Changes in soil aggregate stability and aggregate-associated carbon under different slope positions in a karst region of Southwest China.

Soil aggregates are crucial for reducing soil erosion and enhancing soil organic carbon sequestration. However, knowledge regarding influences of different slope positions on compositions and carbon content for different soil aggregates is limited. Soil samples were collected from various slope positions including dip slope, anti-dip slope and valley depression in the Longtan karst valley of Southwest China. Contents of macroaggregate (> 0.25 mm), microaggregate (0.053-0.25 mm) and silt and clay fraction (< 0.053 mm), and aggregate-associated carbon contents under the three slope positions were measured. Compared to the anti-dip slope, the mean weight diameter under the dip slope and valley depression decreased by 28.48 % and 58.79 %, respectively, while the geometric mean diameter decreased by 39.01 % and 62.57 %, respectively. The mean carbon content in silt and clay fraction was 27.59 % and 21.00 % lower than the macroaggregate- and microaggregate-associated carbon content, respectively. Under the valley depression and dip slope, soil organic carbon contents in bulk soil (37.67 % and 10.36 %, respectively), microaggregate (37.56 % and 4.95 %), and silt and clay fraction (39.99 % and 12.84 %, respectively) were significantly lower than those under the anti-dip slope. However, the difference in macroaggregate-associated carbon content among the three slope positions was not significant. The silt and clay fraction was the major contributor to soil carbon pool in bulk soil in the study area because of its high content. Compared to the anti-dip slope, contribution of macroaggregates to soil carbon pool under the dip slope and valley depression decreased by 25.53 % and 47.95 %, respectively, whereas the contribution of silt and clay fraction increased by 22.68 % and 42.66 %, respectively. These results suggested that the anti-dip slope surpassed both the dip slope and valley depression in carbon sequestration and soil and water conservation in karst regions.

Association of Dietary Vitamin D Intake, Serum 25(OH)D3, 25(OH)D2 with Cognitive Performance in the Elderly.

BACKGROUND: As life expectancy increases, cognitive performance decline in the elderly has become one of the major global challenges. We aimed to evaluate the association of dietary vitamin D (VD), serum 25-hydroxyvitamin D3 (25(OH)D3), 25-hydroxyvitamin D2 (25(OH)D2), and total 25-hydroxyvitamin (25(OH)D) concentration with cognitive performance in older Americans. METHODS: The data from the National Health and Nutrition Examination Survey (NHANES), 2011-2014 was used. The cognitive performance was assessed by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word Learning sub-test, Animal Fluency test, and Digit Symbol Substitution Test (DSST). A binary logistic regression model was applied to evaluate the association between VD and cognitive performance, and restricted cubic spline model was adopted to evaluate the dose-response relationship. RESULTS: While comparing to the lowest dietary VD intake group, the multivariate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of the highest dietary VD intake group were 0.51 (0.36-0.72) for the Animal Fluency test score and 0.45 (0.31-0.66) for DSST score, respectively; and those of serum total 25(OH)D and 25(OH)D3 concentration were 0.68 (0.47-0.97) and 0.62 (0.44-0.86) for DSST score. L-shaped relationships were identified for dietary VD intake, serum total 25(OH)D and 25(OH)D3 concentration with cognition performance. The associations between dietary VD intake, serum total 25(OH)D and cognitive performance were non-significant when stratified by gender. CONCLUSIONS: The study indicates that dietary VD intake, serum total 25(OH)D and 25(OH)D3 concentration were positively associated with cognitive performance. Further studies are needed to clarify the possible effects of dietary VD intake and serum 25(OH)D2, 25(OH)D3 on cognitive performance.

Multiclass retinal disease classification and lesion segmentation in OCT B-scan images using cascaded convolutional networks.

Disease classification and lesion segmentation of retinal optical coherence tomography images play important roles in ophthalmic computer-aided diagnosis. However, existing methods achieve the two tasks separately, which is insufficient for clinical application and ignores the internal relation of disease and lesion features. In this paper, a framework of cascaded convolutional networks is proposed to jointly classify retinal diseases and segment lesions. First, we adopt an auxiliary binary classification network to identify normal and abnormal images. Then a novel, to the best of our knowledge, U-shaped multi-task network, BDA-Net, combined with a bidirectional decoder and self-attention mechanism, is used to further analyze abnormal images. Experimental results show that the proposed method reaches an accuracy of 0.9913 in classification and achieves an improvement of around 3% in Dice compared to the baseline U-shaped model in segmentation.