| 1. |
|
|
| 2. |
- Brown, Matthew, et al.
(författare)
-
Determination of Surface Potential and Electrical Double-Layer Structure at the Aqueous Electrolyte-Nanoparticle Interface
- 2016
-
Ingår i: Physical Review X. - 2160-3308. ; 6:1
-
Tidskriftsartikel (refereegranskat)abstract
- The structure of the electrical double layer has been debated for well over a century, since it mediates colloidal interactions, regulates surface structure, controls reactivity, sets capacitance, and represents the central element of electrochemical supercapacitors. The surface potential of such surfaces generally exceeds the electrokinetic potential, often substantially. Traditionally, a Stern layer of nonspecifically adsorbed ions has been invoked to rationalize the difference between these two potentials; however, the inability to directly measure the surface potential of dispersed systems has rendered quantitative measurements of the Stern layer potential, and other quantities associated with the outer Helmholtz plane, impossible. Here, we use x-ray photoelectron spectroscopy from a liquid microjet to measure the absolute surface potentials of silica nanoparticles dispersed in aqueous electrolytes. We quantitatively determine the impact of specific cations (Li + , Na + , K + , and Cs + ) in chloride electrolytes on the surface potential, the location of the shear plane, and the capacitance of the Stern layer. We find that the magnitude of the surface potential increases linearly with the hydrated-cation radius. Interpreting our data using the simplest assumptions and most straightforward understanding of Gouy-Chapman-Stern theory reveals a Stern layer whose thickness corresponds to a single layer of water molecules hydrating the silica surface, plus the radius of the hydrated cation. These results subject electrical double-layer theories to direct and falsifiable tests to reveal a physically intuitive and quantitatively verified picture of the Stern layer that is consistent across multiple electrolytes and solution conditions.
|
|
| 3. |
- Flannick, Jason, et al.
(författare)
-
Data Descriptor : Sequence data and association statistics from 12,940 type 2 diabetes cases and controls
- 2017
-
Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 4
-
Tidskriftsartikel (refereegranskat)abstract
- To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (> 80% of low-frequency coding variants in similar to ~82 K Europeans via the exome chip, and similar to ~90% of low-frequency non-coding variants in similar to ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
|
|
| 4. |
- Fuchsberger, Christian, et al.
(författare)
-
The genetic architecture of type 2 diabetes
- 2016
-
Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 536:7614, s. 41-47
-
Tidskriftsartikel (refereegranskat)abstract
- The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
|
|
| 5. |
- Gaulton, Kyle J, et al.
(författare)
-
Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.
- 2015
-
Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 47:12, s. 1415-1415
-
Tidskriftsartikel (refereegranskat)abstract
- We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.
|
|
| 6. |
- Manning, Alisa, et al.
(författare)
-
A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk
- 2017
-
Ingår i: Diabetes. - : AMER DIABETES ASSOC. - 0012-1797 .- 1939-327X. ; 66:7, s. 2019-2032
-
Tidskriftsartikel (refereegranskat)abstract
- To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.
|
|
| 7. |
- Muller, Matthew S, et al.
(författare)
-
Non-mydriatic color fundus imaging with the Digital Light Ophthalmoscope
- 2015
-
Konferensbidrag (refereegranskat)abstract
- Purpose To provide non-mydriatic confocal color fundus imaging of sufficient quality for screening for diabetic retinopathy despite dark fundus pigmentation, small pupil, high refractive error, or other anterior segment issues. MethodsNon-mydriatic color fundus images of 34 volunteers (aged 39.2 ± 13.2 yr) were acquired using the Digital Light Ophthalmoscope (DLO). 10 subjects had dark fundi and/or high refractive errors, as well as other anterior segment issues. Unique to retinal cameras, the Digital Light Ophthalmoscope (DLO) uses a digital light projector with LED light sources to provide the illumination for both confocal imaging and fixation stimuli. The DLO projects a series of lines across the fundus that is synchronized to the electronic rolling shutter read-out on a 2D CMOS sensor, providing high contrast confocal imaging that is highly customizable through software. Monochromatic 40 deg field images were acquired with alternating red and green LED illumination at 14.3 Hz and overlayed to present a pseudo-color image to the operator in real time. To reduce pupil constriction and patient discomfort while maintaining strong blood absorption, the green illumination was long-pass filtered with a 570 nm filter, and a 1.5mm entrance pupil and time-averaged power of <30 uW was used. ResultsThe DLO provided gradable quality non-mydriatic fundus images in all tested subjects, including those with dark fundi or pupils < 2 mm, as judged by an EyePACS certified grader. The use of long pass filters in the green LED permitted high contrast, non-mydriatic images with illumination wavelengths >570 nm and limited pupil constriction. Retinal vessels at the 4th branch or smaller, as well as neovascularization in diabetic retinopathy, could be seen. Hyperpigmentation was clearly seen both peripherally as bear tracks and centrally at the posterior pole. The aperture width and color balance can be adjusted to provide high contrast and yet relatively uniform and natural color across the image. ConclusionsThe DLO provides confocal color fundus images in real time without the use of short (< 570nm) wavelength light. Despite recruiting an especially challenging population that included dark fundi, small pupils, high refractive errors, and media issues, we achieved a 100% success rate in obtaining gradable images for screening.
|
|
| 8. |
- Romagnoni, A, et al.
(författare)
-
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
-
Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
-
Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
|
|
| 9. |
|
|
| 10. |
|
|
