Clinical features, polysomnography, and genetics association study of restless legs syndrome in clinic based Chinese patients: A multicenter observational study.

STUDY OBJECTIVES: To systemically describe the clinical features, polysomnography (PSG) finding, laboratory tests and single-nucleotide polymorphisms (SNPs) in a clinic based Chinese primary restless legs syndrome (RLS) population. METHODS: This observational study, conducted from January 2020 to October 2021 across 22 sleep labs in China, recruited 771 patients diagnosed with RLS following the 2014 RLSSG criteria. Clinical data, PSG testing, and laboratory examination and SNPs of patients with RLS were collected. A total of 32 SNPs in 24 loci were replicated using the Asian Screening Array chip, employing data from the Han Chinese Genomes Initiative as controls. RESULTS: In this study with 771 RLS patients, 645 had primary RLS, and 617 has DNA available for SNP study. Among the 645 primary RLS, 59.7% were women. 33% had a family history of RLS, with stronger familial influence in early-onset cases. Clinical evaluations showed 10.4% had discomfort in body parts other than legs. PSG showed that 57.1% of RLS patients had periodic leg movement index (PLMI) of >5/h and 39.1% had PLMI >15/h, respectively; 73.8% of RLS patients had an Apnea-Hypopnea Index (AHI) > 5/h, and 45.3% had an AHI >15/h. The laboratory examinations revealed serum ferritin levels <75 ng/ml in 31.6%, and transferrin saturation (TSAT) of <45% in 88.7% of RLS patients. Seven new SNPs in 5 genes showed a significant allelic association with Chinese primary RLS, with one previously reported (BTBD9) and four new findings (TOX3, PRMT6, DCDC2C, NOS1). CONCLUSIONS: Chinese RLS patients has specific characters in many aspects. A high family history with RLS not only indicates strong genetic influence, but also reminds us to consider the familial effect in the epidemiological study. Newly developed sequencing technique with large samples remains to be done.

Transfer learning of phase transitions in percolation and directed percolation.

The latest advances of statistical physics have shown remarkable performance of machine learning in identifying phase transitions. In this paper, we apply domain adversarial neural network (DANN) based on transfer learning to studying nonequilibrium and equilibrium phase transition models, which are percolation model and directed percolation (DP) model, respectively. With the DANN, only a small fraction of input configurations (two-dimensional images) needs to be labeled, which is automatically chosen, to capture the critical point. To learn the DP model, the method is refined by an iterative procedure in determining the critical point, which is a prerequisite for the data collapse in calculating the critical exponent ν_{⊥}. We then apply the DANN to a two-dimensional site percolation with configurations filtered to include only the largest cluster which may contain the information related to the order parameter. The DANN learning of both models yields reliable results which are comparable to the ones from Monte Carlo simulations. Our study also shows that the DANN can achieve quite high accuracy at much lower cost, compared to the supervised learning.

Multifractality and Network Analysis of Phase Transition.

Many models and real complex systems possess critical thresholds at which the systems shift dramatically from one sate to another. The discovery of early-warnings in the vicinity of critical points are of great importance to estimate how far the systems are away from the critical states. Multifractal Detrended Fluctuation analysis (MF-DFA) and visibility graph method have been employed to investigate the multifractal and geometrical properties of the magnetization time series of the two-dimensional Ising model. Multifractality of the time series near the critical point has been uncovered from the generalized Hurst exponents and singularity spectrum. Both long-term correlation and broad probability density function are identified to be the sources of multifractality. Heterogeneous nature of the networks constructed from magnetization time series have validated the fractal properties. Evolution of the topological quantities of the visibility graph, along with the variation of multifractality, serve as new early-warnings of phase transition. Those methods and results may provide new insights about the analysis of phase transition problems and can be used as early-warnings for a variety of complex systems.