Evaluation of retinal nerve fibre layer thickness as a possible measure of diabetic retinal neurodegeneration in the EPIC-Norfolk Eye Study

Background/aims: Markers to clinically evaluate structural changes from diabetic retinal neurodegeneration (DRN) have not yet been established. To study the potential role of peripapillary retinal nerve fibre layer (pRNFL) thickness as a marker for DRN, we evaluated the relationship between diabetes, as well as glycaemic control irrespective of diabetes status and pRNFL thickness. Methods: Leveraging data from a population-based cohort, we used general linear mixed models (GLMMs) with a random intercept for patient and eye to assess the association between pRNFL thickness (measured using GDx) and demographic, systemic and ocular parameters after adjusting for typical scan score. GLMMs were also used to determine: (1) the relationship between: (A) glycated haemoglobin (HbA1c) irrespective of diabetes diagnosis and pRNFL thickness, (B) diabetes and pRNFL thickness and (2) which quadrants of pRNFL may be affected in participants with diabetes and in relation to HbA1c. Results: 7076 participants were included. After controlling for covariates, inferior pRNFL thickness was 0.94 µm lower (95% CI −1.28 µm to −0.60 µm), superior pRNFL thickness was 0.83 µm lower (95% CI −1.17 µm to −0.49 µm) and temporal pRNFL thickness was 1.33 µm higher (95% CI 0.99 µm to 1.67 µm) per unit increase in HbA1c. Nasal pRNFL thickness was not significantly associated with HbA1c (p=0.23). Similar trends were noted when diabetes was used as the predictor. Conclusion: Superior and inferior pRNFL was significantly thinner among those with higher HbA1c levels and/or diabetes, representing areas of the pRNFL that may be most affected by diabetes

Conduct Disorder and the specifier callous and unemotional traits in the DSM-5

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Detecting retinal neurodegeneration in people with diabetes: Findings from the UK Biobank

IMPORTANCE: Efforts are underway to incorporate retinal neurodegeneration in the diabetic retinopathy severity scale. However, there is no established measure to quantify diabetic retinal neurodegeneration (DRN). OBJECTIVE: We compared total retinal, macular retinal nerve fiber layer (mRNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness among participants with and without diabetes (DM) in a population-based cohort. DESIGN/SETTING/PARTICIPANTS: Cross-sectional analysis, using the UK Biobank data resource. Separate general linear mixed models (GLMM) were created using DM and glycated hemoglobin as predictor variables for retinal thickness. Sub-analyses included comparing thickness measurements for patients with no/mild diabetic retinopathy (DR) and evaluating factors associated with retinal thickness in participants with and without diabetes. Factors found to be significantly associated with DM or thickness were included in a multiple GLMM. EXPOSURE: Diagnosis of DM was determined via self-report of diagnosis, medication use, DM-related complications or glycated hemoglobin level of ≥ 6.5%. MAIN OUTCOMES AND MEASURES: Total retinal, mRNFL and GC-IPL thickness. RESULTS: 74,422 participants (69,985 with no DM; 4,437 with DM) were included. Median age was 59 years, 46% were men and 92% were white. Participants with DM had lower total retinal thickness (-4.57 μm, 95% CI: -5.00, -4.14; p<0.001), GC-IPL thickness (-1.73 μm, 95% CI: -1.86, -1.59; p<0.001) and mRNFL thickness (-0.68 μm, 95% CI: -0.81, -0.54; p<0.001) compared to those without DM. After adjusting for co-variates, in the GLMM, total retinal thickness was 1.99 um lower (95% CI: -2.47, -1.50; p<0.001) and GC-IPL was 1.02 μm lower (95% CI: -1.18, -0.87; p<0.001) among those with DM compared to without. mRNFL was no longer significantly different (p = 0.369). GC-IPL remained significantly lower, after adjusting for co-variates, among those with DM compared to those without DM when including only participants with no/mild DR (-0.80 μm, 95% CI: -0.98, -0.62; p<0.001). Total retinal thickness decreased 0.40 μm (95% CI: -0.61, -0.20; p<0.001), mRNFL thickness increased 0.20 μm (95% CI: 0.14, 0.27; p<0.001) and GC-IPL decreased 0.26 μm (95% CI: -0.33, -0.20; p<0.001) per unit increase in A1c after adjusting for co-variates. Among participants with diabetes, age, DR grade, ethnicity, body mass index, glaucoma, spherical equivalent, and visual acuity were significantly associated with GC-IPL thickness. CONCLUSION: GC-IPL was thinner among participants with DM, compared to without DM. This difference persisted after adjusting for confounding variables and when considering only those with no/mild DR. This confirms that GC-IPL thinning occurs early in DM and can serve as a useful marker of DRN

Winter wheat roots grow twice as deep as spring wheat roots, is this important for N uptake and N leaching losses?

Cropping systems comprising winter catch crops followed by spring wheat could reduce N leaching risks compared to traditional winter wheat systems in humid climates. We studied the soil mineral N (Ninorg) and root growth of winter- and spring wheat to 2.5 m depth during three years. Root depth of winter wheat (2.2 m) was twice that of spring wheat, and this was related to much lower amounts of Ninorg in the 1 to 2.5 m layer after winter wheat (81 kg Ninorg ha-1 less). When growing winter catch crops before spring wheat, N content in the 1 to 2.5 m layer after spring wheat was not different from that after winter wheat. The results suggest that by virtue of its deep rooting, winter wheat may not lead to high levels of leaching as it is often assumed in humid climates. Deep soil and root measurements (below 1 m) in this experiment were essential to answer the questions we posed

Characteristics of C-4 photosynthesis in stems and petioles of C-3 flowering plants

Most plants are known as C-3 plants because the first product of photosynthetic CO2 fixation is a three-carbon compound. C-4 plants, which use an alternative pathway in which the first product is a four-carbon compound, have evolved independently many times and are found in at least 18 families. In addition to differences in their biochemistry, photosynthetic organs of C-4 plants show alterations in their anatomy and ultrastructure. Little is known about whether the biochemical or anatomical characteristics of C-4 photosynthesis evolved first. Here we report that tobacco, a typical C-3 plant, shows characteristics of C-4 photosynthesis in cells of stems and petioles that surround the xylem and phloem, and that these cells are supplied with carbon for photosynthesis from the vascular system and not from stomata. These photosynthetic cells possess high activities of enzymes characteristic of C-4 photosynthesis, which allow the decarboxylation of four-carbon organic acids from the xylem and phloem, thus releasing CO2 for photosynthesis. These biochemical characteristics of C-4 photosynthesis in cells around the vascular bundles of stems of C-3 plants might explain why C-4 photosynthesis has evolved independently many times

A new accuracy measure based on bounded relative error for time series forecasting

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred

Synergistic effect of doping with nitrogen and molybdenum on the photocatalytic properties of thin titania films

Doping of titania with metal and non-metal elements provides a simple and efficient pathway to significant enhancement of photocatalytic properties. In this work titania thin films co-doped with molybdenum and nitrogen were prepared by reactive magnetron sputtering. Additionally, coatings doped only with nitrogen were prepared under identical deposition conditions for comparison purposes. Coatings were annealed at 873 K in air and analysed by Raman spectroscopy, XRD and XPS. Photocatalytic properties of the coatings were evaluated on the basis of the photodegradation rate of methylene blue dye under UV, fluorescent and visible light. It was found that the photocatalytic activity of co-doped samples was significantly higher than that of N-doped coatings. Unlike N-doped titania films, co-doped coatings exhibited high photocatalytic activity under the fluorescent light source and noticeable activity under visible light. The possible mechanism for the enhancement of the photocatalytic activity of Mo-N co-doped titania coatings is discussed

Hydrothermal Growth and Application of ZnO Nanowire Films with ZnO and TiO2Buffer Layers in Dye-Sensitized Solar Cells

This paper reports the effects of the seed layers prepared by spin-coating and dip-coating methods on the morphology and density of ZnO nanowire arrays, thus on the performance of ZnO nanowire-based dye-sensitized solar cells (DSSCs). The nanowire films with the thick ZnO buffer layer (~0.8–1 μm thick) can improve the open circuit voltage of the DSSCs through suppressing carrier recombination, however, and cause the decrease of dye loading absorbed on ZnO nanowires. In order to further investigate the effect of TiO2buffer layer on the performance of ZnO nanowire-based DSSCs, compared with the ZnO nanowire-based DSSCs without a compact TiO2buffer layer, the photovoltaic conversion efficiency and open circuit voltage of the ZnO DSSCs with the compact TiO2layer (~50 nm thick) were improved by 3.9–12.5 and 2.4–41.7%, respectively. This can be attributed to the introduction of the compact TiO2layer prepared by sputtering method, which effectively suppressed carrier recombination occurring across both the film–electrolyte interface and the substrate–electrolyte interface

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases