Krylov integrators for Hamiltonian systems

We consider Arnoldi-like processes to obtain symplectic subspaces for Hamiltonian systems. Large dimensional systems are locally approximated by ones living in low dimensional subspaces, and we especially consider Krylov subspaces and some of their extensions. These subspaces can be utilized in two ways: by solving numerically local small dimensional systems and then mapping back to the large dimension, or by using them for the approximation of necessary functions in exponential integrators applied to large dimensional systems. In the former case one can expect an excellent energy preservation and in the latter this is so for linear systems. We consider second order exponential integrators which solve linear systems exactly and for which these two approaches are in a certain sense equivalent. We also consider the time symmetry preservation properties of the integrators. In numerical experiments these methods combined with symplectic subspaces show promising behavior also when applied to nonlinear Hamiltonian problems.Peer reviewe

Neural network methods in analysing and modelling time varying processes

Statistical data analysis is applied in many fields in order to gain understanding to the complex behaviour of the system or process under interest. For this goal, observations are collected from the process, and models are built in an effort to capture the essential structure from the observed data. In many applications, e.g. process control and pattern recognition, the modeled process is time-dependent, and thus modeling the temporal context is essential. In this thesis, neural network methods in statistical data analysis and especially in temporal sequence processing (TSP) are considered. Neural networks are a class of statistical models, applicable in many tasks from data exploration to regression and classification. Neural networks suitable for TSP can model time dependent phenomena, typically by utilizing delay lines or recurrent connections within the network. Recurrent Self-Organizing Map (RSOM) is an unsupervised neural network model capable of processing pattern sequences. The application of the RSOM with local models in temporal sequence prediction is presented. The RSOM is applied to divide the input pattern sequences into clusters, and local models are estimated corresponding to these clusters. In case studies, time series prediction problems are considered. Prediction results gained from the RSOM model show better performance than the model with conventional Self-Organizing Map. The RSOM can capture temporal context from the pattern sequence, which is useful in the presented prediction tasks. As another application, a neural network model for optimizing a Web cache is proposed. Web caches store recently requested Web objects, and are typically shared by many clients. A caching policy decides which objects are removed when the storage space is full. In the proposed approach a model predicts the value of each cache object by utilizing features extracted from the object. Only syntactic features are used, which enables efficient estimation and application of the model. The caching policy can be optimized based on the predicted values and a cost model designed according to the objectives of the caching. In a case study, different stages and decisions made during the data analysis and model building are presented. The results gained suggest that the proposed approach is useful in the application.reviewe

Aikuisväestön hyvinvointi, terveys ja palvelut – FinSote 2020 : Aikuisten palvelukokemuksissa ja hyvinvoinnissa alueellisia eroja

FinSote-tutkimus tuottaa laajasti tietoa väestön koetusta hyvinvoinnista, elinoloista, terveydentilasta, elintavoista, työ- ja toimintakyvystä sekä laajasti palvelukokemuksista kuten niiden saatavuudesta ja asiakastyytyväisyydestä. Tässä raportissa tarkastellaan kahta keskeistä teemaa: psyykkistä kuormittuneisuutta ja riittämätöntä terveyspalveluiden saantia

Fragaria vesca CONSTANS controls photoperiodic flowering and vegetative development

According to the external coincidence model, photoperiodic flowering occurs when CONSTANS ( CO) mRNA expression coincides with light in the afternoon of long days (LDs), leading to the activation of FLOWERING LOCUS T (FT). CO has evolved in Brassicaceae from other Group Ia CO-like (COL) proteins which do not control photoperiodic flowering in Arabidopsis. COLs in other species have evolved different functions as floral activators or even as repressors. To understand photoperiodic development in the perennial rosaceous model species woodland strawberry, we functionally characterized FvCO, the only Group Ia COL in its genome. We demonstrate that FvCO has a major role in the photoperiodic control of flowering and vegetative reproduction through runners. FvCO is needed to generate a bimodal rhythm of FvFT1 which encodes a floral activator in the LD accession Hawaii-4: a sharp FvCO expression peak at dawn is followed by the FvFT1 morning peak in LDs indicating possible direct regulation, but additional factors that may include FvGI and FvFKF1 are probably needed to schedule the second FvFT1 peak around dusk. These results demonstrate that although FvCO and FvFT1 play major roles in photoperiodic development, the CO-based external coincidence around dusk is not fully applicable to the woodland strawberry.Peer reviewe

Natural Variation in the Control of Flowering and Shoot Architecture in Diploid Fragaria Species

In perennial fruit and berry crops of the Rosaceae family, flower initiation occurs in late summer or autumn after downregulation of a strong repressor TERMINAL FLOWER1 (TFL1), and flowering and fruiting takes place the following growing season. Rosaceous fruit trees typically form two types of axillary shoots, short flower-bearing shoots called spurs and long shoots that are, respectively, analogous to branch crowns and stolons in strawberry. However, regulation of flowering and shoot architecture differs between species, and environmental and endogenous controlling mechanisms have just started to emerge. In woodland strawberry (Fragaria vesca L.), long days maintain vegetative meristems and promote stolon formation by activating TFL1 and GIBBERELLIN 20-OXIDASE4 (GA20ox4), respectively, while silencing of these factors by short days and cool temperatures induces flowering and branch crown formation. We characterized flowering responses of 14 accessions of seven diploid Fragaria species native to diverse habitats in the northern hemisphere and selected two species with contrasting environmental responses, Fragaria bucharica Losinsk. and Fragaria nilgerrensis Schlecht. ex J. Gay for detailed studies together with Fragaria vesca. Similar to F. vesca, short days at 18 degrees C promoted flowering in F. bucharica, and the species was induced to flower regardless of photoperiod at 11 degrees C after silencing of TFL1. F. nilgerrensis maintained higher TFL1 expression level and likely required cooler temperatures or longer exposure to inductive treatments to flower. We also found that high expression of GA20ox4 was associated with stolon formation in all three species, and its downregulation by short days and cool temperature coincided with branch crown formation in F. vesca and F. nilgerrensis, although the latter did not flower. F. bucharica, in contrast, rarely formed branch crowns, regardless of flowering or GA20ox4 expression level. Our findings highlighted diploid Fragaria species as rich sources of genetic variation controlling flowering and plant architecture, with potential applications in breeding of Rosaceous crops.Peer reviewe

Female C57BL/6J Mice Show Alcohol-Seeking Behaviour after Withdrawal from Prolonged Alcohol Consumption in the Social Environment.

Aims Recently we developed a model to study alcohol-seeking behaviour after withdrawal in a social context in female mice. The model raised several questions that we were eager to address to improve methodology. Methods In our model, female mice were group-housed in automated cages with three conditioned (CS+) corners and water in both sides of one separate non-conditioned corner. Water was available with opened doors at all the time of training. We established conditioning by pairing alcohol drinking with light cues. Here, we introduced prolonged access to increasing concentrations of alcohol instead of intermittent access. To study motivation to drink alcohol, we carried out the extinction tests on withdrawal days 1 (WD1) and 10 (WD10). During tests, the light cues were present in conditioned corners, but there was no liquid in the bottles. Results We found that the number of visits and nosepokes in the CS+ corner in the alcohol group was much higher than in the water group. Also, during training, the consumption of alcohol was increasing. In the extinction tests, we found that the number of nosepokes in the CS+ corner increased in the alcohol group on both WD1 and WD10. Conclusions Our study supports that alcohol-seeking behaviour after withdrawal can be modelled and studied in group-housed animals and environments without social isolation. Short Summary: We developed a model to study alcohol drinking behaviour in an enriched and social environment. Long-term conditioning coupling with alcohol reward results in cue-induced alcohol-seeking behaviour in group-housed female C57BL/6J mice. Moreover, a high number of nosepokes on the last day of alcohol drinking conditioning might potentiate alcohol-seeking after withdrawal response.Peer reviewe