(Abridged) The Swift X-Ray Telescope (XRT) reveals some interesting features of early X-ray afterglows, including a distinct rapidly decaying component preceding the conventional afterglow component in many sources, a shallow decay component before the more ``normal'' decay component observed in a good fraction of GRBs (e.g. GRB 050128, GRB 050315, GRB 050319, and GRB 050401), and X-ray flares in nearly half of the afterglows (e.g. GRB 050406, GRB 050502B, GRB 050607, and GRB 050724). In this paper, we systematically analyze the possible physical processes that shape the properties of the early X-ray afterglow lightcurves, and use the data to constrain various models. We suggest that the steep decay component is consistent with the tail emission of the prompt gamma-ray bursts and/or of the X-ray flares. This provides clear evidence that the prompt emission and afterglow emission are two distinct components, supporting the internal origin of the GRB prompt emission. The shallow decay segment observed in a group of GRBs suggests that the forward shock keeps being refreshed for some time. This might be caused either by a long-lived central engine, or by a power law distribution of the shell Lorentz factors, or else by the deceleration of a Poynting flux dominated flow. X-ray flares suggest that the GRB central engine is still active after the prompt gamma-ray emission is over, but with a reduced activity at later times. In some cases, the central engine activity even extends days after the burst trigger. Analyses of early X-ray afterglow data reveal that GRBs are indeed highly relativistic events. Early afterglow data of many bursts, starting from the beginning of the XRT observations, are consistent with the afterglow emission from an interstellar medium (ISM) environment.Comment: emulateapj, 19 pages, ApJ, in press. Conclusions unchange

We investigate the consequences of a continuously injecting central engine on the gamma-ray burst afterglow emission, focusing more specifically on a highly-magnetized millisecond pulsar engine. For initial pulsar parameters within a certain region of the parameter space, the afterglow lightcurves are predicted to show a distinctive achromatic bump feature, the onset and duration of which range from minutes to months, depending on the pulsar and the fireball parameters. The detection of or upper limits on such features would provide constraints on the burst progenitor and on magnetar-like central engine models. An achromatic bump such as that in GRB 000301C afterglow may be caused by a millisecond pulsar with P0=3.4 millisecond and Bp=2.7e14 Gauss.Comment: 5 pages, emulateapj style, to appear in ApJ Letters, updated with the accepted version, a few corrections are mad

Background: The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are the most commonly used scales to detect mild cognitive impairment (MCI) in population-based epidemiologic studies. However, their comparison on which is best suited to assess cognition is scarce in samples from multiple regions of China. Methods: We conducted a cross-sectional analysis of 4923 adults aged ≥55 years from the Community-based Cohort Study on Nervous System Diseases. Objective cognition was assessed by Chinese versions of MMSE and MoCA, and total score and subscores of cognitive domains were calculated for each. Education-specific cutoffs of total score were used to diagnose MCI. Demographic and health-related characteristics were collected by questionnaires. Correlation and agreement for MCI between MMSE and MoCA were analyzed; group differences in cognition were evaluated; and multiple logistic regression model was used to clarify risk factors for MCI. Results: The overall MCI prevalence was 28.6% for MMSE and 36.2% for MoCA. MMSE had good correlation with MoCA (Spearman correlation coefficient = 0.8374, p < 0.0001) and moderate agreement for detecting MCI with Kappa value of 0.5973 (p < 0.0001). Ceiling effect for MCI was less frequent using MoCA versus MMSE according to the distribution of total score. Percentage of relative standard deviation, the measure of inter-individual variance, for MoCA (26.9%) was greater than for MMSE (19.0%) overall (p < 0.0001). Increasing age (MMSE: OR = 2.073 for ≥75 years; MoCA: OR = 1.869 for≥75 years), female (OR = 1.280 for MMSE; OR = 1.163 for MoCA), living in county town (OR = 1.386 and 1.862 for MMSE and MoCA, respectively) or village (OR = 2.579 and 2.721 for MMSE and MoCA, respectively), smoking (OR = 1.373 and 1.288 for MMSE and MoCA, respectively), hypertension (MMSE: OR = 1.278; MoCA: OR = 1.208) and depression (MMSE: OR = 1.465; MoCA: OR = 1.350) were independently associated with greater likelihood of MCI compared to corresponding reference group in both scales (all p < 0.05). Conclusions: MoCA is a better measure of cognitive function due to lack of ceiling effect and with good detection of cognitive heterogeneity. MCI prevalence is higher using MoCA compared to MMSE. Both tools identify concordantly modifiable factors for MCI, which provide important evidence for establishing intervention measures.