Costs and Benefits of a Shared Digital Long-Term Preservation System

Paper presented at the 40th Annual Conference of LIBER (Ligue des Bibliothèques Européennes de Recherche - Association of European Research Libraries) on July 1st, 2011; with the slides used at the presentation.

NADPH-Dependent Thioredoxin System In Regulation of Chloroplast Functions

Photosynthesis, the process in which carbon dioxide is converted into sugars using the energy of sunlight, is vital for heterotrophic life on Earth. In plants, photosynthesis takes place in specific organelles called chloroplasts. During chloroplast biogenesis, light is a prerequisite for the development of functional photosynthetic structures. In addition to photosynthesis, a number of other metabolic processes such as nitrogen assimilation, the biosynthesis of fatty acids, amino acids, vitamins, and hormones are localized to plant chloroplasts. The biosynthetic pathways in chloroplasts are tightly regulated, and especially the reduction/oxidation (redox) signals play important roles in controlling many developmental and metabolic processes in chloroplasts. Thioredoxins are universal regulatory proteins that mediate redox signals in chloroplasts. They are able to modify the structure and function of their target proteins by reduction of disulfide bonds. Oxidized thioredoxins are restored via the action of thioredoxin reductases. Two thioredoxin reductase systems exist in plant chloroplasts, the NADPHdependent thioredoxin reductase C (NTRC) and ferredoxin-thioredoxin reductase (FTR). The ferredoxin-thioredoxin system that is linked to photosynthetic light reactions is involved in light-activation of chloroplast proteins. NADPH can be produced via both the photosynthetic electron transfer reactions in light, and in darkness via the pentose phosphate pathway. These different pathways of NADPH production enable the regulation of diverse metabolic pathways in chloroplasts by the NADPH-dependent thioredoxin system. In this thesis, the role of NADPH-dependent thioredoxin system in the redox-control of chloroplast development and metabolism was studied by characterization of Arabidopsis thaliana T-DNA insertion lines of NTRC gene (ntrc) and by identification of chloroplast proteins regulated by NTRC. The ntrc plants showed the strongest visible phenotypes when grown under short 8-h photoperiod. This indicates that i) chloroplast NADPH-dependent thioredoxin system is non-redundant to ferredoxinthioredoxin system and that ii) NTRC particularly controls the chloroplast processes that are easily imbalanced in daily light/dark rhythms with short day and long night. I identified four processes and the redox-regulated proteins therein that are potentially regulated by NTRC; i) chloroplast development, ii) starch biosynthesis, iii) aromatic amino acid biosynthesis and iv) detoxification of H₂O₂. Such regulation can be achieved directly by modulating the redox state of intramolecular or intermolecular disulfide bridges of enzymes, or by protecting enzymes from oxidation in conjunction with 2-cysteine peroxiredoxins. This thesis work also demonstrated that the enzymatic antioxidant systems in chloroplasts, ascorbate peroxidases, superoxide dismutase and NTRC-dependent 2-cysteine peroxiredoxins are tightly linked up to prevent the detrimental accumulation of reactive oxygen species in plants.

Dispersal and metacommunity dynamics in a soft-sediment benthic system : how well is the seafloor connected?

Connectivity depends on rates of dispersal between communities. For marine soft-sediment communities continued small-scale dispersal as post-larvae and as adults can be equally important in maintaining community composition, as initial recruitment of substrate by pelagic larvae. In this thesis post-larval dispersal strategies of benthic invertebrates, as well as mechanisms by which communities are connected were investigated. Such knowledge on dispersal is scarce, due to the difficulties in actually measuring dispersal directly in nature, and dispersal has not previously been quantified in the Baltic Sea. Different trap-types were used underwater to capture dispersing invertebrates at different sites, while in parallel measuring waves and currents. Local community composition was found to change predictably under varying rates of dispersal and physical connectivity (waves and currents). This response was, however, dependent on dispersal-related traits of taxa. Actively dispersing taxa will be relatively better at maintaining their position, as they are not as dependent on hydrodynamic conditions for dispersal and will be less prone to be passively transported by currents. Taxa also dispersed in relative proportions that were distinctly different from resident community composition and a significant proportion (40 %) of taxa were found to lack a planktonic larval life-stage. Community assembly was re-started in a large-scale manipulative field experiment over one year across several sites, which revealed how patterns of community composition (α-, β- and λ-diversity) change depending on rates of dispersal. Results also demonstrated that in response to small-scale disturbance, initial recruitment was by nearby-dominant species after which other species arrived from successively further away. At later assembly time, the number of coexisting species increased beyond what was expected purely by local niche requirements (species sorting), transferring regional differences in community composition (β-diversity) to the local scale (α-diversity, mass effect). Findings of this thesis complement more theoretical studies in metacommunity ecology by demonstrating that understanding how and when individuals disperse relative to underlying environmental heterogeneity is key to interpreting how patterns of diversity change across different spatial scales. Such information from nature is critical when predicting responses to, for example, different types of disturbances or management actions in conservation.

System-level characterization of Th2 cell development and immune cell responses to ZnO and TiO2 nanoparticles

Asthma and allergy are common diseases and their prevalence is increasing. One of the hypotheses that explains this trend is exposure to inhalable chemicals such as traffi c-related air pollution. Epidemiological research supports this theory, as a correlation between environmental chemicals and allergic respiratory diseases has been found. In addition to ambient airborne particles, one may be exposed to engineered nanosized materials that are actively produced due to their favorable physico-chemical properties compared to their bulk size counterparts. On the cellular level, improper activity of T helper (Th) cells has been connected to allergic reactions. Th cells can differentiate into functionally different effector subsets, which are identifi ed according to their characteristic cytokine profi les resulting in specifi c ability to communicate with other cells. Th2 cells activate humoral immunity and stimulate eradication of extracellular pathogens. However, persistent predominance of Th2 cells is involved in a development of number of allergic diseases. The cytokine environment at the time of antigen recognition is the major factor determining the polarization of a naïve Th cell. Th2 cell differentiation is initiated by IL4, which signals via transcription factor STAT6. Although the importance of this pathway has been evaluated in the mouse studies, the signaling components involved have been largely unknown. The aim of this thesis was to identify molecules, which are under the control of IL4 and STAT6 in Th cells. This was done by using system-level analysis of STAT6 target genes at genome, mRNA and protein level resulting in identifi cation of various genes previously not connected to Th2 cell phenotype acquisition. In the study, STAT6-mediated primary and secondary target genes were dissection from each other and a detailed transcriptional kinetics of Th2 cell polarization of naïve human CD4+ T cells was collected. Integration of these data revealed the hierarchy of molecular events that mediates the differentiation towards Th2 cell phenotype. In addition, the results highlighted the importance of exploiting proteomics tools to complement the studies on STAT6 target genes identifi ed through transcriptional profi ling. In the last subproject, the effects of the exposure with ZnO and TiO2 nanoparticles was analyzed in Jurkat T cell line and in primary human monocyte-derived macrophages and dendritic cells to evaluate their toxicity and potential to cause infl ammation. Identifi cation of ZnO-derived gene expression showed that the same nanoparticles may elicit markedly distinctive responses in different cell types, thus underscoring the need for unbiased profi ling of target genes and pathways affected. The results gave additional proof that the cellular response to nanosized ZnO is due to leached Zn2+ ions. The approach used in ZnO and TiO2 nanoparticle study demonstrated the value of assessing nanoparticle responses through a toxicogenomics approach. The increased knowledge of Th2 cell signaling will hopefully reveal new therapeutic nodes and eventually improve our possibilities to prevent and tackle allergic infl ammatory diseases.

Maritime safety in the Gulf of Finland. Evaluation of the regulatory system

Growing traffic is believed to increase the risk of an accident in the Gulf of Finland. As the consequences of a large oil accident would be devastating in the vulnerable sea area, accident prevention is performed at the international, regional and national levels. Activities of shipping companies are governed with maritime safety policy instruments, which can be categorised into regulatory, economic and information instruments. The maritime regulatory system has been criticised for being inefficient because it has not been able to eliminate the violations that enable accidents. This report aims to discover how maritime governance systems or maritime safety policy instruments could be made more efficient in the future, in order to improve the maritime safety level. The results of the research are based on a literature review and nine expert interviews, with participants from shipping companies, interest groups and authorities. Based on the literature and the interviews, a suggestion can be made that in the future, instead of implementing new policy instruments, maritime safety risks should be eliminated by making the existing system more efficient and by influencing shipping companies’ safety culture and seafarers’ safety related attitudes. Based on this research, it can be stated that the development of maritime safety policy instruments should concentrate on harmonisation, automation and increasing national and cross-border cooperation. These three tasks could be primarily accomplished by developing the existing technology. Human error plays a role in a significant number of maritime accidents. Because of this, improving companies’ safety culture and voluntary activities that go beyond laws are acknowledged as potential ways of improving maritime safety. In the future, maritime regulatory system should be developed into a direction where the private sector has better possibilities to take part in decision-making.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Complement system in cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinoma (cSCC) consists 20% of keratinocytederived non-melanoma skin cancers (NMSC), the incidence of which is increasing globally. cSCC is the most common metastatic skin cancer and it causes approximately 20% of skin cancer-related deaths. At present, there are no molecular markers for predicting which cSCC lesions are aggressive or metastasize rapidly. UV radiation is the most important risk factor for cSCC. During the development of cSCC, normal epidermal keratinocytes are transformed and form actinic keratosis (AK), which progresses to cSCC in situ (cSCCIS, Bowen’s disease) and finally to invasive and metastatic cSCC. Inflammatory factors and cells are a part of cancer microenvironment and cSCC can develop in the chronically irritated skin or in the context of chronic inflammation. The complement system is a central part of innate immunity and it regulates normal immunological and inflammatory processes. In this study, the role of complement system components and inhibitors were studied in the progression of cSCC in culture and in vivo. Elevated expression of complement factor H (CFH), complement factor I (CFI), complement component C3 and complement factor B (CFB) was noted in cSCC cells in culture. The analysis with immunohistochemistry (IHC) revealed that the expression of CFH, CFI, C3 and CFB was specifically noted in tumor cells in vivo. The staining intensity of CFH, CFI, C3 and CFB was also stronger in invasive cSCC than in AK or cSCCIS samples. The knockdown of CFH, CFI and CFB with specific siRNAs decreased cSCC cell viability and migration, whereas the knockdown of C3 reduced only cSCC cell migration. Moreover, the knockdown of CFI, C3 and CFB inhibited growth of cSCC xenograft tumors established in SCID mice in vivo. In these tumors, CFI, C3 and CFB knockdown decreased the number of proliferating cells. Moreover, the knockdown of CFI increased local inflammation and complement activation. This study provides evidence for the roles of CFH, CFI, C3 and CFB in the tumor progression indicating these as molecular biomarkers and putative therapeutic targets of cSCC.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.

Implementing Green IT approach for transferring Big Data over Parallel Data Link

The whole research of the current Master Thesis project is related to Big Data transfer over Parallel Data Link and my main objective is to assist the Saint-Petersburg National Research University ITMO research team to accomplish this project and apply Green IT methods for the data transfer system. The goal of the team is to transfer Big Data by using parallel data links with SDN Openflow approach. My task as a team member was to compare existing data transfer applications in case to verify which results the highest data transfer speed in which occasions and explain the reasons. In the context of this thesis work a comparison between 5 different utilities was done, which including Fast Data Transfer (FDT), BBCP, BBFTP, GridFTP, and FTS3. A number of scripts where developed which consist of creating random binary data to be incompressible to have fair comparison between utilities, execute the Utilities with specified parameters, create log files, results, system parameters, and plot graphs to compare the results. Transferring such an enormous variety of data can take a long time, and hence, the necessity appears to reduce the energy consumption to make them greener. In the context of Green IT approach, our team used Cloud Computing infrastructure called OpenStack. It’s more efficient to allocated specific amount of hardware resources to test different scenarios rather than using the whole resources from our testbed. Testing our implementation with OpenStack infrastructure results that the virtual channel does not consist of any traffic and we can achieve the highest possible throughput. After receiving the final results we are in place to identify which utilities produce faster data transfer in different scenarios with specific TCP parameters and we can use them in real network data links.

Energy Management System for LVDC Island Networks

Recent developments in power electronics technology have made it possible to develop competitive and reliable low-voltage DC (LVDC) distribution networks. Further, islanded microgrids—isolated small-scale localized distribution networks— have been proposed to reliably supply power using distributed generations. However, islanded operations face many issues such as power quality, voltage regulation, network stability, and protection. In this thesis, an energy management system (EMS) that ensures efficient energy and power balancing and voltage regulation has been proposed for an LVDC island network utilizing solar panels for electricity production and lead-acid batteries for energy storage. The EMS uses the master/slave method with robust communication infrastructure to control the production, storage, and loads. The logical basis for the EMS operations has been established by proposing functionalities of the network components as well as by defining appropriate operation modes that encompass all situations. During loss-of-powersupply periods, load prioritizations and disconnections are employed to maintain the power supply to at least some loads. The proposed EMS ensures optimal energy balance in the network. A sizing method based on discrete-event simulations has also been proposed to obtain reliable capacities of the photovoltaic array and battery. In addition, an algorithm to determine the number of hours of electric power supply that can be guaranteed to the customers at any given location has been developed. The successful performances of all the proposed algorithms have been demonstrated by simulations.