Lymph node (LN) metastasis confers gastric cancer (GC) progression, poor survival and cancer-related death. Aberrant activation of Wnt/β-catenin promotes epithelial-mesenchymal transition (EMT) and LN metastasis, whereas the constitutive activation mutation of Wnt/β-catenin is rare in GC, suggesting that the underlying mechanisms enhancing Wnt/β-catenin activation need to be further investigated and understood.Methods: Bioinformatics analyses and immunohistochemistry (IHC) were used to identify and detect LN metastasis-related genes in GC. Cellular functional assays and footpad inoculation mouse model illustrate the biological function of CCT5. Co-immunoprecipitation assays, western blot and qPCR elucidate the interaction between CCT5 and E-cadherin, and the regulation on β-catenin activity.Results: CCT5 is upregulated in LN metastatic GCs and correlates with poor prognosis. In vitro assays prove that CCT5 markedly promotes GC cell proliferation, anti-anoikis, invasion and lymphatic tube formation. Moreover, CCT5 enhances xenograft GC growth and popliteal lymph node metastasis in vivo. Furthermore, CCT5 binds the cytoplasmic domain of E-cadherin and abrogates the interaction between E-cadherin and β-catenin, thereby releasing β-catenin to the nucleus and enhancing Wnt/β-catenin signalling activity and EMT.Conclusion: CCT5 promotes GC progression and LN metastasis by enhancing wnt/β-catenin activation, suggesting a great potential of CCT5 as a biomarker for GC diagnosis and therapy. [ABSTRACT FROM AUTHOR]

Engineers often estimate the amount of liquefaction-induced building settlements (LIBS) as a performance proxy to assess the potential of earthquake-induced damage to buildings. The first robust LIBS models were initially developed in 2017 and 2018 using traditional statistical approaches. More recently, machine learning techniques have started to be used in developing LIBS models. These recent efforts are a step forward in realizing the potential of machine learning in liquefaction engineering; however, they have often considered only one ML technique for a given dataset and typically used only held-out test sets for model assessment. In this study, five ML-based LIBS models with varying flexibility (i.e., ridge regression, partial least square regression — PLSR, random forest, gradient boosting decision tree — GBDT, and support vector regression) are developed using a LIBS database generated by soil–structure numerical simulations of different buildings and soil profiles shaken by ground motions with varying intensity measures. The motivation for considering models with different flexibility is to include different bias–variance trade-offs. Feature selection with different ML techniques indicates that cumulative absolute velocity, spectral acceleration at one second, contact pressure, foundation width, the thickness of the liquefiable layer, and a shearing liquefaction index are important features in estimating LIBS. The developed ML-based models are assessed considering prediction accuracy in test sets, performance against centrifuge tests and case histories, and trends. The assessment indicates that the random forest, GBDT, and SVR models perform best, providing standard deviation reductions up to 40% relative to a multi-linear regression. Specifically, the random forest and GBDT models exhibit a root mean square error (RMSE) of 0.29 and a coefficient of determination (R 2) of 0.93 on test sets, demonstrating a notable improvement compared to a traditional multi-linear regression model, which yields an RMSE of 0.47 and an R 2 of 0.82. Moreover, random forest and GBDT, alongside SVR, show a good performance in centrifuge tests and case histories. Finally, given the scarcity of LIBS models, this study also contributes to treating epistemic uncertainties in estimating LIBS, which is ultimately beneficial for performance-based assessments. • Liquefaction-induced building settlements (LIBS) have significantly affected infrastructure in recent earthquakes. • Most current LIBS models are based on traditional statistical approaches. • Machine learning-based models are proposed and assessed holistically. • The assessments consider statistical robustness and performance in centrifuge tests and case histories. • The developed LIBS models contribute to the treatment of epistemic uncertainties and performance-based earthquake engineering in general. [ABSTRACT FROM AUTHOR]

Spectral–spatial Gabor filtering, which is based on 3-D local harmonic analysis, has been a powerful spectral–spatial feature extraction tool for hyperspectral image (HSI) classification. However, existing spectral–spatial Gabor approaches are prone to oversmoothing, neglecting the existences of edges and negatively affecting the classification. In this article, we propose a new HSI Gabor filtering concept, called spectral–spatial-range Gabor filtering, which intends to restrain edge interference from disturbing local spectral–spatial harmonic components. Contributions and novelties of our work can be identified as follows: 1) an HSI filtering framework is created, which can accommodate various Gabor filtering procedures and hence offer the potential to guide the design of new Gabor filters; 2) following such a unified filtering framework and taking into consideration both local spectral–spatial harmonic characteristics and range domain variations, we develop a new concept of spectral–spatial-range Gabor filtering; and 3) utilizing this proposed Gabor prototype and elaborating mathematical derivations, we achieve a novel discriminative spectral–spatial-range Gabor filtering method, which can deal with discriminative local harmonics and edge interference simultaneously along the spectral–spatial-range domain, obtaining highly discriminative Gabor features while yielding linear computational complexity. Our novel method is evaluated on four real HSI data sets and achieves excellent performances. [ABSTRACT FROM AUTHOR]

The Global Industry Standard on Tailings Management (GISTM) promotes performance-based approaches in geotechnical assessments. Hence, characterizing the spatial variability of deposited tailings is expected to be a key input for some tailings storage facilities (TSFs); however, it has seldom been investigated. In this study, we assess the spatial variability of thickened and conventional tailings, which have been deposited into the same TSF, providing a unique opportunity to investigate two tailings technologies. A dense array of 15 cone penetration tests (CPTus) with an average offset of 1.5 m has been conducted to collect data. In addition to evaluating the spatial variability, the collected information is also used to assess the potential of machine learning (ML) for detrending when deriving random fields. Using a new proposed stationarity score, we find that an ML-based detrending outperforms traditional procedures for most scenarios. In terms of correlation lengths, we find similar ranges for thickened and conventional tailings (vertical: δwv=0.2–0.6 m , horizontal δwh=1.5–4.5 m) and similar distributions, likely influenced by the depositional processes. In contrast, the variance in the conventional tailings is higher, which we attribute to its segregating nature. Finally, by inspecting previous studies on natural soils, we find that the variability of mine tailings (δwh/δwv=2–21) resembles that observed in alluvial deposits, which we attribute to the parallels in the depositional processes. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is a heterogeneous subtype of breast cancer. Anti -PD-1/PD-L1 treatment for advanced TNBC is still limited to PD-L1-positive patients. Ataxia telangiectasia mutated (ATM) is a switch molecule for homologous recombination and repair. In this study, we found a significant negative correlation between ATM and PD-L1 in 4 TNBC clinical specimens by single-cell RNA sequencing (scRNA-seq), which was confirmed by immunochemical staining in 86 TNBC specimens. We then established ATM knockdown TNBC stable cell lines to perform in vitro studies and animal experiments, proving the negative regulation of PD-L1 by ATM via suppression of tumor necrosis factor-alpha (TNF-α), which was confirmed by cytokine array analysis of TNBC cell line and analysis of clinical specimens. We further found that ATM inhibits TNF-α via inactivating JNK/c-Jun by scRNA-seq, Western blot and luciferase reporter assays. Finally, we identified a negative correlation between changes in phospho-ATMS1981 and PD-L1 levels in TNBC post- and pre-neoadjuvant therapy. This study reveals a novel mechanism by which ATM negatively regulates PD-L1 by downregulating JNK/c-Jun/TNF-α in TNBC, shedding light on the wide application of immune checkpoint blockade therapy for treating multi-line-resistant TNBC. • A negative correlation between PD-L1and ATM expression in TNBC. • ATM negatively regulated PD-L1 expression through TNF-α. • ATM inhibits TNF-α by inactivating JNK/c-Jun signaling pathway. • ATM as a potential biomarker for diagnosis and prediction of immunotherapy. • The combination of ATM inhibitors and immunotherapy may benefit more patients with TNBC. [ABSTRACT FROM AUTHOR]

The seismic performance assessment of earth slopes and systems requires an estimate of the seismically-induced slope displacement (D). Modified Newmark-type analyses are useful to develop an estimate of D. Most simplified seismic slope displacement procedures were developed for shallow crustal earthquakes. In contrast, few procedures are available for subduction tectonic settings, which is especially the case for intraslab earthquakes. This study uses the comprehensive NGA-Sub ground motion database to formulate new seismic slope displacement models for subduction zone interface and intraslab earthquakes using a mixed random variable framework, which allows a straightforward implementation in engineering practice. The models are formulated in terms of the sliding mass properties (its yield coefficient and fundamental period), the spectral acceleration at the degraded period of the slope, and earthquake magnitude with peak ground velocity as an optional parameter. The models reveal that D scales differently for interface and intraslab earthquakes, which is contrary to what has been often assumed. The models also estimate displacement-compatible seismic coefficients for pseudostatic slope stability analyses. This study advances the performance-based seismic design of slope systems affected by subduction zone earthquakes. [ABSTRACT FROM AUTHOR]

The assessment of the seismic performance of slope systems often relies on estimating the amount of seismically-induced slope displacements (D) using semi-empirical D models. These models take as inputs the slope properties and ground motion intensity measures (IMs) to provide D estimates that are used in engineering design. However, most of the available D models have been developed for regions affected by shallow crustal seismicity. Comparatively, the available D models for subduction tectonic settings are scarce. Moreover, most existing models have been developed using traditional statistical methods that do not take advantage of modern data-driven approaches. In this study, we develop new machine learning (ML) based D models applicable to subduction earthquake zones (considering both interface and intraslab mechanisms) using the NGA-Sub ground motion database. A systematic feature selection is performed using three ML procedures, finding that the yield coefficient (k y), the initial fundamental period of the slope system (T s), the earthquake magnitude (M), the peak ground velocity (PGV), and the pseudo-spectral acceleration at 1.3 T s (Sa (1.3 T s)) are efficient features for estimating D in subduction earthquake zones. Based on the selected features, we develop five ML-based D models by using modern ML procedures (i.e., ridge regression, random forest, gradient boosting decision tree (GBDT), support vector regression (SVR), and residual neural network (ResNet)). The developed ML-based D models do not need to be restricted to predefined fixed functional forms, as has often been the case for previously developed D models. The ridge regression model is used to represent generic traditional D models as it has a polynomial-based functional form. Compared to the ridge regression model, the other developed ML-based models are able to better capture the complex relationship between D and the slope properties and IMs, showing a better predictive performance on test tests; hence, they outperform traditional models. In addition, we compare the performance of the developed ML-based models in terms of predictive performance, model trends, and computational cost for training. Lastly, the developed ML-based models also enhance the treatment of epistemic uncertainty in the estimation of D, given the scarcity of available robust D models for subduction zone tectonic settings. • Seismically-induced slope displacements (D) are a proxy to assess performance of slope systems. • Machine learning-based models for estimating D in subduction zones are developed. • The proposed models show a better prediction capabilities on test sets than traditional proxies. • The proposed models enhance the treatment of epistemic uncertainties. • This study advances the performance-based seismic assessment of slope systems. [ABSTRACT FROM AUTHOR]

• Steam jet condensation in water flow is studied experimentally and numerically. • The jet plume changes from cone to cylinder-cone as inlet steam pressure rises. • The dynamical pressure peaks at the end of the two-phase mixing region. • Thermal hydraulic parameters in wake region conforms to the jet self-similarity law. • Heat and mass transfer of condensation is almost completed in the two-phase region. Subsonic and sonic steam jet condensation in subcooled water pipe flow is investigated experimentally and numerically. Steam plumes are captured using a high-speed camera, and thermal hydraulic parameters and heat and mass transfer are revealed by a two-fluid model coupled with a phase change model. As the Reynolds number of water increases, the steam plumes show a conical shape and gradually shorten in length. The steam dynamic pressure suddenly drops at the nozzle outlet, and the water dynamic pressure rises sharply to the maximum at the end of the two-phase region where the steam volume fraction is 0.001. Starting from the single-phase region, the pressure, velocity and temperature of the jet are self-similar. The heat and mass transfer mainly occur in the two-phase region with steam volume fractions between 0.01 and 0.99, and the mass transfer reaches the maximum at the phase interface with the steam volume fraction of 0.60. [ABSTRACT FROM AUTHOR]

The current state of practice in the estimation of liquefaction-induced building settlements (LIBS) relies on pseudoprobabilistic approaches in which the estimation of ground motion intensity measures (IMs) is separated from the estimation of LIBS. In contrast, in a performance-based probabilistic approach, the estimation of the IM hazard is coupled with the estimation of the LIBS hazard. Thus, engineers can obtain LIBS estimates that are directly related to a selected design hazard level (or return period), which is more consistent with performance-based design. In this study, we present new developments for the performance-based probabilistic evaluation of LIBS, including 1) the performance-based assessment of LIBS considering the hazard from a single IM in the context of scalar probabilistic seismic hazard assessment (PSHA), 2) the performance-based assessment of LIBS considering the hazard from multiple IMs in the context of vector PSHA, 3) deaggregation of earthquake scenarios from LIBS hazard curves, 4) estimation of LIBS hazard curves in areas where earthquakes from multiple tectonic settings can occur (e.g., shallow crustal, subduction), and 5) treatment of uncertainties (i.e., aleatory and epistemic). The developments are implemented in a computational platform named "LIBS", which is fully coupled with PSHA assessments and facilitates the straightforward performance-based estimation of LIBS in engineering practice. Finally, we perform comparisons of performance-based and pseudoprobabilistic-based estimates of LIBS and share insights from the comparisons. • We present new developments for the performance-based assessment of liquefaction-induced building settlements (LIBS). • An automated CPT-based definition of soil layers is integrated in the developed procedures. • The proposed procedures integrate multiple seismic sources and the use of logic trees to account for epistemic uncertainties. • A procedure to perform magnitude-distance deaggregation of settlement hazard curves is provided. • A software LIBS is implemented for the application of the proposed procedures. • Performance-based procedures that provide hazard-consistent settlements should be used as they are more rational. [ABSTRACT FROM AUTHOR]

Engineers often use semiempirical models, which estimate the amount of seismically-induced slope displacements (D), to evaluate the seismic performance of earth structures and natural slopes. These procedures often use as inputs slope properties, earthquake parameters, and ground motion intensity measures (IMs). In this study, we propose a new set of machine learning (ML) based models to estimate D using the NGA-West2 shallow crustal ground motion database. We consider both the classification of negligible D and its estimation. The selection of features to explain D (which is based on LASSO, Forward Selection, and Random Forest) suggests that the most efficient features are the slope's yield coefficient (k y), its fundamental period (T s), the earthquake magnitude (M w), the peak ground velocity (PGV), and the degraded spectral acceleration at 1.3 T s. Moreover, the feature selection suggests that there is no significant gain in accuracy beyond five features. We formulate 19 different models, considering various ML-based algorithms such as Generalized Linear Models (GLM), Partial Least Square Regressions (PLSR), Principal Component Regressions (PCR), Bagging and Boosting, Random Forest, Polynomial-based regressions, Multi-order regressions, and Kernel-based models. We assess the performance of the proposed models by evaluating test errors, their predictive performance in case histories, and comparisons against existing models. Based on the assessments, we recommend 6 ML-based models to estimate D in engineering practice. • New models for estimating seismically-induced slope displacements are proposed. • Several Machine learning techniques are used to develop the new models. • Key features to estimate seismically-induced slope displacements are identified. • The proposed modes are evaluated using cross-validation and case histories. • Selected models are recommended to be used in engineering practice. [ABSTRACT FROM AUTHOR]

• Two-phase flow regimes are explored in a long pipeline with S-shaped riser system. • Differential pressure signal and neural network are used to identify flow regimes. • High recognition rates are achieved with signals near the outlet of the riser. • Good recognition rates can be obtained if the sample lengths are long enough. Accurate classification of flow regimes in pipeline-riser system is a promising approach for flow assurance in offshore oil fields. Experiments on two-phase flow regimes in a 1657 m long-distance pipeline-riser system were conducted. Statistical features of the differential pressure signals were extracted and input into a neural network classifier to identify flow regimes. The influences of positions along the loop and sample lengths of the differential pressure signals on the recognition rates were discussed. For the 13 differential pressure signals at various positions along the loop, the recognition rates of the signals on the S-shaped riser section, the inclined section and the horizontal section decreases in turn. Recognition rates higher than 92.2% are achieved with signals near the outlet of the riser. The overall recognition rate increases significantly as the sample length increases, and good recognition rates can be obtained if the sample lengths exceed about 74.4 s. [ABSTRACT FROM AUTHOR]

This paper provides an overview and introduction to the development of non-ergodic ground-motion models, GMMs. It is intended for a reader who is familiar with the standard approach for developing ergodic GMMs. It starts with a brief summary of the development of ergodic GMMs and then describes different methods that are used in the development of non-ergodic GMMs with an emphasis on Gaussian process (GP) regression, as that is currently the method preferred by most researchers contributing to this special issue. Non-ergodic modeling requires the definition of locations for the source and site characterizing the systematic source and site effects; the non-ergodic domain is divided into cells for describing the systematic path effects. Modeling the cell-specific anelastic attenuation as a GP, and considerations on constraints for extrapolation of the non-ergodic GMMs are also discussed. An updated unifying notation for non-ergodic GMMs is also presented, which has been adopted by the authors of this issue. [ABSTRACT FROM AUTHOR]

Multiphase flow pattern identification is an urgently desirable approach for flow assurance of gas-liquid transportation in deep-sea pipelines. Experiments are performed on a system with 1657 m horizontal pipe and 16.7 m S-shaped riser. A new method of sample preparation using only data from liquid accumulation stage is proposed for identification of early-stage severe slugging. A set of signal evaluation and selection criteria covering the recognition effect and practicability of signals are proposed for identification of severe slugging, oscillating flow and stable flow. For oilfield applications including above-water and underwater scenarios, pressure signals at different locations of the pipeline-riser system are selected to meet the actual demand with the shortest sampling duration. Using the combination of above-water signals at the top of the riser, the recognition rate of 90.0% is achieved at a sampling duration of 37.2 s. Feature dimension is reduced by principal component analysis, and good recognition results are obtained using the optimal signal combinations in different classifiers. • Severe slugging is investigated in a 1657 m long pipeline-riser system. • Data from liquid accumulation stage is used for identification of severe slugging. • Signal selection criteria covering recognition rate and practicability. • Good recognition rates are achieved with two pressure signals near the riser outlet. [ABSTRACT FROM AUTHOR]

Siglec-15 was recently reported to be an immunosuppressive molecule that is expressed by tumor-associated macrophages and upregulated in some solid tumors. Targeting Siglec-15 is a potential strategy for normalization cancer immunotherapy. Here, we identified the important post-translational modification, N -glycosylation of Siglec-15, which is regulated by glucose uptake. Using a series of glycosidase and glycosylation inhibitors, we demonstrated that Siglec-15 was completely N -glycosylated in vitro and in vivo. The precise glycosylation site was determined. N -glycosylation stabilized Siglec-15 by decreasing its lysosome-dependent degradation. Siglec-15 subcellular distribution detected by immunofluorescence indicated that N -glycosylation promoted Siglec-15 transportation to the cell membrane. The collective observations indicate that targeting the N -glycosylation of Siglec-15 may be an effective supplement to immunotherapy. • Siglec-15 was found to play a critical role in suppressing antigen-specific T cell responses in tumor microenviroment. • Siglec-15 is modified by N -glycosylation and this process is mediated by glucose-uptake. • N -glycosylation also stabilized Siglec-15 by decreasing its lysosome-dependent degradation. • N -glycosylation is necessary for Siglec-15 to transport to the cell membrane and play its further function. [ABSTRACT FROM AUTHOR]

In deep-sea oil and gas, S-shaped riser is the key equipment to transport the multiphase working substance produced underwater to the surface platform. The occurrence of severe slugging will seriously threaten the safe production of offshore oil and gas fields. Therefore, accurate and rapid recognition of hazardous flow patterns such as severe slugging is an urgent need to ensure the safety of multiphase flow in deep-sea long-distance pipelines. This paper has built a set of long S-shaped riser with industrial parameters, including 1687 m horizontal and downward inclined pipelines and 16.7 m S-shaped riser. The gas-liquid two-phase flow patterns, including severe slugging, oscillating flow and stable flow, are studied in this system. The pressure and differential pressure features of different signals are extracted from different positions along the pipeline, and a more comprehensive and reasonable evaluation criterion of signal performance is established. Making use of the advantages of visualization of the structure of decision tree, a new method to evaluate the recognition effect of features is proposed based on its classification principle. The original signal is decomposed into components of different scales through wavelet multi-scale analysis, and then the statistical features with good flow pattern differentiation ability are calculated. It is found that the mean of wavelet approximate component has the best recognition effect among all features. On the basis of feature correlation analysis, 7 features with high recognition rate applicable to 22 signals along the pipeline are optimally selected from 13 original features. For the water differential pressure signal DP 13 , the recognition rate obtained by using its 4 optimal features is 91.4 %, and the error between the recognition rate obtained by using all features is only 0.2 %. The overall recognition rate of the signal is the highest and the practicability is the best. When the sample duration is 6.2s, the recognition rate using the optimal feature reaches 90.8 %, thus ensuring the real-time recognition of the flow pattern while obtaining a high recognition rate. • Two-phase flow patterns are studied on a 1687 m long pipeline with an S-shaped riser. • Decision tree is used to rank features based on effect of flow pattern recognition. • Mean of wavelet approximation component is the best feature to identify flow pattern. • Obtained features with good recognition rate for all signals by correlation analysis. • Recognition rate using optimal features reaches 90.8 % with a sample length of 6.2s. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is a common and lethal disease in which distant metastasis remains the primary cause of death. Paradoxical roles of LOX have been reported in CRC, and the intracellular function of LOX has also recently been determined. Correlations of LOX expression and its intracellular localization with clinicopathological features in CRC patients remain largely unknown. The aim of the present study was to explore the potential roles of LOX in CRC. Methods: LOX messenger RNA expression was assayed by quantitative PCR in eight paired normal mucosa and tumor tissues. Immunohistochemistry was conducted using tissue arrays to investigate LOX expression in 201 CRC patients. Regulation of LOX by YAP and TEAD4 was explored by YAP or TEAD4 short hairpin RNA interference in a LoVo cell line. Results: LOX messenger RNA expression was elevated in some CRC specimens, and LOX nuclear localization was detected in CRC tumor tissues. LOX nuclear localization was found to correlate with lung/hepatic metastasis, elevated serum carcinoembryonic antigen concentration, and mucinous tumor type (P < 0.05). Nuclear LOX expression was found to be associated with poor overall and disease‐free survival (P < 0.05), and postoperative lung/hepatic metastasis (P < 0.05). Knockdown of YAP or TEAD4 induced downregulation of LOX expression. Conclusions: LOX nuclear localization was significantly associated with poor survival in patients with CRC. Nuclear LOX expression was correlated with synchronous or postoperative lung/hepatic metastasis. LOX may prove to be a potential target gene of YAP and TEAD4. [ABSTRACT FROM AUTHOR]

• Steam jets in subcooled water under different steam mass fluxes is analyzed. • A two-fluid model under unsteady conditions are used for numerical simulations. • The fluctuation of the pressure enhances with the increase of axial distance. • The pressure oscillation is related to radial and axial interface oscillations. The cooling process of high-temperature steam ejecting into subcooled liquid widely exists in industrial production, with high mass and heat transfer efficiency. This paper establishes an unsteady numerical simulation method for jet condensation based on the two-fluid model and the thermal phase change model, focusing on the phase interface evolution and pressure fluctuation characteristics of steam jets in the crossflow of subcooled water under different mass fluxes. With the increase of mass flux, the ability of the jet plume to resist water flow is enhanced; thus, the heat and mass transfer at the two-phase interface becomes more stable; the length of the plume increases, and the range of oscillation of the plume length in the axial direction and the radial direction decreases. The pressure vortex that generated by the two-phase region and the subcooled water is more stable with reduced total pressure fluctuations. Under subsonic conditions, bubbles are shed on the leeward side, and the ranges of axial oscillation, radial oscillation and total pressure oscillation are 1.57 d e –3.41 d e , 0.21 d e –0.37 d e , and 0.17 d e –0.32 d e , respectively. The steam plume becomes progressively longer in the axial direction as the steam velocity increases from subsonic to sonic and supersonic speeds, and the range of interfacial oscillations and total pressure fluctuations along the radial direction are further reduced. Under the same working condition, with the increase of axial distance, the momentum of the steam jet decreases, the ability to resist the impact of water flow decreases, and the windward side is greatly affected; the axial and radial amplitude fluctuations of the pressure increases, with the amplitude on the windward side being significantly larger than that on the leeward side, and the maximum difference between the fluctuations on both sides increases continuously. The momentum change of the two-phase interface is intense, which is the main factor of pressure fluctuation. The correlation coefficients of pressure oscillation with radial and axial interface oscillations are 0.9110 and 0.7477, respectively. [ABSTRACT FROM AUTHOR]

• Gas-liquid flow regimes are studied in a 1657 m long pipeline with an S-shaped riser. • Regime boundaries and transition velocities are presented in a flow regime map. • Parameters from wavelet analysis are input into a decision tree to identify regimes. • Minimum sample duration obviously drops with the increase of signal number. • Principal component analysis significantly reduces statistical parameters' dimension. In this paper, we experimentally investigate gas–liquid flows in a 1657 m long pipeline with an S-shaped riser. Boundaries of three different flow regimes and the transition velocities are determined and presented on the flow regime diagram. Differential pressure signals and pressure signal near the platform are firstly decomposed into six scales via a wavelet analysis, and then statistical parameters on each scale are extracted and further input into a decision tree classifier. For a sample duration of 18.6 s, the highest recognition rates of one signal, two-signal, and three-signal rise from 93.2%, 95.0%, to 96.8%. For one signal, two-signal, and three-signal, the shortest sample durations required for a satisfactory recognition rate of 95% obviously become shorter from 44 s, 28 s, to 2.3 s. On the premise of the highest recognition rate 94.5% to 95.0%, one achieves a dramatic dimension reduction of 81%-93% for statistical parameters via a principal component analysis. [ABSTRACT FROM AUTHOR]

To identify conveniently multiphase flow regimes in subsea pipeline-risers, we study in this paper experimentally two-phase flows in a 1657 m long pipeline with an S-shaped riser to simulate field experiment, within a wide range of gas and liquid velocities. Three flow regimes, namely severe slugging, transitional flows, and stable flows, are analyzed based on three differential pressure and one pressure signals at the top of the riser; comparatively speaking, the positions of these signals in the experimental system are similar to those of the sea level signals in industrial fields, which are easy and less expensive to obtain. The obtained signals are decomposed into six scales via a multi-scale wavelet analysis, and further four statistical parameters on each scale are extracted, including mean values, standard deviations, ranges, and mean values of absolute. We compared the effects of six SVM classifiers with different kernel functions on the recognition rate of flow regimes, and it is found the recognition rates of SVM classifier with quadratic and cubic kernel functions are the highest. Further, the principal component analysis is employed to reduce the dimension of statistical parameters and it indicates that the recognition rate tends to increase with the rising number of principal components from 1 to 6, and it remains constant if the principal component number is further increased. Moreover, The results suggest that the recognition rate obtained from the pressure difference between the top of the riser and the separator peaks, and then it comes that from the pressure signal at the top of the riser, and that for the pressure difference signal at the top of the riser is the least satisfying one. As for the optimal differential pressure signals between the top of the riser and the separator, the results show that the recognition rate increases rapidly from 70.2% to 90.4% when the sample duration rising from 2.3 s to 18.6 s, and when the sample duration exceeds 74.4 s, the recognition rate exceeds 92.9% and remains unchanged. • Experiments of air-water flow in a long pipeline with an S-shaped riser are studied. • Sea level signals are acquired and decomposed via a multi-scale wavelet analysis. • Principal component analysis and support vector machine are used to identify regimes. • Recognition rate peaks for pressure difference between the riser top and separator. [ABSTRACT FROM AUTHOR]

Whether TEAD4 itself plays a vital role in the tumorigenesis and development of lung adenocarcinoma remains unclear. In our study, we aim to investigate the expression pattern and biological functions of TEAD4 and further investigate the potential mechanisms. Clinical tumor and paired normal samples were collected for preparing tissue microarray. Western blot and immunohistochemical (IHC) staining of TEAD4 expression in these tissues were conducted to explore the expression pattern. Moreover, A549 cell line was select for investigating the function of TEAD4 for lung adenocarcinoma in vitro and in vivo. RNA sequencing was finally performed to further detect the potential downstream genes. The elevated TEAD4 expression level was observed in tumor tissues and the patients with higher TEAD4 expression tended to have worse overall survival. The knockdown of TEAD4 inhibits A549 cells proliferation ability and migration ability. A total of 431 differentially expressed genes (DEGs), including 239 down-regulated genes and 191 up-regulated genes, were finally identified and some of DEGs were validated. Moreover, knockdown of TEAD4 led to the down-regulation of pERK, which maybe the potential TEAD4-targeted signaling pathway to play the pro-tumorigenic function. The expression level of TEAD4 is high in lung adenocarcinoma tumor tissues and positively associated with worse prognosis. Up-regulation of TEAD4 may lead to excessive transcription and phosphorylation of ERK proteins and therefore accelerates the process of tumor development. Our results demonstrate that overexpression of TEAD4 is a new mechanism of dysregulation of Hippo pathway. • TEAD4 promotes tumor development in patients with lung adenocarcinoma via ERK signaling pathway. • Overexpression of TEAD4 is a new mechanism of dysregulation of Hippo pathway. • The expression level of TEAD4 is high in tumor tissues and positively associated with worse prognosis. [ABSTRACT FROM AUTHOR]

This study presents new developments for the performance-based assessment of seismically-induced slope displacements (D). Performance-based procedures enable a hazard-consistent and rational seismic design of slope systems; hence, their use in practice is appealing. However, they are not the standard in engineering practice because their use is considered too complex to be used for non-critical projects. The developments presented in this study allow the straightforward estimation of displacement hazard curves (DHC) for a wide range of slope systems subjected to earthquakes in different tectonic settings (i.e., shallow crustal, and subduction), considering a rigorous quantification of the existing uncertainties. The new developments include 1) full integration of probabilistic seismic hazard assessments (PSHA), and the estimation of DHCs, 2) automatic estimation of DHC for D models with multiple intensity measures through vector PSHA, 3) estimation of DHC for systems with contributions from multiple tectonic settings, 4) uncertainty treatment (i.e., epistemic and aleatory) on DHCs, through a logic tree scheme, 5) deaggregation of earthquake scenarios from DHCs, and 6) uncertainty quantification on DHCs through the polynomial chaos theory. The new developments are implemented in a MATLAB graphical user interface (GUI) to facilitate its use by engineers and researchers. We discuss illustrative examples and guidelines for the application of the GUI to evaluate the seismic performance of different slope systems that are affected by earthquakes from multiple tectonic settings. • Performance-based procedures allow a rational seismic design of slope systems. • New developments for the performance-based seismic design of slopes are presented. • The hazard of engineering demand parameters should be considered in seismic design. • The proposed procedures can be readily used in the seismic design of slope systems. • A platform for the evaluation of displacement hazard curves is implemented. [ABSTRACT FROM AUTHOR]