Hawking radiation from spherically symmetrical gravitational collapse to an extremal R-N black hole for a charged scalar field

Sijie Gao has recently investigated Hawking radiation from spherically symmetrical gravitational collapse to an extremal R-N black hole for a real scalar field. Especially he estimated the upper bound for the expected number of particles in any wave packet belonging to $\mathcal{H}_{out}$ spontaneously produced from the state $|0>_{in}$, which confirms the traditional belief that extremal black holes do not radiate particles. Making some modifications, we demonstrate that the analysis can go through for a charged scalar field.Comment: 10 pages, 1 figur

Improvement of EEG based brain computer interface by application of tripolar electrodes and independent component analysis

For persons with severe disabilities, a brain computer interface (BCI) may be a viable means of communication, with scalp-recorded electroencephalogram (EEG) being the most common signal employed in the operation of a BCI. Various electrode configurations can be used for EEG recording, one of which was a set of concentric rings that was referred to as a Laplacian electrode. It has been shown that Lapalacian EEG could improve classification in EEG recognition, but the complete advantages of this configuration have not been established. This project included two parts. First, a modeling study was performed using Independent Component Analysis (ICA) to prove that tripolar electrodes could provide better EEG signal for BCI. Next, human experiments were performed to study the application of tripolar electrodes in a BCI model to show that the application of tripolar electrodes and data-segment related parameter selection can improve EEG classification ratio for BCI. In the first part of work, an improved four-layer anisotropic concentric spherical head computer model was programmed, then four configurations of time-varying dipole signals were used to generate the scalp surface signals that would be obtained with tripolar and disc electrodes. Four important EEG artifacts were tested: eye blinking, cheek movements, jaw movements and talking. Finally, a fast fixed-point algorithm was used for signal-independent component analysis (ICA). The results showed that signals from tripolar electrodes generated better ICA separation than signals from disc electrodes for EEG signals, suggesting that tripolar electrodes could provide better EEG signal for BCI. The human experiments were divided into three parts: improvement of the data acquirement system by application of tripolar concentric electrodes and related circuit; development of pre-feature selection algorithm to improve BCI EEG signal classification; and an autoregressive (AR) model and Mahalanobis distance-based linear classifier for BCI classification. In the work, tripolar electrodes and corresponding data acquisition system were developed. Two sets of left/right hand motor imagery EEG signals were acquired. Then the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. The pre-feature selection methods were developed and applied to four data segment-related parameters: the length of the data segment in each trial (LDS), its starting position (SPD), the number of trials (NT) and the AR model order (AR Order). The study showed that, compared to the classification ratio (CR) without parameter selection, the CR was significantly different with an increase by 20% to 30% with proper selection of these data-segment-related parameter values and that the optimum parameter values were subject-dependent, which suggests that the data-segment-related parameters should be individualized when building models for BCI. The experiments also showed that that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes

Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method

BACKGROUND: In recent years, both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging (fMRI) have been widely used for the study of schizophrenia (SCZ). In addition, a few studies have been reported integrating both SNPs data and fMRI data for comprehensive analysis. METHODS: In this study, a novel sparse representation based variable selection (SRVS) method has been proposed and tested on a simulation data set to demonstrate its multi-resolution properties. Then the SRVS method was applied to an integrative analysis of two different SCZ data sets, a Single-nucleotide polymorphism (SNP) data set and a functional resonance imaging (fMRI) data set, including 92 cases and 116 controls. Biomarkers for the disease were identified and validated with a multivariate classification approach followed by a leave one out (LOO) cross-validation. Then we compared the results with that of a previously reported sparse representation based feature selection method. RESULTS: Results showed that biomarkers from our proposed SRVS method gave significantly higher classification accuracy in discriminating SCZ patients from healthy controls than that of the previous reported sparse representation method. Furthermore, using biomarkers from both data sets led to better classification accuracy than using single type of biomarkers, which suggests the advantage of integrative analysis of different types of data. CONCLUSIONS: The proposed SRVS algorithm is effective in identifying significant biomarkers for complicated disease as SCZ. Integrating different types of data (e.g. SNP and fMRI data) may identify complementary biomarkers benefitting the diagnosis accuracy of the disease

Efficient test for nonlinear dependence of two continuous variables

The power comparison of simulation study across Gaussian noise levels (mean = 0, variance = 1/9, 1/4, 4 and 9). (XLSX 11 kb

Complex responses of spring vegetation growth to climate in a moisture-limited alpine meadow.

Since 2000, the phenology has advanced in some years and at some locations on the Qinghai-Tibetan Plateau, whereas it has been delayed in others. To understand the variations in spring vegetation growth in response to climate, we conducted both regional and experimental studies on the central Qinghai-Tibetan Plateau. We used the normalized difference vegetation index to identify correlations between climate and phenological greening, and found that greening correlated negatively with winter-spring time precipitation, but not with temperature. We used open top chambers to induce warming in an alpine meadow ecosystem from 2012 to 2014. Our results showed that in the early growing season, plant growth (represented by the net ecosystem CO2 exchange, NEE) was lower in the warmed plots than in the control plots. Late-season plant growth increased with warming relative to that under control conditions. These data suggest that the response of plant growth to warming is complex and non-intuitive in this system. Our results are consistent with the hypothesis that moisture limitation increases in early spring as temperature increases. The effects of moisture limitation on plant growth with increasing temperatures will have important ramifications for grazers in this system

Causal influences of osteoarthritis on COVID-19: a Mendelian randomization study

ObjectiveAlthough observational and genetic studies have indicated a correlation between OA and COVID-19, it remains uncertain whether osteoarthritis (OA) contributes to the severity of COVID-19. Here, we aimed to investigate the potential causal links between the two.MethodsIn this study, we conducted Mendelian randomization (MR) analysis to investigate whether there is a potential causal connection between OA and COVID-19 outcomes. The analysis utilized publicly available GWAS summary datasets, incorporating data on OA (N = 455,221), SARS-CoV-2 infection (N = 2,597,856), hospitalized COVID-19 (N = 2,095,324), and critical COVID-19 (N = 1,086,211). Additionally, we performed a literature analysis to establish a molecular network connecting OA and COVID-19.ResultsThe MR analysis showed causal effects of OA on hospitalized COVID-19 (OR: 1.21, 95% CI: 1.02–1.43, p = 0.026) and critical COVID-19 (OR: 1.35, 95% CI: 1.09–1.68, p = 0.006) but not on SARS-CoV-2 infection as such (OR: 1.00, 95% CI: 0.92–1.08, p = 0.969). Moreover, the literature-based pathway analysis uncovered a set of specific genes, such as CALCA, ACE, SIRT1, TNF, IL6, CCL2, and others, that were found to mediate the association between OA and COVID-19.ConclusionOur findings indicate that OA elevates the risk of severe COVID-19. Therefore, larger efforts should be made in the prevention of COVID-19 in OA patients

Qi-Shen-Yi-Qi Dripping Pills for the Secondary Prevention of Myocardial Infarction: A Randomised Clinical Trial

Background. Several types of drugs have been recommended for the secondary prevention of myocardial infarction (MI). However, these conventional strategies have several limitations, such as low adherence, high cost, and side effects during long time use. Novel approaches to this problem are still needed. This trial aimed to test the effectiveness and safety of Qi-Shen-Yi-Qi Dripping Pills (QSYQ), a multi-ingredient Chinese patent medicine, for the secondary prevention of MI. Methods and Findings. A total of 3505 eligible patients were randomly assigned to QSYQ group (1746 patients) or aspirin group (1759). Patients took their treatments for 12 months. The final follow-up visit took place 6 months after the end of the trial drugs. The 12-month and 18-month estimated incidences of the primary outcome were 2.98% and 3.67%, respectively, in the QSYQ group. The figures were 2.96% and 3.81% in the aspirin group. No significant difference was identified between the groups. Conclusions. This trial did not show significant difference of primary and secondary outcomes between aspirin and QSYQ in patients who have had an MI. Though inconclusive, the result suggests that QSYQ has similar effects to aspirin in the secondary prevention of MI