A New Fiber Detection Method for LAMOST Based on the Front-illuminated Method

The double revolving fiber positioning technology is one of the key technologies for the success of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST).The accuracy of fiber positioning will directly affect the observation efficiency of LAMOST.To achieve higher fiber positioning accuracy,the original open-loop controlled fiber positioning system urgently needs to be upgraded into a closed-loop control system.The fiber detection is the most important part of the closed-loop controlled fiber positioning system.The back-illuminated detection method is usually used to detect the fiber position by directly detecting the light spot generated at the fiber end in the multi-fiber spectral surveys.In this paper,we introduce a new method to measure the fiber position based on the image of the front-illuminated LAMOST focal plane.The front-illuminated image does not require lighting devices inside the spectrograph,and it could reduce the instability and light pollution in the spectrograph end.Our method measures the fiber position by fitting the profile of the fiber pinhole with a 2D Gaussian function.A series of tests show that the relative position measurement precision of the front-illuminated method is about 012,and the method could have the same accuracy as the back-illuminated method once the system bias is calibrated by a simple radial correction function.The required fiber positioning accuracy of LAMOST is 04,and the new method satisfies the requirement of LAMOST fiber detection accuracy and could be used in the closed-loop fiber control system.

PARK16 rs708730 Polymorphism Decreases Parkinson’s Disease Risk in European Ancestry Population:A Meta-analysis

Parkinson’s disease(PD)is a complex fatal chronic neurodegenerative disease most common in elderly people.The early genome-wide association studies(GWAS)found that the minor allele variant of PARK16 rs708730 polymorphism is a significant protective factor for PD in Caucasian populations.However,these results cannot be repeated by the following studies in Caucasian populations and other populations.We considered that the inconsistency of the findings may be caused by the small-scale samples or the heterogeneity among different populations.Therefore,in this study,we synthesized the previous related GWAS studies through three authoritative sources,and used the large-scale samples(10,645 PD cases and 30,499 controls)to reevaluate the association between rs708730 polymorphism and PD.The results showed that there is no association between them in Asian ancestry population.While,in European ancestry population,we found that the minor allele variant(G)of rs708730 polymorphism is significantly associated with a decreased risk of PD.Collectively,our findings further verified the association of rs708730 with PD and show its genetic heterogeneity among different populations,which can help to develop a better understanding of the PD’s pathogenesis.

Fault-tolerant hamiltonian cycles and paths embedding into locally exchanged twisted cubes

The foundation of information society is computer interconnection network,and the key of information exchange is communication algorithm.Finding interconnection networks with simple routing algorithm and high fault-tolerant performance is the premise of realizing various communication algorithms and protocols.Nowadays,people can build complex interconnection networks by using very large scale integration(VLSI)technology.Locally exchanged twisted cubes,denoted by(s+t+1)-dimensional LeTQ_(s,t) which combines the merits of the exchanged hypercube and the locally twisted cube.It has been proved that the LeTQ_(s,t) has many excellent properties for interconnection networks,such as fewer edges,lower overhead and smaller diameter.Embeddability is an important indicator to measure the performance of interconnection networks.We mainly study the fault tolerant Hamiltonian properties of a faulty locally exchanged twisted cube,LeTQ_(s,t)-(f_(v)+f_(e)),with faulty vertices f_(v) and faulty edges fe.Firstly,we prove that an LeTQ_(s,t) can tolerate up to s-1 faulty vertices and edges when embedding a Hamiltonian cycle,for s≥2,t≥3,and s≤t.Furthermore,we also prove another result that there is a Hamiltonian path between any two distinct fault-free vertices in a faulty LeTQ_(s,t) with up to(s-2)faulty vertices and edges.That is,we show that LeTQ_(s,t) is(s-1)-Hamiltonian and(s-2)-Hamiltonian-connected.The results are proved to be optimal in this paper with at most(s-1)-fault-tolerant Hamiltonicity and(s-2)fault-tolerant Hamiltonian connectivity of LeTQ_(s,t).

Functional characterization of disease/comorbidity-associated IncRNA

Background:Functional characterization of the long noncoding RNAs(IncRNAs)in disease attracts great attention,which results in a limited number of experimentally characterized IncRNAs.The major problems underlying the lack of experimental verifications are considered to come from the significant false-positive assignments and extensive genetic-heterogeneity of disease.These problems are even worse when it comes to the functional characterization in comorbidity(simultaneous/sequential presence of multiple diseases in a patient,and showing much wider prevalence,poorer treatment-response and longer illness-course than a single disease).Methods:Herein,FCCLnc was developed to characterize IncRNA function by(1)integrating diverse SNPs that were associated with 193 diseases standardized by International Classification of Diseases(ICD-11),(2)condition-specific expression of IncRNAs,(3)weighted correlation network of IncRNAs and protein-coding neighboring genes.Results:FCCLnc can characterize IncRNA function in both disease and comorbidity by not only controlling false discovery but also tolerating their disease heterogeneity.Moreover,FCCLnc can provide interactive visualization and full download of IncRNA-centered co-expression network.Conclusion:In summary,FCCLnc is unique in characterizing IncRNA function in diverse diseases and comorbidities and is highly expected to emerge to be an indispensable complement to other available tools.FCCLnc is accessible at https://idrblab.org/fcclnc/.