Profile Likelihood Biclustering

Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees. To address this shortcoming, we propose a new biclustering procedure based on profile likelihood. The procedure applies to a broad range of data modalities, including binary, count, and continuous observations. We prove that the procedure recovers the true row and column classes when the dimensions of the data matrix tend to infinity, even if the functional form of the data distribution is misspecified. The procedure requires computing a combinatorial search, which can be expensive in practice. Rather than performing this search directly, we propose a new heuristic optimization procedure based on the Kernighan-Lin heuristic, which has nice computational properties and performs well in simulations. We demonstrate our procedure with applications to congressional voting records, and microarray analysis.Comment: 40 pages, 11 figures; R package in development at https://github.com/patperry/biclustp

DEMAND ESTIMATION FOR AGRICULTURAL PROCESSING CO-PRODUCTS

Co-products of processing agricultural commodities are often marketed through private transaction rather than through public markets or those in which public transaction information is recorded or available. The resulting lack of historical price information prohibits the use of positive time series techniques to estimate demand. Demand estimates for co-products are of value to both livestock producers, who obtain them for use in livestock rations, and processors, who must sell or otherwise dispose of them. Linear programming has long been used, first by researchers and later as a mainstream tool for nutritionists and producers, to formulate least-cost livestock rations. Here it is used as a normative technique to estimate step function demand schedules for co-products by individual livestock classes within a crop-reporting district. Regression is then used to smooth step function demand schedules by fitting demand data to generalized Leontief cost functions. Seemingly unrelated regression is used to estimate factor demand first adjusted for data censoring using probit analysis. Demand by individual livestock classes is aggregated over the number of livestock within a region. Quantities demanded by beef cows for each of the three co-products considered, sugarbeet pulp, wheat middlings, and potato waste, are large relative to other species because of their predominance in the district. At the current price for sugarbeet pulp, quantity demanded by district livestock is low. However quantity demanded is price elastic and becomes much greater at lower prices. Wheat middlings can be an important component of livestock rations, even at higher prices. At a price slightly below the current price, local livestock demand would exhaust the wheat middlings produced at the district's only wheat processing plant. Potato waste is most appropriate for ruminant diets because these animals are able to consume a large quantity of this high moisture feedstuff. Potato waste can be a cost-effective component in beef and dairy rations. Practically, livestock markets for potato waste must be in close proximity to a potato processing plant. Its high moisture content limits the distance it can be economically transported. At current prices, potato waste can be economically included in the ration for beef cows on a farm nearly 100 miles from the processing plant, although storage challenges may restrict use of the feed to closer operations.co-products, demand estimation, econometrics, linear programming, Agribusiness,

DEMAND ESTIMATION FOR AGRICULTURAL PROCESSING CO-PRODUCTS

Co-products of processing agricultural commodities are often marketed through private transaction rather than through public markets or those in which public transaction information is recorded or available. The resulting lack of historical price information prohibits the use of positive time series techniques to estimate demand. Demand estimates for co-products are of value to both livestock producers, who obtain them for use in livestock rations, and processors, who must sell or otherwise dispose of them. Linear programming has long been used, first by researchers and later as a mainstream tool for nutritionists and producers, to formulate least cost livestock rations. Here it is used as a normative technique to estimate step function demand schedules for co-products by individual livestock classes within a region. Regression is then used to smooth step function demand schedules by fitting demand data to generalized Leontief cost functions. Seemingly unrelated regression is used to estimate factor demand first adjusted for data censoring using probit analysis. Demand by individual livestock classes is aggregated over the number of livestock within a region. Species important to demand for each co-product are identified and own price elasticity for individual livestock classes and all livestock are estimated.Agribusiness, Demand and Price Analysis,

Bayesian analysis of congruence of core genes in Prochlorococcus and Synechococcus and implications on horizontal gene transfer

It is often suggested that horizontal gene transfer is so ubiquitous in microbes that the concept of a phylogenetic tree representing the pattern of vertical inheritance is oversimplified or even positively misleading. “Universal proteins” have been used to infer the organismal phylogeny, but have been criticized as being only the “tree of one percent.” Currently, few options exist for those wishing to rigorously assess how well a universal protein phylogeny, based on a relative handful of well-conserved genes, represents the phylogenetic histories of hundreds of genes. Here, we address this problem by proposing a visualization method and a statistical test within a Bayesian framework. We use the genomes of marine cyanobacteria, a group thought to exhibit substantial amounts of HGT, as a test case. We take 379 orthologous gene families from 28 cyanobacteria genomes and estimate the Bayesian posterior distributions of trees – a “treecloud” – for each, as well as for a concatenated dataset based on putative “universal proteins.” We then calculate the average distance between trees within and between all treeclouds on various metrics and visualize this high-dimensional space with non-metric multidimensional scaling (NMMDS). We show that the tree space is strongly clustered and that the universal protein treecloud is statistically significantly closer to the center of this tree space than any individual gene treecloud. We apply several commonly-used tests for incongruence/HGT and show that they agree HGT is rare in this dataset, but make different choices about which genes were subject to HGT. Our results show that the question of the representativeness of the “tree of one percent” is a quantitative empirical question, and that the phylogenetic central tendency is a meaningful observation even if many individual genes disagree due to the various sources of incongruence

Use of Grounded Theory in Cardiovascular Research

While grounded theory is often cited in the qualitative literature as the methodology, there are few good examples of publications that follow the principles of grounded theory and result in an actual theory. The purpose of this paper is to demonstrate how the Corbin and Strauss (2015) method of grounded theory was used in a study looking at how patients with cardiovascular disease and diabetes develop health literacy skills that are used to manage their condition. The key principles of grounded theory include theoretical sampling, constant comparison, open, axial, and selective coding, the use of memoing, and theoretical saturation. Data collection in this study was in the form of semi-structured interviews of 16 patients with cardiovascular disease and diabetes, and 19 healthcare professionals that care for or educate these patients. Patients were recruited from a primary care medical practice, a cardiology medical practice, patient focused programs provided by the American Heart Association, and social media. Healthcare professionals were recruited from the medical practices, the American Heart Association, and social media. Each interview was recorded, transcribed, and coded. Insights from these interviews led to the development of the health literacy instructional mode, which explores the use of digital tools, instructional approaches, social support, and self-directed learning in the development of health literacy skills, and is an example of the use of grounded theory in cardiovascular research

Understanding Health Literacy Skills in Patients with Cardiovascular Disease and Diabetes

Health literacy is the ability to understand and act on health information and is linked to health outcomes. It is unclear how health literacy skills are developed in patients with complex conditions, such as cardiovascular disease and diabetes. The purpose of this grounded theory study was to gain perspectives of both patients and healthcare professionals on how health literacy skills were developed in patients with cardiovascular disease or diabetes. The research questions addressed how knowledge and skills were acquired, the role of digital tools, instructional strategies used by healthcare professionals, and how the instructional strategies of the healthcare professionals matched the learning preferences and needs of the patients. A social ecological framework was used, which underscored the importance of understanding health literacy from multiple sources. Semistructured interviews were conducted on 19 healthcare professionals and 16 patients. Emergent key themes included: (a) social support plays an important role as a learning opportunity; (b) many patients get their information from internet searches; (c) instructional strategies should be personalized, interactive, social, and relevant; and (d) patients are self-directed learners. Linking of these themes led to the development of the health literacy instructional model, which is a 3-step approach, including an emotional support, behavioral approach, and instructional strategy. Social support was the common element in all 3 phases and was perceived to be key to developing health literacy skills, resulting in the key implication for social change. Recommendations are to consider social support in the development of health literacy instructional strategies

Using Interactive Digital Wall (iWall) Technology to Promote Active Learning

Using Interactive Digital Wall (iWall) Technology to Promote Active Learning Garrat Cheryl Thompson (UNMC), Suhasini Kotcherlakota (UNMC), Patrick Rejda (UNMC), Paul Dye (UNMC) UNMC\u27s iWall technology bridges College of Nursing campuses across the state. The multi-taction iWall consists of from 9-12 high resolution video panels. These panels provide interactive monitor space on which to project class content and simultaneously allow instructor and student interaction with content. The iWalls across the state are connected, allowing interactions between students in different locations. Students at home or sites without iWall are able to view and participate in class activities via webinar technology. This presentation will discuss the use of iWall within the UNMC iEXCEL Visualization Hub to teach information mapping. Time will be allotted for questions and to discuss attendee proposals for use of such technology

Using Interactive Digital Wall (iWall) Technology to Promote Active Learning

Using Interactive Digital Wall (iWall) Technology to Promote Active Learning Garrat Cheryl Thompson (UNMC), Suhasini Kotcherlakota (UNMC), Patrick Rejda (UNMC), Paul Dye (UNMC) UNMC\u27s iWall technology bridges College of Nursing campuses across the state. The multi-taction iWall consists of from 9-12 high resolution video panels. These panels provide interactive monitor space on which to project class content and simultaneously allow instructor and student interaction with content. The iWalls across the state are connected, allowing interactions between students in different locations. Students at home or sites without iWall are able to view and participate in class activities via webinar technology. This presentation will discuss the use of iWall within the UNMC iEXCEL Visualization Hub to teach information mapping. Time will be allotted for questions and to discuss attendee proposals for use of such technology

Writing Across the Curriculum Spring 2021 Faculty Survey

In April 2021 the Docking Institute of Public Affairs conducted an online survey of FHSU faculty members for FHSU’s Writing Across the Curriculum Committee. The survey addressed attitudes, perceptions, and practices about writing assignments in undergraduate courses. This report provides univariate analysis of each survey question