Principal Metabolic Flux Mode Analysis

In recent years, much progress has been achieved in the computational analysis of the metabolic networks, as a consequence of the rapid growth of the omics database. However, current literature analysis algorithms still lack good biological interpretability of the analysis results. Moreover, they can not be applied on a whole-genome level. This thesis assesses the potential of the Principal Metabolic Flux Mode Analysis (PMFA). The PMFA is a novel algorithm that was recently developed, which aims to improve the interpretability of Principal Component Analysis (PCA), through including a stoichiometric regularization to the PCA objective function. The PMFA can determine the flux modes that explain the highest variability in the network and it can also scale-up to a whole-genome level using the sparse version of PMFA. Furthermore, this thesis compares the PMFA to the recent approach Principal Elementary Mode Analysis (PEMA), which also tries to enhance the PCA interpretability. However, this approach is computationally heavy and thus fails to handle the large-scale networks (e.g., whole-genome). In order to further determine the feasibility of the PMFA approach for the analysis of metabolism, a Graph-regularized Matrix Factorization (GMF) was developed analogous to PMFA framework, similarly by adding the network stoichiometric matrix to a graph-structured matrix factorization framework. The results illustrate the potential of PMFA as a metabolic network analysis for identifying fluxes that explain maximum variation in the network and it can be used to analyze whole-genome level. In addition, the results showed that GMF method performed well in predicting active Elementary Modes (EMs) on simulated data but failed to work on large networks, while PEMA had the lowest performance among all methods. Based on the results, future work can be conducted to improve the GMF approach in terms of genome-scale analysis through including sparsity

Investigation of different ML approaches in classification of emotions induced by acute stress

Background: Machine learning is becoming a common tool in monitoring emotion. However, methodological studies of the processing pipeline are scarce, especially ones using subjective appraisals as ground truth. New method: A novel protocol was used to induce cognitive load and physical discomfort, and emotional dimensions (arousal, valence, and dominance) were reported after each task. The performance of five common ML models with a versatile set of features (physiological features, task performance data, and personality trait) was compared in binary classification of subjectively assessed emotions. Results: The psychophysiological responses proved the protocol was successful in changing the mental state from baseline, also the cognitive and physical tasks were different. The optimization and performance of ML models used for emotion detection were evaluated. Additionally, methods to account for imbalanced classes were applied and shown to improve the classification performance. Comparison with existing method(s): Classification of human emotional states often assumes the states are determined by the stimuli. However, individual appraisals vary. None of the past studies have classified subjective emotional dimensions with a set of features including biosignals, personality and behavior. Conclusion: Our data represent a typical setup in affective computing utilizing psychophysiological monitoring: N is low compared to number of features, inter-individual variability is high, and class imbalance cannot be avoided. Our observations are a) if possible, include features representing physiology, behavior and personality, b) use simple models and limited number of features to improve interpretability, c) address the possible imbalance, d) if the data size allows, use nested cross-validation.</p

Monialaisen palvelukäytön ennakointi tekoälyn avulla:Kehittämisen perusteita ja suuntaviivoja

Tässä käsikirjassa tarkastellaan ja jäsennetään monialaisen palvelukäytön ennustamisen ja ennakoinnin kehittämistä ja toteuttamista sosiaali- ja terveydenhuollon organisaatioissa. Käsikirjan tarkastelu kohdentuu erityisesti moderniin tekoälyyn tukeutuvaan ennustamiseen ja ennakointiin. Käsikirja on suunnattu ensisijaisesti hyvinvointialueilla tapahtuvan kehittämistyön tueksi

Piloting a Smart Rollator : User experiences with technology-related motivation and physical activity

Background: Improved life expectancy combined with suboptimal physical activity (PA) represents an increasingly salient public health challenge among the elderly. PA in late life is associated with fewer health problems in old age. Assistive information and communication technology might improve PA and alleviate health problems among the elderly. Objective: This pilot study aimed to quantitatively measure the motivational aspects related to rollator use and, by using qualitative interviews, outline how a Smart Rollator solution would motivate older adults to increase their PA in their everyday lives. Method: A total of 19 subjects between the ages of 63 and 91 years participated in the study. Half of the participants started in a setting in which the application did not provide feedback to the user, and the other half received feedback. A transition occurred (ordinary rollator to Smart Rollator and vice versa) after two months of usage. Motivational aspects were measured before the use of the rollator and after four months. Semi-structured qualitative interviews were conducted with 10 participants to acquire information about their experiences. Results: On the motivation questionnaire, self-perceived mental vitality showed a significant decrease at follow-up, but the total score did not change. Three different types of Smart Rollator users were identified based on the interview data: enthusiastic, practical, and disappointed users. The user types differed from each other, especially regarding user experiences concerning the smart features and intelligent features of the rollator. Conclusion: We conclude that the individual variations in terms of benefiting from the use of the Smart Rollator were large and that some users reported clear advantages using the Smart Rollator. The Smart Rollator elicited emotional reactions and affection, as well as frustration if the user was not able to benefit from the Smart Rollator as expected. Larger sample size is warranted to thoroughly specify the relations between the use of a Smart Rollator, user experiences, and PA.peerReviewe