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Purpose: To develop a model to select appropriate candidates for irradiation stent placement among patients with unresectable pancreatic cancer with malignant biliary obstruction (UPC-MBO).Methods: This retrospective study included 106 patients treated with an irradiation stent for UPC-MBO. These patients were randomly divided into a training group (74 patients) and a validation group (32 patients). A clinical model for predicting restenosis-free survival (RFS) was developed with clinical predictors selected by univariate and multivariate analyses. After integrating the radiomics signature, a combined model was constructed to predict RFS. The predictive performance was evaluated with the concordance index (C-index) in both the training and validation groups. The median risk score of progression in the training group was used to divide patients into high- and low-risk subgroups.Results: Radiomics features were integrated with clinical predictors to develop a combined model. The predictive performance was better in the combined model (C-index, 0.791 and 0.779 in the training and validation groups, respectively) than in the clinical model (C-index, 0.673 and 0.667 in the training and validation groups, respectively). According to the median risk score of 1.264, the RFS was significantly different between the high- and low-risk groups (p < 0.001 for the training group, and p = 0.016 for the validation group).Conclusions: The radiomics-based model had good performance for RFS prediction in patients with UPC-MBO who received an irradiation stent. Patients with slow progression should consider undergoing irradiation stent placement for a longer RFS.

Of all indoor localization techniques, vision-based localization emerges as a promising one, mainly due to the ubiquity of rich visual features. Visual landmarks, which present distinguishing textures, play a fundamental role in visual indoor localization. However, few researches focus on visual landmark labeling. Preliminary arts usually designate a surveyor to select and record visual landmarks, which is tedious and time-consuming. Furthermore, due to structural changes (e.g., renovation), the visual landmark database may be outdated, leading to degraded localization accuracy. To overcome these limitations, we propose VILL, a user-friendly, efficient, and accurate approach for visual landmark labeling. VILL asks a user to sweep the camera to take a video clip of his/her surroundings. In the construction stage, VILL identifies unlabeled visual landmarks from videos adaptively according to the graph-based visual correlation representation. Based on the spatial correlations with selected anchor landmarks, VILL estimates locations of unlabeled ones on the floorplan accurately. In the update stage, VILL formulates an alteration identification model based on the judgments from different users to identify altered landmarks accurately. Extensive experimental results in two different trial sites show that VILL reduces the site survey substantially (by at least 65.9%) and achieves comparable accuracy. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article addresses a systematic review and meta-analysis on the effectiveness of non-pharmacological interventions for reducing pain in individuals with dementia. Topics discussed include the identification of limitations in the study's search and quality assessment processes, the variability in control conditions across studies, and the geographical bias towards Western countries in the reviewed trials.

The conservation of the giant panda (Ailuropoda melanoleuca), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information, which mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images. [ABSTRACT FROM AUTHOR]

The parameter optimization coupled with the control strategy and target driving cycle directly affects the performance of vehicles. This paper proposes an optimization approach for a plug‐in hybrid electric truck (PHET), which considers comprehensive performances including fuel economy, emissions, vehicle drivability, safety, and dynamics. First, 10 initial design parameters are selected from powertrain components and a real‐time energy management strategy (EMS). Then, a definitive screening design (DSD) is proposed to simplify the design parameters. Finally, non‐dominated sorting genetic algorithm‐III (NSGA‐III) is proposed to solve a constrained many‐objective optimization problem with 9 objectives, and the design space is refined through a sensitivity analysis. Simulation results demonstrate that the proposed optimization approach can achieve significant improvements regarding both the comprehensive performances, power repartition, and system efficiency. The simulation is conducted both on Chinese Heavy‐Duty Commercial Vehicle Test Cycle (CHTC) and Urban Dynamometer Driving Schedule for Heavy‐Duty Vehicles (UDDSHDV). In addition, to guide a decision maker (DM) to make trade‐offs among many objectives, preferences are also incorporated into the solutions. [ABSTRACT FROM AUTHOR]

Background: Biopsy Gleason score (GS) is crucial for prostate cancer (PCa) treatment decision-making. Upgrading in GS from biopsy to radical prostatectomy (RP) puts a proportion of patients at risk of undertreatment.Purpose: To develop and validate a radiomics model based on multiparametric magnetic resonance imaging (mp-MRI) to predict PCa upgrading.Study Type: Retrospective, radiomics.Population: A total of 166 RP-confirmed PCa patients (training cohort, n = 116; validation cohort, n = 50) were included.Field Strength/sequence: 3.0T/T2 -weighted (T2 W), apparent diffusion coefficient (ADC), and dynamic contrast enhancement (DCE) sequences.Assessment: PI-RADSv2 score for each tumor was recorded. Radiomic features were extracted from T2 W, ADC, and DCE sequences and Mutual Information Maximization criterion was used to identify the optimal features on each sequence. Multivariate logistic regression analysis was used to develop predictive models and a radiomics nomogram and their performance was evaluated.Statistical Tests: Student's t or chi-square were used to assess the differences in clinicopathologic data between the training and validation cohorts. Receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated.Results: In PI-RADSv2 assessment, 67 lesions scored 5, 70 lesions scored 4, and 29 lesions scored 3. For each sequence, 4404 features were extracted and the top 20 best features were selected. The radiomics model incorporating signatures from the three sequences achieved better performance than any single sequence (AUC: radiomics model 0.868, T2 W 0.700, ADC 0.759, DCE 0.726). The combined mode incorporating radiomics signature, clinical stage, and time from biopsy to RP outperformed the clinical model and radiomics model (AUC: combined model 0.910, clinical model 0.646, radiomics model 0.868). The nomogram showed good performance (AUC 0.910) and calibration (P-values: training cohort 0.624, validation cohort 0.294).Data Conclusion: Radiomics based on mp-MRI has potential to predict upgrading of PCa from biopsy to RP.Level Of Evidence: 3 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2020;52:1239-1248. [ABSTRACT FROM AUTHOR]

Copyright of China Harbour Engineering is the property of Editorial Office of China Harbour Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

T cell immunoglobulin domain and mucin domain‐containing molecule 3 (TIM‐3) is found expression in the surface of terminally differentiated T cells and belongs to the TIM family of type Ⅰ transmembrane proteins. It binds to the ligand Galectin‐9 and mediates T cell apoptosis. As the research progresses, TIM‐3 is also expressed in Th17, NK, monocyte, which binds to ligand and induce immune peripheral tolerance in both mice and man. Numerous researches have demonstrated that TIM‐3 influences liver diseases, including liver‐associated chronic viral infection, liver fibrosis, liver cancer et al and suggest new approaches to intervention. Currently, targeted therapy of TIM‐3 is a new treatment in the field of immunization. Although many studies have proven that TIM‐3 has an inhibitory effect in vivo, the specific mechanism is not clear. Herein, we summarize the important role of TIM‐3 in the regulation of liver disease and prospects for future clinical research. TIM‐3 will provide new targets for improving clinical liver disease. [ABSTRACT FROM AUTHOR]

Purpose: To develop a model to select appropriate candidates for irradiation stent placement among patients with unresectable pancreatic cancer with malignant biliary obstruction (UPC-MBO). Methods: This retrospective study included 106 patients treated with an irradiation stent for UPC-MBO. These patients were randomly divided into a training group (74 patients) and a validation group (32 patients). A clinical model for predicting restenosis-free survival (RFS) was developed with clinical predictors selected by univariate and multivariate analyses. After integrating the radiomics signature, a combined model was constructed to predict RFS. The predictive performance was evaluated with the concordance index (C-index) in both the training and validation groups. The median risk score of progression in the training group was used to divide patients into high- and low-risk subgroups. Results: Radiomics features were integrated with clinical predictors to develop a combined model. The predictive performance was better in the combined model (C-index, 0.791 and 0.779 in the training and validation groups, respectively) than in the clinical model (C-index, 0.673 and 0.667 in the training and validation groups, respectively). According to the median risk score of 1.264, the RFS was significantly different between the high- and low-risk groups (p < 0.001 for the training group, and p = 0.016 for the validation group). Conclusions: The radiomics-based model had good performance for RFS prediction in patients with UPC-MBO who received an irradiation stent. Patients with slow progression should consider undergoing irradiation stent placement for a longer RFS. [ABSTRACT FROM AUTHOR]

With the remarkable proliferation of smart mobile devices, mobile crowdsensing has emerged as a compelling paradigm to collect and share sensor data from surrounding environment. In many application scenarios, due to unavailable wireless network or expensive data transfer cost, it is desirable to offload crowdsensing data traffic on opportunistic device-to-device (D2D) networks. However, coupling between mobile crowdsensing and D2D networks, it raises new technical challenges caused by intermittent routing and indeterminate settings. Considering the operations of data sensing, relaying, aggregating, and uploading simultaneously, in this article, we study collaborative mobile crowdsensing in opportunistic D2D networks. Toward the concerns of sensing data quality, network performance and incentive budget, Minimum-Delay-Maximum-Coverage (MDMC) problem and Minimum-Overhead-Maximum-Coverage (MOMC) problem are formalized to optimally search a complete set of crowdsensing task execution schemes over user, temporal, and spatial three dimensions. By exploiting mobility traces of users, we propose an unified graph-based problem representation framework and transform MDMC and MOMC problems to a connection routing searching problem on weighted directed graphs. Greedy-based recursive optimization approaches are proposed to address the two problems with a divide-and-conquer mode. Empirical evaluation on both real-world and synthetic datasets validates the effectiveness and efficiency of our proposed approaches. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs play important roles in the regulation of cellular processes including cell proliferation, differentiation, and metastasis. The dysregulation of long non-coding RNAs, such as the SPRY4-IT1 (SPRY4 intronic transcript 1), has been associated with various types of malignancies. However, the functional roles and regulatory mechanism of SPRY4-IT1 in ovarian cancer remain to be elucidated. Here, we quantified the expression level of SPRY4- IT1 in ovarian cancer patients and found its downregulation in ovarian cancer tissues compared to the adjacent normal tissues. Patients with lower SPRY4-IT1 expression were associated with a relatively poor prognosis. In consistency, the expression of SPRY4-IT1 was found to be reduced in four human ovarian cancer cell lines compared to normal ovarian epithelial cells. Next, two ovarian cancer cell lines SKOV3 and HO8910 were employed in vitro assays to investigate biological functions of SPRY4-IT1 in ovarian cancer. The cell proliferation was reduced following SPRY4- IT1 overexpression in SKOV3/HO8910 cells based on 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and colony formation assays. The SPRY4-IT1 overexpression also dramatically arrested cell cycle and promoted cell apoptosis. Both wound-healing and transwell-based assays demonstrated that cell migration and invasion were inhibited following SPRY4-IT1 overexpression. Meanwhile, overexpression of SPRY4-IT1 increased E-cadherin and decreased N-cadherin and vimentin protein levels, indicating that SPRY4-IT1 may regulate ovarian cancer cell metastasis through the inhibition of epithelial--mesenchymal transition. Taken together, our findings suggest that SPRY4-IT1 regulates various cellular processes of ovarian cancer cells and its downregulation may contribute to ovarian cancer progression and metastasis partly via affecting the epithelial--mesenchymal transition. [ABSTRACT FROM AUTHOR]

Detection of copy-move forgery has recently attracted much attention. During the past decade, two categories of methods, namely block-based and feature point-based methods, gradually developed. Compared with block-based methods, feature point-based methods exhibit remarkable performance with respect to robustness and computational cost. However, the feature point-based methods are still incomplete especially in terms of forgeries involving small smooth regions. In this paper, we solve this problem by cautiously supplementing redundant feature points and feature fusion. We propose two-stage feature detection to obtain better feature coverage and enhance the matching performance by combining the MROGH and HH descriptor. We evaluated our method on two representative datasets. We use precision, recall and F score to quantitatively evaluate the performance. Experimental results confirm the efficacy of our work. [ABSTRACT FROM AUTHOR]

Contraction-based skeleton extraction methods have the feature that during skeleton extraction process, the correspondence between skeleton and mesh regions can be obtained, which makes this class of algorithm attractive. Besides, among all mesh skeleton extraction methods, contraction-based methods possesses the merits of robustness to noise, rotation invariant and no requirement on additional boundary conditions. However, contraction-based methods still suffer some flaws such as not promising homotopy or centeredness, or not capable of processing non-closed meshes, etc. In this paper, an improved contraction-based skeleton extraction method is proposed which covers the failure of existing methods at non-closed part of a model and increases the rationality of the centeredness correction of the skeleton: First, non-closed models are virtually closed by a preprocessing stage such that models with boundaries can be contracted in the same way as the closed ones. Second, to improve the centeredness of the skeleton, we present a simpler and more effective one-ring area sequence weighting scheme by which the displacements measuring the shift of skeleton nodes can be calculated. Experimental results show the effectiveness of our work. [ABSTRACT FROM AUTHOR]

rough TiO film was fabricated by sol-gel technique, the film exhibits perfect super-hydrophobicity with a contact angle of 150° and glide angle of about 5° after being modified with octyltrimethoxysilane. The super-hydrophobicity was converted to super-hydrophilicity by the irradiation of ultraviolet light for 20 h, the irradiated super-hydrophilic film evolves to be hydrophobic with a contact angle of about 120° on the condition of being placed in dark for 2 weeks. Finally the property of super-hydrophobicity of TiO film modified with octyltrimethoxysilane was discussed on the basis of Cassie's theory. [ABSTRACT FROM AUTHOR]