Multi-chromatic narrow-energy-spread electron bunches from laser wakefield acceleration with dual-color lasers

A method based on laser wakefield acceleration with controlled ionization injection triggered by another frequency-tripled laser is proposed, which can produce electron bunches with low energy spread. As two color pulses co-propagate in the background plasma, the peak amplitude of the combined laser field is modulated in time and space during the laser propagation due to the plasma dispersion. Ionization injection occurs when the peak amplitude exceeds certain threshold. The threshold is exceeded for limited duration periodically at different propagation distances, leading to multiple ionization injections and separated electron bunches. The method is demonstrated through multi-dimensional particle-in-cell simulations. Such electron bunches may be used to generate multi-chromatic X-ray sources for a variety of applications.Comment: 5 pages, 5 figures; accepted by PR

FacetClumps: A Facet-based Molecular Clump Detection Algorithm

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian Facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. Experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, a high consistency between the region of detected clump and that of simulated clump, and is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises $^{13}CO$ ($J = 1-0$) emission line of the MWISP within $11.7^{\circ} \leq l \leq 13.4^{\circ}$, $0.22^{\circ} \leq b \leq 1.05^{\circ}$ and 5 km s$^{-1}$ $\leq v \leq$ 35 km s$^{-1}$ and simulated clumps, reaches 90.2\%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.Comment: 27pages,28figure

Silicon nitride metalenses for unpolarized high-NA visible imaging

As one of nanoscale planar structures, metasurface has shown excellent superiorities on manipulating light intensity, phase and/or polarization with specially designed nanoposts pattern. It allows to miniature a bulky optical lens into the chip-size metalens with wavelength-order thickness, playing an unprecedented role in visible imaging systems (e.g. ultrawide-angle lens and telephoto). However, a CMOS-compatible metalens has yet to be achieved in the visible region due to the limitation on material properties such as transmission and compatibility. Here, we experimentally demonstrate a divergent metalens based on silicon nitride platform with large numerical aperture (NA~0.98) and high transmission (~0.8) for unpolarized visible light, fabricated by a 695-nm-thick hexagonal silicon nitride array with a minimum space of 42 nm between adjacent nanoposts. Nearly diffraction-limit virtual focus spots are achieved within the visible region. Such metalens enables to shrink objects into a micro-scale size field of view as small as a single-mode fiber core. Furthermore, a macroscopic metalens with 1-cm-diameter is also realized including over half billion nanoposts, showing a potential application of wide viewing-angle functionality. Thanks to the high-transmission and CMOS-compatibility of silicon nitride, our findings may open a new door for the miniaturization of optical lenses in the fields of optical fibers, microendoscopes, smart phones, aerial cameras, beam shaping, and other integrated on-chip devices.Comment: 16 pages, 7 figure

Association of erythrocyte n-3 polyunsaturated fatty acids with incident type 2 diabetes in a Chinese population

Summary Background & aims The association between circulating n-3 polyunsaturated fatty acid (PUFA) biomarkers and incident type 2 diabetes in Asian populations remains unclear. We aimed to examine the association of erythrocyte n-3 PUFA with incident type 2 diabetes in a Chinese population. Methods A total of 2671 participants, aged 40–75 y, free of type 2 diabetes at baseline, were included in the present analysis. Incident type 2 diabetes cases (n = 213) were ascertained during median follow-up of 5.6 years. Baseline erythrocyte fatty acids were measured by gas chromatography. We used multivariable Cox regression models to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of type 2 diabetes across quartiles of erythrocyte n-3 PUFA. Results After adjustment for potential confounders, HRs (95% CIs) of type 2 diabetes were 0.68 (0.47, 1.00), 0.77 (0.52, 1.15), and 0.63 (0.41, 0.95) in quartiles 2–4 of docosapentaenoic acid (C22:5n-3) (P-trend = 0.07), compared with quartile 1; and 1.08 (0.74, 1.60), 1.03 (0.70, 1.51), and 0.57 (0.38, 0.86) for eicosapentaenoic acid (C20:5n-3) (P-trend = 0.007). No association was found for docosahexaenoic acid (C22:6n-3) or alpha-linolenic acid (C18:3n-3). Conclusions Erythrocyte n-3 PUFA from marine sources (C22:5n-3 and C20:5n-3), as biomarkers of dietary marine n-3 PUFA, were inversely associated with incident type 2 diabetes in this Chinese population. Future prospective investigations in other Asian populations are necessary to confirm our findings

Study on the Road Network Connectivity Reliability of Valley City Based on Complex Network

Based on the research progress in related fields and the distribution characteristics of road networks in valley cities, the complex network model of a city road network is established to study its connectivity reliability. Taking Lanzhou as the example, several parameters of the complex network abstracted from the road network are calculated and the practical meanings of them are described, respectively. On this basis, through computing the global efficiency and the relative size of the largest connecting subgraph under intentional attacks and random attacks, respectively, the curves of the above two parameters varying with the attacking times are drawn. The detailed investigation of connectivity reliability of Lanzhou road network is done by analyzing the curves’ tendency. Finally, we find that the network of a valley city has a poor connection and has a lot of dead ends. Besides, the average length of the roads is very long and the holistic connectivity reliability is at a lower level; these are suitable to the group-type distribution of valley city’s road network, and the connectivity reliability of the road network is stronger under random attacks than that under intentional attacks

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic