Validation of Pre-/Post-TACE-Predict Models among Patients with Hepatocellular Carcinoma Receiving Transarterial Chemoembolization

This study attempted to validate the prognostic performance of the proposed Pre- and Post-TACE (transarterial chemoembolization)-Predict models, in comparison with other models for prognostication. One-hundred-and-eighty-seven patients with HCC who underwent TACE were recruited. Regarding overall survival (OS), the predictive performance of the Pre-TACE-Predict model (one-year integrated area under the curve (iAUC) 0.685 (95% confidence interval (CI) 0.593–0.772)) was better than that of the Post-TACE-Predict model (iAUC 0.659 (95% CI 0.580–0.742)). However, there was no significant statistical difference between two models at any time point. For comparison between models using pre-treatment factors, the modified hepatoma arterial embolization prognostic (mHAP)-II model demonstrated significantly better predictive performance at one year (iAUC 0.767 (95% CI 0.683–0.847)) compared with Pre-TACE-Predict. For comparison between models using first TACE response, the SNACOR model was significantly more predictive at one year (iAUC 0.778 (95% CI 0.687–0.866) vs. 0.659 (95% CI 0.580–0.742), respectively) and three years (iAUC 0.707 (95% CI 0.646–0.770) vs. 0.624 (95% CI 0.564–0.688), respectively) than the Post-TACE-Predict model. mHAP-II and SNACOR may be preferred over the Pre- and Post-TACE-Predict models, respectively, considering their similar or better performance and the ease of application.

Competition and Predation Models

The chapter “Competition and Predation Models” considers models with two or more interacting species. What needs to happen for there to be “stable equilibria” that contain all possible members of a system? This is where simple models can be useful: these interactions can be represented by mathematical equations, and then solved for conditions that allow species to coexist. This chapter shows three techniques that make it possible to take a model system and determine whether the system has a stable equilibrium with all members present. The basic principles of model stability are presented, as well as how mathematical models can be used to address basic ecological questions in competition and predator-prey systems. Isocline analysis and analytical stability analysis are explained as ways to predict model behavior and are then used to draw inferences about the processes acting in the real world.

Multi-Output Soft Sensor with a Multivariate Filter That Predicts Errors Applied to an Industrial Reactive Distillation Process

The paper deals with the problem of developing a multi-output soft sensor for the industrial reactive distillation process of methyl tert-butyl ether production. Unlike the existing soft sensor approaches, this paper proposes using a soft sensor with filters to predict model errors, which are then taken into account as corrections in the final predictions of outputs. The decomposition of the problem of optimal estimation of time delays is proposed for each input of the soft sensor. Using the proposed approach to predict the concentrations of methyl sec-butyl ether, methanol, and the sum of dimers and trimers of isobutylene in the output product in a reactive distillation column was shown to improve the results by 32%, 67%, and 9.5%, respectively.

Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models

Attention maps, a popular heatmap-based explanation method for Visual Question Answering (VQA), are supposed to help users understand the model by highlighting portions of the image/question used by the model to infer answers. However, we see that users are often misled by current attention map visualizations that point to relevant regions despite the model producing an incorrect answer. Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err. Error maps can indicate when a correctly attended region may be processed incorrectly leading to an incorrect answer, and hence, improve users’ understanding of those cases. To evaluate our new explanations, we further introduce a metric that simulates users’ interpretation of explanations to evaluate their potential helpfulness to understand model correctness. We finally conduct user studies to see that our new explanations help users understand model correctness better than baselines by an expected 30% and that our proxy helpfulness metrics correlate strongly (rho>0.97) with how well users can predict model correctness.

Identification and Validation of the Prognostic Stemness Biomarkers in Bladder Cancer Bone Metastasis

BackgroundBladder urothelial carcinoma (BLCA) is one of the most common urinary system malignancies with a high metastasis rate. Cancer stem cells (CSCs) play an important role in the occurrence and progression of BLCA, however, its roles in bone metastasis and the prognostic stemness biomarkers have not been identified in BLCA.MethodIn order to identify the roles of CSC in the tumorigenesis, bone metastasis and prognosis of BLCA, the RNA sequencing data of patients with BLCA were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) and the differential expressed genes (DEGs) were evaluated and identified. The associations between mRNAsi and the tumorigenesis, bone metastasis, clinical stage and overall survival (OS) were also established. The key prognostic stemness-related genes (PSRGs) were screened by Lasso regression, and based on them, the predict model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic (ROC) curve and the risk score. Additionally, in order to explore the key regulatory network, the relationship among differentially expressing TFs, PSRGs, and absolute quantification of 50 hallmarks of cancer were also identified by Pearson correlation analysis. To verify the identified key TFs and PSRGs, their expression levels were identified by our clinical samples via immunohistochemistry (IHC).ResultsA total of 8,647 DEGs were identified between 411 primary BLCAs and 19 normal solid tissue samples. According to the clinical stage, mRNAsi and bone metastasis of BLCA, 2,383 stage-related DEGs, 3,680 stemness-related DEGs and 716 bone metastasis-associated DEGs were uncovered, respectively. Additionally, compared with normal tissue, mRNAsi was significantly upregulated in the primary BLCA and also associated with the prognosis (P = 0.016), bone metastasis (P < 0.001) and AJCC clinical stage (P < 0.001) of BLCA patients. A total of 20 PSRGs were further screened by Lasso regression, and based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.699). Moreover, we found two key TFs (EPO, ARID3A), four key PRSGs (CACNA1E, LINC01356, CGA and SSX3) and five key hallmarks of cancer gene sets (DNA repair, myc targets, E2F targets, mTORC1 signaling and unfolded protein response) in the regulatory network. The tissue microarray of BLCA and BLCA bone metastasis also revealed high expression of the key TFs (EPO, ARID3A) and PRSGs (SSX3) in BLCA.ConclusionOur study identifies mRNAsi as a reliable index in predicting the tumorigenesis, bone metastasis and prognosis of patients with BLCA and provides a well-applied model for predicting the OS for patients with BLCA based on 20 PSRGs. Besides, we also identified the regulatory network between key PSRGs and cancer gene sets in mediating the BLCA bone metastasis.

Abrasion performance of the scraper conveyor chute under complex working conditions

In the coal mine transportation, the scraper conveyor chute is badly worn. However, the abrasion performance has not been well documented. In this study, the chute was wear tested under a single factor using a modified pin-on-disc apparatus, including normal load, sliding distance, bulk coal characteristics (water content, gangue content), and chute material. Experiments were also carried out with a simultaneous variation of these factors. Generally speaking, the influence of coal characteristics on the chute wear was obviously higher than that of normal load and sliding distance. An improved wear predict model was obtained taking the operational parameters and bulk coal characteristics into account. The wear mechanism involved micro-ploughing, corrosion, adhesion, and fatigue peeling.

A Predict Model to Evaluate the Level of HBV-DNA for the Patients With Chronic Hepatitis B Virus (CHB) in Clinic: a Cross-sectional Study

Background: The previous studies showed the correlation between HBsAg and serum HBV DNA levels were weak or missing. And the relationship of HBeAg and HBV DNA levels was lack.Objective: The study aims to investigate the correlation between HBeAg and HBV DNA levels, and to find an alternative tool to evaluate the HBV DNA levels for clinicians.Methods: We enrolled 1020 patients in this cross-sectional study. We divided the patients into four groups as: HBeAg positivity and negativity groups, high and low HBV DNA levels groups. Further, as to the levels of HBV DNA, we performed subgroups’ in HBeAg-positive and HBeAg-negative groups.Results: The levels of ALT, ALB and HBeAg were the independent factors for the serum HBV DNA in CHB patients. The predict model for the patients with HBeAg-positive was: M1 (high HBV DNA levels) = 1.412 × (1 for HBeAg-positive >16.15 S/CO or 0 for others) + 0.004 × (1 for ALT > 42.5 IU/L or 0 for others) −0.029 × (1 for ALB > 25.5 g/L or 0 for others) + 0.779, and the AUC was 0.606. And the predict model of patients with HBeAg-negative was: M2 (low levels of HBV DNA) = 0.385 − 0.005 × (1 for ALT > 36.5 IU/L or 0 for others) − 0.006 × (1 for TB > 11.15 umol/L or 0 for others), and the AUC was 0.609.Conclusion: HBeAg was an independent risk factor for the patients with HBeAg(+), and when the level of HBeAg was higher than 16.15 S/CO, the patients should have a HBV-DNA test, if not , we should combine with the level of ALT to determine the decision. For the patients with HBeAg(-), we should evaluate the patients to have a HBV-DNA test by the levels of ALT and TB.

Progression signature underlies clonal evolution and dissemination of multiple myeloma

Clonal evolution drives tumor progression, dissemination and relapse in multiple myeloma (MM), with most patients dying of relapsed disease. This multi-stage process requires tumor cells to enter the circulation, extravasate and colonize distant bone marrow (BM) sites. Here, we developed a fluorescent or DNA-barcode clone-tracking system on MM PrEDiCT (Progression through Evolution and Dissemination of Clonal Tumor cells) xenograft mouse model to study clonal behavior within the BM microenvironment. We showed that only the few clones that successfully adapt to the BM microenvironment can enter the circulation and colonize distant BM sites. RNA-sequencing of primary and distant-site MM tumor cells revealed a progression signature sequentially activated along human MM progression and significantly associated with overall survival when evaluated against patient datasets. 28 genes were then computationally predicted to be master regulators (MRs) of MM progression. HMGA1 and PA2G4 were validated in vivo using CRISPR/Cas9 in PrEDiCT model and were shown to be significantly depleted in distant BM sites indicating their role in MM progression and dissemination. Loss of HMGA1 and PA2G4 also compromised the proliferation, migration and adhesion abilities of MM cells in vitro. Overall, our model successfully recapitulates key characteristics of human MM disease progression and identified potential new therapeutic targets for MM.