An Extendable Gaussian Mixture Model for Lane-Based Queue Length Estimation Based on License Plate Recognition Data

Most existing studies on queue length estimation based on license plate recognition (LPR) data require multisection LPR data. Studies based on single-section LPR data cannot ensure the accuracy and stability of the estimates when missed detections occur, which greatly limits the practicability of existing studies. Therefore, using single-section LPR data, this study proposes a lane-based queue length estimation method based on a two-dimensional Gaussian mixture model. First, the LPR data were processed to obtain the departure times and time headway of vehicles. Then, the two-dimensional Gaussian distributions of queued vehicles and nonqueued vehicles were fitted, and the expectation-maximization algorithm was adopted to solve the distribution parameters. Finally, the queuing status of each vehicle was determined, and the lane-based queue length was estimated based on the last identified queued vehicle in the cycle. The empirical results showed that the mean absolute errors (MAEs) of the proposed method were just 1.3 veh/cycle under no missed detections and 2 veh/cycle under a 20% missed detection rate, outperforming existing methods. The simulation results indicate that the proposed method can achieve accurate estimates under various traffic demands. In addition, the proposed method can be extended to real-time applications and multisection LPR systems

Long non-coding RNA H19 regulates E2F1 expression by competitively sponging endogenous miR-29a-3p in clear cell renal cell carcinoma

Abstract Background Numerous recent studies indicate that the long non-coding RNAs (lncRNAs) are frequently abnormal expressed and take critical roles in many cancers. Renal cell carcinoma is the secondary malignant tumors in the urinary system and has high mortality and morbidity. Around 80% of RCCs is clear cell renal cell carcinoma (ccRCC) and is characterized by high metastasis and relapse rate. However, the clinical significances of lncRNAs in ccRCC are still unknown. Methods The human cancer lncRNA PCR array (Yingbio) was performed to detect the differentially expressed lncRNAs in human ccRCC samples. Real-time PCR (RT-PCR), dual-luciferase assay, RNA binding protein immunoprecipitation (RIP) assay, transwell assay, CCK-8 assay, and western blot were performed to explore the molecular mechanism of lncRNAs in ccRCC cell migration and invasion. Results In this study, lncRNA-H19 was high expressed and negatively correlated with miR-29a-3p in ccRCC. By bioinformatics software, dual-luciferase reporter and RIP assays, we verified that miR-29a-3p was identified as a direct target of lncRNA-H19. RT-PCR and western blot demonstrated that down-regulated lncRNA-H19 could affect the expression of miR-29a-3p targeting E2F1 with competitively binding miR-29a-3p. Furthermore, transwell assays indicated that lncRNA-H19 knockdown inhibited cells migration and invasion, but this effect was attenuated by co-transfection of lncRNA-H19 siRNA and miR-29a-3p inhibitor. Over expression of E2F1 could rescue lncRNA-H19 siRNA induced suppression on cell migration and invasion in ccRCC cells. Conclusions These results show a possible competing endogenous RNAs regulatory network involving lncRNA-H19 regulates E2F1 expression by competitively sponging endogenous miR-29a-3p in ccRCC. This mechanism may contribute to a better understanding of ccRCC pathogenesis, and lncRNA-H19 may be further considered as a potential therapeutic target for ccRCC intervention

Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang–Mekong River Basin

A warming climate will intensify the water cycle, resulting in an exacerbation of water resources crises and flooding risks in the Lancang–Mekong River Basin (LMRB). The mitigation of these risks requires accurate streamflow and flood simulations. Process-based and data-driven hydrological models are the two major approaches for streamflow simulations, while a hybrid of these two methods promises advantageous prediction accuracy. In this study, we developed a hybrid physics-data (HPD) methodology for streamflow and flood prediction under the physics-guided neural network modeling framework. The HPD methodology leveraged simulation information from a process-based model (i.e., VIC-CaMa-Flood) along with the meteorological forcing information (precipitation, maximum temperature, minimum temperature, and wind speed) to simulate the daily streamflow series and flood events, using a long short-term memory (LSTM) neural network. This HPD methodology outperformed the pure process-based VIC-CaMa-Flood model or the pure observational data driven LSTM model by a large margin, suggesting the usefulness of introducing physical regularization in data-driven modeling, and the necessity of observation-informed bias correction for process-based models. We further developed a gradient boosting tree method to measure the information contribution from the process-based model simulation and the meteorological forcing data in our HPD methodology. The results show that the process-based model simulation contributes about 30% to the HPD outcome, outweighing the information contribution from each of the meteorological forcing variables (<20%). Our HPD methodology inherited the physical mechanisms of the process-based model, and the high predictability capability of the LSTM model, offering a novel way for making use of incomplete physical understanding, and insufficient data, to enhance streamflow and flood predictions

Collecting Duct Carcinoma of the Kidney: A Single-Center Retrospective Study of 23 Cases

Objective: To explore the clinical, imaging, pathologic features, treatment, and prognostic outcomes in 23 cases of collecting duct carcinoma (CDC) from a single center. Methods: The clinical and imaging findings, pathological features, treatment methods, and outcomes of the 23 patients with CDC confirmed by microscopic examination between 2003 and 2020 at our institution were retrospectively reviewed. Descriptive statistics of demographic and clinical variables were applied. Kaplan–Meier method was used to analyze survival data and log-rank test statistic survival differences between groups. Cox regression analysis was employed to identify variables independently related to overall survival (OS). Results: A total of 23 patients with CDC were identified. The mean age was 50.8 years. Stage III or IV tumors were diagnosed in 82.6% of the patients at diagnosis. The average size of the tumor was 6.58 cm, and the left kidney was more involved than the right. The median OS was 12 months. The OS rates at 1 and 2 years were 43.5% and 26.1%, respectively. Twenty patients underwent nephrectomy, 3 underwent nephroureterectomy, and 9 (39.1%) patients received subsequent therapeutic interventions following surgery. Distant metastasis and no symptoms at initial diagnosis proved to be an independent factor of unfavorable survival in Cox regression analysis. Conclusions: CDC is a rare and highly aggressive malignant renal tumor, and most patients present at an advanced stage at initial diagnosis. More than half of the patients died within 1 year after surgery. Distant metastasis and no clinical symptoms at initial diagnosis were independent risk prognostic factors for patients with CDC

MicroRNA Expression Profiling in Clear Cell Renal Cell Carcinoma: Identification and Functional Validation of Key miRNAs

<div><p>Objective</p><p>This study aims to profile dysregulated microRNA (miRNA) expression in clear cell renal cell carcinoma (ccRCC) and to identify key regulatory miRNAs in ccRCC.</p><p>Methods and Results</p><p>miRNA expression profiles in nine pairs of ccRCC tumor samples at three different stages and the adjacent, non-tumorous tissues were investigated using miRNA arrays. Eleven miRNAs were identified to be commonly dysregulated, including three up-regulated (miR-487a, miR-491-3p and miR-452) and eight down-regulated (miR-125b, miR-142-3p, miR-199a-5p, miR-22, miR-299-3p, miR-29a, miR-429, and miR-532-5p) in tumor tissues as compared with adjacent normal tissues. The 11 miRNAs and their predicted target genes were analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and three key miRNAs (miR-199a-5p, miR-22 and miR-429) were identified by microRNA-gene network analysis. Dysregulation of the three key miRNAs were further validated in another cohort of 15 ccRCC samples, and the human kidney carcinoma cell line 786-O, as compared with five normal kidney samples. Further investigation showed that over-expression of miR-199a-5p significantly inhibited the invasion ability of 786-O cells. Luciferase reporter assays indicated that miR-199a-5p regulated expression of TGFBR1 and JunB by directly interacting with their 3’ untranslated regions. Transfection of miR-199a-5p successfully suppressed expression of TGFBR1 and JunB in the human embryonic kidney 293T cells, further confirming the direct regulation of miR-199a-5p on these two genes.</p><p>Conclusions</p><p>This study identified 11 commonly dysregulated miRNAs in ccRCC, three of which (miR-199a-5p, miR-22 and miR-429) may represent key miRNAs involved in the pathogenesis of ccRCC. Further studies suggested that miR-199a-5p plays an important role in inhibition of cell invasion of ccRCC cells by suppressing expression of TGFBR1 and JunB.</p></div

Three key miRNAs and their predicted target genes in cancer-related pathways.

<p>Three key miRNAs and their predicted target genes in cancer-related pathways.</p

MiRNA expression profiling in ccRCC using microarray analysis.

<p>Unsupervised clustering of differentially expressed miRNAs in the three stages of ccRCC tumors (GI, GII and GIII) as compared with adjacent normal tissues. Three ccRCC tumor tissues from each stage, as well as the adjacent nontumorous tissues were randomly selected and subjected to global miRNA expression profiling. Differential miRNA expression was analyzed by comparison of the tumor tissues with adjacent normal tissues. The miRNAs with more than two-fold change in expression were considered to be differentially expressed. The expression of these miRNA candidates is illustrated in the heat map. The brightest green, black, and brightest red colors represent low, medium, and high expression of miRNAs, respectively.</p

miR-199a-5p decreased invasive ability of ccRCC cells.

<p><b>(A)</b> Overexpression of miR-199a-5p in 786-O cells after miR-199a-5p mimics transfection compared with that in NC cells. <b>(B)</b> miR-199a-5p suppressed the invasive ability of 786-O cells as indicated by the transwell assay. Representative images from transwell assay (upper panel) and quantitative comparison of the invasive ability of cells transfected with miR-199a-5p mimics and NC mimics (lower panel) are shown.</p

miR-199a-5p directly suppressed expression of TGFBR1 and JunB in ccRCC.

<p><b>(A)</b> Relative luciferase activities in 293T cells co-transfected with psiCHECK luciferase reporter containing the miR-199a-5p recognition region and hsa-mir-199a-5p mimics or NC mimics(15 nM). <b>(B)</b> Relative luciferase activities in 293T cells co-transfected with psiCHECK luciferase reporter containing mutated miR-199a-5p recognition region in JunB (JunB-mut) or TGFBR1 (TGFBR-mut) and hsa-mir-199a-5p mimics or NC mimics (50 nM). Level of activity was calculated by normalizing <i>Renilla</i> luciferase to <i>Firefly</i> luciferase. P-values were determined by student <i>t</i>-test. Mean and SD were calculated from three independent experiments. <b>(C)</b> Western blotting showed the protein levels of TGFBR1 and JunB upon expression of miR-199a-5p mimics in human kidney carcinoma 786-O cells. β-actin was used as a loading control. Relative JunB and TGFBR1 protein levels were quantified and expressed as the ratio of JunB or TGFBR1 and β-actin.</p