The Large sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) general survey is a spectroscopic survey that will eventually cover approximately half of the celestial sphere and collect 10 million spectra of stars, galaxies and QSOs. Objects in both the pilot survey and the first year regular survey are included in the LAMOST DR1. The pilot survey started in October 2011 and ended in June 2012, and the data have been released to the public as the LAMOST Pilot Data Release in August 2012. The regular survey started in September 2012, and completed its first year of operation in June 2013. The LAMOST DR1 includes a total of 1202 plates containing 2 955 336 spectra, of which 1 790 879 spectra have observed signalto-noise ratio (SNR) ≥ 10. All data with SNR ≥ 2 are formally released as LAMOST DR1 under the LAMOST data policy. This data release contains a total of 2 204 696 spectra, of which 1 944 329 are stellar spectra, 12 082 are galaxy spectra and 5017 are quasars. The DR1 not only includes spectra, but also three stellar catalogs with measured parameters: late A,FGK-type stars with high quality spectra (1 061 918 entries), A-type stars (100 073 entries), and M-type stars (121 522 entries). This paper introduces the survey design, the observational and instrumental limitations, data reduction and analysis, and some caveats. A description of the FITS structure of spectral files and parameter catalogs is also provided.

Background The outbreak of 2019 coronavirus disease (COVID-19) could increase the risk of depression. However, epidemiological data on outbreak-associated depressive morbidity of female adolescents are not available. This study determines the incidence and correlates of depression among female adolescents aged 11–18 years during the COVID-19 outbreak in mainland China. Methods A large cross-sectional sample, nationwide online survey was conducted during the COVID-19 outbreak. Depression was assessed using the Center for Epidemiologic Studies Depression Scale (CES-D), and the correlative factors of depression were analyzed. Results In this study, 4805 female adolescents were enrolled with a median (range) age of 15 (11–18) years. Of them, 1899 (39.5%) suffered from depression with a CES-D score of > 15. The onset of depression was significantly related to age, grade, distant learning, attitude toward COVID-19, sleep duration, and physical exercise duration. Furthermore, participants aged 15–18 years (OR = 1.755, 95% CI: 1.550–1.987, p < 0.001), participating in distant learning (OR = 0.710, 95% CI: 0.564–0.894, p = 0.004), concerned about COVID-19 (OR = 0.414, 95% CI: 0.212–0.811, p = 0.010), with sleep duration/day of < 6 h (OR = 2.603, 95% CI: 1.946–3.483, p < 0.001),and with physical exercise duration/day < 30 min (OR = 1.641, 95% CI: 1.455–1.850, p < 0.001) represented to be independent factors for suffering from depression. Conclusion During the COVID-19 outbreak, depression was common among female adolescents. Older age, distant learning, concern about COVID-19, short sleep duration, and physical exercise duration represented the independent factors for suffering from depression.

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.