Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and ...consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Evaluating suppliers’ quality performance is one of critical tasks of quality management since it is directly related to quality assurance, improvement and development, especially for manufacturing ...companies. However, the supplier quality evaluation remains challenging due to the involvement of a large amount of qualitative and quantitative data, multi-dimensional attributes, as well as a large number of items and suppliers, etc. In addition, while various multiple criteria decision making methods (MCDMs) exist and can be used for supplier quality evaluation, individual choice and application of the methods often result in inconsistent or conflicting evaluation results. To overcome the challenges, in this study, we develop a decision support system for supplier quality evaluation to deal with the practical complexity and to take advantage of the availability of the big industrial data. The system is presented as a conceptual framework including three modules: the decision matrix and criteria initialization module, the MCDM method selection and implementation module, and the aggregation module. The sequential conduction of each module formulates and solves the multiple supplier and item quality evaluation problem. The implementation of the system can be applied to dynamically monitor and evaluate supplier and item quality performance. Moreover, as the core intelligent part of the decision support system, in the aggregation module, we propose an aggregated MCDM model based on the machine learning concepts. It aims at providing more reliable evaluations across multiple suppliers and items. The aggregation model also contributes to the MCDM literature as an independent method which is not restricted to the purpose of supplier quality evaluation. The methods shed lights on how to construct and solve MCDM problems in the big-data era. Finally, the application of the decision support system is illustrated by a case of a large automotive company. The case shows that our decision support system provide more robust and reliable evaluation results than the traditional individual multiple criteria decision making method.
•An aggregated MCDM model based on Machine Learning concept.•A DSS for supplier quality evaluation dealing with practical complexities with big industrial data.•Qualitative and quantitative data, multi-dimensional attributes, and a large number of items and suppliers.•A case study shows that the system and methodologies are effective.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Several randomized clinical trials showed that aspirin could decrease the incidence of preeclampsia (PE) in women at high risk, but data from sources other than traditional clinical trials that ...investigating the preventive effect of aspirin 75 mg on PE is still lacking, especially in mainland China. We aimed to use Chinese real-world data to estimate the preventive effect of low-dose aspirin (LDA) on PE.
Clinical data of pregnant women who were at high risk of PE and had their first prenatal visit at the affiliated Taicang People's Hospital of Soochow University during November 31, 2018 and May 10, 2021 was retrospectively analyzed. Among the 266 included pregnant women, 115 individuals treated with aspirin 75 mg per day and the other 151 without such treatment were considered as the LDA group and the control group, respectively.
In the LDA group, 64 (55.65%) of 115 pregnant women took aspirin before 16 weeks of gestation. Besides, 12 (10.43%) and 34 (22.52%) women developed PE in the LDA group and control group, respectively; the aspirin prophylaxis was associated with a lower risk of PE (odds ratio = 0.40, 95% confidence interval = 0.20-0.82, P = 0.0098). In addition, LDA is slightly more effective when initiated before 16 weeks of gestation or in those without chronic hypertension, when compared with their counterparts.
Prophylaxis with 75 mg per day of aspirin in high-risk women resulted in a significantly lower incidence of PE than that in the control group.
Members of the FOXO (forkhead O) class of transcription factors are tumor suppressors that also control aging and organismal life span. Mammalian FOXO degradation is proteasome-mediated, although the ...ubiquitin E3 ligase for FOXO factors remains to be defined. We show that MDM2 binds to FOXO1 and FOXO3A and promotes their ubiquitination and degradation, a process apparently dependent on FOXO phosphorylation at AKT sites and the E3 ligase activity of MDM2. Binding of MDM2 to FOXO occurs through the region of MDM2 that directs its cellular trafficking and the forkhead box of FOXO1. MDM2 promotes the ubiquitination of FOXO1 in a cell-free system, and its knockdown by small interfering RNA causes accumulation of endogenous FOXO3A protein in cells and enhances the expression of FOXO target genes. In cells stably expressing a temperature-sensitive p53 mutant, activation of p53 by shifting to permissive temperatures leads to MDM2 induction and degradation of endogenous FOXO3A. These data suggest that MDM2 acts as an ubiquitin E3 ligase, downstream of p53, to regulate the degradation of mammalian FOXO factors.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
FoxO (mammalian forkhead subclass O) proteins are transcription factors acting downstream of the PTEN (phosphatase and tensin homolog deleted on chromosome 10) tumor suppressor. Their activity is ...negatively regulated by AKT-mediated phosphorylation. Our previous studies showed that the transcriptional activity of the androgen receptor (AR) was inhibited by PTEN in an AKT-sensitive manner. Here, we report the repression of the activity of the full-length AR and its N-terminal domain by FoxO1 and the participation of FoxO1 in AR inhibition by PTEN. Ectopic expression of active FoxO1 decreased the transcriptional activity of AR as well as androgen-induced cell proliferation and production of prostate-specific antigen. FoxO1 knock down by RNA interference increased the transcriptional activity of the AR in PTEN-intact cells and relieved its inhibition by ectopic PTEN in PTEN-null cells. Mutational analysis revealed that FoxO1 fragment 150–655, which contains the forkhead box and C-terminal activation domain, was required for AR inhibition. Mammalian two-hybrid and glutathione-S-transferase pull-down assays demonstrated that the inhibition of AR activity by PTEN through FoxO1 involved the interference of androgen-induced interaction of the N- and C-termini of the AR and the recruitment of the p160 coactivators to its N terminus and to the androgen response elements of natural AR target genes. These studies reveal new mechanisms for the inhibition of AR activity by PTEN-FoxO axis and establish FoxO proteins as important nuclear factors that mediate the mutual antagonism between AR and PTEN tumor suppressor in prostate cancer cells.
The studies establish nuclear FoxO factors as mediators of the inhibition of AR N/C interaction and coactivator recruitment by the PTEN tumor suppressor.
To evaluate the diagnostic performance and inter-observer agreement of the American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS) in the diagnosis of ovarian ...masses in children.
From June 2012 to December 2021, 163 ovarian masses in 159 patients with pathologic results were retrospectively analyzed. Each mass was classified into an O-RADS category according to the criteria. The diagnostic performance of O-RADS for detecting malignant ovarian masses was assessed using histopathology as the reference standard. Kappa (
) statistic was used to assess inter-observer agreement between a less-experienced and a well-experienced radiologist.
Out of 163 ovarian masses, 18 (11.0%) were malignant and 145 (89.0%) were benign. The malignancy rates of O-RADS 5, O-RADS 4, and O-RADS 3 masses were 72.7%, 34.6%, and 4.8%, respectively. The area under the receiver operating characteristic curve was 0.944 (95% CI, 0.908-0.981). The optimal cutoff value for predicting malignant ovarian masses was > O-RADS 3 with a sensitivity, specificity, and accuracy of 94.4%, 86.2% and 86.2% respectively. The inter-observer agreement of the O-RADS category was good (
= 0.777).
O-RADS has a high diagnostic performance for children with ovarian masses. It provides an effective malignant risk classification for ovarian masses in children, which shows high consistency between radiologists with different levels of experience.
Background
Growth differentiation factor‐15 (GDF‐15) is a stress response protein and is related to cardiovascular diseases (CVD). This study aimed to investigate the association between GDF‐15 and ...pre‐eclampsia (PE).
Method
The study involved 299 pregnant women, out of which 236 had normal pregnancies, while 63 participants had PE. Maternal serum levels of GDF‐15 were measured by using enzyme‐linked immunosorbent assay kits and then translated into multiple of median (MOM) to avoid the influence of gestational week at blood sampling. Logistic models were performed to estimate the association between GDF‐15 MOM and PE, presenting as odd ratios (ORs) and 95% confidence intervals (CIs).
Results
MOM of GDF‐15 in PE participants was higher compared with controls (1.588 vs. 1.000, p < 0.001). In the logistic model, pregnant women with higher MOM of GDF‐15 (>1) had a 4.74‐fold (95% CI = 2.23–10.08, p < 0.001) increased risk of PE, adjusted by age, preconceptional body mass index, gravidity, and parity.
Conclusions
These results demonstrated that higher levels of serum GDF‐15 were associated with PE. GDF‐15 may serve as a biomarker for diagnosing PE.
Box diagram of multiple of median (MOM) of growth differentiation factor‐15 (GDF‐15) from early‐onset pre‐eclampsia (PE), late‐onset PE, PE, and control.
Key points
Growth differentiation factor‐15 (GDF‐15) is associated with increased risk of pre‐eclampsia.
GDF‐15 may be a potential biomarker of pre‐eclampsia.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Root cause analysis for quality problem solving is critical to improve product quality performance and reduce the quality risk for manufacturers. Subjective conventional methods have been applied ...frequently in past decades. However, due to increasingly complex product and supply chain structures, diverse working conditions, and massive amounts of components, accuracy and efficiency of root cause analysis are progressively challenged in practice. Therefore, data-driven root cause analysis methods have attracted attention lately. In this paper, taking advantage of the availability of big operations data and the rapid development of data science, we design a big data-driven root cause analysis system utilizing Machine Learning techniques to improve the performance of root cause analysis. More specifically, we first propose a conceptual framework of the big data-driven root cause analysis system including three modules of Problem Identification, Root Cause Identification, and Permanent Corrective Action. Furthermore, in the Problem Identification Module, we construct a unified feature-based approach to describe multiple and different types of quality problems by applying a data mining method. In the Root Cause Identification Module, we use supervised Machine Learning (classification) methods to automatically predict the root causes of multiple quality problems. Finally, we illustrate the accuracy and efficiency of the proposed system and algorithms based on actual quality data from a case company. This study contributes to the literature from the following aspects: (i) the integrated system and algorithms can be used directly to develop a computer application to manage and solve quality problems with high concurrences and complexities in any manufacturing process; (ii) a general procedure and method are provided to formulate and describe a large quantity and different types of quality problems; (iii) compared with traditional methods, it is demonstrated using real case data that manufacturing companies can save significant time and cost with our proposed data-driven root cause analysis system; (iv) this study not only aims at improving the quality problem solving practices for a complex manufacturing process but also bridges a gap between the theoretical development of Machining Learning methods and their application in the operations management domain.
•Develop a big data-driven framework of root cause analysis for quality problems.•Consider the multiple types of quality data from multiple data sources.•Construct the feature libraries to describe quality problems by data mining.•Provide ML algorithms to automatically predict root causes of quality problems.•Test the system and algorithms successfully based on the real product quality data.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
ERBB2-directed therapy is now a routine component of therapy for ERBB2-amplified metastatic gastroesophageal adenocarcinomas. However, there is little knowledge of the mechanisms by which these ...tumors develop acquired resistance to ERBB2 inhibition. To investigate this question we sought to characterize cell line models of ERBB2-amplified gastroesophageal adenocarcinoma with acquired resistance to ERBB2 inhibition. We generated lapatinib-resistant (LR) subclones from an initially lapatinib-sensitive ERBB2-amplified esophageal adenocarcinoma cell line, OE19. We subsequently performed genomic characterization and functional analyses of resistant subclones with acquired lapatinib resistance. We identified a novel, acquired SrcE527K mutation in a subset of LR OE19 subclones. Cells with this mutant allele harbour increased Src phosphorylation. Genetic and pharmacologic inhibition of Src resensitized these subclones to lapatinib. Biochemically, Src mutations could activate both the phosphatidylinositol 3-kinase and mitogen activated protein kinase pathways in the lapatinib-treated LR OE19 cells. Ectopic expression of SrcE527K mutation also was sufficient to induce lapatinib resistance in drug-naïve cells. These results indicate that pathologic activation of Src is a potential mechanism of acquired resistance to ERBB2 inhibition in ERBB2-amplified gastroesophageal cancer. Although Src mutation has not been described in primary tumor samples, we propose that the Src hyperactivation should be investigated in the settings of acquired resistance to ERBB2 inhibition in esophageal and gastric adenocarcinoma.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Brain imaging progresses are briefly introduced to emphasize the significance of NIR-II PL imaging based on lanthanide nanoprobes.•Brain delivery strategies and design principles of NIR-II ...lanthanide nanoprobes are detailed for improving brain imaging performance.•Brain imaging studies of NIR-II lanthanide nanoprobes are overviewed, focusing on imaging brain vessels, injuries, and tumors.•Current achievements and future challenges in NIR-II lanthanide nanoprobes based brain imaging are discussed.
Brain research is a cutting-edge topic but it remains challenging to achieve noninvasive sensitive diagnosis of brain diseases. Lanthanide nanocrystals (NCs) with near-infrared II (NIR-II) photoluminescence (PL) have shown great promise for noninvasive through-skull high-resolution imaging. Currently, it still lacks a systematic review discussing the emerging spectroscopic and nanoprobe engineering of NIR-II luminescent lanthanide NCs for specific imaging of brain diseases. Herein, we comprehensively review recent development of NIR-II lanthanide nanoprobes for brain disease imaging in vivo. We first summarize luminescence regulation strategies aiming to enhance the NIR-II PL efficiency of lanthanide NCs. Then, the design strategies of lanthanide nanoprobes are introduced with the effort to improve their brain delivery efficacy and NIR-II PL imaging specificity and sensitivity in the brain. In particular, we comprehensively detail the latest tailoring of lanthanide nanoprobes for NIR-II PL imaging of brain diseases such as brain injuries and tumors. Finally, we propose future opportunities and challenges in the exploration of NIR-II lanthanide nanoprobes and practical imaging studies of brain diseases. This review is anticipated to motivate future engineering of lanthanide nanoprobes for the diagnosis and treatment of brain diseases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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