Frontotemporal dementia is a neurodegenerative disorder with high heterogeneity on the genetic, pathological and clinical level. The familial form of the disease is mainly caused by pathogenic variants of three genes: C9orf72, MAPT and GRN. As there is no clear correlation between the mutation and the clinical phenotype, symptom severity or age of onset, the demand for predictive biomarkers is high. While there is no fluid biomarker for frontotemporal dementia in use yet, there is strong hope that changes of protein concentrations in the blood or cerebrospinal fluid can aid prognostics many years before symptoms develop. Increasing amounts of data are becoming available because of long-term studies of families affected by familial frontotemporal dementia, but its analysis is time-consuming and work intensive. In the scope of this project a pipeline was built for the automated analysis of proteomics data. Specifically, it aims to identify proteins useful for differentiation between two groups by using random forest, a supervised machine learning method. The analysis results of the pipeline for a data set containing blood plasma protein concentration of healthy controls and participants affected by frontotemporal dementia were promising and the generalized functioning of the pipeline was proven with an independent breast cancer proteomics data set.

Background: Knowledge within organizations can play a vital role for organizational development. The role of sharing knowledge in public organizations by means of the use of information systems have not been studied to a larger extent. During 2016 the thirteen municipalities within Region Jönköping’s län adhered to a so-called digital agenda to develop the municipal organizations and service delivery. One of the goals of the digital agenda was to increase knowledge sharing by digital means between municipalities.  Purpose: The purpose of the thesis was to investigate how knowledge sharing practices taking place between municipalities in region Jönköping’s län. The authors performed a pilot case study in the educational department within three municipalities.  Method: This study based on qualitative research and data were gathered through semi-structured interviews and analyzed according to the conventional content analysis. Semi-structured interviews were performed based on the theoretical frameworks of Nonaka’s Model of Knowledge Management, which resulted in an interview guide with open-ended questions. Conventional content was used for qualitative data analysis.  Conclusion: According to our analysis we have found that knowledge sharing in public organization is generally seen as one of the most important elements that should be wisely managed. Collaboration in public sector basically depend on the so many things and it starts with the government initiative but ends with public awareness. It is also very important that organizations can manage knowledge resources more successfully if employees are willingly to share their knowledge with colleagues. People of organizations are quite comfortable with collaborative technologies because the advance of the internet and related technologies. In the public sector worker or employees should motivated, get more encouragement and support by the leaders.

The brain – the organ that allows us to be aware of our surroundings – consists of a complex network of neurons, which seemingly allows the human brain to be able of abstract thinking, emotions, and cognitive function. To learn how the brain is capable of this, the two main branches of neuroscience study either neurons in detail, or how they communicate within neuronal networks. Both these branches often tackle the complexity using a combination of experiments and mathematical modelling. A third and less studied aspect of neuroscience concerns the neurovascular coupling (NVC), for which my research group has previously developed mathematical models. However, these NVC models have still not integrated valuable data from rodents and primates, and the NVC models are also not connected to existing neuronal network models. In this project, I address both of these two shortcomings. First, an existing model for the NVC was connected with a simple model for neuronal networks, establishing a connection between the NVC models and the software NEURON. Second, we established a way to preserved information from NVC data from rodents and mice into NVC models humans. This work thus connects the previously developed NVC model both with data from other species and with other types of models. This brings us one step closer to a more holistic and interconnected understanding of the brain and its many intriguing cognitive and physiological functions.

Over the course of this project, I demonstrate the utility of a 4-phase analysis pipeline in the context of cancer therapy and the associated search for antineoplastic drug candidates. I showcase a repeatable means for generating lists of potential targets which may be used in conjunction with methods like small molecule screening as part of a search for broadly effective antineoplastic agents.  By using publicly available variant call format (VCF) data sourced from the 1000 genomes project, global human population-wide data for non-sex chromosomes was filtered and transformed in a 4-phase process to obtain high population frequency, heterozygotic, nonsynonymous single nucleotide variants (nsSNVs) residing in functional domains of proteins. Through manual filtration combined with software-assisted annotation, I obtained a ranked list of 50 top scoring annotated variants across the human autosome, all residing in known protein domains. Additionally, a single top variant was selected for proof-of-concept structure prediction and visualization. When the methodology outlined herein is coupled to additional loss-of-heterozygosity (LOH) prevalence data across cancer genomes, it may be used to identify candidate variants which collectively represent potential loss-of-heterozygosity based collateral lethalities (CL) in the underlying cancer. Furthermore, under the assumption that subsequent methods like small molecule screening succeed in finding molecule(s) targeting a structural aspect of one of these variants, any subsequently developed therapeutic approaches may possess broader therapeutic utility dependent upon the strictness of the initial heterozygotic filtering threshold applied at the onset of the project pipeline. When combined with additional cancer data, the recreation of such gene lists at other degrees of heterozygotic thresholding can allow for the creation of lists of autosomal loss-of-heterozygosity gene candidates, representing potential collateral lethality targets with varied degrees of utility dependent upon the strictness of the initial filtration threshold.

Microbial fuel cell (MFC) technology is a renewable energy source that employs microorganisms as biocatalysts to degrade substrates into electrons and protons, and then transfer the electrons to the anode electrode. Electron transfer rates by microorganisms depend on many factors as well as on their diverse electron transfer mechanisms. The present study compared cytochromes, flavoproteins, electron transfer complexes, redoxins and other extracellular membrane proteins that have direct involvement in electron transfer mechanisms in Escherichia coli str. K-12 MG1655, Rhodopseudomonas pulastris DX-1 and Shewanella oneidensis MR-1. Escherichia coli str. The results showed that K-12 MG1655 had a more diverse range of extracellular proteins for electron transfer mechanisms compared to Rhodopseudomonas pulastris DX-1 and Shewanella oneidensis MR-1. Escherichia coli str. K-12 MG1655 expressed more flavoproteins, redoxin and electron transfer complex related proteins that had direct involvement in electron transfer mechanisms compared to two other bacterial species indicating that it may be able to transfer more electrons when employed in MFC technique. Escherichia coli str. K-12 MG1655 expressed 16 cytochromes, 9 flavoproteins, 6 redoxins, 6 electron transport complexes, 1 hypothetical and 1 oxidoreductase proteins. On the other hand, Rhodopseudomonas pulastris DX-1 and Shewanella oneidensis MR-1 expressed 26 and 35 cytochromes proteins. But these two bacterial species expressed less flavoproteins and redoxin related proteins and they didn’t express any electron transport complexes or hypothetical and oxidoreductase related proteins for electron transfer. STRING and SMART results suggested that the identified proteins transferred electrons either by connecting with other types of identified proteins in the constructed gene network or independently by taking part in oxidation-reduction reaction, metal ion reduction reaction or by their FMN binding activities.

This thesis presents the work done during my master degree projects under the supervision of Alexander Suh and Francisco J. Ruiz-Ruano. My work focused on the development of in-silico methods to improve the assembly of the Germline Restricted Chromosome (GRC) of songbirds, more specifically that of zebra finch.GRCs are a good example of the popular saying "The exception that proves the rule". For a very long time, it was assumed that every cell in a healthy multicellular organism carries the same genetic information. Cytogenetic evidence dating back as far as early XX century suggests that this is not always the case, as it has been documented that certain organisms carry supernumerary B chromosomes, which are dispensable chromosomes that are not part of the normal karyotype of a species. GRCs are often regarded as a special case of B chromosomes, where every individual from a species carries an additional chromosome whose presence is restricted to germline cells only. GRCs presence has been documented in insects, hagfishes and songbirds. A peculiar case of GRCs is that of zebra finch, whose GRC has an estimated size of over 150 Mb, accounting for over 10% of zebra finch total genome size. Despite the first cytogenetic evidence of zebra finch GRC dating back to 1998, it was only last year that the first comprehensive genomic study about this relatively large chromosome was published. This study shed some light on the gene content of the GRC in zebra finch, revealing that the GRC of zebra finch mostly consists of paralogs of A chromosomal genes. The GRC assembly and annotation that were published as part of this study included 115 GRC-linked genes that were identified through germline/soma read mapping, as well as 36 manually curated scaffolds with a median length of 3.6 kb. Considering the conspicuous size of the GRC of zebra finch, it is clear that this is a very fragmented and likely incomplete GRC assembly. There are many factors that can have a negative impact on assembly completeness and contiguity. In the GRC case, these factors collectively affect coverage in ways that are not properly handled by available genome assemblers. In the course of my master degree project I developed kFish, a bioinformatic software to perform alignment-free enrichment of GRC-linked barcodes from a 10x Genomics linked-read DNA Chromium library. kFish uses an iterative approach where the k-mer content of a set of GRC-linked sequences is compared with that of reads corresponding to each individual 10x Genomics barcode. This comparison allows kFish to identify likely GRC-linked barcodes, and then only use reads corresponding to these barcodes when trying to assemble the GRC. First benchmarking results generated using five GRC-linked genes from zebra finch as reference sequences, show that kFish is not only capable of assembling already known GRC-linked sequences, but also new ones with high confidence. kFish can do all of this in a matter of hours, using only few gigabytes of system memory, while previous efforts took over two days to assemble zebra finch genome and identify GRC-linked scaffolds using an approach based on read mapping. High quality genome assemblies and annotations are the foundations of modern genomics research, the lack of which greatly limits the breadth of the questions that can be answered. There is still a lot that we do not understand about GRCs, and part of this is due to the lack of high quality GRC assemblies and annotations. Producing such an assembly will likely require an integrated approach, where multiple sequencing technologies as well as bleeding edge bioinformatic tools such as kFish, are combined together to produce an high quality assembly, which will be crucial to unravel the mystery of GRCs function and evolutionary history.

Schizophrenia and bipolar disorder are two severe mental disorders that affect more than 65 million individuals worldwide. The aim of thisproject was to find co-prediction mechanisms for genes associated with schizophrenia and bipolar disorder using a multi-omics data set and a transparent machine learning approach. The overall purpose of theproject was to further understand the biological mechanisms of these complex disorders. In this work, publicly available multi-omics data collected from post-mortem brain tissue were used. The omics types included were gene expression, DNA methylation, and SNP array data. The data consisted of samples from individuals with schizophrenia, bipolar disorder, and healthy controls. Individuals with schizophrenia or bipolar disorder were considered as a combined CASE class. Using machine learning techniques, a multi-omics pipeline was developedto integrate these data in a manner such that all types were adequately represented. A feature selection was performed on methylation and SNP data, where the most important sites were estimated and mapped to their corresponding genes. Next, those genes were intersected with the gene expression data, and another feature selection was performed on the gene expression data. The most important genes were used to develop an interpretable rule-based model with an accuracy of 88%. The model wasthen visualized as a network. The graph highlighted genes that may be of biological importance, including CACNG8, RTN4, TERT, OSBPL8, and ANTXR1. Moreover, strong co-predictions were found, most notable between CNKSR4 and KDM4C in CASE samples. However, further investigations would need to be performed in order to prove that these are real biological interactions. Through the methods developed and the results found in this project, we hope to shed new light towards analyzing multi-omics data as well as to reveal more about the underlying mechanisms of psychiatric disorders.

During the last decade, the use of Next-Generation Sequencing(NGS) technologies has sky-rocketed. The vast amount of data produced by these platforms require processing and analysis. This is usually performed at locations remote from the sequencing facilities thereby introducing the need for data-transportation to the place of analysis. The use of internet transfer would greatly facilitate the process, however since NGS data is considered to be personal sensitive information the handling of the data is highly regulated by the General Data Protection Regulation(GDPR). During this project, a web-based application was developed for the privacy-protecting transfer of personal sensitive data, implementing an in-motion encryption scheme which ensures data integrity and authenticity. The application consists of three scripts: the HTML web page with JavaScript functionality, a PHP script responsible for connection establishment and integrity verification, and a Python script executing the majority of the server-side operations. The resulting application uses the symmetric encryption algorithm AES in GCM mode, using a key size of 128 bits and transfers 60 Kibibytes of the file at a time. The key is established by using the asymmetric RSA encryption scheme with a 4096 bit key pair. SHA-256 is used for verifying the integrity of the transferred files. The JavaScript encryption speed is 584 MB/s and the Python decryption speed 251 MB/s. While the focus of the project was to optimize the application for NGS data, it is not limited to this type of file and can transfer different formats, enabling the use in multiple different fields.

In the current environment of severe climate change, studying the adaptability of Norway spruce to the environment, that is, local adaptation is of great significance for helping to protect forest tree species and genetic breeding. As a structural variation, copy number variations (CNVs) have been proved to play an important role in shaping population structure and local adaptation in marine species, going beyond traditional studies focusing only on SNPs. Therefore, this experiment was to investigate the association of genotypes, including CNVs and SNPs, with local adaptation in Norway spruce. About 5.631% of CNVs were screened from SNPs, and the population structure of Norway spruce was detected based on the data of SNPs and CNVs. Then, the associations between genotypes (SNPs and CNVs) and the environmental variables are calculated by the model, considering the effects of population structure. Finally, the relationship between CNVs and SNPs and local adaptation of Norway spruce was investigated by redundancy analysis (RDA). The results preliminarily revealed that SNPs and CNVs had certain effects on the local adaptation of Norway spruce and a significant correlation with various environmental factors. However, the results indicated comparing to SNPs, CNVs had no significant effect on the local adaptation of Norway spruce.

The increasing use of plastics has elevated the risk of exposure to environmental pollutants such as plasticisers in the general population, making it necessary to understand the possible long term health consequences of the same. In this study we aim to understand how DEHP affects the gene expression in mice models and if it causes disruptions to its cellular pathways. Two datasets, GSE18564 and GSE14920 comprising of 15 and 60 samples respectively were downloaded from GEO database for analysis. Quality control checks were done using Principal Component Analysis and quantile normalisation. Differentially expressed genes were found using LIMMA model, following which only top 20 genes were selected for pathway analysis using KEGG and Gene Ontology. DEHP was found to be associated with chemical carcinogenesis, including negative regulation of extrinsic apoptotic signaling pathway and fatty acid metabolism. Furthermore, it seems likely that PPAR-alpha might play a key role in DEHP related metabolic disruption. Further studies are required to better elucidate the effect of DEHP on individual metabolic pathway implicated in this thesis.