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The Finnish language is one that offers two translations of the concept ‘democracy,’ demokratia and kansanvalta (people’s power), which have remained in active political use. We analyse the existence of two terms as a linguistic affordance, providing political agents with possibilities for resisting and supporting the prevailing interpretations of democracy. We ask how and where the different versions occur in parliamentary speech (1980–2021) and in the MPs’ interviews (1998–2018). In quantitative analysis, we study the relative appearance of words close to these terms. In qualitative analysis, we study such terms (e.g. representative, Finnish and western) that have different profiles with demokratia and kansanvalta and study how these terms characterise and shape democracy. This way, we are able to question the dictionary-based understanding of these terms as synonyms. The difference between them is both geographical, kansanvalta referring more strictly to domestic phenomena, and functional, since demokratia covers most of the issues of procedural democracy and kansanvalta more distinctively the realisation of the presumed will of the people.

Abstract Background and objective Emergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation that could alleviate the problem. There has been continued academic interest in ED forecasting but the number of used explanatory variables has been low, limited mainly to calendar and weather variables. In this study we investigate whether predictive accuracy of next day arrivals could be enhanced using high number of potentially relevant explanatory variables and document two feature selection processes that aim to identify which subset of variables is associated with number of next day arrivals. Performance of such predictions over longer horizons is also shown. Methods We extracted numbers of total daily arrivals from Tampere University Hospital ED between the time period of June 1, 2015 and June 19, 2019. 158 potential explanatory variables were collected from multiple data sources consisting not only of weather and calendar variables but also an extensive list of local public events, numbers of website visits to two hospital domains, numbers of available hospital beds in 33 local hospitals or health centres and Google trends searches for the ED. We used two feature selection processes: Simulated Annealing (SA) and Floating Search (FS) with Recursive Least Squares (RLS) and Least Mean Squares (LMS). Performance of these approaches was compared against autoregressive integrated moving average (ARIMA), regression with ARIMA errors (ARIMAX) and Random Forest (RF). Mean Absolute Percentage Error (MAPE) was used as the main error metric. Results Calendar variables, load of secondary care facilities and local public events were dominant in the identified predictive features. RLS-SA and RLS-FA provided slightly better accuracy compared ARIMA. ARIMAX was the most accurate model but the difference between RLS-SA and RLS-FA was not statistically significant. Conclusions Our study provides new insight into potential underlying factors associated with number of next day presentations. It also suggests that predictive accuracy of next day arrivals can be increased using high-dimensional feature selection approach when compared to both univariate and nonfiltered high-dimensional approach. Performance over multiple horizons was similar with a gradual decline for longer horizons. However, outperforming ARIMAX remains a challenge when working with daily data. Future work should focus on enhancing the feature selection mechanism, investigating its applicability to other domains and in identifying other potentially relevant explanatory variables.

High-content screening prompted the development of software enabling discrete phenotypic analysis of single cells. Here, the authors show that supervised continuous machine learning can drive novel discoveries in diverse imaging experiments and present the Regression Plane module of Advanced Cell Classifier.

Objective Growth-based determination of pubertal onset timing would be cheap and practical. We aimed to determine this timing based on pubertal growth markers. Secondary aims were to estimate the differences in growth between cohorts and identify the role of overweight in onset timing. Design This multicohort study includes data from three Finnish cohorts—the Type 1 Diabetes Prediction and Prevention (DIPP, N = 2,825) Study, the Special Turku Coronary Risk Factor Intervention Project (STRIP, N = 711), and the Boy cohort (N = 66). Children were monitored for growth and Tanner staging (except in DIPP). Methods The growth data were analyzed using a Super-Imposition by Translation And Rotation growth curve model, and pubertal onset analyses were run using a time-to-pubertal onset model. Results The time-to-pubertal onset model used age at peak height velocity (aPHV), peak height velocity (PHV), and overweight status as covariates, with interaction between aPHV and overweight status for girls, and succeeded in determining the onset timing. Cross-validation showed a good agreement (71.0% for girls, 77.0% for boys) between the observed and predicted onset timings. Children in STRIP were taller overall (girls: 1.7 [95% CI: 0.9, 2.5] cm, boys: 1.0 [0.3, 2.2] cm) and had higher PHV values (girls: 0.13 [0.02, 0.25] cm/year, boys: 0.35 [0.21, 0.49] cm/year) than those in DIPP. Boys in the Boy cohort were taller (2.3 [0.3, 4.2] cm) compared with DIPP. Overweight girls showed pubertal onset at 1.0 [0.7, 1.4] year earlier compared with other girls. In boys, there was no such difference. Conclusions The novel modeling approach provides an opportunity to evaluate the Tanner breast/genital stage–based pubertal onset timing in cohort studies including longitudinal data on growth but lacking pubertal follow-up.

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.