Innan jag började läsa arkitektur var jag dansare i många år. Då låg jag ofta på golvet och tittade i taket. Så pass länge att taket tillslut blev en vägg. Jag har också snurrat mig yr så att rummets gränser suddats ut, så att upp blev ner och ner blev upp. Med den erfarenheten har jag sökt efter ett sätt att radera bort gränsen för vad som är en vägg, ett golv och ett tak. En kulturskola ska vara en plats för lärande, lust och kreativitet. En plats där möjligheterna är många och begränsningarna få. Där barnen och ungdomarna inte upplever normer kring vad som är normalt, utan en plats där de får exprimentera och inte fastna i begrepp av vad som är rätt och fel. Att komma till Kulturskolan i Älvsjö ska ge eleverna känslan av den taklösa byggnaden som inte begränsar utan snarare visar på alla de möjligheter en plats för lärande och reativitet har. Byggnaden är en samlad kropp med rumsligheter i fasad. I kontrast till grannen Stockholmsmässan där all verksamhet sker i hemlighet och bakom slutna väggar visar byggnaden upp sin verksamhet för det kringliggande Älvsjö. Jag har sökt efter en byggnad där barnen får vara kvar i sin öppna värld. Där det inte finns ett rätt svar utan där svaren är många. Eleverna ska kunna vrida på huvudet och världen ska vara sig lik eller lägga sig på golvet och upp liknar ner. Det blir en byggnad utan tak eller ett fotavtryck mot himmlen. / Before I started studying architecture, I was a dancer for many years. I often laid on the floor and looked at the ceiling. For so long that the ceiling was transformed into a wall. I have also twisted and turned myself dizzy in rooms so boundaries have become blurred, where up has been down and down has been up. With that experience, I have been looking for a way to erase the limits of what is a wall, a floor and a ceiling. A Kulturskola should be a place for learning, desire and creativity. A place where the opportunities are many and the limitations are few. Where children and young people  not experience  norms of what is normal, but where they can experiment and not get caught up in the concept of what is right and wrong. Beeing at the Kulturskola in Älvsjö should give the students the feeling of the roofless building which does not limit but rather shows all the potential of a place for learning and creativity. In contrast to the neighboring Stockholm International Fairs where all the activity takes place in secret and behind closed walls, this buildning shows Älvsjö what is happening inside. I have searched for a building where children can be left in their open world. Students should be able to turn their heads and the world should be the same or lie down on the floor and up is similar to down. It is a building without a roof or a footprint to the sky.

In the context of search, it may be advantageous in some use-cases to have documents saved in a graph database rather than a document-orientated database. Graph databases are able to model relationships between objects, in this case documents, in ways which allow for efficient retrieval, as well as search queries that are slightly more specific or complex. This report will attempt to explore the possibilities of storing an existing set of documents into a graph database. A Named Entity Recognizer was used on a set of news articles in order to extract entities from each news article’s body of text. News articles that contain the same entities are then connected to each other in the graph. Ideas to improve this entity extraction are also explored. The method of evaluation that was utilized in this report proved not to be ideal for this task in that only a relative measure was given, not an absolute one. As such, no absolute answer with regards to the quality of the method can be presented. It is clear that improvements can be made, and the result should be subject to further study. / I ett sökkontext kan det vara födelaktigt att i några användarscenarion utgå från dokument lagrade i en grafdatabas gentemot en dokument-orienterad databas. Grafdatabaser kan modellera förhållanden mellan objekt, som i detta fall är dokument, på ett sätt som ökar effektiviteten för vissa mer specifika eller komplexa sökfrågor. Denna rapport utforskar möjligheterna i att lagra existerande dokument i en grafdatabas. En Named Entity Recognizer används för att extrahera entiter från en stor samling nyhetsartiklar. Nyhetsartiklar som innehåller samma entiteter är sedan kopplade till varandra i grafen. Dessutom undersöks möjligheter till att förbättra extraheringen av entiteter. Evalueringsmetoden som användes visade sig mindre än ideal, då endast en relativ snarare än absolut bedömning kan göras av den slutgiltiga grafen. Därav kan inget slutgiltigt svar ges angående grafens och metodens kvalitet, men resultatet bör vara av intresse för framtida undersökningar.

Sharing data is an important part of the progress of science in many fields. In the largely deep learning dominated field of natural language processing, textual resources are in high demand. In certain domains, such as that of medical records, the sharing of data is limited by ethical and legal restrictions and therefore requires anonymization. The process of manual anonymization is tedious and expensive, thus automated anonymization is of great value. Since medical records consist of unstructured text, pieces of sensitive information have to be identified in order to be masked for anonymization. Named-entity recognition (NER) is the subtask of information extraction named entities, such as person names or locations, are identified and categorized. Recently, models that leverage unsupervised training on large quantities of unlabeled training data have performed impressively on the NER task, which shows promise in their usage for the problem of anonymization. In this study, a small set of medical records was annotated with named-entity tags. Because of the lack of any training data, a BERT model already fine-tuned for NER was then evaluated on the evaluation set. The aim was to find out how well the model would perform on NER on medical records, and to explore the possibility of using the model to anonymize medical records. The most positive result was that the model was able to identify all person names in the dataset. The average accuracy for identifying all entity types was however relatively low. It is discussed that the success of identifying person names shows promise in the model’s application for anonymization. However, because the overall accuracy is significantly worse than that of models fine-tuned on domain-specific data, it is suggested that there might be better methods for anonymization in the absence of relevant training data.

Nucleotide excision repair (NER) and ataxia telangiectasia mutated (ATM)/ATR (ATM- and RAD3-related) NA damage checkpoints are among the major pathways that affect the chemotherapeutic efficiency of the anticancer rug cisplatin. Xeroderma pigmentosum group A (XPA) protein plays a crucial role in NER including both global enome repair (GG-NER) and transcription-coupled repair (TC-NER) subpathways, and has been a potential target for mproving cisplatin therapeutic effects. We report here that XPA translocates from the cytosol into the nucleus after NA damage induced by UV irradiation and cisplatin, a mimetic of UV damage, in human cells with or without p53 deficiency. However, the damage-induced response of XPA nuclear import was significantly slower in p53-deficient cells than in p53-proficient cells. We also found that while XPA is imported into the nucleus upon cisplatin or UV damage in an ATR-dependent manner in p53-proficient A549 lung cancer cells, the ATR checkpoint pathway has no effect on the XPA nuclear import in p53-deficient H1299 lung cancer cells. Similarly, the XPA nuclear translocation is not regulated by ATM checkpoint or by p38MAPK/MK2 either. Our findings suggest that NER is independent on the major DNA damage checkpoint pathways in H1299 (p53-/-) cells and that DNA damage responses are mechanistically different between p53-proficient and p53-deficient cells. Our results also highlight the possibility of selectively targeting XPA nuclear import as a way to sensitize cisplatin anticancer activity, but targeting ATR/ATM-dependent checkpoints may not be helpful in killing p53-deficient cancer cells.

This thesis explores the training of a deep neural network based named entity recognizer in an end-to-end privacy preserved setting where dataset creation and model training happen in an environment with minimal manual interventions. With the improvement of accuracy in Deep Learning Models for practical tasks, a rising concern is satisfying the demand for training data for these models amidst the concerns on the data privacy. Several scenarios of data protection are suggested in the recent past due to public concerns hence the legal guidelines to enforce them. A promising new development is the decentralized model training on isolated datasets, which eliminates the compromises of privacy upon providing data to a centralized entity. However, in this federated setting curating the data source is still a privacy risk mostly in unstructured data sources such as text. We explore the feasibility of automatic dataset annotation for a Named Entity Recognition (NER) task and training a deep learning model with it in two federated learning settings. We explore the feasibility of utilizing a dataset created in this manner for fine-tuning a stateof- the-art deep learning language model for the downstream task of named entity recognition. We also explore this novel setting of deep learning NLP model and federated learning for its deviation from the classical centralized setting. We created an automatically annotated dataset containing around 80,000 sentences, a manual human annotated test set and tools to extend the dataset with more manual annotations. We observed the noise from automated annotation can be overcome to a level by increasing the dataset size. We also contributed to the federated learning framework with state-of-the-art NLP model developments. Overall, our NER model achieved around 0.80 F1-score for recognition of entities in sentences.

The new General Data Protection Regulation (GDPR) Act will apply to all companies within the European Union after 25 May. This means stricter legal requirements for companies that in some way store personal data. The goal of this project is therefore to make it easier for companies to meet the new legal requirements. This by creating a tool that searches file systems and visually shows the user in a graphical user interface which files contain personal data. The tool uses Named entity recognition with the Linear chain conditional random field algorithm which is a type of supervised learning method in machine learning. This algorithm is used in the project to find names and addresses in files. The different models are trained with different parameters and the training is done using the stanford NER library in Java. The models are tested by a test file containing 45,000 words where the models themselves can predict all classes to the words in the file. The models are then compared with each other using the measurements of precision, recall and F-score to find the best model. The tool also uses Regular Expression to find emails, IP numbers, and social security numbers. The result of the final machine learning model shows that it does not find all names and addresses, but that can be improved by increasing exercise data. However, this is something that requires a more powerful computer than the one used in this project. An analysis of how the Swedish language is built would also need to be done to apply the most appropriate parameters for the training of the model. / Den nya lagen General data protection regulation (GDPR) började gälla för alla företag inom Europeiska unionen efter den 25 maj. Detta innebär att det blir strängare lagkrav för företag som på något sätt lagrar personuppgifter. Målet med detta projekt är därför att underlätta för företag att uppfylla de nya lagkraven. Detta genom att skapa ett verktyg som söker igenom filsystem och visuellt visar användaren i ett grafiskt användargränssnitt vilka filer som innehåller personuppgifter. Verktyget använder Named Entity Recognition med algoritmen Linear Chain Conditional Random Field som är en typ av ”supervised” learning metod inom maskininlärning. Denna algoritm används för att hitta namn och adresser i filer. De olika modellerna tränas med olika parametrar och träningen sker med hjälp av biblioteket Stanford NER i Java. Modellerna testas genom en testfil som innehåller 45 000 ord där modellerna själva får förutspå alla klasser till orden i filen. Modellerna jämförs sedan med varandra med hjälp av mätvärdena precision, recall och F-score för att hitta den bästa modellen. Verktyget använder även Regular expression för att hitta e- mails, IP-nummer och personnummer. Resultatet på den slutgiltiga maskininlärnings modellen visar att den inte hittar alla namn och adresser men att det är något som kan förbättras genom att öka träningsdata. Detta är dock något som kräver en kraftfullare dator än den som användes i detta projekt. En undersökning på hur det svenska språket är uppbyggt skulle även också behöva göras för att använda de lämpligaste parametrarna vid träningen av modellen.

Syftet med denna studie är att undersöka antivaccin-diskurser på Facebooksidan Kavla Ner och jämföra dem med tre antivaccin-diskurser tidigare funna av Kata (2010). Tidigare forskning visar att antivaccin-grupper utnyttjar postmodernistiskt tänkande och att sociala medier är ett kraftfullt verktyg vad gäller åsiktsbildning om vaccin. Därtill kan konspirationsteorier som sprids om vaccin bidra till en ökad tro på att vaccin är farligt.Materialet som analyseras är 30 inlägg från Facebooksidan Kavla Ner och studien bygger på Faircloughs (1992) tredimensionella modell för kritisk diskursanalys. Texterna har analyserats med hjälp av analysverktygen lexikala val och modalitet. Detta görs med utgångspunkt i teorier om ideologi och diskurs, postmodernism samt kulturell hegemoni.Resultatet av analysen visar förekomsten av antivaccin-diskurser och att två av de funna diskurserna stämmer överens med de Kata (2010) funnit. Därtill gjordes nya upptäckter i form av en ny diskurs och de kategoriserande teman Kata funnit utvecklades. Resultatet visade att en stor mängd av inläggen är färgade av ett postmodernistiskt tänkande. Konspirationsteorier visade sig också vara vanligt förekommande i inläggen. Slutsatsen av studien är att de sociala praktiker som påverkar antivaccin-diskursen även skulle kunna påverka andra områden, vilket ytterligare förklarar vikten av att förstå fenomenet vaccinmotstånd. / The purpose of this bachelor thesis is to examine anti-vaccine discourses on the Kavla Ner Facebook page and to compare them to three anti-vaccine discourses previously found by Kata (2010). Research shows that anti-vaccine communities utilize postmodern ways of thought, and that social media is a powerful tool when it comes to affecting people's opinions about vaccination. Furthermore, conspiracy theories which are spread on the internet can contribute to belief that vaccines are dangerous.The material which has been analyzed consists of 30 posts from the Kavla Ner Facebook page and the method is based on Fairclough’s (1992) three-dimensional model for critical discourse analysis. The texts have been analyzed using the tools lexical choices and modality, while drawing on theories about ideology and discourse, postmodernism and cultural hegemony.The result shows the presence of anti-vaccine discourses on the Facebook page, and that two of the discourses are consistent with those found by Kata (2010). In addition, a new discourse was found and the categorizing themes by Kata were expanded. Conspiracy theories proved to be common in the posts. The results also showed that a large number of the posts are characterized by postmodernist thinking. Finally, it is concluded that the social practices which influence the anti-vaccine discourse could affect other areas, which further explains the importance of understanding the phenomenon of vaccine resistance.

The sensitivity of Xeroderma pigmentosa (XP) patients to sunlight has spurred the discovery and genetic and biochemical analysis of the eight XP gene products (XPA-XPG plus XPV) responsible for this disorder. These studies also have served to elucidate the nucleotide excision repair (NER) process, especially the critical role played by the XPA protein. More recent studies have shown that NER also involves numerous other proteins normally employed in DNA metabolism and cell cycle regulation. Central among these is ataxia telangiectasia and Rad3-related (ATR), a protein kinase involved in intracellular signaling in response to DNA damage, especially DNA damage-induced replicative stresses. This review summarizes recent findings on the interplay between ATR as a DNA damage signaling kinase and as a novel ligand for intrinsic cell death proteins to delay damage-induced apoptosis, and on ATR’s regulation of XPA and the NER process for repair of UV-induced DNA adducts. ATR’s regulatory role in the cytosolic-to-nuclear translocation of XPA will be discussed. In addition, recent findings elucidating a non-NER role for XPA in DNA metabolism and genome stabilization at ds-ssDNA junctions, as exemplified in prematurely aging progeroid cells, also will be reviewed.

Single cell gel electrophoresis (SCGE), more commonly known as the Comet assay, is an uncomplicated, affordable and versatile method for investigating DNA damage and repair. Existing comet–assay based methods were modified and applied in this study in order to examine the effects of different perturbations on the DNA repair capacity of different samples. Mitochondrial functioning has a vast effect on overall cell physiology and does not simply involve the production of energy in the form of ATP that sustains common biological processes, but is also associated with important cellular occurrences such as apoptosis and ROS production. It is suggested that a change in mitochondrial function may lead to extensive ROS production which may negatively affect macromolecules, including proteins involved in DNA repair pathways, and impaired energy formation which in turn may hamper the proper occurrence of energy driven processes. Complex I and ?III knock–down systems established in 143B cells are used to investigate the effect that perturbations of the energy metabolism may have on DNA repair capacity. Metallothioneins (MTs) are known to play an imperative role in trace element homeostasis and detoxification of metals and are effective ROS scavengers. The prooxidant environment that heavy metal imbalance causes may result in mutagenesis and transformation through DNA damage. It is suggested that an imbalance in the metal homeostasis caused by MT knock–out may create an environment favourable for DNA damage formation and at the same time impair DNA repair pathways. Because of the multi–functionality and involvement of metallothioneins in such a wide variety of biological processes, it was considered interesting and essential to extend the investigation on the effect of the absence of metallothioneins on DNA repair. A metallothionein I and ?II knock–out mouse model is employed to determine the effect of MT knock–out on DNA repair capacity. It was clear from the results obtained that transfection of cells, as used to investigate a perturbation in the energy metabolism in 143B cells, has an impairing effect on DRC. It was also confirmed that metallothioneins play an important and diverse role in cell biology since the absence thereof inhibits both BER and NER. / Thesis (M.Sc. (Biochemistry))--North-West University, Potchefstroom Campus, 2011.

Single cell gel electrophoresis (SCGE), more commonly known as the Comet assay, is an uncomplicated, affordable and versatile method for investigating DNA damage and repair. Existing comet–assay based methods were modified and applied in this study in order to examine the effects of different perturbations on the DNA repair capacity of different samples. Mitochondrial functioning has a vast effect on overall cell physiology and does not simply involve the production of energy in the form of ATP that sustains common biological processes, but is also associated with important cellular occurrences such as apoptosis and ROS production. It is suggested that a change in mitochondrial function may lead to extensive ROS production which may negatively affect macromolecules, including proteins involved in DNA repair pathways, and impaired energy formation which in turn may hamper the proper occurrence of energy driven processes. Complex I and ?III knock–down systems established in 143B cells are used to investigate the effect that perturbations of the energy metabolism may have on DNA repair capacity. Metallothioneins (MTs) are known to play an imperative role in trace element homeostasis and detoxification of metals and are effective ROS scavengers. The prooxidant environment that heavy metal imbalance causes may result in mutagenesis and transformation through DNA damage. It is suggested that an imbalance in the metal homeostasis caused by MT knock–out may create an environment favourable for DNA damage formation and at the same time impair DNA repair pathways. Because of the multi–functionality and involvement of metallothioneins in such a wide variety of biological processes, it was considered interesting and essential to extend the investigation on the effect of the absence of metallothioneins on DNA repair. A metallothionein I and ?II knock–out mouse model is employed to determine the effect of MT knock–out on DNA repair capacity. It was clear from the results obtained that transfection of cells, as used to investigate a perturbation in the energy metabolism in 143B cells, has an impairing effect on DRC. It was also confirmed that metallothioneins play an important and diverse role in cell biology since the absence thereof inhibits both BER and NER. / Thesis (M.Sc. (Biochemistry))--North-West University, Potchefstroom Campus, 2011.