| 1. |
- Glimelius, Bengt, et al.
(författare)
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U-CAN : a prospective longitudinal collection of biomaterials and clinical information from adult cancer patients in Sweden.
- 2018
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Ingår i: Acta Oncologica. - : Taylor & Francis. - 0284-186X .- 1651-226X. ; 57:2, s. 187-194
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Tidskriftsartikel (refereegranskat)abstract
- Background: Progress in cancer biomarker discovery is dependent on access to high-quality biological materials and high-resolution clinical data from the same cases. To overcome current limitations, a systematic prospective longitudinal sampling of multidisciplinary clinical data, blood and tissue from cancer patients was therefore initiated in 2010 by Uppsala and Umeå Universities and involving their corresponding University Hospitals, which are referral centers for one third of the Swedish population.Material and Methods: Patients with cancer of selected types who are treated at one of the participating hospitals are eligible for inclusion. The healthcare-integrated sampling scheme encompasses clinical data, questionnaires, blood, fresh frozen and formalin-fixed paraffin-embedded tissue specimens, diagnostic slides and radiology bioimaging data.Results: In this ongoing effort, 12,265 patients with brain tumors, breast cancers, colorectal cancers, gynecological cancers, hematological malignancies, lung cancers, neuroendocrine tumors or prostate cancers have been included until the end of 2016. From the 6914 patients included during the first five years, 98% were sampled for blood at diagnosis, 83% had paraffin-embedded and 58% had fresh frozen tissues collected. For Uppsala County, 55% of all cancer patients were included in the cohort.Conclusions: Close collaboration between participating hospitals and universities enabled prospective, longitudinal biobanking of blood and tissues and collection of multidisciplinary clinical data from cancer patients in the U-CAN cohort. Here, we summarize the first five years of operations, present U-CAN as a highly valuable cohort that will contribute to enhanced cancer research and describe the procedures to access samples and data.
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| 2. |
- Idowu, Samuel O., 1985-
(författare)
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Applied Machine Learning in District Heating System
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In an increasingly applied domain of pervasive computing, sensing devices are being deployed progressively for data acquisition from various systems through the use of technologies such as wireless sensor networks. Data obtained from such systems are used analytically to advance or improve system performance or efficiency. The possibility to acquire an enormous amount of data from any target system has made machine learning a useful approach for several large-scale analytical solutions. Machine learning has proved viable in the area of the energy sector, where the global demand for energy and the increasingly accepted need for green energy is gradually challenging energy supplies and the efficiency in its consumption.This research, carried out within the area of pervasive computing, aims to explore the application of machine learning and its effectiveness in the energy sector with dependency on sensing devices. The target application area readily falls under a multi-domain energy grid which provides a system across two energy utility grids as a combined heat and power system. The multi-domain aspect of the target system links to a district heating system network and electrical power from a combined heat and power plant. This thesis, however, focuses on the district heating system as the application area of interest while contributing towards a future goal of a multi-domain energy grid, where improved efficiency level, reduction of overall carbon dioxide footprint and enhanced interaction and synergy between the electricity and thermal grid are vital goals. This thesis explores research issues relating to the effectiveness of machine learning in forecasting heat demands at district heating substations, and the key factors affecting domestic heat load patterns in buildings.The key contribution of this thesis is the application of machine learning techniques in forecasting heat energy consumption in buildings, and our research outcome shows that supervised machine learning methods are suitable for domestic thermal load forecast. Among the examined machine learning methods which include multiple linear regression, support vector machine, feed forward neural network, and regression tree, the support vector machine performed best with a normalized root mean square error of 0.07 for a 24-hour forecast horizon. In addition, weather and time information are observed to be the most influencing factors when forecasting heat load at heating network substations. Investigation on the effect of using substation's operational attributes, such as the supply and return temperatures, as additional input parameters when forecasting heat load shows that the use of substation's internal operational attributes has less impact.
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| 3. |
- Sundström, Johan, Professor, 1971-, et al.
(författare)
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Rationale for a Swedish cohort consortium
- 2019
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Ingår i: Upsala Journal of Medical Sciences. - : Taylor & Francis Group. - 0300-9734 .- 2000-1967. ; 124:1, s. 21-28
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Tidskriftsartikel (refereegranskat)abstract
- We herein outline the rationale for a Swedish cohort consortium, aiming to facilitate greater use of Swedish cohorts for world-class research. Coordination of all Swedish prospective population-based cohorts in a common infrastructure would enable more precise research findings and facilitate research on rare exposures and outcomes, leading to better utilization of study participants' data, better return of funders' investments, and higher benefit to patients and populations. We motivate the proposed infrastructure partly by lessons learned from a pilot study encompassing data from 21 cohorts. We envisage a standing Swedish cohort consortium that would drive development of epidemiological research methods and strengthen the Swedish as well as international epidemiological competence, community, and competitiveness.
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