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Background The phospholipase A2 Group 6 (PLA2G6, also known as PLA2, PARK14, and iPLA2) gene encodes a group VIA calcium-independent phospholipase A2. Genetic polymorphism of PLA2G6 has been indicated to be involved in conferring susceptibility for Parkinson’s disease (PD), whereas conclusive results have not been obtained. Thus, we intended to conduct a systematic review to determine if PLA2G6 genetic variation confers a greater susceptibility to PD. Methods All case-control studies that investigated the association of the PLA2G6 polymorphisms with the risk of PD published before 15 July 2018 were included. The literature was comprehensively searched and identified in five English databases (EBSCO, Pubmed, OVID, EMBASE and ISI Web of Knowledge) and four Chinese databases (Wanfang database, Chinese Biomedical Literature Database, China Academic Journals Database and VIP database). We performed analyses of study characteristics, heterogeneity, and forest plot in analyses analogous to dominant, codominant and additive models with the pooled odds ratio (OR) in fixed- or random-effects models as the measure of association. Results A total of 664 potentially relevant studies were retrieved with the initial search, of which eight studies fulfilled the inclusion criteria, and included 2,779 PD patients and 3,291 control participants,. Among all the reported 27 genetic variants, 15 single nucleotide polymorphisms (SNPs) were present only in patients, and only five available SNPs (rs2267369, rs140758033, c.1959T>A (Gly653Gly), rs76718524, rs199935023) were pooled in the meta-analysis. However, there was no evidence for a significant association between the five SNPs and PD risk in dominant, codominant and allele models, suggesting a lack of association between PLA2G6 genetic variation and PD susceptibility. Conclusion The present study assessed the association of PLA2G6 genetic polymorphism with the risk PD, and the result strongly demonstrates that PLA2G6 polymorphism is not associated with PD susceptibility.

Somnambulism is defined as a state of dissociated consciousness triggered by impaired arousal, which results in partial wakefulness and partial sleep. No effective therapy or medication has been available for treating children with somnambulism. Herein we present a case in a 4.5-year-old girl, who presented with somnambulism associated with separation anxiety disorder every night in a week. The girl received formal assessment and appropriate interventions, and the symptoms disappeared within a week. The treatment was carried out in 5 stages: diagnosis of the disease, establishment of trust, information collection, supervision, and individualized psychotherapy. Although dreams in childhood can be difficult to interpret, close observation of the behaviors in sleepwalking, as a special form of dream, in addition to the more precise description by the guardians, still provides useful clues to understand those dreams. For children with somnambulism, early intervention with psychotherapy can significantly decrease the false revival of the unconscious desires, and thus may serve as a treatment option other than medications.

Fault management of message processing networks requires fault localization on the higher layers. In this article, we propose a new fault localization technique for complex message processing networks, to locate specific network equipments that are highly correlated with the given service quality degradation occurrence. Our proposed technique utilizes the attempt and failure counters of message processing, and consists of two phases: processing failure model derivation and anomaly detection using the derived model. As a concrete example, the application in mobile core networks is described and evaluated. The simulation shows that our proposed technique is excellent in both accuracy and sensitivity.

Today's fault detection in commercial networks is still done in an inefficient way with alarms and threshold violations that treat measured indexes separately. To provide a practical network fault detection approach for the actual network operation, we investigate many actual network fault occurrences, and discover that many network faults can be characterized by unbalanced variation among measured network indexes that occur prior to or during an network fault occurrence. According to this general model of network fault, we propose a practical method for network fault detection that automatically extracts the unbalanced variation among measured indexes without the necessity of recognizing the physical meaning of them. Our evaluation shows that the proposed approach is applicable for the majority of measured indexes in the commercial networks, is efficient and scalable in performance, and with acceptable detection accuracy.