The authors of this study analysed the increasing use of personalized marketing and consumer concerns regarding the access to personal information. The purpose was to find out how companies could react to these concerns. Several theoretical concepts were explored, such as Personal Data, Personalized Marketing, Privacy Concerns, Privacy Policies, Consumer Trust and Consumer Behaviour. Facebook Inc. was analysed as an example to address the problem. An online survey was conducted on university students and two interviews were performed with representatives from the Data Inspection Board in Sweden. The main findings were that individuals seem to approve of personalized marketing but are concerned about their privacy. Companies should therefore inform their consumers on how personal data is used for personalized marketing and respect their rights and take governmental regulations into consideration.

Syftet med denna studie var att belysa kulturell kompetens och dess innebörd för sjuksköterskan. Syftet var även ta reda på hur sjuksköterskan kan förbättra sin kulturella kompetens för att kunna ge en individanpassad omvårdnad i ett mångkulturellt samhälle. En systematisk litteraturstudie har genomförts. Till resultatet användes 12 artiklar som var kvalitativa och kvantitativa. Kulturell kompetens innebar för sjuksköterskan att ha kunskap om olika kulturer för att kunna integrera patientens kultur i omvårdnaden. Det krävdes medvetenhet och förståelse för hur den egna kulturen påverkade den enskilda individen. Lyhördhet inbegrep respekt och uppskattning av mångfald. Det framkom att trots sjuksköterskors användning av helhetssyn i omvårdnaden så är den kulturella kompetensen generellt låg. Sjuksköterskans kulturella kompetens förbättrades genom de erfarenheter som gavs i kulturella möten och genom att interagera med människor med olika kulturella bakgrunder. Genom utbildning kunde en kunskapsbas byggas upp. Skapandet av gemensamma arbetsmodeller och riktlinjer hjälpte sjuksköterskan att försäkra sig om att den vård som gavs var kulturellt kompetent. Studien visade att sjuksköterskans kulturella kompetens har betydelse och påverkar hur individanpassad omvårdnaden blir. Förbättring byggde på önskan och motivation att vilja bli bättre. Processen till att bli mer kulturellt kompetent startade under utbildningen till sjuksköterska och innebar ett livslångt lärande.

Cells behave as complex systems with regulatory processes that make use of many elements such as switches based on thresholds, memory, feedback, error-checking, and other components commonly encountered in electrical engineering. It is therefore not surprising that these complex systems are amenable to study by engineering methods. A great deal of effort has been spent on observing how cells store, modify, and use information. Still, an understanding of how one uses this knowledge to exert control over cells within a living organism is unavailable. Our prime objective is "Personalized Cancer Therapy" which is based on characterizing the treatment for every individual cancer patient. Knowing how one can systematically alter the behavior of an abnormal cancerous cell will lead towards personalized cancer therapy. Towards this objective, it is required to construct a model for the regulation of the cell and utilize this model to devise effective treatment strategies. The proposed treatments will have to be validated experimentally, but selecting good treatment candidates is a monumental task by itself. It is also a process where an analytic approach to systems biology can provide significant breakthrough. In this dissertation, theoretical frameworks towards effective treatment strategies in the context of probabilistic Boolean networks, a class of gene regulatory networks, are addressed. These proposed analytical tools provide insight into the design of effective therapeutic interventions.

Abstract According to recent clinical treatment, doctors give patients medicines based on clinical experience and biochemical data. However, biochemical data simply provides an initial physiology reaction. Although the data is enough for doctors to diagnose diseases, it does not help much for doctors to indicate the most useful medicine. Therefore, doctors will use the first line, cheap or low dose medicine to cure patients by previous clinical experience. It will not only extend the time of treatment but also lower the medical quality. Not to mention the side effects and increases the cost. Consequently, using SNP¡]Single Nucleotide Polymorphism¡^will help doctors to find out different patients¡¦ genotype and forecast the result of medicine. It will control disease efficiently and decrease the medical costs. Methods: This study will be discussed with an accurate test of how to check the genotypes of diabetes mellitus and predict the result of treatment from pharmacogenetic. The method was using PCR (Polymerase Chain Reaction) and RFLP (Restriction Fragment Length Polymorphism) to analyze patients¡¦ different genotype. Besides, this study uses the One-Way ANOVA to interpret the relationship between ABCC8-E16 and type 2 diabetes. In conclusion, the antidiabetic drugs- Sulfonylurea derivatives are suitable for ABCC8-E16 genotype patients. This result can be a reference for doctors to remedy diabetics. It will not only save the cost but also shorten the time of treatment, and it will impact deeply for personalized medicine in the future. type 2 diabetes, Sulfonylurea, SNP, PCR, RFLP, pharmacogenetic, personalized medicine

Authors: Malin Karlsson, Sandra Karlsson, Amanda Malmberg Tutor: Dr. Setayesh Sattari Examiner: Prof. Anders Pehrsson Background: Consumers increasingly purchase products online due to the widespread use of the Internet. The decision for consumers to purchase online is predicted by their purchase intentions, which in turn is affected by their information privacy concerns. There is a lack of research on IPC and purchase intentions in the context of online personalized advertising. Purpose: To extend the understanding of purchase intentions considering information privacy concerns and involvement in the context of online personalized advertising. Methodology: A survey in form of a questionnaire was conducted in order to gather the information necessary to be able to analyse the relationship between IPC and purchase intentions in the context of online personalized advertising. The sample consists of 18-70 year olds from cities in southern Sweden. Conclusion: Conclusions drawn in this thesis is that when applied in the context of online personalized advertising, there is no significant relationship between IPC and purchase intentions. However, involvement is suggested as having a positive relationship to purchase intentions, as well as a positive moderating effect on the relationship between IPC and purchase intention in the context of online personalized advertising. Keywords: Purchase intentions, Information privacy concerns (IPC), Online personalized advertising, Involvement.

About one in eight women in the United States will develop breast cancer over the course of her lifetime. Moreover, patient-to-patient variability in disease progression continues to complicate clinical decisions in diagnosis and treatment for breast cancer patients. Early detection of tumors is a key factor influencing patient survival, and advancements in diagnostic and imaging techniques has allowed clinicians to spot smaller sized lesions. There has also been an increase in premature treatments of non-malignant lesions because there is no clear way to predict whether these lesions will become invasive over time. Patient variability due to genetic polymorphisms has been investigated, but studies on variability at the level of cellular activity have been extremely limited. An individual’s biochemical milieu of cytokines, growth factors, and other stimuli contain a myriad of cues that pre-condition cells and induce patient variability in response to tumor progression or treatment. Circulating white blood cells called monocytes respond to these cues and enter tissues to differentiate into monocyte-derived macrophages (MDMs) and osteoclasts that produce cysteine cathepsins, powerful extracellular matrix proteases. Cathepsins have been mechanistically linked to accelerated tumor growth and metastasis. This study aims to elucidate the variability in disease progression among patients by examining the variability of protease production from tissue-remodeling macrophages and osteoclasts. Since most extracellular cues initiate multiple signaling cascades that are interconnected and dynamic, this current study uses a systems biology approach known as cue-signal-response (CSR) paradigm to capture this complexity comprehensively. The novel and significant finding of this study is that we have identified and predicted donor-to-donor variability in disease modifying cysteine cathepsin activities in macrophages and osteoclasts. This study applied this novel finding to the context of tumor invasion and showed that variability in tumor associated macrophage cathepsin activity and their inhibitor cystatin C level mediates variability in cancer cell invasion. These findings help to provide a minimally invasive way to identify individuals with particularly high remodeling capabilities. This could be used to give insight into the risk for tumor invasion and develop a personalized therapeutic regime to maximize efficacy and chance of disease free survival.

This thesis introduces an efficient and robust approach for 3D reconstruction of complete head models with consistent parameterization and personalized shapes from several possible inputs. The system input consists of Cyberware laser-scanned data where we perform scanning task as well as publically available face data where (i) facial expression may or may not exist and (ii) only partial information of head may exist, for instance only front face part without back part of the head. Our method starts with a surface reconstruction approach to either transfer point clouds to a mesh structure or to fill missing points on a triangular mesh. Then, it is followed by a registration process which unifies the representation of all meshes. Afterward, a photo-cloning method is used to extract an adequate set of features in a semi-automatic way on snapshots taken from front and left views of provided range data. We modify Radial Basis Functions (RBFs) deformation so that it would be based on not only distance, but also regional information. Using feature point sets and modified RBFs deformation, a generic mesh can be manipulated in a way that closed eyes and mouth movements like separating upper lip and lower lip can be properly handled. In other word, such mesh modification method makes construction of various facial expressions possible. Moreover, new functions are added where a generic model can be manipulated based on feature point sets to consequently recover missing parts such as ears, back of the head and neck in the input face. After feature-based deformation using modified radial basis functions, a fine mesh modification method based on model points follows to extract the fine details from the available range data. Then, some post refinement procedures employing RBFs deformation and averaging neighboring points are carried out to make the surface of reconstructed 3D head smoother and uniform. Due to existence of flaws and defects on the mesh surface such as flipped triangles, self-intersections or degenerate faces, an automatic repairing approach is leveraged to clean up the entire surface of the mesh. The experiments which are performed on various models show that our method is robust and efficient in terms of accurate full head reconstruction from input data and execution time, respectively. In our method, it is also aimed to use minimum user interaction as much as possible.

Populations are aging and the prevalence of chronic disease, persisting for many years, is increasing. The most common, non-communicable chronic diseases in developed countries are; cardiovascular disease (CVD), type 2 diabetes, obesity, arthritis and specific cancers. Chronic diseases such as cardiovascular disease, type 2 diabetes and obesity have high prevalence and develop over the course of life due to a number of interrelated factors including genetic predisposition, nutrition and lifestyle. With the development and completion of human genome sequencing, we are able to trace genes responsible for proteins and metabolites that are linked with these diseases. A computerized model focused on organizing knowledge related to genes, nutrition and the three chronic diseases, namely, cardiovascular disease, type 2 diabetes and obesity has been developed for the Ontology-Based Personalized Risk Evaluation for Chronic Disease Project. This model is a Protégé-based ontological representation which has been developed for entering and linking concepts and data for these three chronic diseases. This model facilitates to identify interrelationships between concepts. The ontological representation provides the framework into which information on individual patients, disease symptoms, gene maps, diet and life history can be input, and risks, profiles, and recommendations derived. Personal genome and health data could provide a guide for designing and building a medical health administration system for taking relevant annual medical tests, e.g. gene expression level changes for health surveillance. One method, called transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk of chronic disease. This personalized approach has been used for two different chronic diseases, predicting the risk of cardiovascular disease and predicting the risk of type 2 diabetes. For predicting the risk of cardiovascular disease, the National Nutrition Health Survey 97 data from New Zealand population has been used. This data contains clinical, anthropometric and nutritional variables. For predicting risk of type 2 diabetes, data from the Italian population with clinical and genetic variables has been used. It has been discovered that genes responsible for causing type 2 diabetes are different in male and female samples. A framework to integrate the personalized model and the chronic disease ontology is also developed with the aim of providing support for further discovery through the integration of the ontological representation in order to build an expert system in genes of interest and relevant dietary components.

Populations are aging and the prevalence of chronic disease, persisting for many years, is increasing. The most common, non-communicable chronic diseases in developed countries are; cardiovascular disease (CVD), type 2 diabetes, obesity, arthritis and specific cancers. Chronic diseases such as cardiovascular disease, type 2 diabetes and obesity have high prevalence and develop over the course of life due to a number of interrelated factors including genetic predisposition, nutrition and lifestyle. With the development and completion of human genome sequencing, we are able to trace genes responsible for proteins and metabolites that are linked with these diseases. A computerized model focused on organizing knowledge related to genes, nutrition and the three chronic diseases, namely, cardiovascular disease, type 2 diabetes and obesity has been developed for the Ontology-Based Personalized Risk Evaluation for Chronic Disease Project. This model is a Protégé-based ontological representation which has been developed for entering and linking concepts and data for these three chronic diseases. This model facilitates to identify interrelationships between concepts. The ontological representation provides the framework into which information on individual patients, disease symptoms, gene maps, diet and life history can be input, and risks, profiles, and recommendations derived. Personal genome and health data could provide a guide for designing and building a medical health administration system for taking relevant annual medical tests, e.g. gene expression level changes for health surveillance. One method, called transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk of chronic disease. This personalized approach has been used for two different chronic diseases, predicting the risk of cardiovascular disease and predicting the risk of type 2 diabetes. For predicting the risk of cardiovascular disease, the National Nutrition Health Survey 97 data from New Zealand population has been used. This data contains clinical, anthropometric and nutritional variables. For predicting risk of type 2 diabetes, data from the Italian population with clinical and genetic variables has been used. It has been discovered that genes responsible for causing type 2 diabetes are different in male and female samples. A framework to integrate the personalized model and the chronic disease ontology is also developed with the aim of providing support for further discovery through the integration of the ontological representation in order to build an expert system in genes of interest and relevant dietary components.

Populations are aging and the prevalence of chronic disease, persisting for many years, is increasing. The most common, non-communicable chronic diseases in developed countries are; cardiovascular disease (CVD), type 2 diabetes, obesity, arthritis and specific cancers. Chronic diseases such as cardiovascular disease, type 2 diabetes and obesity have high prevalence and develop over the course of life due to a number of interrelated factors including genetic predisposition, nutrition and lifestyle. With the development and completion of human genome sequencing, we are able to trace genes responsible for proteins and metabolites that are linked with these diseases. A computerized model focused on organizing knowledge related to genes, nutrition and the three chronic diseases, namely, cardiovascular disease, type 2 diabetes and obesity has been developed for the Ontology-Based Personalized Risk Evaluation for Chronic Disease Project. This model is a Protégé-based ontological representation which has been developed for entering and linking concepts and data for these three chronic diseases. This model facilitates to identify interrelationships between concepts. The ontological representation provides the framework into which information on individual patients, disease symptoms, gene maps, diet and life history can be input, and risks, profiles, and recommendations derived. Personal genome and health data could provide a guide for designing and building a medical health administration system for taking relevant annual medical tests, e.g. gene expression level changes for health surveillance. One method, called transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk of chronic disease. This personalized approach has been used for two different chronic diseases, predicting the risk of cardiovascular disease and predicting the risk of type 2 diabetes. For predicting the risk of cardiovascular disease, the National Nutrition Health Survey 97 data from New Zealand population has been used. This data contains clinical, anthropometric and nutritional variables. For predicting risk of type 2 diabetes, data from the Italian population with clinical and genetic variables has been used. It has been discovered that genes responsible for causing type 2 diabetes are different in male and female samples. A framework to integrate the personalized model and the chronic disease ontology is also developed with the aim of providing support for further discovery through the integration of the ontological representation in order to build an expert system in genes of interest and relevant dietary components.