The Role of Feminine and Masculine Norms in College Women's Alcohol Use

Current literature suggests not only that men and women can conform to both feminine and masculine norms, but that women who adhere to certain masculine norms may be at greater risk for problematic alcohol use. This study examined conformity to both masculine and feminine norms, and how conformity to distinct norms influenced heavy episodic drinking and alcohol-related problems among a sample of underage college women (N= 645). Results demonstrated that the masculine norms risk-taking and emotional control were associated with increased HED, while the masculine norm power over women was associated with a decrease in HED. Traditional feminine norms, including modesty and sexual fidelity, were associated with a decrease in HED and alcohol-related problems. The feminine norm relational was associated with increased HED, while the norms thinness and appearance were associated with increased alcohol-related problems. The study’s theoretical and clinical implications are discussed.

A combination of amino acids and caffeine enhances sprint running capacity in a hot, hypoxic environment.

The 2005 Illinois Drought

Illinois State Water Survey

The Water Cycle and Water Budgets in Illinois: A Framework for Drought and Water Supply Planning

Illinois State Water Survey

Insights to Key Questions about Climate Change

Illinois State Water Survey

Visitors' engagement and authenticity: Japanese heritage consumption

Understanding the sense of authenticity of heritage attractions is important for tourism management and marketing because presentation, interpretation and verification has a direct bearing on motivations to visit and engage with heritage tourism sites. This paper establishes relationships among the concepts of culturally specific motivation, perception of authenticity, engagement and attendant behavioral consequences based on domestic visitors' experiences at Japanese heritage sites. It further extends Kolar and Zabkar's (2010) model of authenticity by including concepts of serious leisure, heritage related behaviors, self-connection and their effects over engagement using Partial Least Square, whereby both formative and reflective scales are included. The structural model is tested with a sample of 768 visitors in a culturally specific setting of Japanese heritage sites. The empirical validation of the conceptual model supports the research hypotheses. These findings contribute to a better understanding of visitors' perceptions and valuation of authenticity in Japanese tourist attractions. Several implications can be drawn from the study findings and interesting directions for future research are provided.

Using technology to overcome the tyranny of space: information provision and wayfinding.

Urban wayfinding technology offers many possibilities by which older people and mobility-impaired users can overcome the barriers encountered on every-day journeys in the built environment. Previous work has highlighted the extent to which personal mobility and independence are significant determinants of the quality of life amongst both elderly and visually impaired groups. The paper outlines the development of the auditory location finder (ALF), which is a beacon-based local information system designed to enhance the wayfinding activities of these, and potentially other, user-groups in the community. The proposed system provides the user with an audio message, which is obtained on request via a small portable hand unit. The messages inform the user of their whereabouts and give information about the area that they are currently in. The development of the device involves issues such as message content and structure, route choice, orientation, landmarks, clues and the extent of user reliance on technology. Preliminary trials have been carried out in a UK city and have obtained initial user feedback to help underpin the technological development of the device and its potential application. The paper concludes by outlining the importance of new urban technology and the way in which such local information systems can potentially contribute to overcoming particular patterns of exclusion experienced by mobility-impaired groups, such as the visually impaired

SOCIAL AND ECOLOGICAL FACTORS INFLUENCING COLLECTIVE BEHAVIOR IN GIANT DANIO

A fundamental problem in biology is understanding how and why things group together. Collective behavior is observed on all organismic levels - from cells and slime molds, to swarms of insects, flocks of birds, and schooling fish, and in mammals, including humans. The long-term goal of this research is to understand the functions and mechanisms underlying collective behavior in groups. This dissertation focuses on shoaling (aggregating) fish. Shoaling behaviors in fish confer foraging and anti-predator benefits through social cues from other individuals in the group. However, it is not fully understood what information individuals receive from one another or how this information is propagated throughout a group. It is also not fully understood how the environmental conditions and perturbations affect group behaviors. The specific research objective of this dissertation is to gain a better understanding of how certain social and environmental factors affect group behaviors in fish. I focus on two ecologically relevant decision-making behaviors: (i) rheotaxis, or orientation with respect to a flow, and (ii) startle response, a rapid response to a perceived threat. By integrating behavioral and engineering paradigms, I detail specifics of behavior in giant danio Devario aequipinnatus (McClelland 1893), and numerically analyze mathematical models that may be extended to group behavior for fish in general, and potentially other groups of animals as well. These models that predict behavior data, as well as generate additional, testable hypotheses. One of the primary goals of neuroethology is to study an organism's behavior in the context of evolution and ecology. Here, I focus on studying ecologically relevant behaviors in giant danio in order to better understand collective behavior in fish. The experiments in this dissertation provide contributions to fish ecology, collective behavior, and biologically-inspired robotics.

Genetic control of development in the parasitic nematode Haemonchus contortus by microRNAs

The parasitic nematode Haemonchus contortus has a major impact on the welfare and economic sustainability of small ruminant farming throughout the world. Increasing drug resistance requires the development of novel therapeutic agents. To further this process, we examined the fundamental biology of development in H. contortus, specifically, the potential role of microRNAs (miRNAs). miRNAs are short, non-coding RNA molecules that negatively regulate gene expression. In the free-living nematode Caenorhabditis elegans, miRNAs regulate a variety of genes including those involved in development. This thesis describes the expression patterns, potential targets and possible functions of miRNAs in H. contortus throughout development.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Cirrhosis Diagnosis and Liver Fibrosis Staging: Transient Elastometry Versus Cirrhosis Blood Test.

International audience

Applications of Photoinduced Electron Transfer Chemistry: Photoremovable Protecting Groups and Carbon Dioxide Conversion

Traditional organic chemistry has long been dominated by ground state thermal reactions. The alternative to this is excited state chemistry, which uses light to drive chemical transformations. There is considerable interest in using this clean renewable energy source due to concerns surrounding the combustion byproducts associated with the consumption of fossil fuels. The work presented in this text will focus on the use of light (both ultraviolet and visible) for the following quantitative chemical transformations: (1) the release of compounds containing carboxylic acid and alcohol functional groups and (2) the conversion of carbon dioxide into other useable chemicals. Chapters 1-3 will introduce and explore the use of photoremovable protecting groups (PPGs) for the spatiotemporal control of molecular concentrations. Two new PPGs are discussed, the 2,2,2-tribromoethoxy group for the protection of carboxylic acids and the 9-phenyl-9-tritylone group for the protection of alcohols. Fundamental interest in the factors that affect C–X bond breaking has driven the work presented in this text for the release of carboxylic acid substrates. Product analysis from the UV photolysis of 2,2,2-tribromoethyl-(2′-phenylacetate) in various solvents results in the formation of H–atom abstraction products as well as the release of phenylacetic acid. The deprotection of alcohols is realized through the use of UV or visible light photolysis of 9-phenyl-9-tritylone ethers. Central to this study is the use of photoinduced electron transfer chemistry for the generation of ion diradicals capable of undergoing bond-breaking chemistry leading to the release of the alcohol substrates. Chapters 4 and 5 will explore the use of N-heterocyclic carbenes (NHCs) as a catalyst for the photochemical reduction of carbon dioxide. Previous experiments have demonstrated that NHCs can add to CO2 to form stable zwitterionic species known as N-heterocylic-2-carboxylates (NHC–CO2). Work presented in this text illustrate that the stability of these species is highly dependent on solvent polarity, consistent with a lengthening of the imidazolium to carbon dioxide bond (CNHC–CCO2). Furthermore, these adducts interact with excited state electron donors resulting in the generation of ion diradicals capable of converting carbon dioxide into formic acid.