e-Distance Weighted Support Vector Regression

We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR).e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle these two issues. In addition, we use both dual coordinate descent (CD) and averaged stochastic gradient descent (ASGD) strategies to make e-DWSVR scalable to large scale problems. We report promising results obtained by e-DWSVR in comparison with existing methods on several benchmark datasets

Large Margin Distribution Machine Recursive Feature Elimination

We gratefully thank Dr Teng Zhang and Prof Zhi-Hua Zhou for providing the source code of “LDM” source code and their kind technical assistance. This work is supported by the National Natural Science Foundation of China (Nos. 61472159, 61572227) and Development Project of Jilin Province of China (Nos. 20160204022GX, 2017C033). This work is also partially supported by the 2015 Scottish Crucible Award funded by the Royal Society of Edinburgh and the 2016 PECE bursary provided by the Scottish Informatics & Computer Science Alliance (SICSA).Postprin

Phenotypic plasticity masks range-wide genetic differentiation for vegetative but not reproductive traits in a short-lived plant

Genetic differentiation and phenotypic plasticity jointly shape intraspecific trait variation, but their roles differ among traits. In short-lived plants, reproductive traits may be more genetically determined due to their impact on fitness, whereas vegetative traits may show higher plasticity to buffer short-term perturbations. Combining a multi-treatment greenhouse experiment with observational field data throughout the range of a widespread short-lived herb, Plantago lanceolata, we (1) disentangled genetic and plastic responses of functional traits to a set of environmental drivers and (2) assessed how genetic differentiation and plasticity shape observational trait–environment relationships. Reproductive traits showed distinct genetic differentiation that largely determined observational patterns, but only when correcting traits for differences in biomass. Vegetative traits showed higher plasticity and opposite genetic and plastic responses, masking the genetic component underlying field-observed trait variation. Our study suggests that genetic differentiation may be inferred from observational data only for the traits most closely related to fitness

Evaluation of causality between ADHD and Parkinson's disease:Mendelian randomization study

In a retrospective cohort study, patients with attention-deficit hyperactivity disorder (ADHD) and psychostimulant prescription were associated with increased risk of Parkinson's disease (PD). It is unclear whether ADHD per se or psychostimulant prescription is associated with PD. We aim to determine if genetic correlation or/and causal association exists between ADHD and PD using summary statistics obtained from the largest meta-analysis of genome-wide association studies of ADHD (20,183 cases; 35,191 controls) and PD (26,421 cases; 442,271 controls). Genetic correlation was tested between ADHD and PD by linkage disequilibrium score regression. Causal estimate was assessed by inverse-variance weighted (IVW) method as the main mendelian randomization analysis, with sensitivity analyses to detect horizontal pleiotropy. Weak and inverse genetic correlation existed between ADHD and PD (r=-0.100;SE=0.045;P = 0.026). Univariable IVW analysis with 10 and 77 genetic instruments respectively revealed null association for ADHD with PD (OR=0.930 per doubling in odds of ADHD; 95% CI:0.792–1.092) and PD with ADHD (OR=0.986 per doubling in odds of PD; 95% CI:0.956–1.015). Multivariable IVW analyses adjusted for BMI/smoking also revealed null association of ADHD with PD. Using 58 PD-associated genetic instruments, multivariable IVW analysis with/without adjustment for BMI/smoking suggested a weak and inverse causal association for PD on ADHD, but cautious interpretation is required. This well-powered study did not support causality between ADHD and PD. The observed positive association between ADHD and PD is more likely to be caused by unmeasured confounders. As psychostimulant use is associated with high risk of early-onset PD, future research should focus on this area