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År 1855 klassificerades slotten i vindistriktet runt Bordeaux. Denna klassificering fick stort genomslag och används än idag. Klassificeringen har emellertid kritiserats för att ej representera den faktiska kvaliteten av dess innefattande viner. Trots det används den som ett facit över vinernas storhet. Detta arbete har undersökt samband mellan klassificeringen och vinernas kvalitet idag. Resultatet av litteraturstudien fann samband mellan bordeaux, pris och marknadsföring. Vidare redogörs för att begreppet kvalitet är svårdefinierat då flertalet aktörer tolkar kvalitet på skilda sätt. Influensen av terroir är ej utan vinmakaren en relevant faktor för de sensoriska egenskaperna i vinet. Faktisk kvalitet är mindre betydelsefullt än förväntad kvalitet ur ett marknadsperspektiv. Arbetet diskuterar kvalitetens förankring och vikt i klassificeringen från 1855. Marknadsföring är en stark variabel vid prissättning av viner från Bordeaux i vilken förväntad kvalitet vägde tyngst för priset. Vinkritiker är inflytelserika i en vinproducents förväntade kvalitet. Diskussionen behandlar även olika ståndpunkter i användande av begreppet terroir. De innefattande vinernas faktiska kvalitet var av mindre vikt när klassificeringen genomfördes. Rankningen har idag lett till en generellt högre kvalitet bland vinerna. Klassificeringen har även skänkt status åt regionen och dess viner. / B-uppsatser

This thesis describes the procedure employed in writing and testing programed instruction on the subject of biosystematics. It briefly discusses similar studies that have been done with programed and other self-instructional materials. A review of the literature provides evidence that there is a need for such instructional materials in our modern schools.The thesis then describes the procedure that the writers followed in the writing, testing, and revising of the programed text, Principles of Biosystematics. Recorded in the appendices are the testing data which include the students# pre- and posttest scores and the item-analysis of the examinations used to test the first and second drafts of the program. These data were used to determine what parts of the program seemed weak or poorly developed, Such segments of the program were modified before being incorporated in the final draft of the programed textbook which, along with the accompanying teacher's manual, is also located in the appendices.

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This thesis explores the classification of emotions in song lyrics, using automatic approaches applied to a novel corpus of 100 popular songs. I use crowd sourcing via Amazon Mechanical Turk to collect line-level emotions annotations for this collection of song lyrics.  I then build classifiers that rely on textual features to automatically identify the presence of one or more of the following six Ekman emotions: anger, disgust, fear, joy, sadness and surprise. I compare different classification systems and evaluate the performance of the automatic systems against the manual annotations. I also introduce a system that uses data collected from the social network Twitter. I use the Twitter API to collect a large corpus of tweets manually labeled by their authors for one of the six emotions of interest. I then compare the classification of emotions obtained when training on data automatically collected from Twitter versus data obtained through crowd sourced annotations.

Controlling spatial-temporal gene expression patterns is a fundamental task for maize growth and development. With the emergence of massively parallel sequencing, genome-wide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. In one project, using publicly available data, a Gene Co-expression Network (GCN) was constructed and used for gene function prediction, candidate gene selection and improving understanding of regulatory pathways. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and ranked aggregation strategy on network performance using libraries from 1266 maize samples was conducted. Three normalization methods (VST, CPM, RPKM) and ten inference methods, including six correlation and four mutual information (MI) methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than MI methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks. In another project, a maize mutant, transgene reactivated 9-1 (tgr9-1) in the transcriptional gene silencing (TGS) pathway, was cloned. The B-A translocation lines were used to map tgr9-1 on chromosome 3 and this result was consistent with molecular markers. To further locate tgr9-1, next-generation sequencing (NGS) combined with bulk segregant analysis was applied to the tgr9-1 mapping population. Using coexpression analysis, our result indicates a maize dicer-like3a (Zmdcl3a) gene is a high-confidence candidate gene for tgr9. Zmdcl3a is involved in the RNA-directed DNA methylation (RdDM) pathway. This pathway is driven by two plant-specific DNA-dependent RNA polymerases, Polymerase IV (Pol IV) and Polymerase V (Pol V). Several kinds of non-coding RNAs are involved, including long single-stranded RNAs, double-stranded RNAs, and small interfering RNAs. The identification of tgr9-1 uncovered the role of non-coding RNAs in TGS and revealed the diversity of TGS pathways in maize. One primary focus of gene regulation study is by studying transcription factors (TFs). Transcription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Little is known about tissue-specific gene regulation through TFs in maize. In this project, a network approach was applied to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, shoot apical meristem and seed) in maize. We used GENIE3 machine-learning algorithm combined with the large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Although many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. The GRNs were also validated by ChIP-Seq datasets (KN1, FEA4, and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. / A Dissertation submitted to the Department of Biological Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2018. / April 2, 2018. / GENE EXPRESSION, MAIZE, NETWORK, RDDM, SMALL RNA, TRANSCRIPTION FACTOR / Includes bibliographical references. / Karen M. McGinnis, Professor Directing Dissertation; Alan R. Lemmon, University Representative; Kathryn M. Jones, Committee Member; Brian P. Chadwick, Committee Member; Jonathan H. Dennis, Committee Member.

Our ordinary concept of perception contains a seeming tension: we distinguish perception from thought on the grounds that it is a direct awareness of mind-independent objects through their effects on our senses; yet we also allow that what we see (hear, feel, etc) is determined by how we interpret or classify the data that comes through our senses. Theorists of perception disagree over which of these intuitions should prevail, with some maintaining that concepts are in play all the way down and others that perceptual awareness is wholly immediate and concrete. But we do not have to choose. This dissertation argues that the patterns of perception sustain a distinctive form of nonconceptual classification, in which property spaces organize sensory matter so as to preserve rather than discard its concreteness and detail. What then is classification without concepts? What sort of abstraction, generality, representation, or form does it entail? And what ramifications then for thinking about the roots of language and reason, and of our awareness of the external world?