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1.
  • 2019
  • swepub:Mat_article_t (swepub:level_refereed_t)
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2.
  • Kattge, Jens, et al. (creator_code:aut_t)
  • TRY plant trait database - enhanced coverage and open access
  • 2020
  • record:In_t: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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3.
  • de Jong, Yde, et al. (creator_code:aut_t)
  • PESI - a taxonomic backbone for Europe
  • 2015
  • record:In_t: Biodiversity Data Journal. - 1314-2836 .- 1314-2828. ; 3, s. 1-51
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Reliable taxonomy underpins communication in all of biology, not least nature conservation and sustainable use of ecosystem resources. The flexibility of taxonomic interpretations, however, presents a serious challenge for end-users of taxonomic concepts. Users need standardised and continuously harmonised taxonomic reference systems, as well as high-quality and complete taxonomic data sets, but these are generally lacking for non-specialists. The solution is in dynamic, expertly curated web-based taxonomic tools.The Pan-European Species-directories Infrastructure (PESI) worked to solve this key issue by providing a taxonomic e-infrastructure for Europe. It strengthened the relevant social (expertise) and information (standards, data and technical) capacities of five major community networks on taxonomic indexing in Europe, which is essential for proper biodiversity assessment and monitoring activities. The key objectives of PESI were: 1) standardisation in taxonomic reference systems, 2) enhancement of the quality and completeness of taxonomic data sets and 3) creation of integrated access to taxonomic information.This paper describes the results of PESI and its future prospects, including the involvement in major European biodiversity informatics initiatives and programs.
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4.
  • Gallo, Selene, et al. (creator_code:aut_t)
  • Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies
  • 2023
  • record:In_t: Molecular Psychiatry. - : SPRINGERNATURE. - 1359-4184 .- 1476-5578. ; 28:7, s. 3013-3022
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.
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5.
  • Javaheripour, Nooshin, et al. (creator_code:aut_t)
  • Altered resting-state functional connectome in major depressive disorder : a mega-analysis from the PsyMRI consortium
  • 2021
  • record:In_t: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 11:1
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers (). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 +/- 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 +/- 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN.
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6.
  • Petrov, Dmitry, et al. (creator_code:aut_t)
  • Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
  • 2017
  • record:In_t: Machine learning in medical imaging. MLMI (Workshop). - Cham : Springer International Publishing. ; 10541, s. 371-378
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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7.
  • Walter, Susanna, et al. (creator_code:aut_t)
  • Measuring the impact of gastrointestinal inconvenience and symptoms on perceived health in the general population - validation of the Short Health Scale for gastrointestinal symptoms (SHS-GI)
  • 2021
  • record:In_t: Scandinavian Journal of Gastroenterology. - : Informa UK Limited. - 0036-5521 .- 1502-7708. ; 56:12, s. 1406-1463
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Objectives Gastrointestinal (GI) symptoms are intimately related to our wellbeing. The Short Health Scale for GI symptoms (SHS-GI) is a simple questionnaire to measure the impact of GI inconvenience and symptoms on quality of life. The aim was to validate the SHS-GI in a general population sample and to compare it with SHS-data across different patient groups.Method A subsample of 170 participants from a population-based colonoscopy study completed the Rome II questionnaire, GI diaries, psychological questionnaire (hospital anxiety and depression scale) and SHS-GI at follow-up investigation. Psychometric properties of SHS-GI as an overall score were determined by performing a confirmatory factor analysis (CFA). Spearman correlation between SHS total score and symptoms was calculated in the general population sample. SHS-GI data was compared with SHS data from patients with inflammatory bowel disease (IBD) and fecal incontinence (FI).Results As expected, the general population rated their impact of GI inconvenience on quality of life as better than the patient populations in terms of all aspects of the SHS-GI. The CFA showed a good model fit meeting all fit criteria in the general population. Cronbach's alpha for the total scale was 0.80 in the general population sample and ranged from 0.72 in the FI sample to 0.88 and 0.89 in the IBD samples.Conclusions SHS-GI demonstrated appropriate psychometric properties in a sample of the normal population. We suggest that SHS-GI is a valid simple questionnaire suitable for measuring the impact of GI symptoms and inconvenience on quality of life in both general and patient populations.
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8.
  • Abbasi, R., et al. (creator_code:aut_t)
  • All-particle cosmic ray energy spectrum measured with 26 IceTop stations
  • 2013
  • record:In_t: Astroparticle physics. - : Elsevier BV. - 0927-6505 .- 1873-2852. ; 44, s. 40-58
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • We report on a measurement of the cosmic ray energy spectrum with the IceTop air shower array, the surface component of the IceCube Neutrino Observatory at the South Pole. The data used in this analysis were taken between June and October, 2007, with 26 surface stations operational at that time, corresponding to about one third of the final array. The fiducial area used in this analysis was 0.122 km(2). The analysis investigated the energy spectrum from 1 to 100 PeV measured for three different zenith angle ranges between 0 degrees and 46 degrees. Because of the isotropy of cosmic rays in this energy range the spectra from all zenith angle intervals have to agree. The cosmic-ray energy spectrum was determined under different assumptions on the primary mass composition. Good agreement of spectra in the three zenith angle ranges was found for the assumption of pure proton and a simple two-component model. For zenith angles theta < 30 degrees, where the mass dependence is smallest, the knee in the cosmic ray energy spectrum was observed at about 4 PeV, with a spectral index above the knee of about -3.1. Moreover, an indication of a flattening of the spectrum above 22 PeV was observed. 
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9.
  • Abbasi, R., et al. (creator_code:aut_t)
  • An absence of neutrinos associated with cosmic-ray acceleration in gamma-ray bursts
  • 2012
  • record:In_t: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 484:7394, s. 351-354
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Very energetic astrophysical events are required to accelerate cosmic rays to above 10(18) electronvolts. GRBs (c-ray bursts) have been proposed as possible candidate sources(1-3). In the GRB 'fireball' model, cosmic-ray acceleration should be accompanied by neutrinos produced in the decay of charged pions created in interactions between the high-energy cosmic-ray protons and gamma-rays(4). Previous searches for such neutrinos found none, but the constraints were weak because the sensitivity was at best approximately equal to the predicted flux(5-7). Here we report an upper limit on the flux of energetic neutrinos associated with GRBs that is at least a factor of 3.7 below the predictions(4,8-10). This implies either that GRBs are not the only sources of cosmic rays with energies exceeding 10(18) electronvolts or that the efficiency of neutrino production is much lower than has been predicted.
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10.
  • Abbasi, R., et al. (creator_code:aut_t)
  • Background studies for acoustic neutrino detection at the South Pole
  • 2012
  • record:In_t: Astroparticle physics. - : Elsevier BV. - 0927-6505 .- 1873-2852. ; 35:6, s. 312-324
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • The detection of acoustic signals from ultra-high energy neutrino interactions is a promising method to measure the flux of cosmogenic neutrinos expected on Earth. The energy threshold for this process depends strongly on the absolute noise level in the target material. The South Pole Acoustic Test Setup (SPATS), deployed in the upper part of four boreholes of the IceCube Neutrino Observatory, has monitored the noise in Antarctic ice at the geographic South Pole for more than two years down to 500 m depth. The noise is very stable and Gaussian distributed. Lacking an in situ calibration up to now, laboratory measurements have been used to estimate the absolute noise level in the 10-50 kHz frequency range to be smaller than 20 mPa. Using a threshold trigger, sensors of the South Pole Acoustic Test Setup registered acoustic events in the IceCube detector volume and its vicinity. Acoustic signals from refreezing IceCube holes and from anthropogenic sources have been used to test the localization of acoustic events. An upper limit on the neutrino flux at energies E-v>10(11) GeV is derived from acoustic data taken over eight months. (C) 2011 Elsevier B.V. All rights reserved.
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