University of Turku

About: University of Turku is a education organization based out in Turku, Finland. It is known for research contribution in the topics: Population & Galaxy. The organization has 16296 authors who have published 45124 publications receiving 1505428 citations. The organization is also known as: Turun yliopisto & Åbo universitet.

Nasal swab versus nasopharyngeal aspirate for isolation of respiratory viruses

Abstract: To determine the usefulness of nasal swabs as a simple method for detection of respiratory viruses, we compared nasal swabs and nasopharyngeal aspirates obtained at the same time from the opposite nostrils of 230 children with upper respiratory infection. The sensitivity of nasal swabs was comparable to that of nasopharyngeal aspirates for the detection of all major respiratory viruses except respiratory syncytial virus.

Sudden death and sudden birth of entanglement in common structured reservoirs

Abstract: We study the exact entanglement dynamics of two qubits in a common structured reservoir. We demonstrate that for certain classes of entangled states, entanglement sudden death occurs, while for certain initially factorized states, entanglement sudden birth takes place. The backaction of the non-Markovian reservoir is responsible for revivals of entanglement after sudden death has occurred, and also for periods of disentanglement following entanglement sudden birth.

Linking floristic patterns with soil heterogeneity and satellite imagery in ecuadorian amazonia

Abstract: Florisitic ground surveys in tropical rain forests are laborious and time consuming, so we tested to what degree reflectance differences visible in Landsat Thematic Mapper (TM) satellite images can be used to predict differences in florisitic composition and species richness among rain forest sites. To gain ecological understanding of the rain forest ecosystem, we also tested to what extent variation in these vegetation characteristics can be explained by edaphic site conditions. The study was conducted in a relatively homogeneous area of Amazonian rain forest in Yasuni National Park, Ecuador. We established 27 transects of 5 m × 500 m within an area of ∼20 km × 25 km to study edaphic and floristic patterns mainly within the tierra firme (non-inundated) forest. In each transect, soil samples were collected for chemical and textural analyses, and the abundance of each species belonging to two understory plant groups, pteridophytes (ferns and fern allies) and the Melastomataceae, was assessed. Floristic similarity between transect pairs varied widely and ranged from almost no overlap in species composition to very high overlap. The among-transect floristic similarity patterns of the two plant groups were strongly correlated with each other no matter whether presence–absence or abundance data were used. The floristic similarity patterns were also strongly correlated with the similarity in pixel values of the infrared bands in the Landsat TM satellite image and with the similarity in most of the measured soil variables. Similarity in species richness, on the contrary, was neither correlated with similarity in pixel values nor with similarity in most of the soil variables. We conclude that reflectance patterns in satellite images can be efficiently used to predict landscape-scale floristic and edaphic patterns in tierra firme rain forest. Predicting patterns in species richness, on the other hand, is not possible in the same straightforward manner. These results have important practical implications for land use and conservation planning as well as for ecological and biodiversity research. Corresponding Editor: C. A. Wessman.

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or with Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis

Abstract: Importance Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses. Objective To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning. Design, Setting, and Participants This multisite naturalistic study followed up patients in CHR states, with ROD, and with recent-onset psychosis, and healthy control participants for 18 months in 7 academic early-recognition services in 5 European countries. Participants were recruited between February 2014 and May 2016, and data were analyzed from April 2017 to January 2018. Main Outcomes and Measures Performance and generalizability of prognostic models. Results A total of 116 individuals in CHR states (mean [SD] age, 24.0 [5.1] years; 58 [50.0%] female) and 120 patients with ROD (mean [SD] age, 26.1 [6.1] years; 65 [54.2%] female) were followed up for a mean (SD) of 329 (142) days. Machine learning predicted the 1-year social-functioning outcomes with a balanced accuracy of 76.9% of patients in CHR states and 66.2% of patients with ROD using clinical baseline data. Balanced accuracy in models using structural neuroimaging was 76.2% in patients in CHR states and 65.0% in patients with ROD, and in combined models, it was 82.7% for CHR states and 70.3% for ROD. Lower functioning before study entry was a transdiagnostic predictor. Medial prefrontal and temporo-parieto-occipital gray matter volume (GMV) reductions and cerebellar and dorsolateral prefrontal GMV increments had predictive value in the CHR group; reduced mediotemporal and increased prefrontal-perisylvian GMV had predictive value in patients with ROD. Poor prognoses were associated with increased risk of psychotic, depressive, and anxiety disorders at follow-up in patients in the CHR state but not ones with ROD. Machine learning outperformed expert prognostication. Adding neuroimaging machine learning to clinical machine learning provided a 1.9-fold increase of prognostic certainty in uncertain cases of patients in CHR states, and a 10.5-fold increase of prognostic certainty for patients with ROD. Conclusions and Relevance Precision medicine tools could augment effective therapeutic strategies aiming at the prevention of social functioning impairments in patients with CHR states or with ROD.

Vulnerable Children in Varying Classroom Contexts: Bystanders' Behaviors Moderate the Effects of Risk Factors on Victimization

Abstract: We examined whether the bystanders' behaviors in bullying situations influence vulnerable students' risk for victimization. The sample consisted of 6,980 primary school children from Grades 3–5, who were nested within 378 classrooms in 77 schools. These students filled out Internet-based questionnaires in their schools' computer labs. The results from multilevel models indicated that the associations between victimization and its two risk factors—social anxiety and peer rejection—were strongest in classrooms that were high in reinforcing bullying and low in defending the victims. This suggests that bystanders' behaviors in bullying situations moderate the effects of individual and interpersonal risk factors for victimization. Influencing these behaviors might be an effective way to protect vulnerable children from victimization.