Indicators and tools for assessing sustainability impacts of the forest bioeconomy

The sustainable use of renewable resources has become an important issue worldwide in the move towards a less fossil-fuel-intensive future. Mainstream method for fulfilling this aim is to increase the share of renewable energy and materials to substitute fossil fuels and to become fully independent from fossil fuels over the long-term. However, the environmental sustainability of this endeavor has been questioned. In addition, economic and social sustainability issues are also much debated topics in this particular context. Forest resources are often thought to contribute partially to achieving a so-called “carbon-neutral society”. In this review, we discuss sustainability issues of using forest biomass. We present several sustainability indicators for ecological, economic and social dimensions and discuss the issues in applying them in sustainability impact assessments (SIAs). We also present a number of tools and methods previously used in conducting SIAs. We approach our study from the perspective of the Finnish forestry; in addition, various aspects regarding the application of SIAs in a broader context are also presented. One of the key conclusions of the study is that although sufficient data are available to measure many indicators accurately, the impacts may be very difficult to assess (e.g. impact of greenhouse gases on biodiversity) for conducting a holistic SIA. Furthermore, some indicators, such as “biodiversity”, are difficult to quantify in the first place. Therefore, a mix of different methods, such as Multi-criteria Assessment, Life-cycle Assessment or Cost-Benefit Analysis, as well as different approaches (e.g. thresholds and strong/weak sustainability) are needed in aggregating the results of the impacts. SIAs are important in supporting and improving the acceptability of decision-making, but a certain degree of uncertainty will always have to be tolerated

Systeemisen muutoksen johtaminen lastensuojelussa : Opas esimiehille ja johtajille

Oppaan tarkoitus on lisätä esimiesten ja johtajien ymmärrystä systeemisestä ajattelusta. Samalla se antaa työkaluja lastensuojelussa toteutettavan systeemiseen työtapaan tähtäävän muutoksen läpiviemiseen. Uudenlaiseen työtapaan siirtyminen koskettaa lukuisia työn eri ulottuvuuksia: arvoja, rakenteita, osaamista, yhteistyötä ja kustannuksia. Lastensuojelutyön muuttaminen systeemiseksi ei kuitenkaan tapahdu pelkästään sosiaalityön sisällä vaan se edellyttää muutoksia myös muissa lasten, nuorten ja perheiden palveluissa. Muutosta on luotsattava kaikilla organisaation johtamisen tasoilla: lähiesimiesten työstä aina kuntien ja kuntayhtymien ylimpään johtoon saakka. Opas on suunnattu erityisesti lastensuojelun esimiehille ja päälliköille sekä ylemmissä johtotehtävissä toimiville henkilöille, jotka vastaavat lastensuojelun asioista. Tämän ohella myös muiden julkisten palveluiden esimiehet ja johtajat voivat löytää oppaasta hyödyllisiä näkökulmia, suuntaviivoja ja työkaluja omaan johtamistyöhönsä

DSP p.(Thr2104Glnfs*12) variant presents variably with early onset severe arrhythmias and left ventricular cardiomyopathy

Background Dilated cardiomyopathy (DCM) is a condition characterized by dilatation and systolic dysfunction of the left ventricle in the absence of severe coronary artery disease or abnormal loading conditions. Mutations in the titin (TTN) and lamin A/C (LMNA) genes are the two most significant contributors in familial DCM. Previously mutations in the desmoplakin (DSP) gene have been associated with arrhythmogenic right ventricular cardiomyopathy (ARVC) and more recently with DCM. Methods We describe the cardiac phenotype related to a DSP mutation which was identified in ten unrelated Finnish index patients using next-generation sequencing. Sanger sequencing was used to verify the presence of this DSP variant in the probands' relatives. Medical records were obtained, and clinical evaluation was performed. Results We identified DSP c.6310delA, p.(Thr2104Glnfs*12) variant in 17 individuals of which 11 (65%) fulfilled the DCM diagnostic criteria. This pathogenic variant presented with left ventricular dilatation, dysfunction and major ventricular arrhythmias. Two patients showed late gadolinium enhancement (LGE) and myocardial edema on cardiac magnetic resonance imaging (MRI) that may suggest inflammatory process at myocardium. Conclusions The patients diagnosed with DCM showed an arrhythmogenic phenotype as well as SCD at young age supporting the recently proposed concept of arrhythmogenic cardiomyopathy. This study also demonstrates relatively low penetrance of truncating DSP variant in the probands' family members by the age of 40. Further studies are needed to elucidate the possible relations between myocardial inflammation and pathogenic DSP variants.Peer reviewe

Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression

Motivation: A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype–phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals. Results: We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method’s ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study. Availability and implementation: R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

metaCCA : summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies.Peer reviewe