SDSS J013127.34−-032100.1: A newly discovered radio-loud quasar at z=5.18z=5.18 with extremely high luminosity

Only very few z>5 quasars discovered to date are radio-loud, with a radio-to-optical flux ratio (radio-loudness parameter) higher than 10. Here we report the discovery of an optically luminous radio-loud quasar, SDSS J013127.34-032100.1 (J0131-0321 in short), at z=5.18+-0.01 using the Lijiang 2.4m and Magellan telescopes. J0131-0321 has a spectral energy distribution consistent with that of radio-loud quasars. With an i-band magnitude of 18.47 and radio flux density of 33 mJy, its radio-loudness parameter is ~100. The optical and near-infrared spectra taken by Magellan enable us to estimate its bolometric luminosity to be L_bol ~ 1.1E48 erg/s, approximately 4.5 times greater than that of the most distant quasar known to date. The black hole mass of J0131-0321 is estimated to be 2.7E9 solar masses, with an uncertainty up to 0.4 dex. Detailed physical properties of this high-redshift, radio-loud, potentially super-Eddington quasar can be probed in the future with more dedicated and intensive follow-up observations using multi-wavelength facilities.Comment: 5 pages, 3 figures, accepted to ApJ

The Double-Edges Role of Copper in the Fate of Amyloid Beta in the Presence of Anti-Oxidants

The biological fate of amyloid beta (Aβ) species is a fundamental question in Alzheimer’s disease (AD) pathogenesis. The competition between clearance and aggregation of Aβs is critical for the onset of AD. Copper has been widely considered to be an inducer of harmful crosslinking of Aβs, and an important triggering factor for the onset of AD. In this report, however, we present data to show that copper can also be an inducer of Aβ degradation in the presence of a large excess of well-known intrinsic (such as dopamine) or extrinsic (such as vitamin C) anti-oxidants. The degraded fragments were identified using SDS-Page gels, and validated via nanoLC-MS/MS. A tentative mechanism for the degradation was proposed and validated with model peptides. In addition, we performed electrophysiological analysis to investigate the synaptic functions in brain slices, and found that in the presence of a significant excess of vitamin C, Cu(II) could prevent an Aβ-induced deficit in synaptic transmission in the hippocampus. Collectively, our evidence strongly indicated that a proper combination of copper and anti-oxidants might have a positive effect on the prevention of AD. This double-edged function of copper in AD has been largely overlooked in the past. We believe that our report is very important for fully understanding the function of copper in AD pathology

The association of serum uric acid with cognitive impairment and ATN biomarkers

BackgroundCognitive impairment (CI) has become a worldwide health problem. The relationship between CI and uric acid (UA) is contradictory.ObjectiveWe included participants with a full spectrum of CI, from cognitively unimpaired (CU) to dementia, from the Chongqing Ageing & Dementia Study (CADS).MethodsFirst, we identified the relationships between serum UA (sUA) and cognitive function in different stages of CI. Second, we analyzed these relationships among different stages and types of CI. Finally, we explored the association between sUA and amyloid/tangle/neurodegeneration (ATN) biomarkers.ResultsWe recruited 427 participants from the CADS, including 382 participants with mini-mental state examination (MMSE) evaluation. The levels of sUA were positively correlated with MMSE scores (p < 0.001), and the correlation was prominent in the course of dementia and in the type of Alzheimer’s disease (AD). The levels of UA had a positive correlation with plasma amyloid-β 42 (Aβ42) (p = 0.004). Higher levels of sUA weakened the correlation of MMSE scores with CSF ATN biomarkers and the correlation of CSF Aβ42 with tau.ConclusionUA is positively correlated with cognitive function, especially in the advanced stage of AD. The probable neuroprotective effects of sUA mainly act on Aβ42 and the downstream pathological cascade

Dendritic integration in mouse retinal ganglion cells

Understanding how a neuron integrates the large number of synaptic inputs across its dendritic arbour is critical to understand neural computations. The central nervous system comprises a large variety of neuron types that differ in their morphology, physiology and functional role within the circuit. However, little is known about how cell-type-specific differences in dendritic integration arise from general features such as neuronal morphology and intrinsic membrane properties. Here, retinal ganglion cells, which relay the visual system’s first computations to the brain, represent an exquisite model. They are functionally and morphologically diverse yet defined, and they allow studying dendritic integration in a functionally relevant context. In this thesis, I systematically investigate the dendritic integration of visual information in four types of mouse retinal ganglion cells (transient Off alpha, transient Off mini, sustained Off, and F-miniOff), which receive similar excitatory inputs, but display different visual responses and dendritic morphologies. Using two-photon imaging of dendritic calcium signals from individual cells, and biophysical modelling, I demonstrate that these retinal ganglion cells exhibit diverse type-specific spatio-temporal dendritic integration profiles: In transient Off alpha cells, dendritic receptive fields displayed little spatial overlap, indicating a dendritic arbour that is partitioned in largely isolated regions. In contrast, dendritic receptive fields in the other three cell types overlapped greatly and were offset to the soma in transient Off mini and sustained Off cells, suggesting strong synchronization of dendritic signals likely due to back-propagation of somatic signals. Also, the temporal correlation of dendritic signals varied extensively among these types, with transient Off mini cells displaying the highest correlation across their dendritic arbour. Modelling suggests that morphology alone cannot explain these differences in dendritic integration, but instead specific combinations of dendritic morphology and ion channel densities are required. Together, these results reveal how neurons exhibit distinct dendritic integration profiles, tuned towards their type-specific computations in their circuits and highlight the interplay between morphology and channel complement as a key contributor in shaping dendritic integration

In Silico Study of Polypharmacology with Ligand-based Interaction Fingerprint

The past years have witnessed the versatile applications of interaction fingerprint method, including three-dimensional structure analysis, docking-pose clustering and filtering, scoring function improvement and enhancing enrichment of virtual screening. However, it’s still unclear whether it’s possible to study the polypharmacology with such a strategy. We have explored this important question by assessing the performance of ligand-based interaction fingerprint (LIFt), a new approach providing insights into the potential targets for the specific small-molecule drug. According to our results, it’s found that LIFt could recognize most of the native targets for the promiscuous kinase inhibitor staurosporine on the basis of experimental determined complex structures. In addition, with assistance of physics-based docking and sampling techniques, LIFt can predict the kinase-selectivity profile as well as the unexpected off-targets for the established drug or drug candidates with appreciated accuracy. More encouragingly, a prospective prediction of new kinase target for the anticancer drug candidate TN-16 was experimentally validated, which suggests the promise of LIFt in practical use of polypharmacology study

Central and peripheral pulmonary sclerosing pneumocytomas: multi-phase CT study and comparison with Ki-67

This study aimed to evaluate the multi-phase CT findings of central and peripheral pulmonary sclerosing pneumocytomas (PSPs) and compared them with Ki-67 to reveal their neoplastic nature

Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion

Frame repetition (FR) is a common temporal-domain tampering operator, which is often used to increase the frame rate of video sequences. Existing methods detect FR forgery by analyzing residual variation or similarity between video frames; however, these methods are easily interfered with by noise, affecting the stability of detection performance. This paper proposes a noise-level based detection method which detects the varying noise level over time to determine whether the video is forged by FR. Wavelet coefficients are first computed for each video frame, and median absolute deviation (MAD) of wavelet coefficients is used to estimate the standard deviation of Gaussian noise mixed in each video frame. Then, fast Fourier transform (FFT) is used to calculate the amplitude spectrum of the standard deviation curve of the video sequence, and to provide the peak-mean ratio (PMR) of the amplitude spectrum. Finally, according to the PMR obtained, a hard threshold decision is taken to determine whether the standard deviation bears periodicity in the temporal domain, in which way FR forgery can be automatically identified. The experimental results show that the proposed method ensures a large PMR for the forged video, and presents a better detection performance when compared with the existing detection methods

Total spray tray (TST) for distillation columns: A new generation tray with lower pressure drop

As a critical gas-liquid contact component, trays play an important role in the performance of distillation column. In this paper, the hydrodynamics of a new type of tray, the total spray tray (TST), were investigated. Taking air-water as the working medium, the pressure drop with different F0 was tested under atmospheric pressure. The results indicate that there is no flooding with the tray and thus the wider operation flexibility can be provided by TST. Furthermore, the pressure drop of TST is much lower than that of other trays. The above properties of TST would benefit the production expansion and energy decreasing in distillation operation greatly

An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning

For global optimal control strategy, it is not only necessary to know the driving cycle in advance but also difficult to implement online because of its large calculation volume. As an artificial intelligent-based control strategy, reinforcement learning (RL) is applied to an energy management strategy of a super-mild hybrid electric vehicle. According to time-speed datasets of sample driving cycles, a stochastic model of the driver’s power demand is developed. Based on the Markov decision process theory, a mathematical model of an RL-based energy management strategy is established, which assumes the minimum cumulative return expectation as its optimization objective. A policy iteration algorithm is adopted to obtain the optimum control policy that takes the vehicle speed, driver’s power demand, and state of charge (SOC) as the input and the engine power as the output. Using a MATLAB/Simulink platform, CYC_WVUCITY simulation model is established. The results show that, compared with dynamic programming, this method can not only adapt to random driving cycles and reduce fuel consumption of 2.4%, but also be implemented online because of its small calculation volume