This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely, the proximal policy optimization (PPO) method is used to control the mass flow rate of four synthetic jets symmetrically located on the upper and lower sides of a cylinder immersed in a two-dimensional flow domain. The learning environment supports four flow configurations with Reynolds numbers 100, 200, 300, and 400, respectively. A new smoothing interpolation function is proposed to help the PPO algorithm learn to set continuous actions, which is of great importance to effectively suppress problematic jumps in lift and allow a better convergence for the training process. It is shown that the DRL controller is able to significantly reduce the lift and drag fluctuations and actively reduce the drag by ∼5.7%, 21.6%, 32.7%, and 38.7%, at Re = 100, 200, 300, and 400, respectively. More importantly, it can also effectively reduce drag for any previously unseen value of the Reynolds number between 60 and 400. This highlights the generalization ability of deep neural networks and is an important milestone toward the development of practical applications of DRL to active flow control.

Cigarette smoking is the best established modifiable risk factor for pancreatic cancer. Genetic factors that underlie smoking-related pancreatic cancer have previously not been examined at the genome-wide level. Taking advantage of the existing Genome-wide association study (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study in 2028 cases and 2109 controls to examine gene-smoking interactions at pathway/gene/single nucleotide polymorphism (SNP) level. Using the likelihood ratio test nested in logistic regression models and ingenuity pathway analysis (IPA), we examined 172 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, 3 manually curated gene sets, 3 nicotine dependency gene ontology pathways, 17 912 genes and 468 114 SNPs. None of the individual pathway/gene/SNP showed significant interaction with smoking after adjusting for multiple comparisons. Six KEGG pathways showed nominal interactions (P < 0.05) with smoking, and the top two are the pancreatic secretion and salivary secretion pathways (major contributing genes: RAB8A, PLCB and CTRB1). Nine genes, i.e. ZBED2, EXO1, PSG2, SLC36A1, CLSTN1, MTHFSD, FAT2, IL10RB and ATXN2 had P interaction < 0.0005. Five intergenic region SNPs and two SNPs of the EVC and KCNIP4 genes had P interaction < 0.00003. In IPA analysis of genes with nominal interactions with smoking, axonal guidance signaling $$\left(P=2.12\times 1{0}^{-7}\right)$$ and α-adrenergic signaling $$\left(P=2.52\times 1{0}^{-5}\right)$$ genes were significantly overrepresented canonical pathways. Genes contributing to the axon guidance signaling pathway included the SLIT/ROBO signaling genes that were frequently altered in pancreatic cancer. These observations need to be confirmed in additional data set. Once confirmed, it will open a new avenue to unveiling the etiology of smoking-associated pancreatic cancer.

Concurrent diabetes has been linked with an increased risk of death in many cancers, but findings in pancreatic cancer have been inconsistent. We performed a systematic review and meta-analysis to assess the effect of diabetes on survival in patients with pancreatic cancer. Of 4, 463 original articles, 41 were included in the review; 29 studies with 33 risk estimates were included in the meta-analysis. In the overall comparison of patients with pancreatic cancer and diabetes with their nondiabetic counterparts, the former had significantly higher all-cause mortality (pooled HR: 1.13; 95% CI: 1.04–1.22). Subgroup analyses showed that diabetes was associated with poor survival in patients with resectable disease (HR: 1.37; 95% CI: 1.15–1.63) but not in those with unresectable disease (HR: 1.07; 95% CI: 0.89–1.29). The HR (95% CI) was 1.52 (1.20–1.93) for patients with new-onset diabetes (≤2 years of diabetes duration) and 1.22 (0.83–1.80) for those with longstanding diabetes (>2 years). Diabetes was associated with higher mortality overall in patients with pancreatic cancer. The effect of diabetes on overall survival was associated with the stages of tumor and the duration of diabetes.

ABO blood type has previously been identified as a risk factor for thrombosis and pancreatic cancer (PC). The aim of the study is to demonstrate the associations between ABO blood type and other clinical factors with the risk of thromboembolism (TE) in patients with PC. We conducted a retrospective study in 670 patients with pathologically confirmed pancreatic adenocarcinoma at the University of Texas MD Anderson Cancer Center. Clinical information was retrieved from medical records. ABO blood type was determined serologically and/or genetically. Logistic regression models, Kaplan–Meier plot, log-rank test, and Cox proportional hazard regression models were employed in data analysis. The incidence of TE was 35.2% in 670 patients who did not have TE prior to cancer diagnosis. Pulmonary embolism (PE) and deep vein thrombosis (DVT) consisted 44.1% of the TE events. Non-O blood type, pancreatic body/tail tumors, previous use of antithrombotic medication, and obesity (body mass index >30 kg/m2) were significant predictors for TE in general. Blood type A and AB, low hemoglobin level (≤10 g/dL), obesity, metastatic tumor, and pancreatic body/tail tumors were significant predictors for PE and DVT. Patients with metastatic tumor or pancreatic body/tail tumors had a much higher frequency of early TE events (≤3 months after cancer diagnosis); and early TE occurrence was a significant independent predictor for increased risk of death. These observations suggest that ABO non-O blood type is an independent predictor for TE in PC. A better understanding of the risk factors for TE in PC may help to identify patients who are most likely to benefit from prophylactic anticoagulation therapy.

As whole-exome/genome sequencing data become increasingly available in genetic epidemiology research consortia, there is emerging interest in testing the interactions between rare genetic variants and environmental exposures that modify the risk of complex diseases. However, testing rare-variant-based gene-by-environment interactions (GxE) is more challenging than testing the genetic main effects due to the difficulty in correctly estimating the latter under the null hypothesis of no GxE effects and the presence of neutral variants. In response, we have developed a family of powerful and data-adaptive GxE tests, called “aGE” tests, in the framework of the adaptive powered score test, originally proposed for testing the genetic main effects. Using extensive simulations, we show that aGE tests can control the type I error rate in the presence of a large number of neutral variants or a nonlinear environmental main effect, and the power is more resilient to the inclusion of neutral variants than that of existing methods. We demonstrate the performance of the proposed aGE tests using Pancreatic Cancer Case-Control Consortium Exome Chip data. An R package “aGE” is available at http://github.com/ytzhong/projects/.

Background Obesity and diabetes are potentially alterable risk factors for pancreatic cancer. Genetic factors that modify the associations of obesity and diabetes with pancreatic cancer have previously not been examined at the genome-wide level. Methods Using GWAS genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study of 2,028 cases and 2,109 controls to examine gene-obesity and gene-diabetes interactions in relation to pancreatic cancer risk by employing the likelihood ratio test (LRT) nested in logistic regression models and Ingenuity Pathway Analysis (IPA). Results After adjusting for multiple comparisons, a significant interaction of the chemokine signaling pathway with obesity (P = 3.29 × 10−6) and a near significant interaction of calcium signaling pathway with diabetes (P = 1.57 × 10−4) in modifying the risk of pancreatic cancer was observed. These findings were supported by results from IPA analysis of the top genes with nominal interactions. The major contributing genes to the two top pathways include GNGT2, RELA, TIAM1 and GNAS. None of the individual genes or SNPs except one SNP remained significant after adjusting for multiple testing. Notably, SNP rs10818684 of the PTGS1 gene showed an interaction with diabetes (P = 7.91 × 10−7) at a false discovery rate of 6%. Conclusions Genetic variations in inflammatory response and insulin resistance may affect the risk of obesity and diabetes-related pancreatic cancer. These observations should be replicated in additional large datasets. Impact Gene-environment interaction analysis may provide new insights into the genetic susceptibility and molecular mechanisms of obesity- and diabetes-related pancreatic cancer.

N-nitroso compounds (NOCs) are among the most potent dietary and pancreatic carcinogens. N-nitrosodiethylamine (NDEA) and N-nitrosodimethylamine (NDMA) are the most prevalent NOCs identified in foods. Using a validated and comprehensive N-nitroso database developed to estimate total NOCs and important individual NOCs from food intake, we investigated dietary exposure to NOCs in relation to pancreatic cancer in a large matched case-control study. Selfadministered food frequency questionnaires were collected from 957 pathologically confirmed pancreatic ductal adenocarcinoma cases and 938 frequency-matched controls. For each food item, frequency of intake and portion size in grams was multiplied by the estimated NOC concentration from the N-nitroso database. Multiple unconditional logistic regression models were used to estimate the odds ratios (OR) and 95% confidence intervals (CIs) for pancreatic cancer risk by quartiles of NOCs and major food group contributors to NOCs, with the lowest quartile as referent. Following adjustment for confounders, we observed significant positive associations for NDEA (OR Q4 versus Q1 = 2.28, 95% CI = 1.71-3.04, P trend < 0.0001) and NDMA from plant sources (OR Q4 versus Q1 = 1.93, 95% CI = 1.42-2.61, P trend < 0.0001) with pancreatic cancer. The major food groups related to NDEA and NDMA intakes in this population were fermented cheese, pizza, grains, seafood and beer. No associations of intake of nitrate or total NOCs were observed; nitrite was inversely associated with pancreatic cancer. Although some of our findings probably reflect reverse causation bias due to lower meat intake in cases with latent disease, biologically plausible findings for pancreatic carcinogens, NDEA and NDMA, warrant further prospective investigation.

Previous findings on the association of genetic factors and pancreatic cancer survival are limited and inconsistent. In a two-stage study, we analyzed the existing genome-wide association study dataset of 868 pancreatic cancer patients from MD Anderson Cancer Center in relation to overall survival using Cox regression. Top hits were selected for replication in another 820 patients from the same institution using the Taqman genotyping method. Functional annotation, pathway analysis, and gene expression analysis were conducted using existing software and databases. We discovered genome-wide significant associations of patient survival with three imputed SNPs which, in complete LD (r2=1), were intronic SNPs of the PAIP2B (rs113988120) and DYSF genes (rs112493246 and rs138529893) located on chromosome 2. The variant alleles were associated with a 3.06-fold higher risk of death (95% confidence interval [CI]=2.10-4.47, P = 6.4 × 10−9) after adjusting for clinical factors. Eleven SNPs were tested in the replication study and the association of rs113988120 with survival was confirmed (HR: 1.57, 95%CI: 1.13-2.20, P = 0.008). In silico analysis found rs1139988120 might lead to altered motif. This locus is in LD (D′=0.77) with 3 eQTL SNPs near or belong to the NAGK and MCEE genes. According to The Cancer Genome Atlas data and our previous RNA-sequencing data, the mRNA expression level of PAIP2B but not NAGK, MCEE or DYSF was significantly lower in pancreatic tumors than in normal adjacent tissues. Additional validation efforts and functional studies are warranted to demonstrate whether PAIP2B is a novel tumor suppressor gene and a potential therapeutic target for pancreatic cancer.