Automated Particle Identification through Regression Analysis of Size, Shape and Colour

Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false). As such the computer program should be able to ”predict” with reasonable level of confidence if a given particle belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a logistic regression equation as they proved to have a relatively high predictive value on their own

Extended twin study of alcohol use in Virginia and Australia

Drinking alcohol is a normal behavior in many societies, and prior studies have demonstrated it has both genetic and environmental sources of variation. Using two very large samples of twins and their first-degree relatives (Australia ≈ 20,000 individuals from 8,019 families; Virginia ≈ 23,000 from 6,042 families), we examine whether there are differences: (1) in the genetic and environmental factors that influence four interrelated drinking behaviors (quantity, frequency, age of initiation, and number of drinks in the last week), (2) between the twin-only design and the extended twin design, and (3) the Australian and Virginia samples. We find that while drinking behaviors are interrelated, there are substantial differences in the genetic and environmental architectures across phenotypes. Specifically, drinking quantity, frequency, and number of drinks in the past week have large broad genetic variance components, and smaller but significant environmental variance components, while age of onset is driven exclusively by environmental factors. Further, the twin-only design and the extended twin design come to similar conclusions regarding broad-sense heritability and environmental transmission, but the extended twin models provide a more nuanced perspective. Finally, we find a high level of similarity between the Australian and Virginian samples, especially for the genetic factors. The observed differences, when present, tend to be at the environmental level. Implications for the extended twin model and future directions are discussed

Cross-cultural comparison of genetic and cultural transmission of smoking initiation using an extended twin kinship model

Background: Considerable evidence from twin and adoption studies indicates that genetic and shared environmental factors play a role in the initiation of smoking behavior. Although twin and adoption designs are powerful to detect genetic and environmental influences, they do not provide information on the processes of assortative mating and parent–offspring transmission and their contribution to the variability explained by genetic and/or environmental factors. Methods: We examined the role of genetic and environmental factors in individual differences for smoking initiation (SI) using an extended kinship design. This design allows the simultaneous testing of additive and non-additive genetic, shared and individual-specific environmental factors, as well as sex differences in the expression of genes and environment in the presence of assortative mating and combined genetic and cultural transmission, while also estimating the regression of the prevalence of SI on age. A dichotomous lifetime ‘ever’ smoking measure was obtained from twins and relatives in the ‘Virginia 30,000’ sample and the ‘Australian 25,000’. Results: Results demonstrate that both genetic and environmental factors play a significant role in the liability to SI. Major influences on individual differences appeared to be additive genetic and unique environmental effects, with smaller contributions from assortative mating, shared sibling environment, twin environment, cultural transmission, and resulting genotype-environment covariance. Age regression of the prevalence of SI was significant. The finding of negative cultural transmission without dominance led us to investigate more closely two possible mechanisms for the lower parent–offspring correlations compared to the sibling and DZ twin correlations in subsets of the data: (1) age × gene interaction, and (2) social homogamy. Neither of the mechanism provided a significantly better explanation of the data. Conclusions: This study showed significant heritability, partly due to assortment, and significant effects of primarily non-parental shared environment on liability to SI

Meiotic DSB patterning: A multifaceted process

Meiosis is a specialized two-step cell division responsible for genome haploidization and the generation of genetic diversity during gametogenesis. An integral and distinctive feature of the meiotic program is the evolutionarily conserved initiation of homologous recombination (HR) by the developmentally programmed induction of DNA double-strand breaks (DSBs). The inherently dangerous but essential act of DSB formation is subject to multiple forms of stringent and self-corrective regulation that collectively ensure fruitful and appropriate levels of genetic exchange without risk to cellular survival. Within this article we focus upon an emerging element of this control—spatial regulation—detailing recent advances made in understanding how DSBs are evenly distributed across the genome, and present a unified view of the underlying patterning mechanisms employed

A Spatially Distributed Water Balance Based on Physical, Isotropic and Airborne Remotely Sensed Data

Introduction: The objective of this research to develop a spatially distributed water balance model based on the integration of spatially distributed data. Progress this year has consisted of model development, instrument acquisition, installation and development of experimental procedures, and baseline data collection. The original research plan called for detailed observations related to the water balance over the year September 1991 to August 1992. The detailed measurements were to start with accumulation of the snowpack followed by melt and evapotranspiration measurements from March to August. The objective was to measure the energy balance parameters starting with the peak accumulation, through the melt and infiltration phases, the greenup of vegetation, the peak evapotranspiration period and the dry-down and senescence of grasses and other species in the upper Sheep Creek sub-basin of the Reynolds Creek Experimental Watershed. Groundwater depths as well as run-off in the stream were to be measured and samples of the snowpack water, soil water, groundwater and run-off water were to be taken for isotopic tracing. Unfortunately, average snow accumulation was well below average last winter. April 1st, snow course measurement indicated that the snowpack at the Reynolds mountain sub-basin was only 30% of normal. In the Upper Sheep Creek sub-basin, which usually has a 10-m high drift during this time of the year there was less than 0.5 m of snow. After consultation with the USDA scientists from the Northwest Watershed Research Center, we decided in February to postpone the field campaign for one year. In retrospect this was wise because there was no runoff reponse from Upper Sheep Creek (the basin where we plan to do our detailed model development) and few of the groundwater wells had measureable response, we would have had nothing to measure. The intensive field campaign will be conducted in Spring 1993. Although a repeat of last year\u27s condition is possible, the chances are very low and we are hopeful of better snowfall. A revised project time schedule is shown on the next page. This has us finished the field measurement in August 1993. Given this it is unlikely that all data reduction and modeling will be complete by August 15, 1993 so we will need until May 1993, a 9-month no-cost extention to complete the data analysis. In this report we describe our progress in terms of data base development, model development, and modeling based on data from earlier years. Some of this will be presented at the AGU Fall meeting in San Francisco in Devember (see abstract on page 3). We also developed snow-isotope fractionation studies, evapotranspiration model development, installation of soil moisture measurement equipment, and database development. Abstract: A distributed mass balance appraoch is being developed to model spatially variable hydrologic processes in an arid mountain watershed. The model will be applied to Upper Sheep Creek, a 26 ha catchment within the Reynolds Creek ARS Experimental Watershed, near Boise, ID. The model is based on a DEM representation of basin topography. A mass balance equation relating moisture inflow, outflow, and the change in storage is resolved to give the moisture defecit in each DEM cell. Moisture input is subsurface flow from up-gradient DEM cells and surface influx from rain or a spatially distributed energy-balance snowmelt model. Subsurface outlfow is determined from topographic slope and transmissivity, which is a function of moisture content. We illustrate the effects of topography on the areal distribution of soil moisture and the time variation of streamflow in Upper Sheep Creek and compare our results with field observations and streamflow measurements

Channel integrated optoelectronic tweezer chip for microfluidic particle manipulation

Light patterned electrical fields have been widely used for the manipulation of microparticles, from cells to microscopic electronic components. In this work, we explore a novel electromechanical phenomenon for particle focusing and sorting where the electrical field patterns are shaped by a combination of the light patterned photoconductor and the channel geometry. This effect results from the combination of particle polarisation described by the Clausius–Mossotti relation and the engineering of large electric gradients produced by choosing the channels height to suit the size of the particles being manipulated. The matched geometry increases the distortion of the field created by a combination of the illuminated photoconductor and the particles themselves and hence the non-uniformity of the field they experience. We demonstrate a new channel integration strategy which allows the creation of precisely defined channel structures in the OET device. By defining channels in photoresist sandwiched between upper and lower ITO coated glass substrates we produce robust channels of well controlled height tailored to the particle. Uniquely, the top substrate is attached before photolithographically defining the channels. We demonstrate versatile control using this effect with dynamically reconfigurable light patterns allowing the retention against flow, focusing and sorting of micro particles within the channels. Contrary to traditional designs, this channel integrated device allows patterned micro channels to be used in conjunction with conductive top and bottom electrodes producing optimal conditions for the dielectrophoretic manipulation as demonstrated by the rapid flow (up to 5mm s−1 ) in which the particles can be focuse

Evaluation of the Water Footprint of Beef Cattle Production in Nebraska

Data were compiled on feed usage to model the amount of water needed to produce beef in typical Nebraska production systems. Production systems where cows were wintered on corn residue utilized 18% less water than systems utilizing native range as a wintering source, because of water allocations. Therefore, the water footprint (gallons of water required to produce one pound of boneless meat) was decreased by 18%. In addition, increasing the dietary inclusion of distillers grains from 0% to 40% decreased the water footprint in the finishing phase by 29%, again based on water allocation. Utilizing corn residue and distillers grains in Nebraska beef cattle systems decreases the overall water footprint of production. Additionally, the water footprint of the systems analyzed was 80% green water as rain, minimizing the environmental impact of beef production on freshwater use and ecological water balance