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SLC16A family members play crucial roles in tumorigenesis and tumor progression. However, the exact role of distinct members in the SLC16A family in human pancreatic cancer remains unclear. Integrated bioinformatics analysis for the identification of therapeutic targets for certain cancers based on transcriptomics, proteomics and high-throughput sequencing could help us obtain novel information and understand potential underlying molecular mechanisms. In the present study, we investigated SLC16A family members in pancreatic cancer through accumulated data from GEO (Gene Expression Omnibus), TCGA (The Cancer Genome Atlas) and other available databases. The expression profile, clinical application significance and prognostic value of the SLC16A family for patients with pancreatic cancer were explored. SLC16A1, SLC16A3 and SLC16A13 exhibited biomarker potential for prognosis, and we further identified their related genes and regulatory networks, revealing core molecular pathways that require further investigation for pancreatic cancer.

Soil classification and analysis are essential for understanding soil properties and serve as a foundation for various engineering projects. Traditional methods of soil classification rely heavily on costly and time-consuming laboratory and in-situ tests. In this study, Support Vector Machine (SVM) models were trained for soil classification using 649 Cone Penetration Test (CPT) datasets, specifically utilizing cone tip resistance ( ) and sleeve friction ( ) as input variables. Pearson correlation and sensitivity analysis confirmed that these variables are highly correlated with the classification results. To enhance classification performance, 25 optimization algorithms were applied, and the models were validated against an independent dataset of 208 CPT records. The results revealed that 23 of the algorithms successfully improved the SVM classification accuracy. Among these, 18 algorithms achieved higher accuracy than the current engineering standard, the “Code for in-situ Measurement of Railway Engineering Geology.” Notably, the Thermal Exchange Optimization (TEO) algorithm resulted in the most significant improvement, increasing the accuracy of the original SVM model by 10% and exceeding the standard by 4.3%. Moreover, the models were thoroughly evaluated using Monte Carlo simulations, confusion matrices, ROC curves, and 10 key performance metrics. In conclusion, integrating evolutionary algorithms with SVM for soil classification offers a promising approach to enhancing the efficiency and accuracy of soil analysis in engineering applications.

Background Synaptogyrin-2 (SYNGR2), as a member of synaptogyrin gene family, is overexpressed in several types of cancer. However, the role of SYNGR2 in pan-cancer is largely unexplored. Methods From the TCGA and GEO databases, we obtained bulk transcriptomes, and clinical information. We examined the expression patterns, prognostic values, and diagnostic value of SYNGR2 in pan-cancer, and investigated the relationship of SYNGR2 expression with tumor mutation burden (TMB), microsatellite instability (MSI), immune infiltration, and immune checkpoint (ICP) genes. The gene set enrichment analysis (GSEA) software was used to perform pathway analysis. Besides, we built a nomogram of liver hepatocellular carcinoma patients (LIHC) and validated its prediction accuracy. Results SYNGR2 was highly expressed in most cancers. The high expression of SYNGR2 significantly reduced the overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) in multiple types of cancer. Also, receiver operating characteristic (ROC) curve analysis demonstrated that SYNGR2 showed high accuracy in distinguishing cancerous tissues from normal ones. Moreover, SYNGR2 expression was correlated with TMB, MSI, immune scores, and immune cell infiltrations. We also analyzed the association of SYNGR2 with immunotherapy response in LIHC. Finally, a nomogram including SYNGR2 and pathologic T, N, M stage was built and exhibited good predictive power for the OS, DSS, and PFI of LIHC patients. Conclusion Overall, SYNGR2 is a critical oncogene in various tumors. SYNGR2 participates in the carcinogenic progression, and may contribute to the immune infiltration in tumor microenvironment. Our study suggests that SYNGR2 can serve as a predictor related to prognosis in pan-cancer, especially LIHC.

Objective The predictive biomarkers of immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC) still need to be further explored. This study aims to establish a new immune prognosis biomarker to predict the clinical outcomes of hepatocellular carcinoma patients receiving immune checkpoint inhibitors. Methods The subjects of this study were 151 HCC patients receiving ICIs at Harbin Medical University Cancer Hospital from January 2018 to December 2021. This study collected a wide range of blood parameters from patients before treatment and used Cox’s regression analysis to identify independent prognostic factors in blood parameters, as well as their β coefficient. The hepatocellular carcinoma immune prognosis score (HCIPS) was established through Lasso regression analysis and COX multivariate analysis. The cut-off value of HCIPS was calculated from the receiver operating characteristic (ROC) curve. Finally, the prognostic value of HCIPS was validated through survival analysis, stratified analyses, and nomograms. Results HCIPS was composed of albumin (ALB) and thrombin time (TT), with a cut-off value of 0.64. There were 56 patients with HCIPS < 0.64 and 95 patients with HCIPS ≥ 0.64, patients with low HCIPS were significantly related to shorter progression-free survival (PFS) (13.10 months vs. 1.63 months, P  < 0.001) and overall survival (OS) (14.83 months vs. 25.43 months, P  < 0.001). HCIPS has also been found to be an independent prognostic factor in this study. In addition, the stratified analysis found a significant correlation between low HCIPS and shorter OS in patients with tumor size ≥ 5 cm ( P of interaction = 0.032). The C-index and 95% CI of the nomograms for PFS and OS were 0.730 (0.680–0.779) and 0.758 (0.711–0.804), respectively. Conclusions As a new score established based on HCC patients receiving ICIs, HCIPS was significantly correlated with clinical outcomes in patients with ICIs and might serve as a new biomarker to predict HCC patients who cloud benefit from ICIs.

Novel view synthesis and 3D reconstruction have been extensively studied. Three-dimensional Gaussian Splatting (3DGS) has gained popularity due to its rapid training and real-time rendering capabilities. However, RGB imaging is highly dependent on ideal illumination conditions. In low-light situations such as at night or in the presence of occlusions, RGB images often suffer from blurred contours or even complete failure in imaging, which severely restricts the application of 3DGS in such scenarios. Thermal imaging technology, on the other hand, serves as an effective complement. Thermal images are solely influenced by heat sources and are immune to illumination conditions. This unique property enables them to clearly identify the contour information of objects in low-light environments. Nevertheless, thermal images exhibit significant limitations in presenting texture details due to their sensitivity to temperature variations rather than surface texture features. To capitalize on the strengths of both, we propose thermal geometrically accurate Gaussian Splatting (TGA-GS), a novel Gaussian Splatting model. TGA-GS is designed to leverage RGB and thermal information to generate high-quality meshes in low-light conditions. Meanwhile, given low-resolution thermal images and low-light RGB images as inputs, our method can generate high-resolution thermal and RGB images from novel viewpoints. Moreover, we also provide a real thermal imaging dataset captured with a handheld thermal infrared camera. This not only enriches the information content of the images but also provides a more reliable data basis for subsequent computer vision tasks in low-light scenarios.

Limited pedestrian microcosmic simulation models focus on the interactions between pedestrians and vehicles at unmarked roadways. Pedestrians tend to head to the destinations directly through the shortest path. So, pedestrians have inclined trajectories pointing destinations. Few simulation models have been established to describe the mechanisms underlying the inclined trajectories when pedestrians cross unmarked roadways. To overcome these shortcomings, achieve solutions for optimal design features before implementation, and help to make the design more rational, the paper establishes a modified social force model for interactions between pedestrians and vehicles at unmarked roadways. To achieve this goal, stop/go decision-making model based on gap acceptance theory and conflict avoidance models were developed to make social force model more appropriate in simulating pedestrian crossing behaviors at unmarked roadways. The extended model enables the understanding and judgment ability of pedestrians about the traffic environment and guides pedestrians to take the best behavior to avoid conflict and keep themselves safe. The comparison results of observed pedestrians’ trajectories and simulated pedestrians’ trajectories at one unmarked roadway indicate that the proposed model can be used to simulate pedestrian crossing behaviors at unmarked roadways effectively. The proposed model can be used to explore pedestrians’ trajectories variation at unmarked roadways and improve pedestrian safety facilities.

Pancreatic cancer (PC) is one of the leading causes of cancer-related deaths worldwide. Due to the shortage of effective biomarkers for predicting survival and diagnosing PC, the underlying mechanism is still intensively investigated but poorly understood. Long noncoding RNAs (lncRNAs) provide biological functional diversity and complexity in protein regulatory networks. Scientific studies have revealed the emerging functions and regulatory roles of lncRNAs in PC behaviors. It is worth noting that some in-depth studies have revealed that lncRNAs are significantly associated with the initiation and progression of PC. As lncRNAs have good properties for both diagnostic and prognostic prediction due to their translation potential, we herein address the current understanding of the multifaceted roles of lncRNAs as regulators in the molecular mechanism of PC. We also discuss the possibility of using lncRNAs as survival biomarkers and their contributions to the development of targeted therapies based on the literature. The present review, based on what we know about current research findings, may help us better understand the roles of lncRNAs in PC.

A lot of privacy protection technologies have been proposed, but most of them are independent and aim at protecting some specific privacy. There is hardly enough deep study into the attributes of privacy. To minimize the damage and influence of the privacy disclosure, the important and sensitive privacy should be a priori preserved if all privacy pieces cannot be preserved. This paper focuses on studying the attributes of the privacy and proposes privacy information security classification (PISC) model. The privacy is classified into four security classifications by PISC, and each classification has its security goal, respectively. Google search engine is taken as the research platform to collect the related data for study. Based on the data from the search engine, we got the security classifications of 53 pieces of privacy.

To estimate the capacity of roundabouts more accurately, the priority rank of each stream is determined through the classification technique given in the Highway Capacity Manual 2010 (HCM2010), which is based on macroscopical analysis of the relationship between entry flow and circulating flow. Then a conflict matrix is established using the additive conflict flow method and by considering the impacts of traffic characteristics and limited priority with high volume. Correspondingly, the conflict relationships of streams are built using probability theory. Furthermore, the entry capacity model of roundabouts is built, and sensitivity analysis is conducted on the model parameters. Finally, the entrance delay model is derived using queuing theory, and the proposed capacity model is compared with the model proposed by Wu and that in the HCM2010. The results show that the capacity calculated by the proposed model is lower than the others for an A-type roundabout, while it is basically consistent with the estimated values from HCM2010 for a B-type roundabout.

Speed is a fundamental measure of traffic performance for highway systems. There were lots of results for the speed characteristics of motorized vehicles. In this article, we studied the speed distribution for mixed bicycle traffic which was ignored in the past. Field speed data were collected from Hangzhou, China, under different survey sites, traffic conditions, and percentages of electric bicycle. The statistics results of field data show that the total mean speed of electric bicycles is 17.09 km/h, 3.63 km/h faster and 27.0% higher than that of regular bicycles. Normal, log-normal, gamma, and Weibull distribution models were used for testing speed data. The results of goodness-of-fit hypothesis tests imply that the log-normal and Weibull model can fit the field data very well. Then, the relationships between mean speed and electric bicycle proportions were proposed using linear regression models, and the mean speed for purely electric bicycles or regular bicycles can be obtained. The findings of this article will provide effective help for the safety and traffic management of mixed bicycle traffic.