Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

Insights into post-translational processing of beta-galactosidase in an animal model resembling late infantile human G-gangliosidosis

G(M1)-gangliosidosis is a lysosomal storage disorder caused by a deficiency of ss-galactosidase activity. Human GM1-gangliosidosis has been classified into three forms according to the age of clinical onset and specific biochemical parameters. In the present study, a canine model for type II late infantile human GM1-gangliosidosis was investigated 'in vitro' in detail. For a better understanding of the molecular pathogenesis underlying G(M1)-gangliosidosis the study focused on the analysis of the molecular events and subsequent intracellular protein trafficking of beta-galactosidase. In the canine model the genetic defect results in exclusion or inclusion of exon 15 in the mRNA transcripts and to translation of two mutant precursor proteins. Intracellular localization, processing and enzymatic activity of these mutant proteins were investigated. The obtained results suggested that the beta-galactosidase C-terminus encoded by exons 15 and 16 is necessary for correct C-terminal proteolytic processing and enzyme activity but does not affect the correct routing to the lysosomes. Both mutant protein precursors are enzymatically inactive, but are transported to the lysosomes clearly indicating that the amino acid sequences encoded by exons 15 and 16 are necessary for correct folding and association with protective protein/cathepsin A, whereas the routing to the lysosomes is not influenced. Thus, the investigated canine model is an appropriate animal model for the human late infantile form and represents a versatile system to test gene therapeutic approaches for human and canine G(M1)-gangliosidosis.

A Bayesian approach to estimating the regression coefficients of a multinomial logit model

A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ordinal scale response categories is presented. A Monte Carlo method is used to construct the posterior distribution of the link function. The link function is treated as an arbitrary scalar function. Then the Gauss-Markov theorem is used to determine a function of the link which produces a random vector of coefficients. The posterior distribution of the random vector of coefficients is used to estimate the regression coefficients. The method described is referred to as a Bayesian generalized least square (BGLS) analysis. Two cases involving multinominal logit models are described. Case I involves a cumulative logit model and Case II involves a proportional-odds model. All inferences about the coefficients for both cases are described in terms of the posterior distribution of the regression coefficients. The results from the BGLS method are compared to maximum likelihood estimates of the regression coefficients. The BGLS method avoids the nonlinear problems encountered when estimating the regression coefficients of a generalized linear model. The method is not complex or computationally intensive. The BGLS method offers several advantages over Bayesian approaches. ^

Analysis of life expectancy using linear and nonlinear models

Life expectancy has consistently increased over the last 150 years due to improvements in nutrition, medicine, and public health. Several studies found that in many developed countries, life expectancy continued to rise following a nearly linear trend, which was contrary to a common belief that the rate of improvement in life expectancy would decelerate and was fit with an S-shaped curve. Using samples of countries that exhibited a wide range of economic development levels, we explored the change in life expectancy over time by employing both nonlinear and linear models. We then observed if there were any significant differences in estimates between linear models, assuming an auto-correlated error structure. When data did not have a sigmoidal shape, nonlinear growth models sometimes failed to provide meaningful parameter estimates. The existence of an inflection point and asymptotes in the growth models made them inflexible with life expectancy data. In linear models, there was no significant difference in the life expectancy growth rate and future estimates between ordinary least squares (OLS) and generalized least squares (GLS). However, the generalized least squares model was more robust because the data involved time-series variables and residuals were positively correlated. ^

STUDYING AGGREGATE FORMATION BY AMYOTROPHIC LATERAL SCLEROSIS-ASSOCIATED MUTANT SOD1 PROTEIN IN DROSOPHILA MODEL

A common pathological hallmark of most neurodegenerative disorders is the presence of protein aggregates in the brain. Understanding the regulation of aggregate formation is thus important for elucidating disease pathogenic mechanisms and finding effective preventive avenues and cures. Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, is a selective neurodegenerative disorder predominantly affecting motor neurons. The majority of ALS cases are sporadic, however, mutations in superoxide dismutase 1 (SOD1) are responsible for about 20% of familial ALS (fALS). Mutated SOD1 proteins are prone to misfold and form protein aggregates, thus representing a good candidate for studying aggregate formation. The long-term goal of this project is to identify regulators of aggregate formation by mutant SOD1 and other ALS-associated disease proteins. The specific aim of this thesis project is to assess the possibility of using the well-established Drosophila model system to study aggregation by human SOD1 (hSOD1) mutants. To this end, using wild type and the three mutant hSOD1 (A4V, G85R and G93A) most commonly found among fALS, I have generated 16 different SOD1 constructs containing either eGFP or mCherry in-frame fluorescent reporters, established and tested both cell- and animal-based Drosophila hSOD1 models. The experimental strategy allows for clear visualization of ectopic hSOD1 expression as well as versatile co-expression schemes to fully investigate protein aggregation specifically by mutant hSOD1. I have performed pilot cell-transfection experiments and verified induced expression of hSOD1 proteins. Using several tissue- or cell type-specific Gal4 lines, I have confirmed the proper expression of hSOD1 from established transgenic fly lines. Interestingly, in both Drosophila S2 cells and different fly tissues including the eye and motor neurons, robust aggregate formation by either wild type or mutant hSOD1 proteins was not observed. These preliminary observations suggest that Drosophila might not be a good experimental organism to study aggregation and toxicity of mutant hSOD1 protein. Nevertheless this preliminary conclusion implies the potential existence of a potent protective mechanism against mutant hSOD1 aggregation and toxicity in Drosophila. Thus, results from my SOD1-ALS project in Drosophila will help future studies on how to best employ this classic model organism to study ALS and other human brain degenerative diseases.

Planimetric ice-flow convergence and flow-orthonormal strain rate across the Antarctic Ice Sheet, with links to model result files

Fast-flowing ice streams discharge most of the ice from the interior of the Antarctic Ice Sheet coastward. Understanding how their tributary organisation is governed and evolves is essential for developing reliable models of the ice sheet's response to climate change. Despite much research on ice-stream mechanics, this problem is unsolved, because the complexity of flow within and across the tributary networks has hardly been interrogated. Here I present the first map of planimetric flow convergence across the ice sheet, calculated from satellite measurements of ice surface velocity, and use it to explore this complexity. The convergence map of Antarctica elucidates how ice-stream tributaries draw ice from the interior. It also reveals curvilinear zones of convergence along lateral shear margins of streaming, and abundant convergence ripples associated with nonlinear ice rheology and changes in bed topography and friction. Flow convergence on ice-stream tributaries and their feeding zones is markedly uneven, and interspersed with divergence at distances of the order of kilometres. For individual drainage basins as well as the ice sheet as a whole, the range of convergence and divergence decreases systematically with flow speed, implying that fast flow cannot converge or diverge as much as slow flow. I therefore deduce that flow in ice-stream networks is subject to mechanical regulation that limits flow-orthonormal strain rates. These properties and the gridded data of convergence and flow-orthonormal strain rate in this archive provide targets for ice- sheet simulations and motivate more research into the origin and dynamics of tributarization.

Age model for ODP Leg 175 sites

Upwelling along the western coast of Africa south of the equator may be partitioned into three major areas, each having its own dynamics and history: (1) the eastern equatorial region, comprising the Congo Fan and the area of Mid-Angola; (2) the Namibia upwelling system, extending from the Walvis Ridge to Lüderitz; and (3) the Cape Province region, where upwelling is subdued. The highest nutrient contents in thermocline waters are in the northern region, the lowest in the southern one. Wind effects are at a maximum near the southern end of the Namibia upwelling system, and maximum productivity occurs near Walvis Bay, where the product between upwelling rate and nutrient content of upwelled waters is at a maximum. In the Congo/Angola region, opal tends to follow organic carbon quite closely in the Quaternary record. However, organic carbon has a strong precessional component, while opal does not. Despite relatively low opal content, sediments off Angola show the same patterns as those off the Congo; thus, they are part of the same regime. The spectrum shows nonlinear interference patterns between high- and low-latitude forcing, presumably tied to thermocline fertility and wind. On Walvis Ridge, as in the Congo-Angola region, the organic matter record behaves normally; that is, supply is high during glacial periods. In contrast, interglacial periods are favorable for opal deposition. The pattern suggests reduction in silicate content of the thermocline during glacial periods. The reversed phase (opal abundant during interglacials) persists during the entire Pleistocene and can be demonstrated deep into the Pliocene, not just on Walvis Ridge but all the way to the Oranje River and off the Cape Province. From comparison with other regions, it appears that silicate is diminished in the global thermocline, on average, whenever winds become strong enough to substantially shorten the residence time of silicate in upper waters (Walvis Hypothesis, solving the Walvis Paradox of reversed phase in opal deposition). The central discovery during Leg 175 was the documentation of a late Pliocene opal maximum for the entire Namibia upwelling system (early Matuyama Diatom Maximum [MDM]). The maximum is centered on the period between the end of the Gauss Chron and the beginning of the Olduvai Chron. A rather sharp increase in both organic matter deposition and opal deposition occurs near 3 Ma in the middle of the Gauss Chron, in association with a series of major cooling steps. As concerns organic matter, high production persists at least to 1 Ma, when there are large changes in variability, heralding subsequent pulsed production periods. From 3 to 2 Ma, organic matter and opal deposition run more or less parallel, but after 2 Ma opal goes out of phase with organic matter. Apparently, this is the point when silicate becomes limiting to opal production. Thus, the MDM conundrum is solved by linking planetary cooling to increased mixing and upwelling (ramping up to the MDM) and a general removal of silicate from the upper ocean through excess precipitation over global supply (ramping down from the MDM). The hypothesis concerning the origin of the Namibia opal acme or MDM is fundamentally the same as the Walvis Hypothesis, stating that glacial conditions result in removal of silicate from the thermocline (and quite likely from the ocean as a whole, given enough time). The Namibia opal acme, and other opal maxima in the latest Neogene in other regions of the ocean, marks the interval when a cooling ocean selectively removes the abundant silicate inherited from a warm ocean. When the excess silicate is removed, the process ceases. According to the data gathered during Leg 175, major upwelling started in the late part of the late Miocene. Presumably, this process contributed to the drawing down of carbon dioxide from the atmosphere, helping to prepare the way for Northern Hemisphere glaciation.

Trade Coefficients and the Role of Elasticity in a Spatial CGE Model Based on the Armington Assumption

The Armington Assumption in the context of multi-regional CGE models is commonly interpreted as follows: Same commodities with different origins are imperfect substitutes for each other. In this paper, a static spatial CGE model that is compatible with this assumption and explicitly considers the transport sector and regional price differentials is formulated. Trade coefficients, which are derived endogenously from the optimization behaviors of firms and households, are shown to take the form of a potential function. To investigate how the elasticity of substitutions affects equilibrium solutions, a simpler version of the model that incorporates three regions and two sectors (besides the transport sector) is introduced. Results indicate: (1) if commodities produced in different regions are perfect substitutes, regional economies will be either autarkic or completely symmetric and (2) if they are imperfect substitutes, the impact of elasticity on the price equilibrium system as well as trade coefficients will be nonlinear and sometimes very sensitive.