Pharyngeal and Cervical Cancer Incidences Significantly Correlate with Personal UV Doses Among Whites in the United States

Because we found UV-exposed oral tissue cells have reduced DNA repair and apoptotic cell death compared with skin tissue cells, we asked if a correlation existed between personal UV dose and the incidences of oral and pharyngeal cancer in the United States. We analyzed the International Agency for Research on Cancer\u27s incidence data for oral and pharyngeal cancers by race (white and black) and sex using each state\u27s average annual personal UV dose. We refer to our data as ‘white’ rather than ‘Caucasian,’ which is a specific subgroup of whites, and ‘black’ rather than African-American because blacks from other countries around the world reside in the U.S. Most oropharyngeal carcinomas harboured human papilloma virus (HPV), so we included cervical cancer as a control for direct UV activation. We found significant correlations between increasing UV dose and pharyngeal cancer in white males (p=0.000808) and females (p=0.0031) but not in blacks. Shockingly, we also found cervical cancer in whites to significantly correlate with increasing UV dose (p=0.0154). Thus, because pharyngeal and cervical cancer correlate significantly with increasing personal UV dose in only the white population, both direct (DNA damage) and indirect (soluble factors) effects may increase the risk of HPV-associated cancer

Setting optimal production lot sizes and planned lead times in a job shop

In this research, we model a job shop that produces a set of discrete parts in a make-to-stock setting. The intent of the research is to develop a planning model to determine the optimal tactical policies that minimise the relevant manufacturing costs subject to workload variability and capacity limits. We consider two tactical decisions, namely the production lot size for each part and the planned lead time for each work station. We model the relevant manufacturing costs, entailing production overtime costs and inventory-related costs, as functions of these tactical decisions. We formulate a non-linear optimisation model and implement it in the Excel spreadsheet. We test the model with actual factory data from our research sponsor. The results are consistent with our intuition and demonstrate the potential value from jointly optimising over these tactical policies

Localization of adaptive variants in human genomes using averaged one-dependence estimation.

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios

Velocity-based Storage Assignment in Semi-automated Storage Systems

Our research focuses on the storage decision in a semi-automated storage system, where the inventory is stored on mobile storage pods. In a typical system, each storage pod carries a mixture of items, and the inventory of each item is spread over multiple storage pods. These pods are transported by robotic drives to stationary stations on the boundary of the storage zone where associates conduct pick or stow operations. The storage decision is to decide to which storage location within the storage zone to return a pod upon the completion of a pick or stow operation. The storage decision has a direct impact on the total travel time, and hence the workload of the robotic drives. We develop a fluid model to analyze the performance of velocity-based storage policies. We characterize the maximum possible improvement from applying a velocity-based storage policy in comparison to the random storage policy. We show that class-based storage with two or three classes can achieve most of the potential benefits and that these benefits increase with greater variation in the pod velocities. To validate the findings, we build a discrete-time simulator with real industry data. We observe an 8% to 10% reduction in the travel distance with a 2-class or 3-class storage policy, depending on the parameter settings. From a sensitivity analysis we establish the robustness of the class-based storage policies as they continue to perform well under a broad range of warehouse settings including different zoning strategies, resource utilization and space utilization levels