Recent Advances in Biomarkers for Parkinson’s Disease

Parkinson’s disease (PD) is one of the common progressive neurodegenerative disorders with several motor and non-motor symptoms. Most of the motor symptoms may appear at a late stage where most of the dopaminergic neurons have been already damaged. In order to provide better clinical intervention and treatment at the onset of disease, it is imperative to find accurate biomarkers for early diagnosis, including prodromal diagnosis and preclinical diagnosis. At the same time, these reliable biomarkers can also be utilized to monitor the progress of the disease. In this review article, we will discuss recent advances in the development of PD biomarkers from different aspects, including clinical, biochemical, neuroimaging and genetic aspects. Although various biomarkers for PD have been developed so far, their specificity and sensitivity are not ideal when applied individually. So, the combination of multimodal biomarkers will greatly improve the diagnostic accuracy and facilitate the implementation of personalized medicine

The Effects of SNCA rs894278 on Resting-State Brain Activity in Parkinson’s Disease

The pathogenesis of Parkinson’s disease (PD) is not well established. The rs894278 polymorphism of SNCA has been associated with PD. We performed this study to investigate the relationship between rs894278 and PD status on resting-state brain activity, by analyzing the amplitude of low-frequency fluctuation (ALFF). A total of 81 PD patients and 64 healthy controls were recruited. Disease severity and PD stage were evaluated in PD patients using the unified Parkinson’s disease rating scale (UPDRS) and the Hoehn and Yahr (HY) scale, while the cognitive function of all participants was assessed using the mini-mental state examination (MMSE). All participants were genotyped for the rs894278 SNP and underwent a resting state functional magnetic resonance imaging scan. We found that the ALFF values of PD patients in the lingual gyrus and left caudate were lower than those of HCs; and the ALFF values for the right fusiform of participants with G allele were lower than those of participants without G allele. And we further revealed higher ALFF values in bilateral fusiform in rs894278-G carriers than in rs894278-G non-carriers in the PD group and lower ALFF values in bilateral fusiform in rs894278-G carriers than in rs894278-G non-carriers in the HC group. Our findings show that rs894278 and PD status interactively affect the brain activity of PD patients and HCs, and changes in the brain connectomes may play a key role in the pathogenesis of PD. Thus, our work sheds light on the mechanism underlying PD pathogenesis

Towards Effective Bug Triage with Software Data Reduction Techniques

International audienceSoftware companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance