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1. External busbars for improving current generation in multijunction solar cells

Author

Vuorinen, Marianna, Aho, Arto, Anttola, Elina, Fihlman, Antti, Polojärvi, Ville, Aho, Timo, Isoaho, Riku, Nikander, Veikka, Hietalahti, Arttu, Tukiainen, Antti, and Guina, Mircea

Subjects
Physics - Applied Physics
Abstract

We report an improved device fabrication process employed in the development of an advanced front contact grid design employing external busbars. The advanced fabrication process results in enhanced solar cell performance measured at one-sun illumination. In this grid configuration the busbar area is located outside the active solar cell and the grid fingers travel across the mesa sidewalls. With this design the solar cell size can be scaled down without limitations as the area of the busbar is not restricting the component size. Thus, the demonstrated design is beneficial especially for micro-concentrator solar cells. In general, this approach minimizes the power losses originating from the grid shadowing and dark area related voltage losses. The performance of the proposed design is validated by fabricating GaInP/GaAs/GaInNAsSb triple-junction solar cells employing the grid design with external busbars. Light-biased current-voltage and electroluminescence characteristics of the cells reveal that an additional contact GaAs etching step prior to front contact metal deposition is needed to ensure good photovoltaic performance with a fill factor of 85% at one-sun illumination. The improvement is attributed to removing the plasma-damaged material layer that can extend to a depth beyond 100 nm, leading to resistive losses.

Published
2024

5. Using neural biomarkers to personalize dosing of vagus nerve stimulation

Author

Berthon, Antonin, Wernisch, Lorenz, Stoukidi, Myrta, Thornton, Michael, Tessier-Lariviere, Olivier, Fortier-Poisson, Pascal, Mamen, Jorin, Pinkney, Max, Lee, Susannah, Sarkans, Elvijs, Annecchino, Luca, Appleton, Ben, Garsed, Philip, Patterson, Bret, Gonshaw, Samuel, Jakopec, Matjaz, Shunmugam, Sudhakaran, Edwards, Tristan, Tukiainen, Aleksi, Jennings, Joel, Lajoie, Guillaume, Hewage, Emil, and Armitage, Oliver

Published
2024
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10. Marked difference in liver fat measured by histology vs. magnetic resonance-proton density fat fraction: A meta-analysis

Author

Qadri, Sami, Vartiainen, Emilia, Lahelma, Mari, Porthan, Kimmo, Tang, An, Idilman, Ilkay S, Runge, Jurgen H, Juuti, Anne, Penttilä, Anne K, Dabek, Juhani, Lehtimäki, Tiina E, Seppänen, Wenla, Arola, Johanna, Arkkila, Perttu, Stoker, Jaap, Karcaaltincaba, Musturay, Pavlides, Michael, Loomba, Rohit, Sirlin, Claude B, Tukiainen, Taru, and Yki-Järvinen, Hannele

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Digestive Diseases, Liver Disease, Chronic Liver Disease and Cirrhosis, Biomedical Imaging, Oral and gastrointestinal, Fatty liver, Metabolic dysfunction-associated steatotic liver disease, Magnetic resonance imaging, Magnetic resonance spectroscopy, Biopsy, Histology, Hepatocytes, Pathologists, Triglycerides, Transcriptome, Systematic review, Meta-analysis, Clinical sciences
Abstract

Background & aimsPathologists quantify liver steatosis as the fraction of lipid droplet-containing hepatocytes out of all hepatocytes, whereas the magnetic resonance-determined proton density fat fraction (PDFF) reflects the tissue triacylglycerol concentration. We investigated the linearity, agreement, and correspondence thresholds between histological steatosis and PDFF across the full clinical spectrum of liver fat content associated with non-alcoholic fatty liver disease.MethodsUsing individual patient-level measurements, we conducted a systematic review and meta-analysis of studies comparing histological steatosis with PDFF determined by magnetic resonance spectroscopy or imaging in adults with suspected non-alcoholic fatty liver disease. Linearity was assessed by meta-analysis of correlation coefficients and by linear mixed modelling of pooled data, agreement by Bland-Altman analysis, and thresholds by receiver operating characteristic analysis. To explain observed differences between the methods, we used RNA-seq to determine the fraction of hepatocytes in human liver biopsies.ResultsEligible studies numbered 9 (N = 597). The relationship between PDFF and histology was predominantly linear (r = 0.85 [95% CI, 0.80-0.89]), and their values approximately coincided at 5% steatosis. Above 5% and towards higher levels of steatosis, absolute values of the methods diverged markedly, with histology exceeding PDFF by up to 3.4-fold. On average, 100% histological steatosis corresponded to a PDFF of 33.0% (29.5-36.7%). Targeting at a specificity of 90%, optimal PDFF thresholds to predict histological steatosis grades were ≥5.75% for ≥S1, ≥15.50% for ≥S2, and ≥21.35% for S3. Hepatocytes comprised 58 ± 5% of liver cells, which may partly explain the lower values of PDFF vs. histology.ConclusionsHistological steatosis and PDFF have non-perfect linearity and fundamentally different scales of measurement. Liver fat values obtained using these methods may be rendered comparable by conversion equations or threshold values.Impact and implicationsMagnetic resonance-proton density fat fraction (PDFF) is increasingly being used to measure liver fat in place of the invasive liver biopsy. Understanding the relationship between PDFF and histological steatosis fraction is important for preventing misjudgement of clinical status or treatment effects in patient care. Our analysis revealed that histological steatosis fraction is often significantly higher than PDFF, and their association varies across the spectrum of fatty liver severity. These findings are particularly important for physicians and clinical researchers, who may use these data to interpret PDFF measurements in the context of histologically evaluated liver fat content.

Published
2024

12. Genetic drivers and cellular selection of female mosaic X chromosome loss

Author

Liu, Aoxing, Genovese, Giulio, Zhao, Yajie, Pirinen, Matti, Zekavat, Seyedeh M., Kentistou, Katherine A., Yang, Zhiyu, Yu, Kai, Vlasschaert, Caitlyn, Liu, Xiaoxi, Brown, Derek W., Hudjashov, Georgi, Gorman, Bryan R., Dennis, Joe, Zhou, Weiyin, Momozawa, Yukihide, Pyarajan, Saiju, Tuzov, Valdislav, Pajuste, Fanny-Dhelia, Aavikko, Mervi, Sipilä, Timo P., Ghazal, Awaisa, Huang, Wen-Yi, Freedman, Neal D., Song, Lei, Gardner, Eugene J., Sankaran, Vijay G., Palotie, Aarno, Ollila, Hanna M., Tukiainen, Taru, Chanock, Stephen J., Mägi, Reedik, Natarajan, Pradeep, Daly, Mark J., Bick, Alexander, McCarroll, Steven A., Terao, Chikashi, Loh, Po-Ru, Ganna, Andrea, Perry, John R. B., and Machiela, Mitchell J.

Published
2024
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13. AI MyData: Fostering Middle School Students' Engagement with Machine Learning through an Ethics-Infused AI Curriculum

Author

Ismaila Temitayo Sanusi, Fred Martin, Ruizhe Ma, Joseph E. Gonzales, Vaishali Mahipal, Solomon Sunday Oyelere, Jarkko Suhonen, and Markku Tukiainen

Abstract

As initiatives on AI education in K-12 learning contexts continues to evolve, researchers have developed curricula among other resources to promote AI across grade levels. Yet, there is a need for more effort regarding curriculum, tools, and pedagogy, as well as assessment techniques to popularize AI at the middle school level. Drawing on prior work, we created original curriculum activities with innovative use of existing technology, a new computational teaching tool, and a series of approaches and assessments to evaluate students' engagement with the learning resources. Our curriculum called AI MyData comprises elements of ML and data science infused with ethical orientation. In this article, we describe the novel AI curriculum and further discuss how we engaged students in learning and critiquing AI ethical dilemmas. We gathered data from two pilot studies conducted in the Northeast United States, one Artificial Intelligence Afterschool (AIA) program, and one virtual AI summer camp. The AIA program was carried out in a local public school with four middle school students aged 12 to 13; the program consisted of eleven 2-hour sessions. The summer camp consisted of 2-hour sessions over 4 consecutive days, with 18 students aged 12 to 15. We facilitated both pilot programs with hands-on plugged and unplugged activities. The method of capturing data included artifact collection, structured interviews, written assessments, and a pre- to post-questionnaire tapping participants' dispositions about AI and its societal implication. Participant artifacts, written assessments, survey, observation, and analysis of tasks completed revealed that the children improved in their knowledge of AI. In addition, the AI curriculum units and accompanying approaches developed for this study successfully engaged the participants, even without prior knowledge of related concepts. We also found an indication that introducing ethics of AI to adolescents will help their development as ethically responsive citizens. Our study results also indicate that lessons establishing links with students' personal lives (e.g., letting students choose personally meaningful datasets) and societal implications using unplugged activities and interactive tools were particularly valuable for promoting AI and the integration of AI in middle school education across the subject domains and settings. Based on these results, we discuss our findings, identify their limitations, and propose future work.

Published
2024
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14. A Systematic Literature Review of Empirical Research on ChatGPT in Education

Author

Yazid Albadarin, Mohammed Saqr, Nicolas Pope, and Markku Tukiainen

Abstract

Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023--the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli's (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell's (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Published
2024
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15. Learning Machine Learning with Young Children: Exploring Informal Settings in an African Context

Author

Ismaila Temitayo Sanusi, Kissinger Sunday, Solomon Sunday Oyelere, Jarkko Suhonen, Henriikka Vartiainen, and Markku Tukiainen

Abstract

Background and context: Researchers have been investigating ways to demystify machine learning for students from kindergarten to twelfth grade (K-12) levels. As little evidence can be found in the literature, there is a need for additional research to understand and facilitate the learning experience of children while also considering the African context. Objective: The purpose of this study was to explore how young children teach and develop their understanding of machine learning based technologies in playful and informal settings. Method: Using a qualitative methodological approach through fine-grained analysis of video recordings and interviews, we analysed how 18 children aged 3-13 years constructed their interactions with a machine-based technology (Google's Teachable Machine). Findings: This study provides empirical support for the claim that Google's Teachable Machine contributes to the development of data literacy and conceptual understanding across K-12 irrespective of the learners' backgrounds. The results also confirmed children's ability to infer the relationship between their own expressions and the output of the machine learning-based tool, thus, identifying the input-output relationships in machine learning. In addition, this study opens a discussion around differentials in emerging technology use across different contexts through participatory learning. Implications: The results provide a baseline for future research on the topic and preliminary evidence to discern how children learn about machine learning in the African K-12 context.

Published
2024
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16. Author Correction: Genetic drivers and cellular selection of female mosaic X chromosome loss

Author

Liu, Aoxing, Genovese, Giulio, Zhao, Yajie, Pirinen, Matti, Zekavat, Seyedeh M., Kentistou, Katherine A., Yang, Zhiyu, Yu, Kai, Vlasschaert, Caitlyn, Liu, Xiaoxi, Brown, Derek W., Hudjashov, Georgi, Gorman, Bryan R., Dennis, Joe, Zhou, Weiyin, Momozawa, Yukihide, Pyarajan, Saiju, Tuzov, Valdislav, Pajuste, Fanny-Dhelia, Aavikko, Mervi, Sipilä, Timo P., Ghazal, Awaisa, Huang, Wen-Yi, Freedman, Neal D., Song, Lei, Gardner, Eugene J., Sankaran, Vijay G., Palotie, Aarno, Ollila, Hanna M., Tukiainen, Taru, Chanock, Stephen J., Mägi, Reedik, Natarajan, Pradeep, Daly, Mark J., Bick, Alexander, McCarroll, Steven A., Terao, Chikashi, Loh, Po-Ru, Ganna, Andrea, Perry, John R. B., and Machiela, Mitchell J.

Published
2024
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17. Lateral Ordering of InAs Quantum Dots on Cross-hatch Patterned GaInP

Author

Hakkarainen Teemu, Schramm Andreas, Tukiainen Antti, Ahorinta Risto, Toikkanen Lauri, and Guina Mircea

Subjects
Molecular beam epitaxy, III-V semiconductors, Quantum dots, Ordering, InAs, GaInP, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Abstract We report the use of partially relaxed tensile as well as compressively strained GaInP layers for lateral ordering of InAs quantum dots with the aid of misfit dislocation networks. The strained layers and the InAs QDs were characterized by means of atomic force microscopy, scanning electron microscopy, and X-ray reciprocal space mapping. The QD-ordering properties of compressive GaInP are found to be very similar with respect to the use of compressive GaInAs, while a significantly stronger ordering of QDs was observed on tensile GaInP. Furthermore, we observed a change of the major type of dislocation in GaInP layers as the growth temperature was modified.

Published
2010
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18. Multiscale in modelling and validation for solar photovoltaics

Author

Abu Hamed Tareq, Adamovic Nadja, Aeberhard Urs, Alonso-Alvarez Diego, Amin-Akhlaghi Zoe, Auf der Maur Matthias, Beattie Neil, Bednar Nikola, Berland Kristian, Birner Stefan, Califano Marco, Capan Ivana, Cerne Bostjan, Chilibon Irinela, Connolly James. P., Cortes Juan Frederic, Coutinho Jose, David Christin, Deppert Knut, Donchev Vesselin, Drev Marija, Ehlen Boukje, Ekins-Daukes Nicholas, Even Jacky, Fara Laurentiu, Fuertes Marron David, Gagliardi Alessio, Garrido Blas, Gianneta Violetta, Gomes Maria, Guillemoles Jean-Francois, Guina Mircea, Halme Janne, Hocevar Mateja, Jacak Lucjan, Jacak Witold, Jaksic Zoran, Joseph Lejo k., Kassavetis Spyridon, Kazukauskas Vaidotas, Kleider Jean-Paul, Kluczyk Katarzyna, Kopecek Radovan, Krasovec Ursa Opara, Lazzari Jean-Louis, Lifshitz Efrat, Loncaric Martin, Madsen Søren Peder, Vega Antonio Marti, Mencaraglia Denis, Messing Maria E., Armando Felipe Murphy, Nassiopoulou Androula G., Neijm Ahmed, Nemcsics Akos, Neto Victor, Pedesseau Laurent, Persson Clas, Petridis Konstantinos, Popescu Lacramioara, Pucker Georg, Radovanović Jelena, Rimada Julio C., Ristova Mimoza, Savic Ivana, Savin Hele, Sendova-Vassileva Marushka, Sengul Abdurrahman, Silva José, Steiner Ullrich, Storch Jan, Stratakis Emmanuel, Tao Shuxia, Tomanek Pavel, Tomić Stanko, Tukiainen Antti, Turan Rasit, Ulloa Jose Maria, Wang Shengda, Yuksel Fatma, Zadny Jaroslav, and Zarbakhsh Javad

Subjects
multi-scale modelling, solar cells, third generation photovoltaics, semiconductors, nano structures, device simulation, Renewable energy sources, TJ807-830
Abstract

Photovoltaics is amongst the most important technologies for renewable energy sources, and plays a key role in the development of a society with a smaller environmental footprint. Key parameters for solar cells are their energy conversion efficiency, their operating lifetime, and the cost of the energy obtained from a photovoltaic system compared to other sources. The optimization of these aspects involves the exploitation of new materials and development of novel solar cell concepts and designs. Both theoretical modeling and characterization of such devices require a comprehensive view including all scales from the atomic to the macroscopic and industrial scale. The different length scales of the electronic and optical degrees of freedoms specifically lead to an intrinsic need for multiscale simulation, which is accentuated in many advanced photovoltaics concepts including nanostructured regions. Therefore, multiscale modeling has found particular interest in the photovoltaics community, as a tool to advance the field beyond its current limits. In this article, we review the field of multiscale techniques applied to photovoltaics, and we discuss opportunities and remaining challenges.

Published
2018
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19. Intensive Robotics Course Projects Improve Higher Education Students’ Intrinsic Motivation and Learning Outcomes

Author

Jormanainen, Ilkka, Yigzaw, Samuel, Tukiainen, Markku, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Balogh, Richard, editor, Obdržálek, David, editor, and Fislake, Martin, editor

Published
2024
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20. Positive Customer Experience is Enhanced by Effective Agile Practices

Author

Piiroinen, Riina, Jormanainen, Ilkka, Tukiainen, Markku, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Hyrynsalmi, Sami, editor, Münch, Jürgen, editor, Smolander, Kari, editor, and Melegati, Jorge, editor

Published
2024
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24. Performace of Dilute Nitride Triple Junction Space Solar Cell Grown by MBE

Author

Aho Arto, Isoaho Riku, Tukiainen Antti, Polojärvi Ville, Raappana Marianna, Aho Timo, and Guina Mircea

Subjects
Environmental sciences, GE1-350
Abstract

Dilute nitride arsenide antimonide compounds offer widely tailorable band-gaps, ranging from 0.8 eV to 1.4 eV, for the development of lattice-matched multijunction solar cells with three or more junctions. Here we report on the performance of GaInP/GaAs/GaInNAsSb solar cell grown by molecular beam epitaxy. An efficiency of 27% under AM0 conditions is demonstrated. In addition, the cell was measured at different temperatures. The short circuit current density exhibited a temperature coefficient of 0.006 mA/cm2/°C while the corresponding slope for the open circuit voltage was −6.8 mV/°C. Further efficiency improvement, up to 32%, is projected by better current balancing and structural optimization.

Published
2017
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25. 31% European InGaP/GaAs/InGaAs Solar Cells for Space Application

Author

Campesato Roberta, Tukiainen Antti, Aho Arto, Gori Gabriele, Isoaho Riku, Greco Erminio, and Guina Mircea

Subjects
Environmental sciences, GE1-350
Abstract

We report a triple junction InGaP/GaAs/InGaNAs solar cell with efficiency of ~31% at AM0, 25 °C fabricated using a combined molecular beam epitaxy (MBE) and metal-organic chemical vapour deposition (MOCVD) processes. The prototype cells comprise of InGaNAs (Indium Gallium Nitride Arsenide) bottom junction grown on a GaAs (Gallium Arsenide) substrate by MBE and middle and top junctions deposited by MOCVD. Repeatable cell characteristics and uniform efficiency pattern over 4-inch wafers were obtained. Combining the advantages offered by MBE and MOCVD opens a new perspective for fabrication of high-efficiency space tandem solar cells with three or more junctions. Results of radiation resistance of the sub-cells are also presented and critically evaluated to achieve high efficiency in EOL conditions.

Published
2017
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26. A Systematic Review of Teaching and Learning Machine Learning in K-12 Education

Author

Sanusi, Ismaila Temitayo, Oyelere, Solomon Sunday, Vartiainen, Henriikka, Suhonen, Jarkko, and Tukiainen, Markku

Abstract

The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field.

Published
2023
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27. Design, Development, and Evaluation of a Virtual Reality Game-Based Application to Support Computational Thinking

Author

Agbo, Friday Joseph, Oyelere, Solomon Sunday, Suhonen, Jarkko, and Tukiainen, Markku

Abstract

Computational thinking (CT) has become an essential skill nowadays. For young students, CT competency is required to prepare them for future jobs. This competency can facilitate students' understanding of programming knowledge which has been a challenge for many novices pursuing a computer science degree. This study focuses on designing and implementing a virtual reality (VR) game-based application (iThinkSmart) to support CT knowledge. The study followed the design science research methodology to design, implement, and evaluate the first prototype of the VR application. An initial evaluation of the prototype was conducted with 47 computer science students from a Nigerian university who voluntarily participated in an experimental process. To determine what works and what needs to be improved in the iThinkSmart VR game-based application, two groups were randomly formed, consisting of the experimental (n = 21) and the control (n = 26) groups respectively. Our findings suggest that VR increases motivation and therefore increase students' CT skills, which contribute to knowledge regarding the affordances of VR in education and particularly provide evidence on the use of visualization of CT concepts to facilitate programming education. Furthermore, the study revealed that immersion, interaction, and engagement in a VR educational application can promote students' CT competency in higher education institutions (HEI). In addition, it was shown that students who played the iThinkSmart VR game-based application gained higher cognitive benefits, increased interest and attitude to learning CT concepts. Although further investigation is required in order to gain more insights into students learning process, this study made significant contributions in positioning CT in the HEI context and provides empirical evidence regarding the use of educational VR mini games to support students learning achievements.

Published
2023
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29. Neurotrophic tyrosine receptor kinase gene fusions in adult and pediatric patients with solid tumors: a clinicogenomic biobank and record linkage study of expression frequency and patient characteristics from Finland

Author

Wei Zhang, Arndt A. Schmitz, Roosa E. Kallionpää, Merja Perälä, Niina Pitkänen, Mikko Tukiainen, Erika Alanne, Korinna Jöhrens, Renate Schulze-Rath, Bahman Farahmand, and Jihong Zong

Subjects
Clinicogenomic, NTRK gene fusions, solid tumors, pediatrics, epidemiology, biobank, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background: Neurotrophic tyrosine receptor kinase (NTRK) gene fusions are oncogenic drivers. Using the Auria Biobank in Finland, we aimed to identify and characterize patients with these gene fusions, and describe their clinical and tumor characteristics, treatments received, and outcomes. Material and methods: We evaluated pediatrics with any solid tumor type and adults with colorectal cancer (CRC), non-small cell lung cancer (NSCLC), sarcoma, or salivary gland cancer. We determined tropomyosin receptor kinase (TRK) protein expression by pan-TRK immunohistochemistry (IHC) staining of tumor samples from the Auria Biobank, scored by a certified pathologist. NTRK gene fusion was confirmed by next generation sequencing (NGS). All 2,059 patients were followed-up starting 1 year before their cancer diagnosis. Results: Frequency of NTRK gene fusion tumors was 3.1% (4/127) in pediatrics, 0.7% (8/1,151) for CRC, 0.3% (1/288) for NSCLC, 0.9% (1/114) for salivary gland cancer, and 0% (0/379) for sarcoma. Among pediatrics there was one case each of fibrosarcoma (TPM3::NTRK1), Ewing’s sarcoma (LPPR1::NTRK2), primitive neuroectodermal tumor (DAB2IP::NTRK2), and papillary thyroid carcinoma (RAD51B::NTRK3). Among CRC patients, six harbored tumors with NTRK1 fusions (three fused with TPM3), one harbored a NTRK3::GABRG1 fusion, and the other a NTRK2::FXN/LPPR1 fusion. Microsatellite instability was higher in CRC patients with NTRK gene fusion tumors versus wild-type tumors (50.0% vs. 4.4%). Other detected fusions were SGCZ::NTRK3 (NSCLC) and ETV6::NTRK3 (salivary gland cancer). Four patients (three CRC, one NSCLC) received chemotherapy; one patient (with CRC) received radiotherapy. Conclusion: NTRK gene fusions are rare in adult CRC, NSCLC, salivary tumors, sarcoma, and pediatric solid tumors.

Published
2024
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30. Assessment of accessibility in virtual laboratories: a systematic review

Author

Fitsum G. Deriba, Mohammed Saqr, and Markku Tukiainen

Subjects
virtual laboratory, accessible educational environment, inclusive technologies, online learning, online experiment, Education (General), L7-991
Abstract

In an era of rapid evolution in educational technologies, Virtual Labs (V-Labs) have emerged as a promising solution, fundamentally altering how learners engage with scientific concepts and experiments. Despite their potential, ensuring their effectiveness and inclusivity in terms of accessibility to diverse students remains a challenging task. Currently, there is limited insight into the accessibility of V-Labs, a gap that our study aims to address. This study seeks to ascertain the effectiveness of V-Labs in terms of accessibility and inclusivity. We synthesized empirical studies, reviewing 36 articles published between 2000 and 2023. Of these articles, 69% of the studies were conducted in higher education and covered a wide range of learning environments. Our study revealed that 47.3% of the studies focused on various engineering subjects. Our findings provide insight into V-Labs’ accessibility from four key perspectives: (a) students with limited abilities, (b) diverse cultural and linguistic backgrounds, (c) instructional design features and content availability, and (d) interaction supporting features. We also identified existing gaps in the accessibility of the V-Labs from the four perspectives. Furthermore, we examined the assessment methods of V-Labs, shed light on the aspects that are evaluated, and underscored the need for future work on assessment strategies.

Published
2024
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31. TWINGEN: protocol for an observational clinical biobank recall and biomarker cohort study to identify Finnish individuals with high risk of Alzheimer’s disease

Author

Tommi Vasankari, Henri Vähä-Ypyä, Oskari Heikinheimo, Athena Matakidou, Jaakko Kaprio, Eero Vuoksimaa, Ying Wu, Kai Kaarniranta, Peeter Karihtala, Wei Zhou, Caroline Fox, Sanna-Kaisa Herukka, Sarah Smith, Apinya Lertratanakul, Katri Kaukinen, Johannes Kettunen, Minna Männikkö, Eeva Sliz, Markus Perola, Veikko Salomaa, Hilkka Soininen, Ilkka Kalliala, Mika Kähönen, Hao Chen, Andrey Loboda, Laure Morin-Papunen, Terhi Piltonen, Anders Mälarstig, Jason Miller, Jussi Hernesniemi, Henrike Heyne, Sirkku Peltonen, Daniel Gordin, Masahiro Kanai, Benjamin Challis, Juha Sinisalo, David Rice, Fredrik Åberg, Aarno Palotie, Samuli Ripatti, Oili Kaipiainen-Seppänen, Aki Havulinna, Satu Strausz, Tuomo Kiiskinen, Sanni Ruotsalainen, Jukka Koskela, Tuula Palotie, Mark Daly, Howard Jacob, Heiko Runz, Sally John, Robert Plenge, Mark McCarthy, Julie Hunkapiller, Dawn Waterworth, Petri Virolainen, Terhi Kilpi, Jukka Partanen, Anne Pitkäranta, Veli-Matti Kosma, Outi Tuovila, Raimo Pakkanen, Shameek Biswas, Xinli Hu, Johanna Schleutker, Mikko Arvas, Olli Carpen, Reetta Hinttala, Arto Mannermaa, Valtteri Julkunen, Anne Remes, Reetta Kälviäinen, Mikko Hiltunen, Jukka Peltola, Pentti Tienari, Juha Rinne, Adam Ziemann, Jeffrey Waring, Nizar Smaoui, Anne Lehtonen, Susan Eaton, Sanni Lahdenperä, Natalie Bowers, Edmond Teng, Fanli Xu, David Pulford, Martti Färkkilä, Sampsa Pikkarainen, Airi Jussila, Timo Blomster, Mikko Kiviniemi, Markku Voutilainen, Fedik Rahimov, Joseph Maranville, Tim Lu, Kirsi Kalpala, Melissa Miller, Linda McCarthy, Kari Eklund, Antti Palomäki, Pia Isomäki, Laura Pirilä, Johanna Huhtakangas, Marla Hochfeld, Nan Bing, Jorge Esparza Gordillo, Nina Mars, Tarja Laitinen, Margit Pelkonen, Paula Kauppi, Hannu Kankaanranta, Terttu Harju, Hubert Chen, Teemu Niiranen, Kaj Metsärinne, Marja-Riitta Taskinen, Tiinamaija Tuomi, Jari Laukkanen, Audrey Chu, Jaakko Parkkinen, Heikki Joensuu, Tuomo Meretoja, Lauri Aaltonen, Annika Auranen, Päivi Auvinen, Klaus Elenius, Relja Popovic, Bridget Riley-Gillis, Jennifer Schutzman, Heli Lehtonen, Stefan McDonough, Diptee Kulkarni, Terhi Ollila, Hannu Uusitalo, Erich Strauss, Kaisa Tasanen, Laura Huilaja, Katariina Hannula-Jouppi, Teea Salmi, Leena Koulu, David Choy, Anu Jalanko, Risto Kajanne, Mari Kaunisto, Chia-Yen Chen, Robert Yang, Kirsi Auro, Clement Chatelain, Mitja Kurki, Juha Karjalainen, Kimmo Palin, Priit Palta, Susanna Lemmelä, Manuel Rivas, Arto Lehisto, Andrea Ganna, Vincent Llorens, Kati Kristiansson, Kati Hyvärinen, Jarmo Ritari, Katri Pylkäs, Minna Karjalainen, Tuomo Mantere, Eeva Kangasniemi, Sami Heikkinen, Eija Laakkonen, Anu Loukola, Päivi Laiho, Tuuli Sistonen, Essi Kaiharju, Markku Laukkanen, Elina Järvensivu, Sini Lähteenmäki, Lotta Männikkö, Regis Wong, Hannele Mattsson, Tero Hiekkalinna, Kati Donner, Kalle Pärn, Elina Kilpeläinen, Hannele Laivuori, Harri Siirtola, Sirpa Soini, Teijo Kuopio, Ioanna Tachmazidou, Katja Kivinen, Pekka Nieminen, Adam Platt, Sanna Toppila-Salmi, Antti Mäkitie, Elmo Saarentaus, Kristiina Aittomäki, Elisabeth Widen, Marja Vääräsmäki, Erkki Isometsä, Laura Addis, Mika Helminen, Taneli Raivio, Mart Kals, Majd Mouded, Hanna Ollila, Vuokko Anttonen, Tarja Kokkola, Robert Graham, Amanda Elliott, Ali Abbasi, Bridget Riley-Gills, Dirk Paul, Katherine Klinger, Deepak Raipal, Antti Hakanen, Raisa Serpi, Johanna Mäkelä, Mengzhen Liu, Neha Raghavan, Adriana Huertas-Vazquez, Nicole Renaud, Roosa Kallionpää, John Eicher, Minna Raivio, Juulia Partanen, Riitta Lahesmaa, Glenda Lassi, Joanna Betts, Rajashree Mishra, Felix Vaura, Joel Rämö, Mary Pat Reeve, Johanna Mattson, Sauli Vuoti, Esa Pitkänen, Joni A Turunen, Stephanie Loomis, Pirkko Pussinen, Aino Salminen, Tuula Salo, Ulla Palotie, Maria Siponen, Liisa Suominen, Päivi Mäntylä, Ulvi Gursoy, Kirsi Sipilä, Venla Kurra, Laura Kotaniemi-Talonen, Outi Uimari, Taru Tukiainen, Niko Välimäki, Janet Kumar, Juha Mehtonen, Shabbeer Hassan, Pietro Della Briotta Parolo, Mutaamba Maasha, Javier Garcia-Tabuenca, Jiwoo Lee, Kristin Tsuo, Nina Pitkänen, Eero Punkka, Huei-Yi Shen, Mervi Aavikko, L. Elisa Lahtela, Timo P. Sipilä, Awaisa Ghazal, Sami Koskelainen, Teemu Paajanen, Shuang Luo, Javier Gracia-Tabuenca, Tiina Luukkaala, Iida Vähätalo, Marco Hautalahti, Tom Southerington, Paula Iso-Markku, Jaana Suvisaari, Zhihao Ding, Qingqin S Li, Amy Hart, Perttu Terho, Alessandro Porello, Anastasia Kytölä, Antti Aarnisalo, Aoxing Liu, Argyro Bizaki-Vallaskangas, Auli Toivola, Debby Ngo, Dermot Reilly, Ekaterina Khramtsova, Elisa Rahikkala, Eric Green, Eveliina Salminen, Fabiana Farias, George Okafo, Heidi Silven, Heli Salminen-Mankonen, Henna Palin, Iiris Hovatta, Jaakko Tyrmi, Jae-Hoon Sul, Jenni Aittokallio, Jyrki Pitkänen, Karen He, Katriina Aalto-Setälä, Maarit Niinimäki, Malla-Maria Linna, Marc Jung, Margaret G. Ehm, Marianna Niemi, Meijian Guan, Mike Mendelson, Minna Brunfeldt, Natalia Pujol, Nathan Lawless, Oluwaseun Alexander Dada, Rigbe Weldatsadik, Riikka Arffman, Rion Pendergrass, Sahar Mozaffari, Samuel Lessard, Sanna Siltanen, Shanmukha Sampath Padmanabhuni, Simonne Longerich, Susanna Savukoski, Thomas Damm Als, Timo Hiltunen, Tomi P. Mäkelä, Triin Laisk, Tytti Willberg, Varpu Jokimaa, Aija Kyttälä, Toni T Saari, Aino Aaltonen, Sari Aaltonen, Sari Kärkkäinen, Hilkka Liedes, Miina Ollikainen, Teemu Palviainen, Ilona Ruotsalainen, Mia Urjansson, Markus M Forsberg, Jaakko Lähteenmäki, Pia Nyberg, Jani Tikkanen, Mari E. Niemi, and Rodos Rodosthenous

Subjects
Medicine
Abstract

Introduction A better understanding of the earliest stages of Alzheimer’s disease (AD) could expedite the development or administration of treatments. Large population biobanks hold the promise to identify individuals at an elevated risk of AD and related dementias based on health registry information. Here, we establish the protocol for an observational clinical recall and biomarker study called TWINGEN with the aim to identify individuals at high risk of AD by assessing cognition, health and AD-related biomarkers. Suitable candidates were identified and invited to participate in the new study among THL Biobank donors according to TWINGEN study criteria.Methods and analysis A multi-centre study (n=800) to obtain blood-based biomarkers, telephone-administered and web-based memory and cognitive parameters, questionnaire information on lifestyle, health and psychological factors, and accelerometer data for measures of physical activity, sedentary behaviour and sleep. A subcohort is being asked to participate in an in-person neuropsychological assessment (n=200) and wear an Oura ring (n=50). All participants in the TWINGEN study have genome-wide genotyping data and up to 48 years of follow-up data from the population-based older Finnish Twin Cohort (FTC) study of the University of Helsinki. The data collected in TWINGEN will be returned to THL Biobank from where it can later be requested for other biobank studies such as FinnGen that supported TWINGEN.Ethics and dissemination This recall study consists of FTC/THL Biobank/FinnGen participants whose data were acquired in accordance with the Finnish Biobank Act. The recruitment protocols followed the biobank protocols approved by Finnish Medicines Agency. The TWINGEN study plan was approved by the Ethics Committee of Hospital District of Helsinki and Uusimaa (number 16831/2022). THL Biobank approved the research plan with the permission no: THLBB2022_83.

Published
2024
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32. Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning

Author

Macua, Sergio Valcarcel, Davies, Ian, Tukiainen, Aleksi, and de Cote, Enrique Munoz

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Systems and Control
Abstract

We propose a fully distributed actor-critic architecture, named Diff-DAC, with application to multitask reinforcement learning (MRL). During the learning process, agents communicate their value and policy parameters to their neighbours, diffusing the information across a network of agents with no need for a central station. Each agent can only access data from its local task, but aims to learn a common policy that performs well for the whole set of tasks. The architecture is scalable, since the computational and communication cost per agent depends on the number of neighbours rather than the overall number of agents. We derive Diff-DAC from duality theory and provide novel insights into the actor-critic framework, showing that it is actually an instance of the dual ascent method. We prove almost sure convergence of Diff-DAC to a common policy under general assumptions that hold even for deep-neural network approximations. For more restrictive assumptions, we also prove that this common policy is a stationary point of an approximation of the original problem. Numerical results on multitask extensions of common continuous control benchmarks demonstrate that Diff-DAC stabilises learning and has a regularising effect that induces higher performance and better generalisation properties than previous architectures., Comment: 27 pages, 8 figures

Published
2021
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34. Thrombosis risk in single- and double-heterozygous carriers of factor V Leiden and prothrombin G20210A in FinnGen and the UK Biobank

Author

Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Viollet, Coralie, Petrovski, Slavé, Chen, Chia-Yen, John, Sally, Okafo, George, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Pendergrass, Rion, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Mälarstig, Anders, Vlahiotis, Anna, Klinger, Katherine, Chatelain, Clement, Gossel, Matthias, Estrada, Karol, Graham, Robert, Waterworth, Dawn, O´Donnell, Chris, Renaud, Nicole, Mäkelä, Tomi P., Kaprio, Jaakko, Ruddock, Minna, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Raivio, Taneli, Tikkanen, Jani, Serpi, Raisa, Kristiansson, Kati, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Ding, Zhihao, Jung, Marc, Tuoken, Hanati, Biswas, Shameek, Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Hu, Xinli, Hedman, Åsa, Obeidat, Ma´en, Chung, Jonathan, Zierer, Jonas, Niemi, Mari, Ripatti, Samuli, Schleutker, Johanna, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Runz, Heiko, Lahdenperä, Sanni, Bowers, Natalie, Teng, Edmond, Xu, Fanli, Addis, Laura, Eicher, John, Li, Qingqin S., He, Karen, Khramtsova, Ekaterina, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Lu, Tim, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Lertratanakul, Apinya, Hochfeld, Marla, Gordillo, Jorge Esparza, Farias, Fabiana, Bing, Nan, Laitinen, Tarja, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Chen, Hubert, Betts, Joanna, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Niiranen, Teemu, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Elliott, Amanda, Reeve, Mary Pat, Ruotsalainen, Sanni, Paul, Dirk, Chu, Audrey, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Meretoja, Tuomo, Joensuu, Heikki, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Auvinen, Päivi, Elenius, Klaus, Pitkänen, Esa, Popovic, Relja, Fabre, Margarete, Schutzman, Jennifer, Kulkarni, Diptee, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A., Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Liu, Mengzhen, Loomis, Stephanie, Strauss, Erich, Chen, Hao, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Choy, David, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Laivuori, Hannele, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Kumar, Janet, Hovatta, Iiris, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Rahikkala, Elisa, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della Briotta Parolo, Pietro, Zhou, Wei, Maasha, Mutaamba, Lemmelä, Susanna, Rivas, Manuel, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Heyne, Henrike, Rämö, Joel, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Hyvärinen, Kati, Ritari, Jarmo, Pylkäs, Katri, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Kallio, Lila, Wahlfors, Tiina, Punkka, Eero, Siltanen, Sanna, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Cooper, Helen, Öller, Denise, Leinonen, Rasko, Palin, Henna, Linna, Malla-Maria, Kanai, Masahiro, Zheng, Zhili, Lahtela, L. Elisa, Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Dada, Oluwaseun Alexander, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Donner, Kati, Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Luo, Shuang, Padmanabhuni, Shanmukha Sampath, Niemi, Marianna, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Tammerluoto, Jyrki, Smith, Sarah, Southerington, Tom, Lehto, Petri, Ryu, Justine, Rämö, Joel T., Jurgens, Sean J., Sanna-Cherchi, Simone, Bauer, Kenneth A., Haj, Amelia, Choi, Seung Hoan, Ellinor, Patrick T., and Bendapudi, Pavan K.

Published
2024
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36. High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases

Author

Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Paul, Dirk, Petrovski, Slavé, Runz, Heiko, John, Sally, Okafo, George, Lawless, Nathan, Salminen-Mankonen, Heli, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Mälarstig, Anders, Klinger, Katherine, Chatelain, Clement, Gossel, Matthias, Estrada, Karol, Graham, Robert, Yang, Robert, ODonnell, Chris, Mäkelä, Tomi P., Kaprio, Jaakko, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Pitkäranta, Anne, Raivio, Taneli, Tikkanen, Jani, Serpi, Raisa, Laitinen, Tarja, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Pakkanen, Raimo, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Chen, Chia-Yen, Ding, Zhihao, Jung, Marc, Biswas, Shameek, Pendergrass, Rion, Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Hu, Xinli, Hedman, Åsa, Rivas, Manuel, Waterworth, Dawn, Renaud, Nicole, Obeidat, Maen, Ripatti, Samuli, Schleutker, Johanna, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Abbasi, Ali, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Lahdenperä, Sanni, Bowers, Natalie, Teng, Edmond, Xu, Fanli, Addis, Laura, Eicher, John, Li, Qingqin S., He, Karen, Khramtsova, Ekaterina, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Lu, Tim, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Lertratanakul, Apinya, Viollet, Coralie, Hochfeld, Marla, Gordillo, Jorge Esparza, Farias, Fabiana, Bing, Nan, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Chen, Hubert, Betts, Joanna, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Niiranen, Teemu, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Elliott, Amanda, Reeve, Mary Pat, Ruotsalainen, Sanni, Chu, Audrey, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Meretoja, Tuomo, Joensuu, Heikki, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Auvinen, Päivi, Elenius, Klaus, Pitkänen, Esa, Popovic, Relja, Fabre, Margarete, Schutzman, Jennifer, Kulkarni, Diptee, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A., Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Liu, Mengzhen, Loomis, Stephanie, Strauss, Erich, Chen, Hao, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Choy, David, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Laivuori, Hannele, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Kumar, Janet, Hovatta, Iiris, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Als, Thomas Damm, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Rahikkala, Elisa, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della Briotta Parolo, Pietro, Zhou, Wei, Maasha, Mutaamba, Lemmelä, Susanna, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Heyne, Henrike, Rämö, Joel, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Kristiansson, Kati, Hyvärinen, Kati, Ritari, Jarmo, Pylkäs, Katri, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Kallio, Lila, Wahlfors, Tiina, Punkka, Eero, Siltanen, Sanna, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Cooper, Helen, Öller, Denise, Leinonen, Rasko, Palin, Henna, Linna, Malla-Maria, Kanai, Masahiro, Zheng, Zhili, Lahtela, L. Elisa, Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Dada, Oluwaseun Alexander, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Donner, Kati, Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Pärn, Kalle, Kals, Mart, Luo, Shuang, Padmanabhuni, Shanmukha Sampath, Niemi, Marianna, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Tammerluoto, Jyrki, Smith, Sarah, Southerington, Tom, Lehto, Petri, Youssef, Omar, Zidi-Mouaffak, Yossra H.S., and Tamlander, Max

Published
2024
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47. FinnGen provides genetic insights from a well-phenotyped isolated population

Author

Kurki, Mitja I., Karjalainen, Juha, Palta, Priit, Sipilä, Timo P., Kristiansson, Kati, Donner, Kati M., Reeve, Mary P., Laivuori, Hannele, Aavikko, Mervi, Kaunisto, Mari A., Loukola, Anu, Lahtela, Elisa, Mattsson, Hannele, Laiho, Päivi, Della Briotta Parolo, Pietro, Lehisto, Arto A., Kanai, Masahiro, Mars, Nina, Rämö, Joel, Kiiskinen, Tuomo, Heyne, Henrike O., Veerapen, Kumar, Rüeger, Sina, Lemmelä, Susanna, Zhou, Wei, Ruotsalainen, Sanni, Pärn, Kalle, Hiekkalinna, Tero, Koskelainen, Sami, Paajanen, Teemu, Llorens, Vincent, Gracia-Tabuenca, Javier, Siirtola, Harri, Reis, Kadri, Elnahas, Abdelrahman G., Sun, Benjamin, Foley, Christopher N., Aalto-Setälä, Katriina, Alasoo, Kaur, Arvas, Mikko, Auro, Kirsi, Biswas, Shameek, Bizaki-Vallaskangas, Argyro, Carpen, Olli, Chen, Chia-Yen, Dada, Oluwaseun A., Ding, Zhihao, Ehm, Margaret G., Eklund, Kari, Färkkilä, Martti, Finucane, Hilary, Ganna, Andrea, Ghazal, Awaisa, Graham, Robert R., Green, Eric M., Hakanen, Antti, Hautalahti, Marco, Hedman, Åsa K., Hiltunen, Mikko, Hinttala, Reetta, Hovatta, Iiris, Hu, Xinli, Huertas-Vazquez, Adriana, Huilaja, Laura, Hunkapiller, Julie, Jacob, Howard, Jensen, Jan-Nygaard, Joensuu, Heikki, John, Sally, Julkunen, Valtteri, Jung, Marc, Junttila, Juhani, Kaarniranta, Kai, Kähönen, Mika, Kajanne, Risto, Kallio, Lila, Kälviäinen, Reetta, Kaprio, Jaakko, Kerimov, Nurlan, Kettunen, Johannes, Kilpeläinen, Elina, Kilpi, Terhi, Klinger, Katherine, Kosma, Veli-Matti, Kuopio, Teijo, Kurra, Venla, Laisk, Triin, Laukkanen, Jari, Lawless, Nathan, Liu, Aoxing, Longerich, Simonne, Mägi, Reedik, Mäkelä, Johanna, Mäkitie, Antti, Malarstig, Anders, Mannermaa, Arto, Maranville, Joseph, Matakidou, Athena, Meretoja, Tuomo, Mozaffari, Sahar V., Niemi, Mari E. K., Niemi, Marianna, Niiranen, Teemu, O´Donnell, Christopher J., Obeidat, Ma´en, Okafo, George, Ollila, Hanna M., Palomäki, Antti, Palotie, Tuula, Partanen, Jukka, Paul, Dirk S., Pelkonen, Margit, Pendergrass, Rion K., Petrovski, Slavé, Pitkäranta, Anne, Platt, Adam, Pulford, David, Punkka, Eero, Pussinen, Pirkko, Raghavan, Neha, Rahimov, Fedik, Rajpal, Deepak, Renaud, Nicole A., Riley-Gillis, Bridget, Rodosthenous, Rodosthenis, Saarentaus, Elmo, Salminen, Aino, Salminen, Eveliina, Salomaa, Veikko, Schleutker, Johanna, Serpi, Raisa, Shen, Huei-yi, Siegel, Richard, Silander, Kaisa, Siltanen, Sanna, Soini, Sirpa, Soininen, Hilkka, Sul, Jae Hoon, Tachmazidou, Ioanna, Tasanen, Kaisa, Tienari, Pentti, Toppila-Salmi, Sanna, Tukiainen, Taru, Tuomi, Tiinamaija, Turunen, Joni A., Ulirsch, Jacob C., Vaura, Felix, Virolainen, Petri, Waring, Jeffrey, Waterworth, Dawn, Yang, Robert, Nelis, Mari, Reigo, Anu, Metspalu, Andres, Milani, Lili, Esko, Tõnu, Fox, Caroline, Havulinna, Aki S., Perola, Markus, Ripatti, Samuli, Jalanko, Anu, Laitinen, Tarja, Mäkelä, Tomi P., Plenge, Robert, McCarthy, Mark, Runz, Heiko, Daly, Mark J., and Palotie, Aarno

Published
2023
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48. The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections

Author

Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Paul, Dirk, Petrovski, Slavé, Runz, Heiko, John, Sally, Okafo, George, Lawless, Nathan, Salminen-Mankonen, Heli, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Mälarstig, Anders, Klinger, Katherine, Chatelain, Clement, Gossel, Matthias, Estrada, Karol, Graham, Robert, Yang, Robert, O´Donnell, Chris, Mäkelä, Tomi P., Kaprio, Jaakko, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Pitkäranta, Anne, Raivio, Taneli, Tikkanen, Jani, Serpi, Raisa, Laitinen, Tarja, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Pakkanen, Raimo, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Chen, Chia-Yen, Ding, Zhihao, Jung, Marc, Biswas, Shameek, Pendergrass, Rion, Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Hu, Xinli, Hedman, Åsa, Rivas, Manuel, Waterworth, Dawn, Renaud, Nicole, Obeidat, Ma´en, Ripatti, Samuli, Schleutker, Johanna, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Abbasi, Ali, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Lahdenperä, Sanni, Bowers, Natalie, Teng, Edmond, Xu, Fanli, Addis, Laura, Eicher, John, Li, Qingqin S., He, Karen, Khramtsova, Ekaterina, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Lu, Tim, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Lertratanakul, Apinya, Viollet, Coralie, Hochfeld, Marla, Gordillo, Jorge Esparza, Farias, Fabiana, Bing, Nan, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Chen, Hubert, Betts, Joanna, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Niiranen, Teemu, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Elliott, Amanda, Reeve, Mary Pat, Ruotsalainen, Sanni, Chu, Audrey, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Meretoja, Tuomo, Joensuu, Heikki, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Auvinen, Päivi, Elenius, Klaus, Pitkänen, Esa, Popovic, Relja, Fabre, Margarete, Schutzman, Jennifer, Kulkarni, Diptee, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A., Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Liu, Mengzhen, Loomis, Stephanie, Strauss, Erich, Chen, Hao, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Choy, David, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Laivuori, Hannele, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Kumar, Janet, Hovatta, Iiris, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Als, Thomas Damm, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Rahikkala, Elisa, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della Briotta Parolo, Pietro, Zhou, Wei, Maasha, Mutaamba, Lemmelä, Susanna, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Heyne, Henrike, Rämö, Joel, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Kristiansson, Kati, Hyvärinen, Kati, Ritari, Jarmo, Pylkäs, Katri, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Kallio, Lila, Wahlfors, Tiina, Punkka, Eero, Siltanen, Sanna, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Leinonen, Rasko, Palin, Henna, Linna, Malla-Maria, Kanai, Masahiro, Zheng, Zhili, Lahtela, L. Elisa, Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Dada, Oluwaseun Alexander, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Donner, Kati, Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Pärn, Kalle, Kals, Mart, Luo, Shuang, Padmanabhuni, Shanmukha Sampath, Niemi, Marianna, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Tammerluoto, Jyrki, Smith, Sarah, Southerington, Tom, Lehto, Petri, Jones, Samuel E., Maisha, Fahrisa I., Strausz, Satu J., Lammi, Vilma, Cade, Brian E., Tervi, Anniina, Helaakoski, Viola, Broberg, Martin E., Lane, Jacqueline M., Redline, Susan, Saxena, Richa, and Ollila, Hanna M.

Published
2023
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50. Risk of Midlife Stroke After Adverse Pregnancy Outcomes: The FinnGen Study

Author

Miller, Eliza C., Kauko, Anni, Tom, Sarah E., Laivuori, Hannele, Niiranen, Teemu, Bello, Natalie A., Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Paul, Dirk, Matakidou, Athena, Platt, Adam, Runz, Heiko, John, Sally, Okafo, George, Lawless, Nathan, Salminen-Mankonen, Heli, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Hunkapiller, Julie, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Fox, Caroline, Mälarstig, Anders, Klinger, Katherine, Raipal, Deepak, Green, Eric, Graham, Robert, Yang, Robert, Donnell, Chris O´, Mäkelä, Tomi P., Kaprio, Jaakko, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Pitkäranta, Anne, Raivio, Taneli, Serpi, Raisa, Laitinen, Tarja, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Pakkanen, Raimo, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Chen, Chia-Yen, Runz, Heiko, Ding, Zhihao, Jung, Marc, Biswas, Shameek, Pendergrass, Rion, Hunkapiller, Julie, Ehm, Margaret G., Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Mälarstig, Anders, Hu, Xinli, Klinger, Katherine, Graham, Robert, Green, Eric, Mozaffari, Sahar, Waterworth, Dawn, Renaud, Nicole, Obeidat, Ma´en, Ripatti, Samuli, Schleutker, Johanna, Perola, Markus, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Laukkanen, Jari, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Abbasi, Ali, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Runz, Heiko, Lahdenperä, Sanni, Biswas, Shameek, Hunkapiller, Julie, Bowers, Natalie, Teng, Edmond, Pendergrass, Rion, Xu, Fanli, Pulford, David, Auro, Kirsi, Addis, Laura, Eicher, John, Li, Qingqin S, He, Karen, Khramtsova, Ekaterina, Raghavan, Neha, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Daly, Mark, Abbasi, Ali, Waring, Jeffrey, Smaoui, Nizar, Rahimov, Fedik, Lehtonen, Anne, Lu, Tim, Bowers, Natalie, Pendergrass, Rion, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Hu, Xinli, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Abbasi, Ali, Waring, Jeffrey, Rahimov, Fedik, Lertratanakul, Apinya, Smaoui, Nizar, Lehtonen, Anne, Hochfeld, Marla, Bowers, Natalie, Pendergrass, Rion, Esparza Gordillo, Jorge, Auro, Kirsi, Waterworth, Dawn, Farias, Fabiana, Kalpala, Kirsi, Bing, Nan, Hu, Xinli, Laitinen, Tarja, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Smaoui, Nizar, Lassi, Glenda, Eaton, Susan, Chen, Hubert, Pendergrass, Rion, Bowers, Natalie, Betts, Joanna, Auro, Kirsi, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Kähönen, Mika, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Laukkanen, Jari, Elliott, Amanda, Pat Reeve, Mary, Ruotsalainen, Sanni, Challis, Benjamin, Paul, Dirk, Hunkapiller, Julie, Bowers, Natalie, Pendergrass, Rion, Chu, Audrey, Auro, Kirsi, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Miller, Melissa, Meretoja, Tuomo, Joensuu, Heikki, Carpén, Olli, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Elenius, Päivi Auvinen Klaus, Schleutker, Johanna, Pitkänen, Esa, Mars, Nina, Daly, Mark, Popovic, Relja, Waring, Jeffrey, Riley-Gillis, Bridget, Lehtonen, Anne, Schutzman, Jennifer, Hunkapiller, Julie, Bowers, Natalie, Pendergrass, Rion, Kulkarni, Diptee, Auro, Kirsi, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A, Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Pitkänen, Esa, Liu, Mengzhen, Runz, Heiko, Loomis, Stephanie, Strauss, Erich, Bowers, Natalie, Chen, Hao, Pendergrass, Rion, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Smaoui, Nizar, Rahimov, Fedik, Lehtonen, Anne, Choy, David, Pendergrass, Rion, Waterworth, Dawn, Kalpala, Kirsi, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Pendergrass, Rion, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Kettunen, Johannes, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Pat Reeve, Mary, Daly, Mark, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Liu, Mengzhen, Riley-Gillis, Bridget, Pendergrass, Rion, Kumar, Janet, Auro, Kirsi, Hovatta, Iiris, Chen, Chia-Yen, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Damm Als, Thomas, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Salminen, Eveliina, Rahikkala, Elisa, Kettunen, Johannes, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Ripatti, Samuli, Daly, Mark, Karjalainen, Juha, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della, Pietro, Parolo, Briotta, Zhou, Wei, Maasha, Mutaamba, Hassan, Shabbeer, Lemmelä, Susanna, Rivas, Manuel, Niemi, Mari E., Palotie, Aarno, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Tukiainen, Taru, Pat Reeve, Mary, Heyne, Henrike, Mars, Nina, Rämö, Joel, Saarentaus, Elmo, Ollila, Hanna, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Elliott, Amanda, Kristiansson, Kati, Arvas, Mikko, Hyvärinen, Kati, Ritari, Jarmo, Carpén, Olli, Kettunen, Johannes, Pylkäs, Katri, Sliz, Eeva, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Mannermaa, Arto, Laakkonen, Eija, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Terho, Perttu, Soini, Sirpa, Partanen, Jukka, Punkka, Eero, Serpi, Raisa, Siltanen, Sanna, Kosma, Veli-Matti, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Palin, Henna, Linna, Malla-Maria, Kurki, Mitja, Karjalainen, Juha, Della, Pietro, Parolo, Briotta, Lehisto, Arto, Mehtonen, Juha, Zhou, Wei, Kanai, Masahiro, Maasha, Mutaamba, Havulinna, Aki, Lemmelä, Susanna, Kiiskinen, Tuomo, Elisa Lahtela, L., Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Alexander Dada, Oluwaseun, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Ruotsalainen, Sanni, Donner, Kati, Sipilä, Timo P., Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Kristiansson, Kati, Lemmelä, Susanna, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Palta, Priit, Pärn, Kalle, Kals, Mart, Luo, Shuang, Laitinen, Tarja, Pat Reeve, Mary, Sampath Padmanabhuni, Shanmukha, Niemi, Marianna, Siirtola, Harri, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Pitkänen, Jyrki, Hautalahti, Marco, Mäkelä, Johanna, Smith, Sarah, and Southerington, Tom

Published
2023
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