BACKGROUND: Vitamin D plays a role in cancer tumorogenesis and acts through the vitamin D receptor (VDR). Although African Americans have the lowest serum vitamin D levels, supplementation has not yielded a significant improvement in cancer. Gene polymorphisms in VDR may play a role. There is a dearth of information on VDR gene polymorphisms and colorectal cancer (CRC) among under-represented ethnic groups. In this study, the authors examined whether VDR gene single nucleotide polymorphisms (SNPs) were associated with CRC in predominately African American and Hispanic study participants. METHODS: Blood samples were collected from 378 participants, including a group of 78 patients with CRC (cases), a group of 230 noncancer participants without polyps (controls without polyps), and a group of 70 noncancer participants with polyps (controls with polyps). The 4 polymorphic SNPs in VDR (FokI, BsmI, TaqI, and ApaI) were assessed using the polymerase chain reaction-restriction fragment length polymorphism method. RESULTS: There was a significant association of the VDR-FokI FF genotype with CRC cases (odds ratio, 2.9; P 5.036) compared with the controls without polyps. The most common VDR-FokI genotype in the overall study population was the FF genotype (46%). However, upon breakdown by ethnicity, the FF genotype was the most common in African American participants (61%), and the Ff genotype was the most common in Hispanic=Latino participants (49%). When the association was assessed in a multivariate model, there was no significant association with any VDR polymorphism and CRC cases (P >.05). The other 3 polymorphic variants of VDR (BsmI, TaqI, and ApaI) were not associated with CRC. CONCLUSIONS: The results from this study suggest that genetic variation of the VDR-FokI SNPs may influence CRC risk, particularly in African American cohorts. Cancer 2014;120:1387-93.

We sought to assess the significance of an incidental finding of colorectal wall thickening (CRWT) on computed tomography (CT) scan in African-American and Hispanic patients. We retrospectively reviewed charts of African-American and Hispanic patients from January 1994 to December 2005. Those patients were included in whom the colonoscopy was performed due to incidental CRWT on CT scan. Patients with a history or a family history of colorectal malignancy, inflammatory bowel disease, or colorectal surgery, with an incomplete colonoscopic examination, or <18 years of age were excluded. Endoscopic and pathological findings were abstracted. Thirty-two patients met the criteria. Endoscopic examination was abnormal in 21 (65.6%). The positive predictive value of CRWT for abnormal endoscopic examination was 65.6%. Abnormal endoscopic examination revealed diverticulosis in 9 (43%), erythematous mucosa in 8 (38%), polyps in 6 (29%), mass in 2 (9%), thickened folds in 1 (5%), and diverticulitis in 1 (5%). Histopathological findings revealed colitis in 7 (33%), adenoma in 4 (19%), hyperplastic polyps in 4 (19%), adenocarcinoma in 2 (9%), lymphoid aggregates in 2 (9%), melanosis coli in 1 (5%), and normal in 1 (5%) in the abnormal examination group. Abnormal endoscopic examination was found in 65.6% of patients. The prevalence of colitis, adenomas, and malignancy was high, therefore abnormal CRWT warrants further endoscopic evaluation.

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Integrated operational decision-making in chemical plants is important for improving profitability. Integrated scheduling and control frameworks have been developed to enhance coordination between tactical and operational decisions. As such frameworks start to incorporate more features (e.g., uncertainty, detailed nonlinear process models) the computation time and resources that they require may increase significantly and lead to solutions that cannot be implemented online. Leveraging deep learning models that can capture process nonlinearities without a significant computational burden is a potential solution to this problem. Motivated by the above, we propose a two-stage stochastic integrated deep learning−scheduling−optimal control framework that can be used to model any batch process and can be solved efficiently. The benefits are demonstrated using a prototype case study.

The increasing production of shale gas and, consequently, natural gas liquids (NGLs) provides opportunities to expand the U.S. chemical industry, leading to questions about how to best use these resources. We consider targets for the strategic use of ethane, the most abundant of the NGLs, by evaluating the impact of a potential, new catalytic dehydrogenation technology for converting ethane to ethylene and then evaluating potential, new catalytic oligomerization processes for converting ethylene to 1-butylene and to 1-octene. To conduct these evaluations, we introduce a new, nonlinear, industry-wide, optimization-based network model of the U.S. petrochemical and refining industries. Unlike previous linear models of this type, the nonlinear model accounts for changes in intermediate prices and, thus, process costs as new technology is added to the industry network. A method for propagating cost and price changes, permitting the solution of the nonlinear optimization problem as a sequence of linear problems, is developed and utilized. Using network models for this study, we account for and identify the direct and secondary consequences of introducing new technology on the rest of the industry. For each new technology evaluated, we determine the production level of the technology in the optimal industry network. By doing this over a wide range of net process cost points, a maximum adoption cost (the net process cost beyond which the technology would not be adopted) can be identified, and its sensitivity to the assumed product yield determined for each new technology can be studied. The maximum adoption costs can be viewed as targets for future catalyst research, reaction engineering, and process development work. Scenarios in which the ethane supply is constrained to current values and in which it is unconstrained are considered.