Grounded in framing theory, this thesis presents a qualitative content analysis of newspaper coverage of genetically modified foods by two Chinese newspapers- People’s Daily and Southern Metropolis, in 2000-2017. The political, economic, public opinion and legal implications involved have made the reports of genetically modified (GM) foods present different framing, themes, and positions between People’s Daily and Southern Metropolis. This study aims to examine the various frames used in the coverage of GM foods in two major Chinese newspapers that operate within different media frameworks. Results of the content analysis illustrated that significant differences existed in the newspapers in their framing of GM foods, the themes of GM foods, the positions, news source, and media attributes. While the frames used by People’s Daily and Southern Metropolis were similar in the emphasis on the importance of “Supervision to GM product”, “Worries & Potential Risk/Safety”, and “International perspective”, Southern Metropolis presented a significance of pluralistic standpoint than People’s Daily whose attitude was likely to neutral and pro-GMO. People’s Daily preferred to use the frames of “Advanced Technology”, “International Perspective” or “Economic Consequences”, whereas Southern Metropolis preferred to produce the framed stories of “Worries & Potential Risk/Safety”.

Semantic labeling is becoming more and more popular among researchers in computer vision and machine learning. Many applications, such as autonomous driving, tracking, indoor navigation, augmented reality systems, semantic searching, medical imaging are on the rise, requiring more accurate and efficient segmentation mechanisms. In recent years, deep learning approaches based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have dramatically emerged as the dominant paradigm for solving many problems in computer vision and machine learning. The main focus of this thesis is to investigate robust approaches that can tackle the challenging semantic labeling tasks including semantic instance segmentation and scene understanding. In the first approach, we convert the classic variational Level Set method to a learnable deep framework by proposing a novel definition of contour evolution named Recurrent Level Set (RLS). The proposed RLS employs Gated Recurrent Units to solve the energy minimization of a variational Level Set functional. The curve deformation processes in RLS is formulated as a hidden state evolution procedure and is updated by minimizing an energy functional composed of fitting forces and contour length. We show that by sharing the convolutional features in a fully end-to-end trainable framework, RLS is able to be extended to Contextual Recurrent Level Set (CRLS) Networks to address semantic segmentation in the wild problem. The experimental results have shown that our proposed RLS improves both computational time and segmentation accuracy against the classic variational Level Set-based methods whereas the fully end-to-end system CRLS achieves competitive performance compared to the state-of-the-art semantic segmentation approaches on PAS CAL VOC 2012 and MS COCO 2014 databases. The second proposed approach, Contextual Recurrent Residual Networks (CRRN), inherits all the merits of sequence learning information and residual learning in order to simultaneously model long-range contextual infor- mation and learn powerful visual representation within a single deep network. Our proposed CRRN deep network consists of three parts corresponding to sequential input data, sequential output data and hidden state as in a recurrent network. Each unit in hidden state is designed as a combination of two components: a context-based component via sequence learning and a visualbased component via residual learning. That means, each hidden unit in our proposed CRRN simultaneously (1) learns long-range contextual dependencies via a context-based component. The relationship between the current unit and the previous units is performed as sequential information under an undirected cyclic graph (UCG) and (2) provides powerful encoded visual representation via residual component which contains blocks of convolution and/or batch normalization layers equipped with an identity skip connection. Furthermore, unlike previous scene labeling approaches [1, 2, 3], our method is not only able to exploit the long-range context and visual representation but also formed under a fully-end-to-end trainable system that effectively leads to the optimal model. In contrast to other existing deep learning networks which are based on pretrained models, our fully-end-to-end CRRN is completely trained from scratch. The experiments are conducted on four challenging scene labeling datasets, i.e. SiftFlow, CamVid, Stanford background, and SUN datasets, and compared against various state-of-the-art scene labeling methods.

Imaging of cellular samples has for several hundred years been a way for scientists to investigate biological systems. With the discovery of immunofluorescence labeling in the 1940’s and later genetic fluorescent protein labeling in the 1980’s the most important part in imaging, contrast and specificity, was drastically improved. Eversince, we have seen a increased use of fluorescence imaging in biological research, and the application and tools are constantly being developed further. Specific ion imaging has long been a way to discern signaling events in cell systems. Through use of fluorescent ion reporters, ionic concentrations can be measured inliving cells as result of applied stimuli. Using Ca2+ imaging we have demonstrated that there is a inverse influence by plasma membrane voltage gated calcium channels on angiotensin II type 1 receptor (a protein involved in blood pressure regulation). This has direct implications in treatment of hypertension (high blood pressure),one of the most common serious diseases in the western civilization today with approximately one billion afflicted adults world wide in 2016. Extending from this more lower resolution live cell bioimaging I have moved into super resolution imaging. This thesis includes works on the interpretation of super resolution imaging data of the neuronal Na+, K+ - ATPase α3, a receptor responsible for maintaining cell homeostasis during brain activity. The imaging data is correlated with electrophysiological measurements and computer models to point towards possible artefacts in super resolution imaging that needs to be taken into account when interpreting imaging data. Moreover, I proceeded to develop a software for single-molecule localization microscopy analysis aimed for the wider research community and employ this software to identify expression artifacts in transiently transfected cell systems. In the concluding work super-resultion imaging was used to map out the early steps of the intrinsic apoptotic signaling cascade in space and time. Using superresoultion imaging, I mapped out in intact cells at which time points and at which locations the various proteins involved in apoptotic regulation are activated and interact. / Avbildning av biologiska prover har i flera hundra år varit ett sätt för forskare att undersöka biologiska system. Med utvecklingen av immunofluoresens inmärkn-ing och fluoresens-mikroskopi förbättrades de viktigaste aspekterna av mikroskopi,kontrast och specificitet. Sedan 1941 har vi sett kontinuerligt mer mångsidigt och frekvent användning av fluorosense-mikroskopi i biologisk forskning. Jon-mikroskopi har länge varit en metod att studera signalering i cell-system. Genom användning av fluorosenta jon-sensorer går det att mäta variationer avjon koncentrationer i levande celler som resultat av yttre påverkan. Genom att använda Ca2+ mikroskopi har jag visat att det finns en omvänd koppling mellan kalcium-kanaler i plasma-membran och angiotensin II typ 1 receptorn (ett proteininvolverat i blodtrycksreglering). Detta har direkta implikationer för behandlingav högt blodtryck, en av de mer vanliga sjukdomarna i västvärlden idag med överen miljard drabbade patienter i världen 2016. Efter detta projekt vidgades mitt fokus till att inkludera superupplösnings-mikroskopi. Denna avhandling inkluderar ett arbete fokuserat på tolkningen av superupplösnings-mikroskopi data från neuronal Na+, K+ - ATPase α3, en jon-pump som återställer cellernas jonbalans i samband med cell signalering. Mikroskopi-datan korreleras mot elektrofysiologi experiment och modeller för att illustrera möjliga artefakter i superupplösnings-mikroskopi som måste tas i beaktande i samband med tolkning av data. Jag fortsatte med att utveckla mjukvara för analys av data från singel-molekyl-lokalisations-mikroskopi där fokuset för mjukvaran framförallt varit på användarvänligheten. Detta då jag hoppas att den kommer vara användbar för ett bredare forskingsfält. Mjukvaran användes även i ett separat projekt för att identifiera överuttrycks-artefakter i transfekterade celler. I det avslutande arbetet använder jag superupplösnings-mikroskopi för att karakterisera de tidiga stegen i mitokondriell apoptos. Jag identifierar när och var i cellen de olika proteinerna involverade i apoptos signaleringen är aktiverade och interagerar. / <p>QC 20171003</p>

No description available.

Vår rapport bygger på ungdomar bosatta i ett socioekonomiskt utsatt område och deras tankar kring framtiden, deras bostadsområde och andra faktorer som spelar in. Vår förförståelse kring det valda ämnet grundar sig på vår egen uppväxt samt erfarenheter då vi själva är bosatta i ett socioekonomiskt utsatt område. Syftet med studien har varit att få en ökad förståelse samt belysa några ungdomars uppfattningar och tankar om sin framtid samt hur olika faktorer har en påverkan på deras framtidsmöjligheter. Denna studie har utförts med en kvalitativ metod, genom semistrukturerade intervjuer och gruppintervjuer. Deltagarna i denna studie är ungdomar i åldern 18–27 år. Studiens resultat visar att ungdomarna upplever att deras bostadsområde är segregerat och avskilt från samhället. De uppger även att det råder kriminalitet och oroligheter i området. Men trots problematiken trivs de i sitt bostadsområde. Stämpling av samhället, etnicitet och religion är faktorer som ungdomarna uppger kan komma att vara ett hinder för deras framtid. / Our report is based on young people living in a socioeconomically exposed area and their thoughts about the future, their residential area and other factors that matters. Our pre-understanding of the chosen subject is based on our own upbringing and experiences as we live in a socio-economically vulnerable area. The purpose of the study has been to gain a better understanding and to highlight some young people's perceptions and thoughts about their future and how different factors influence their future opportunities. This study has been conducted with a qualitative method, through semi-structured interviews and group interviews. The participants in this study are young people aged 18-27 years. The study's results show that the young people find that their residential area is segregated and separated from the rest of the society. They also state that their residential area facing problems with criminality and riots. But despite the problems they see their residential area as their home and they feel secure there. Society labeling, ethnicity and religion are factors that the youths feel may be an obstacle to their future.

The current study implements a speech perception experiment that interrogates local perceptions of Spanish varieties in Miami. Participants (N=292) listened to recordings of three Spanish varieties (Peninsular, Highland Colombian, and Post-Castro Cuban) and were given background information about the speakers, including the parents’ country of origin. In certain cases, the parents’ national-origin label matched the country of origin of the speaker, but otherwise the background information and voices were mismatched. The manipulation distinguishes perceptions determined by bottom-up cues (dialect) from top-down ones (social information). Participants then rated each voice for a range of personal characteristics and answered hypothetical questions about the speakers’ employment, family, and income. Results show clear top-down effects of the social information that often drive perceptions up or down depending on the traits themselves. Additionally, the data suggest differences in perceptions between Hispanic/non-Hispanic and Cuban/non-Cuban participants, although the Cuban participants do not drive the Hispanic participants’ perceptions.

Cancer remains one of the world’s most devastating diseases, with more than 10 million new cases every year. However, traditional treatments have proven insufficient for successful medical management of cancer due to the chemotherapeutics’ difficulty in achieving therapeutic concentrations at the target site, non-specific cytotoxicity to normal tissues, and limited systemic circulation lifetime. Although, a concerted effort has been placed in developing and successfully employing nanoparticle(NP)-based drug delivery vehicles successfully mitigate the physiochemical and pharmacological limitations of chemotherapeutics, work towards controlling the subcellular fate of the carrier, and ultimately its payload, has been limited. Because efficient therapeutic action requires drug delivery to specific organelles, the subcellular barrier remains critical obstacle to maximize the full potential of NP-based delivery vehicles. The aim of my dissertation work is to better understand how NP-delivery vehicles’ structural, chemical, and physical properties affect the internalization method and subcellular localization of the nanocarrier. In this work we explored how side-chain and backbone modifications affect the conjugated polymer nanoparticle (CPN) toxicity and subcellular localization. We discovered how subtle chemical modifications had profound consequences on the polymer’s accumulation inside the cell and cellular retention. We also examined how complexation of CPN with polysaccharides affects uptake efficiency and subcellular localization. This work also presents how changes to CPN backbone biodegradability can significantly affect the subcellular localization of the material. A series of triphenyl phosphonium-containing CPNs were synthesized and the effect of backbone modifications have on the cellular toxicity and intracellular fate of the material. A mitochondrial-specific polymer exhibiting time-dependent release is reported. Finally, we present a novel polymerization technique which allows for the controlled incorporation of electron-accepting benzothiadiazole units onto the polymer chain. This facilitates tuning CPN emission towards red emission. The work presented here, specifically, the effect that side-chain and structure, polysaccharide formulation and CPN degradability have on material’s uptake behavior, can help maximize the full potential of NP-based delivery vehicles for improved chemotherapeutic drug delivery.

Perception plays an important role in the human assessment process. This construction of an image is based on many factors, including race, religion, national origin, income, gender, marital status - in short, the summation of our life experiences. An important consideration for the methodology consumers utilize for purchase decisions has been also referred to as the "Country of Origin" (COO) effect. This individual bias based on perceptions and attitudes has a significant impact on how consumers make their purchasing decisions. The 2008 Farm Bill legislation contained a requirement for country of origin labeling (COOL) for beef, lamb, pork, and other products. This paper examined some of the 2008 Farm Bill's impact on consumer's purchasing decisions regarding beef and related meat protein products. Of a potential population of over 700 respondents, 161 responses were received. Correspondence analysis and preferences indicated by the respondents were used to analyze the results. Respondents were mostly well educated, white, in higher income brackets, and split about 60%-40% between male and female. Food safety is considered important and respondents generally believe that the US food supply is safe. Over 40% of respondents had no knowledge of COOL and its background, but about 70% of respondents believed that COOL should be mandatory for all meat products studied. The majority of respondents (111 of 161) indicated that they would be willing to pay $0.25 to $1.00 per pound more for COOL. However when asked about the likeliness to pay, 25%-30% indicated that they were "Not at all Likely" or "Somewhat Likely" to pay. The correspondence analysis results indicated that beef was a product eaten more often, but considered expensive. Chicken was eaten more often and considered inexpensive and healthful. Turkey was a product considered healthful and inexpensive. Lamb was considered expensive, while pork was inconclusive in the two dimensions used. The results of this study provide information for producers, purveyors, retailers, and the government on the importance of COOL as perceived by consumers, and their willingness to pay for this labeling.

Chloroperoxidase (CPO) is a heme-containing glycoprotein secreted by the marine fungus Caldariomyces fumago. Chloroperoxidase contains one ferriprotoporphyrin IX prosthetic group per molecule and catalyzes a variety of reactions, such as halogenation, peroxidation and epoxidation. The versatile catalytic activities of CPO coupled with the increasing demands for chiral synthesis have attracted an escalating interest in understanding the mechanistic and structural properties of this enzyme. In order to better understand the mechanisms of CPO-catalyzed enantioselective reactions and to fine-tune the catalytic properties of chloroperoxidase, asparagine 74 (N74) located in the narrow substrate access channel of CPO was replaced by a bulky, nonpolar valine and a polar glutamine using site-directed mutagenesis. The CPO N74 mutants displayed significantly enhanced activity toward nonpolar substrates compared to wild-type CPO as a result of changes in space and polarity of the heme distal environment. More interestingly, N74 mutants showed dramatically decreased chlorination and catalase activity but significantly enhanced epoxidation activity as a consequence of improved kinetic perfection introduced by the mutation as reflected by the favorable changes in kcat and kcat/KM of these reactions. It is also noted that the N74V mutant is capable of decomposing cyanide, the most notorious poison for many hemoproteins, as judged by the unique binding behavior of N74V with potassium cyanide. Histidine 105 (H105) was replaced by a nonpolar amino acid alanine using site-directed mutagenesis. The CPO H105 mutant (H105A) displayed dramatically decreased chlorination and catalase activity possibly because of the decreased polarity in the heme distal environment and loss of the hydrogen bonds between histidine 105 and glutamic acid 183. However, significantly increased enantioselectivity was observed for the epoxidation of bulky styrene derivatives. Furthermore, my study provides strong evidence for the proposed histidine/cysteine ligand switch in chloroperoxidase, providing experimental support for the structure of the 420-nm absorption maximum for a number of carbon monoxide complexes of heme-thiolate proteins. For the NMR study, [dCPO(heme)] was produced using 90% deuterated growth medium with excess heme precursors and [dCPO(Phe)] was grown in the same highly deuterated medium that had been supplemented with excess natural phenylalanine. To make complete heme proton assignments, NMR spectroscopy has been performed for high-resolution structural characterization of [dCPO(heme)] and [dCPO(Phe)] to achieve unambiguous and complete heme proton assignments, which also allows important amino acids close to the heme active center to be determined.

Contexto: A Anotação de Papéis Semânticos (APS) é uma tarefa da área de Processamento de Línguas Naturais (PLN) que permite detectar os eventos descritos nas sentenças e os participantes destes eventos (Palmer et al., 2010). A APS responde perguntas como Quem?, Quando?, Onde?, O quê?, e Por quê?, dentre outras e, sendo assim, é importante para várias aplicações de PLN. Para anotar automaticamente um texto com papéis semânticos, a maioria dos sistemas atuais emprega técnicas de Aprendizagem de Máquina (AM). Porém, alguns papéis semânticos são previsíveis e, portanto, não necessitam ser tratados via AM. Além disso, a grande maioria das pesquisas desenvolvidas em APS tem dado foco ao inglês, considerando as particularidades gramaticais e semânticas dessa língua, o que impede que essas ferramentas e resultados sejam diretamente transportados para outras línguas. Revisão da Literatura: Para o português do Brasil, há três trabalhos finalizados recentemente que lidam com textos jornalísticos, porém com performance inferior ao estado da arte para o inglês. O primeiro (Alva- Manchego, 2013) obteve 79,6 de F1 na APS sobre o córpus PropBank.Br; o segundo (Fonseca, 2013), sem fazer uso de um treebank para treinamento, obteve 68,0 de F1 sobre o córpus PropBank.Br; o terceiro (Sequeira et al., 2012) realizou anotação apenas dos papéis Arg0 (sujeito prototípico) e Arg1 (paciente prototípico) no córpus CETEMPúblico, com performance de 31,3 pontos de F1 para o primeiro papel e de 19,0 de F1 para o segundo. Objetivos: O objetivo desse trabalho de mestrado é avançar o estado da arte na APS do português brasileiro no gênero jornalístico, avaliando o desempenho de um sistema de APS treinado com árvores sintáticas geradas por um parser automático (Bick, 2000), sem revisão humana, usando uma amostragem do córpus PLN-Br. Como objetivo adicional, foi avaliada a robustez da tarefa de APS frente a gêneros diferentes, testando o sistema de APS, treinado no gênero jornalístico, em uma amostra de revisões de produtos da web. Esse gênero não foi explorado até então na área de APS e poucas de suas características foram formalizadas. Resultados: Foi compilado o primeiro córpus de opiniões sobre produtos da web, o córpus Buscapé (Hartmann et al., 2014). A diferença de performance entre um sistema treinado sobre árvores revisadas e outro sobre árvores não revisadas ambos no gênero jornalístico foi de 10,48 pontos de F1. A troca de gênero entre as fases de treinamento e teste, em APS, é possível, com perda de performance de 3,78 pontos de F1 (córpus PLN-Br e Buscapé, respectivamente). Foi desenvolvido um sistema de inserção de sujeitos não expressos no texto, com precisão de 87,8% no córpus PLN-Br e de 94,5% no córpus Buscapé. Foi desenvolvido um sistema, baseado em regras, para anotar verbos auxiliares com papéis semânticos modificadores, com confiança de 96,76% no córpus PLN-Br. Conclusões: Foi mostrado que o sistema de Alva-Manchego (2013), baseado em árvores sintáticas, desempenha melhor APS do que o sistema de Fonseca (2013), independente de árvores sintáticas. Foi mostrado que sistemas de APS treinados sobre árvores sintáticas não revisadas desempenham melhor APS sobre árvores não revisadas do que um sistema treinado sobre dados gold-standard. Mostramos que a explicitação de sujeitos não expressos nos textos do Buscapé, um córpus do gênero de opinião de produtos na web, melhora a performance da sua APS. Também mostramos que é possível anotar verbos auxiliares com papéis semânticos modificadores, utilizando um sistema baseado em regras, com alta confiança. Por fim, mostramos que o uso do sentido do verbo, como feature de AM, para APS, não melhora a perfomance dos sistemas treinados sobre o PLN-Br e o Buscapé, por serem córpus pequenos. / Background: Semantic Role Labeling (SRL) is a Natural Language Processing (NLP) task that enables the detection of events described in sentences and the participants of these events (Palmer et al., 2010). SRL answers questions such as Who?, When?, Where?, What? and Why? (and others), that are important for several NLP applications. In order to automatically annotate a text with semantic roles, most current systems use Machine Learning (ML) techniques. However, some semantic roles are predictable, and therefore, do not need to be classified through ML. In spite of SRL being well advanced in English, there are grammatical and semantic particularities that prevents full reuse of tools and results in other languages. Related work: For Brazilian Portuguese, there are three studies recently concluded that performs SRL in journalistic texts. The first one (Alva-Manchego, 2013) obtained 79.6 of F1 on the SRL of the PropBank.Br corpus; the second one (Fonseca, 2013), without using a treebank for training, obtained 68.0 of F1 for the same corpus; and the third one (Sequeira et al., 2012) annotated only the Arg0 (prototypical agent) and Arg1 (prototypical patient) roles on the CETEMPúblico corpus, with a perfomance of 31.3 of F1 for the first semantic role and 19.0 for the second one. None of them, however, reached the state of the art of the English language. Purpose: The goal of this masters dissertation was to advance the state of the art of SRL in Brazilian Portuguese. The training corpus used is from the journalistic genre, as previous works, but the SRL annotation is performed on non-revised syntactic trees, i.e., generated by an automatic parser (Bick, 2000) without human revision, using a sampling of the corpus PLN-Br. To evaluate the resulting SRL classifier in another text genre, a sample of product reviews from web was used. Until now, product reviews was a genre not explored in SRL research, and few of its characteristics are formalized. Results: The first corpus of web product reviews, the Buscapé corpus (Hartmann et al., 2014), was compiled. It is shown that the difference in the performance of a system trained on revised syntactic trees and another trained on non-revised trees both from the journalistic genre was of 10.48 of F1. The change of genres during the training and testing steps in SRL is possible, with a performance loss of 3.78 of F1 (corpus PLN-Br and Buscapé, respectively). A system to insert unexpressed subjects reached 87.8% of precision on the PLN-Br corpus and a 94.5% of precision on the Buscapé corpus. A rule-based system was developed to annotated auxiliary verbs with semantic roles of modifiers (ArgMs), achieving 96.76% confidence on the PLN-Br corpus. Conclusions: First we have shown that Alva-Manchego (2013) SRL system, that is based on syntactic trees, performs better annotation than Fonseca (2013)s system, that is nondependent on syntactic trees. Second the SRL system trained on non-revised syntactic trees performs better over non-revised trees than a system trained on gold-standard data. Third, the explicitation of unexpressed subjects on the Buscapé texts improves their SRL performance. Additionally, we show it is possible to annotate auxiliary verbs with semantic roles of modifiers, using a rule-based system. Last, we have shown that the use of the verb sense as a feature of ML, for SRL, does not improve the performance of the systems trained over PLN-Br and Buscapé corpus, since they are small.