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| 2. |
- Sathyendranath, Shubha, et al.
(författare)
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An Ocean-Colour Time Series for Use in Climate Studies : The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)
- 2019
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 19:19
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Tidskriftsartikel (refereegranskat)abstract
- Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
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- Atkins, Isabelle, et al.
(författare)
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Transcriptome-Wide Association Study Identifies New Candidate Susceptibility Genes for Glioma
- 2019
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Ingår i: Cancer Research. - : American Association for Cancer Research. - 0008-5472 .- 1538-7445. ; 79:8, s. 2065-2071
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have so far identified 25 loci associated with glioma risk, with most showing specificity for either glioblastoma (GBM) or non-GBM tumors. The majority of these GWAS susceptibility variants reside in noncoding regions and the causal genes underlying the associations are largely unknown. Here we performed a transcriptome-wide association study to search for novel risk loci and candidate causal genes at known GWAS loci using Genotype-Tissue Expression Project (GTEx) data to predict cis-predicted gene expression in relation to GBM and non-GBM risk in conjunction with GWAS summary statistics on 12,488 glioma cases (6,183 GBM and 5,820 non-GBM) and 18,169 controls. Imposing a Bonferroni-corrected significance level of P < 5.69 x 10(-6), candidate novel risk locus for GBM (mean Z = 4.43; P = 5.68 x 10(-6)). GALNT6 resides at least 55 Mb away from any previously identified glioma risk variant, while all other 30 significantly associated genes were located within 1 Mb of known GWAS-identified loci and were not significant after conditioning on the known GWAS-identified variants. These data identify a novel locus (GALNT6 at 12q13.33) and 30 genes at 12 known glioma risk loci associated with glioma risk, providing further insights into glioma tumorigenesis.Significance: This study identifies new genes associated with glioma risk, increasing understanding of how these tumors develop.
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| 5. |
- Torp, N. C., et al.
(författare)
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Is it time to rethink standard dosage of exposure-based cognitive behavioral therapy for pediatric obsessive-compulsive disorder?
- 2019
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Ingår i: Psychiatry Research. - : Elsevier BV. - 0165-1781. ; 281
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Identifying factors associated with early treatment response is important, because it can help allocate limited resources in psychiatric care more appropriately. This study examined baseline characteristics of participants with early response to exposure-based cognitive behavior therapy (CBT) for pediatric obsessive-compulsive disorder (OCD). Method: 269 participants with OCD, aged 7-17 years, were enrolled in a 14-weeks CBT program. We identified participants with early response to treatment, (CY-BOCS total score of <= 15), by the seventh session. Results: At week 7, 248 (92.2%) participants were assessed, 38.3% (95% CI 32.4-44.5%, n = 95) were identified as treatment responders. Univariate analyses showed that six baseline characteristics were significantly associated with early treatment response: young age, lower levels of symptom severity, functional impairment, internalizing- and externalizing problems, depressive symptoms, and family accommodation. Conclusions: These results suggested that treatment plans for younger children with moderate OCD symptoms and no major comorbid disorder should include briefer and less resource demanding treatment formats than the commonly recommended and applied standard doses of 15 CBT sessions.
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- Eckel-Passow, Jeanette E., et al.
(författare)
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Using germline variants to estimate glioma and subtype risks
- 2019
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Ingår i: Neuro-Oncology. - : Oxford University Press. - 1522-8517 .- 1523-5866. ; 21:4, s. 451-461
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Tidskriftsartikel (refereegranskat)abstract
- Background: Twenty-five single nucleotide polymorphisms (SNPs) are associated with adult diffuse glioma risk. We hypothesized that the inclusion of these 25 SNPs with age at diagnosis and sex could estimate risk of glioma as well as identify glioma subtypes.Methods: Case-control design and multinomial logistic regression were used to develop models to estimate the risk of glioma development while accounting for histologic and molecular subtypes. Case-case design and logistic regression were used to develop models to predict isocitrate dehydrogenase (IDH) mutation status. A total of 1273 glioma cases and 443 controls from Mayo Clinic were used in the discovery set, and 852 glioma cases and 231 controls from UCSF were used in the validation set. All samples were genotyped using a custom Illumina OncoArray.Results: Patients in the highest 5% of the risk score had more than a 14-fold increase in relative risk of developing an IDH mutant glioma. Large differences in lifetime absolute risk were observed at the extremes of the risk score percentile. For both IDH mutant 1p/19q non-codeleted glioma and IDH mutant 1p/19q codeleted glioma, the lifetime risk increased from almost null to 2.3% and almost null to 1.7%, respectively. The SNP-based model that predicted IDH mutation status had a validation concordance index of 0.85.Conclusions: These results suggest that germline genotyping can provide new tools for the initial management of newly discovered brain lesions. Given the low lifetime risk of glioma, risk scores will not be useful for population screening; however, they may be useful in certain clinically defined high-risk groups.
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