A large-scale overexpression screen in Saccharomyces cerevisiae identifies previously uncharacterized cell cycle genes

We have undertaken an extensive screen to identify Saccharomyces cerevisiae genes whose products are involved in cell cycle progression. We report the identification of 113 genes, including 19 hypothetical ORFs, which confer arrest or delay in specific compartments of the cell cycle when overexpressed. The collection of genes identified by this screen overlaps with those identified in loss-of-function cdc screens but also includes genes whose products have not previously been implicated in cell cycle control. Through analysis of strains lacking these hypothetical ORFs, we have identified a variety of new CDC and checkpoint genes.

Mineral surfaces and bioavailability of heavy metals: A molecular-scale perspective

There is a continual influx of heavy metal contaminants and pollutants into the biosphere from both natural and anthropogenic sources. A complex variety of abiotic and biotic processes affects their speciation and distribution, including adsorption onto and desorption from mineral surfaces, incorporation in precipitates or coprecipitates, release through the dissolution of minerals, and interactions with plants and microbes. Some of these processes can effectively isolate heavy metals from the biosphere, whereas others cause their release or transformation to different species that may be more (or less) bioavailable and/or toxic to organisms. Here we focus on abiotic adsorption and precipitation or coprecipitation processes involving the common heavy metal contaminant lead and the metalloids arsenic and selenium in mine tailings and contaminated soils. We have used extremely intense x-rays from synchrotron sources and a structure-sensitive method known as x-ray absorption fine structure (XAFS) spectroscopy to determine the molecular-level speciation of these elements at concentrations of 50 to several thousand ppm in the contaminated environmental samples as well as in synthetic sorption samples. Our XAFS studies of As and Pb in the mine tailings show that up to 50% of these contaminants in the samples studied may be present as adsorbed species on mineral surfaces, which makes them potentially more bioavailable than when present in sparingly soluble solid phases. Our XAFS studies of Se(VI) sorption on Fe2+-containing sulfates show that this element undergoes redox reactions that transform it into less bioavailable and less toxic species. This type of information on molecular-level speciation of heavy metal and metalloid contaminants in various environmental settings is needed to prioritize remediation efforts and to assess their potential hazard to humans and other organisms.

Constraining storm-scale forecasts of deep convective initiation with surface weather observations

Thesis (Ph.D.)--University of Washington, 2016-06

A theoretical exploration of catchment-scale sediment delivery

This paper proposes a theoretical explanation of the variations of the sediment delivery ratio (SDR) versus catchment area relationships and the complex patterns in the behavior of sediment transfer processes at catchment scale. Taking into account the effects of erosion source types, deposition, and hydrological controls, we propose a simple conceptual model that consists of two linear stores arranged in series: a hillslope store that addresses transport to the nearest streams and a channel store that addresses sediment routing in the channel network. The model identifies four dimensionless scaling factors, which enable us to analyze a variety of effects on SDR estimation, including (1) interacting processes of erosion sources and deposition, (2) different temporal averaging windows, and (3) catchment runoff response. We show that the interactions between storm duration and hillslope/channel travel times are the major controls of peak-value-based sediment delivery and its spatial variations. The interplay between depositional timescales and the travel/residence times determines the spatial variations of total-volume-based SDR. In practical terms this parsimonious, minimal complexity model could provide a sound physical basis for diagnosing catchment to catchment variability of sediment transport if the proposed scaling factors can be quantified using climatic and catchment properties.

Blurred edges look faint, and faint edges look sharp:The effect of a gradient threshold in a multi-scale edge coding model

A multi-scale model of edge coding based on normalized Gaussian derivative filters successfully predicts perceived scale (blur) for a wide variety of edge profiles [Georgeson, M. A., May, K. A., Freeman, T. C. A., & Hesse, G. S. (in press). From filters to features: Scale-space analysis of edge and blur coding in human vision. Journal of Vision]. Our model spatially differentiates the luminance profile, half-wave rectifies the 1st derivative, and then differentiates twice more, to give the 3rd derivative of all regions with a positive gradient. This process is implemented by a set of Gaussian derivative filters with a range of scales. Peaks in the inverted normalized 3rd derivative across space and scale indicate the positions and scales of the edges. The edge contrast can be estimated from the height of the peak. The model provides a veridical estimate of the scale and contrast of edges that have a Gaussian integral profile. Therefore, since scale and contrast are independent stimulus parameters, the model predicts that the perceived value of either of these parameters should be unaffected by changes in the other. This prediction was found to be incorrect: reducing the contrast of an edge made it look sharper, and increasing its scale led to a decrease in the perceived contrast. Our model can account for these effects when the simple half-wave rectifier after the 1st derivative is replaced by a smoothed threshold function described by two parameters. For each subject, one pair of parameters provided a satisfactory fit to the data from all the experiments presented here and in the accompanying paper [May, K. A. & Georgeson, M. A. (2007). Added luminance ramp alters perceived edge blur and contrast: A critical test for derivative-based models of edge coding. Vision Research, 47, 1721-1731]. Thus, when we allow for the visual system's insensitivity to very shallow luminance gradients, our multi-scale model can be extended to edge coding over a wide range of contrasts and blurs. © 2007 Elsevier Ltd. All rights reserved.

Developing a forest data portal to support multi-scale decision making

Forests play a pivotal role in timber production, maintenance and development of biodiversity and in carbon sequestration and storage in the context of the Kyoto Protocol. Policy makers and forest experts therefore require reliable information on forest extent, type and change for management, planning and modeling purposes. It is becoming increasingly clear that such forest information is frequently inconsistent and unharmonised between countries and continents. This research paper presents a forest information portal that has been developed in line with the GEOSS and INSPIRE frameworks. The web portal provides access to forest resources data at a variety of spatial scales, from global through to regional and local, as well as providing analytical capabilities for monitoring and validating forest change. The system also allows for the utilisation of forest data and processing services within other thematic areas. The web portal has been developed using open standards to facilitate accessibility, interoperability and data transfer.

A design and implementation methodology for high variety electronics assembly systems

Small indigenous manufacturers of electronic equipment are coming under increasingly severe pressure to adopt a strong defensive position against large multinational and Far Eastern companies. A common response to this threat has been for these firms to adopt a 'market driven' business strategy based on quality and customer service, rather than a 'technology led' strategy which uses technical specification and price to compete. To successfully implement this type of strategy there is a need for production systems to be redesigned to suit the new demands of marketing. Increased range and fast response require economy of scope rather t ban economy or scale while the organisation's culture must promote quality and process consciousness. This paper describes the 'Modular Assembly Cascade' concept which addresses these needs by applying the principles of flexible manufacturing (FMS) and just in time (,JlT) to electronics assembly. A methodology for executing the concept is also outlined. This is called DRAMA (Design Houtirw !'or· Adopting Modular Assembly).

Small-scale Fisheries and the Global Economy: Understanding Common-pool Resource Governance in the Context of Market Pressures, Neoliberal Policies, and Transnational Institutions

The purpose of this dissertation is to contribute to a better understanding of how global seafood trade interacts with the governance of small-scale fisheries (SSFs). As global seafood trade expands, SSFs have the potential to experience significant economic, social, and political benefits from participation in export markets. At the same time, market connections that place increasing pressures on resources pose risks to both the ecological and social integrity of SSFs. This dissertation seeks to explore the factors that mediate between the potential benefits and risks of global seafood markets for SSFs, with the goal of developing hypotheses regarding these relationships.

The empirical investigation consists of a series of case studies from the Yucatan Peninsula, Mexico. This is a particularly rich context in which to study global market connections with SSFs because the SSFs in this region engage in a variety of market-oriented harvests, most notably for octopus, groupers and snappers, lobster, and sea cucumber. Variation in market forms and the institutional diversity of local-level governance arrangements allows the dissertation to explore a number of examples.

The analysis is guided primarily by common-pool resource (CPR) theory because of the insights it provides regarding the conditions that facilitate collective action and the factors that promote long-lasting resource governance arrangements. Theory from institutional economics and political ecology contribute to the elaboration of a multi-faceted conceptualization of markets for CPR theory, with the aim of facilitating the identification of mechanisms through which markets and CPR governance actually interact. This dissertation conceptualizes markets as sets of institutions that structure the exchange of property rights over fisheries resources, affect the material incentives to harvest resources, and transmit ideas and values about fisheries resources and governance.

The case studies explore four different mechanisms through which markets potentially influence resource governance: 1) Markets can contribute to costly resource governance activities by offsetting costs through profits, 2) markets can undermine resource governance by generating incentives for noncompliance and lead to overharvesting resources, 3) markets can increase the costs of resource governance, for example by augmenting monitoring and enforcement burdens, and 4) markets can alter values and norms underpinning resource governance by transmitting ideas between local resource users and a variety of market actors.

Data collected using participant observation, survey, informal and structured interviews contributed to the elaboration of the following hypotheses relevant to interactions between global seafood trade and SSFs governance. 1) Roll-back neoliberalization of fisheries policies has undermined cooperatives’ ability to achieve financial success through engagement with markets and thus their potential role as key actors in resource governance (chapter two). 2) Different relations of production influence whether local governance institutions will erode or strengthen when faced with market pressures. In particular, relations of production in which fishers own their own means of production and share the collective costs of governance are more likely to strengthen resource governance while relations of production in which a single entrepreneur controls capital and access to the fishery are more likely to contribute to the erosion of resource governance institutions in the face of market pressures (chapter three). 3) By serving as a new discursive framework within which to conceive of and talk about fisheries resources, markets can influence norms and values that shape and constitute governance arrangements.

In sum, the dissertation demonstrates that global seafood trade manifests in a diversity of local forms and effects. Whether SSFs moderate risks and take advantage of benefits depends on a variety of factors, and resource users themselves have the potential to influence the outcomes of seafood market connections through local forms of collective action.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Creating restoration landscapes: partnerships in large-scale conservation in the UK

It is increasingly recognized that ecological restoration demands conservation action beyond the borders of existing protected areas. This requires the coordination of land uses and management over a larger area, usually with a range of partners, which presents novel institutional challenges for conservation planners. Interviews were undertaken with managers of a purposive sample of large-scale conservation areas in the UK. Interviews were open-ended and analyzed using standard qualitative methods. Results show a wide variety of organizations are involved in large-scale conservation projects, and that partnerships take time to create and demand resilience in the face of different organizational practices, staff turnover, and short-term funding. Successful partnerships with local communities depend on the establishment of trust and the availability of external funds to support conservation land uses. We conclude that there is no single institutional model for large-scale conservation: success depends on finding institutional strategies that secure long-term conservation outcomes, and ensure that conservation gains are not reversed when funding runs out, private owners change priorities, or land changes hands.

Efficient algorithms to solve scheduling problems with a variety of optimization criteria

La programmation par contraintes est une technique puissante pour résoudre, entre autres, des problèmes d’ordonnancement de grande envergure. L’ordonnancement vise à allouer dans le temps des tâches à des ressources. Lors de son exécution, une tâche consomme une ressource à un taux constant. Généralement, on cherche à optimiser une fonction objectif telle la durée totale d’un ordonnancement. Résoudre un problème d’ordonnancement signifie trouver quand chaque tâche doit débuter et quelle ressource doit l’exécuter. La plupart des problèmes d’ordonnancement sont NP-Difficiles. Conséquemment, il n’existe aucun algorithme connu capable de les résoudre en temps polynomial. Cependant, il existe des spécialisations aux problèmes d’ordonnancement qui ne sont pas NP-Complet. Ces problèmes peuvent être résolus en temps polynomial en utilisant des algorithmes qui leur sont propres. Notre objectif est d’explorer ces algorithmes d’ordonnancement dans plusieurs contextes variés. Les techniques de filtrage ont beaucoup évolué dans les dernières années en ordonnancement basé sur les contraintes. La proéminence des algorithmes de filtrage repose sur leur habilité à réduire l’arbre de recherche en excluant les valeurs des domaines qui ne participent pas à des solutions au problème. Nous proposons des améliorations et présentons des algorithmes de filtrage plus efficaces pour résoudre des problèmes classiques d’ordonnancement. De plus, nous présentons des adaptations de techniques de filtrage pour le cas où les tâches peuvent être retardées. Nous considérons aussi différentes propriétés de problèmes industriels et résolvons plus efficacement des problèmes où le critère d’optimisation n’est pas nécessairement le moment où la dernière tâche se termine. Par exemple, nous présentons des algorithmes à temps polynomial pour le cas où la quantité de ressources fluctue dans le temps, ou quand le coût d’exécuter une tâche au temps t dépend de t.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Optimization of the large scale synthesis of the LSF-20 cathode material for SOFCs

Póster presentado en: 21st World Hydrogen Energy Conference 2016. Zaragoza, Spain. 13-16th June, 2016

Preliminary Validation of the "Plausibility of Intraracial Marriage Scale": A Study of Upwardly Mobile African American Women

Like many Americans, African American women often view marriage as an important life goal; however, it is likely that many also view it as one that is out of reach (Dixon, 2009; Wallace, 2014). The present study sought to address this issue by developing a new measure to assess African American women’s experiences in this regard in hopes of shedding light on how it impacts their dating and mating experiences. A total of 251 heterosexual African American women who were either enrolled in college or who had completed some form of postsecondary education completed an online survey assessing a variety of topics including their attitudes and emotions regarding a shortage of marriageable men, their standards to marry, and their willingness to date interracially. Results from this study established preliminary construct validity for a new scale assessing African American women’s attitudes and emotions about the shortage of “marriageable” African American men.