Baseline concentrations of biliary PAH metabolites in perch (Perca fluviatilis) in the open Gulf of Finland and in two coastal areas

Female perch (Perca fluviatilis) were sampled annually in late summer from 2006 to 2009 from the open sea of the eastern Gulf of Finland off Haapasaari island to monitor baseline biliary PAH metabolite concentrations. In addition, two coastal locations were sampled in 2008. PAH metabolite concentrations were compared between the open sea and coastal samples and between the sampling years and examined in relation to the body characteristics of perch. Of the PAH metabolites, only 1-hydroxypyrene (1 -OH pyrene) was detected at quantifiable levels in the bile of nearly all perch individuals. There were some annual differences but no temporal trend in the concentration of biliary 1-OH pyrene in perch from Haapasaari. At the coastal locations, 1-OH pyrene concentrations in the bile of perch were significantly higher than in the open sea Haapasaari area, probably due to greater contamination of the coastal sites and differences in feeding behaviour. No correlations between the body characteristics of perch and 1 -OH pyrene concentrations were detected. It is concluded that PAH metabolites in the bile of fish could be measured in the Gulf of Finland to detect oil spills in the open sea, and the cost-effective total fluorescence method could be used in such monitoring programmes. (C) 2017 Elsevier B.V. All rights reserved.Peer reviewe

The intake of inorganic arsenic from long grain rice and rice-based baby food in Finland : Low safety margin warrants follow up

AbstractWe evaluated total and inorganic arsenic levels in long grain rice and rice based baby foods on Finnish market. Inorganic arsenic was analysed with an HPLC–ICP-MS system. The total arsenic concentration was determined with an ICP-MS method. In this study, the inorganic arsenic levels in long grain rice varied from 0.09 to 0.28mg/kg (n=8) and the total arsenic levels from 0.11 to 0.65mg/kg. There was a good correlation between the total and inorganic arsenic levels in long grain rice at a confidence level of 95%. The total arsenic levels of rice-based baby foods were in the range 0.02 – 0.29mg/kg (n=10), however, the level of inorganic arsenic could only be quantitated in four samples, on average they were 0.11mg/kg. Our estimation of inorganic arsenic intake from long grain rice and rice-based baby food in Finland indicate that in every age group the intake is close to the lowest BMDL0.1 value 0.3μg/kg bw/day set by EFSA. According to our data, the intake of inorganic arsenic should be more extensively evaluated

Cognitive and Neuropsychiatric Symptom Differences in Early Stages of Alzheimer's Disease: Kuopio ALSOVA Study

Background/Aim: Alzheimer’s disease (AD) causes impairment in memory and other cognitive functions as well as neuropsychiatric symptoms and limitations in the activities of daily living (ADL). The aim of this study was to examine whether demographic variables, dementia severity, ADL and neuropsychiatric symptoms are associated with cognition in very mild or mild AD. Methods: We analyzed the baseline data of 236 patients with very mild or mild AD participating in a prospective AD follow-up study (ALSOVA). The Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery total score was used in the evaluation of the global cognitive performance. Results: Cognition was associated with dementia severity and ADL but not with neuropsychiatric symptoms. ADL functions were associated with both cognitive performance and neuropsychiatric symptoms. Conclusion: Even patients with very mild or mild AD may exhibit neuropsychiatric symptoms not related to cognitive impairment. The results of this study emphasize the importance of taking a multidimensional approach to the diagnostic and prognostic evaluation of AD patients already in the early stages of the disease

Tekoäly viranomaistoiminnassa - eettiset kysymykset ja yhteiskunnallinen hyväksyttävyys

Tekoälyn ja ohjelmistorobottien käyttö lisääntyy - myös viranomaistoiminnassa - muuttaen prosesseja ja toimintatapoja. Uusien teknologioiden ja järjestelmien käyttöönotolla on moninaisia vaikutuksia niin kansalaisten, virkamiesten kuin koko yhteiskunnankin näkökulmasta. Yhteiskunnan arvojen mukainen eettinen toiminta ja siitä kumpuava luottamus tulisi säilyttää myös tekoälyn aikakaudella. Tässä selvityksessä on aluksi tarkasteltu käsityksiä etiikasta ja hyväksyttävyydestä yleisesti ja erityisesti teknologian kehittämisen ja käyttöönoton yhteydessä. Tuloksena on esitetty selvitystyön aikana kehitetty eettinen toimintamalli. Koska eettisyys on vahvasti kontekstisidonnaista, on tekoälyn eettisyyttä viran-omaistoiminnassa tarkasteltu käyttötapausten avulla, joita selvitettiin haastattelemalla 13 valtionhallinnon toimijaa. Käyttötapausten eettisiä ja hyväksyttävyyskysymyksiä tarkasteltiin työpajoissa. Hyväksyttävyyttä selvitettiin lisäksi tekemällä kansalaiskysely ja analysoimalla internetissä käytyä keskustelua tekoälystä ja sen käytöstä viranomaistoiminnassa. Raportissa on esitetty vahvimmin esille tulleet eettisyyteen ja hyväksyttävyyteen liittyvät kysymykset sekä kysymyksiin liittyviä ratkaisuehdotuksia. Tekoäly viranomaistoiminnassa saatetaan nähdä eettisten ongelmien aiheuttajana, mutta se avaa paljon myös positiivisia mahdollisuuksia, kuten monenlaisia laadun parantamisen keinoja. Siten tekoäly viisaasti käytettynä on eettisyyden ja yhteiskunnallisen hyväksyttävyyden kannalta positiivisen muutok-sen mahdollistaja ja voi parhaimmillaan jopa ratkaista eettisyyteen ja yhteiskunnalliseen hyväksyttävyyteen liittyviä ongelmia

Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.Peer reviewe

Very High Spatial Resolution Soil Moisture Observation of Heterogeneous Subarctic Catchment Using Nonlocal Averaging and Multitemporal SAR Data

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.Peer reviewe