A New Species of Genus Microhyla (Amphibia: Anura: Microhylidae) from Zhejiang Province, China

We described a new species, Microhyla beilunensis sp. nov., from Zhejiang Province of China. Phylogenetic analyses based on the mitochondrial 12S, 16S and CO1 gene sequences suggested that the new taxon was distinctly separated from its congeners and closed to M. mixtura and M. okinavensis. Morphologically, the new species could be identified from its congeners except M. mixtura by several characters: (1) rudimentary webs on toe base; (2) absence of disks and dorsal median longitudinal grooves on finger tips; (3) presence of disks and dorsal median longitudinal grooves on toe tips. As well, the new species could be identified from topotype M. mixtura by the combination of characters: (1) apart from the stripes, bar-shaped and oval-shaped patterns, the rounded spots present on the dorsum of body and legs; (2) the outer metacarpal tubercles prominently larger than the inner one; (3) of males, the ratios of HW, IND, UEW and LAW to SVL of the new species were significantly larger than those of M. mixtura (P < 0.01), and the ratios of SL, IOD, LAHL, HLL, TL, TFL and FL to SVL of the new species were significantly less than those of M. mixtura (P < 0.05)

Enhancing Satellite Clock Bias Prediction Accuracy in the Case of Jumps with an Improved Grey Model

High accuracy and reliable predictions of the bias of in-orbit atomic clocks are crucial to the application of satellites, while their clocks cannot transfer time information with the earth stations. It brings forward a new short-term, mid-long-term, and long-term prediction approach with the grey predicting model (GM(1, 1)) improved by the least absolute deviations (GM(1, 1)-LAD) when there are abnormal cases (larger fluctuations, jumps, and/or singular points) in SCBs. Firstly, it introduces the basic GM(1, 1) models. As the parameters of the conventional GM(1, 1) model determined by the least squares method (LSM) is not the best in these cases, leading to magnify the fitting errors at the abnormal points, the least absolute deviations (LAD) is used to optimize the conventional GM(1, 1) model. Since the objective function is a nondifferentiable characteristic, some function transformation is inducted. Then, the linear programming and the simplex method are used to solve it. Moreover, to validate the prediction performances of the improved model, six prediction experiments are performed. Compared with those of the conventional GM(1, 1) model and autoregressive moving average (ARMA) model, results indicate that (1) the improved model is more adaptable to SCBs predictions of the abnormal cases; (2) the root mean square (RMS) improvement for the improved model are 5.7%∼81.7% and 6.6%∼88.3%, respectively; (3) the maximum improvement of the pseudorange errors (PE) and mean absolute errors (MAE) for the improved model could reach up to 88.30%, 89.70%, and 87.20%, 85.30%, respectively. These results suggest that our improved method can enhance the prediction accuracy and PE for these abnormal cases in SCBs significantly and effectively and deliver a valuable insight for satellite navigation

西藏自治区小树蛙属一新种

Volume: 3Start Page: 67End Page: 6

A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method

High-accuracy and dependable prediction of the bias of space-borne atomic clocks is extremely crucial for the normal operation of the satellites in the case of interrupted communication. Currently, the clock bias prediction for the Chinese BeiDou Navigation Satellite System (BDS) remains still a huge challenge. To develop a high-precision approach for forecasting satellite clock bias (SCB) in allusion to analyze the shortcomings of the exponential smoothing (ES) model, a modified ES model is proposed hereof, especially for BDS-2 satellites. Firstly, the basic ES models and their prediction mechanism are introduced. As the smoothing coefficient is difficult to determine, this leads to increasing fitting errors and poor forecast results. This issue is addressed by introducing a dynamic &ldquo;thick near thin far (TNTF)&rdquo; principle based on the sliding windows (SW) to optimize the best smoothing coefficient. Furthermore, to enhance the short-term forecasted accuracy of the ES model, the gray model (GM) is adopted to learn the fitting residuals of the ES model and combine the forecasted results of the ES model with the predicted results of the GM model from error learning (ES + GM). Compared with the single ES models, the experimental results show that the short-term forecast based on the ES + GM models is improved remarkably, especially for the combination of the three ES model and GM model (ES3 + GM). To further improve the medium-term prediction accuracy of the ES model, the new algorithms in ES with GM error learning based on the SW (ES + GM + SW) are presented. Through examples analysis, compared with the single ES2 (ES3) model, results indicate that (1) the average forecast precision of the new algorithms ES2 + GM + SW (ES3 + GM + SW) can be dramatically enhanced by 49.10% (56.40%) from 5.56 ns (6.77 ns) to 2.83 ns (2.95 ns); (2) the average forecast stability of the new algorithms ES2 + GM + SW (ES3 + GM + SW) is also observably boosted by 53.40% (49.60%) from 8.99 ns (16.13 ns) to 4.19 ns (8.13 ns). These new coupling forecast models proposed in this contribution are more effective in clock bias prediction both forecast accuracy and forecast stability

One-dimensional Maximum Entropy Image Segmentation Algorithm Based on the Small Field of View of Measuring Robot Star Map

As one of the fundamental problems in processing star map,image segmentation plays a significant part in ensuring precise field astronomical survey.Image binarization is the key procedure in the image segmentation,but it is extremely difficult to extract star targets from complex sky background using conventional threshold segmentation algorithms.Considering that the Leica video measurement robot TS50i shows features such as the small field of view,single star point,weak target,and single peak,one-dimensional maximum entropy method is firstly proposed to split the star maps.The proposed algorithm is verified by comparison with conventional threshold segmentation algorithms.It is indicated that the one-dimensional maximum entropy algorithm can achieve satisfied binarization processing results while adequately preserve the image information at the same time.Simulation experiments using real star maps show that the extraction method based on this algorithm is accurate and reliable with an accuracy of an order of magnitude better than requirements of the field first-class astronomical survey,hence it can satisfy the need of precise field astronomical survey

A self-healing injectable hydrogel integrated with enzymatic and nonenzymatic antioxidants as artificial antioxidant defense system for diabetic wound healing

Chronic diabetic wounds seriously threaten human health due to their intractable nature in a complex pathological microenvironment that contains oxidative stress, persistent inflammatory status, hypoxia and bacterial infections, of which oxidative damage contributing the most. Applied materials that can mimic the natural intracellular antioxidant defense system are perceived to have huge therapeutic potential. Herein, we present a novel strategy to prepare a versatile nanocomposite hydrogel by incorporating the nanoenzyme (manganese dioxide (MnO2)) and nonenzymatic antioxidant components (polydopamine (PDA)) in a dynamic hydrogel network composed of thioctic acid and tannic acid (TA). The prepared hydrogel exhibited outstanding adhesive and injectable properties, making it adapt perfectly to wounds with different shapes and depths. More importantly, encapsulation of PDA@MnO2 nanoparticles (NPs) conferred the hydrogel with superior antioxidant performance to effectively scavenge multiple types of reactive nitrogen and oxygen species (RNOS) and reduce the inflammatory response by regulating macrophage polarization. Meanwhile, the hydrogel showed high-performance catalyzing ability to transfer hydrogen peroxide (H2O2) into oxygen (O2), which could further alleviate hypoxic wound environment. Moreover, the nanocomposite hydrogel exhibited excellent near-infrared (NIR) photothermal antibacterial effect. Collectively, our findings indicate that the synthesized nanocomposite hydrogel shows potential for use in a clinical setting to treat diabetic wounds

Mild NIR controlled NO-Releasing adenine-based composite hydrogel with excellent Antimicrobial, wound adaptiveness and angiogenic capabilities for rapid bacterial-infected wounds healing

Development of wound dressings with antimicrobial activity and promotion of sustained wound healing to reduce the use of antibiotics in infected wounds is important. In this study, we developed a thermosensitive ABA triblock copolymer poly {(N-isopropyl acrylamide)–co-[(6-((3-(6-amino-purin-9-yl)propanoyl)oxy)hexyl acrylates)]-b-PEO-b-Poly (N-isopropyl acrylamide)–co-{[(6-((3-(6-amino-purin-9-yl)propanoyl)oxy)hexyl acrylates)]} as matrix and participating nitric oxide (NO) donor (N,N'-dis-sec-butyl-N,N'-dinitroso-1,4-phenylenediamine, BNN6) loaded SiO2@PDA nanoparticles composed of hydrogel dressings (Gel-APB) for efficient antimicrobial and sustainable treatment of wounds. Thanks to the presence of poly (N-isopropyl acrylamide) (PNIPAM) and unique ABA triblock structure, the NAONA copolymer would self-assemble into temperature-sensitive injectable hydrogel with excellent dermal adaptation to dynamic wounds. Meanwhile, the biobase adenine endowed the hydrogel dressing with self-healing ability. Under 808 nm wavelength irradiation, the hydrogel was able to elevate to a mild temperature (∼48 °C) and release NO on demand, which efficiently kills E. coli and S. aureus, realizing highly efficient and mild photothermal antimicrobial therapy (PTAT). In addition, full-thickness skin wound modeling demonstrated that Gel-APB hydrogel dressing accelerated tissue regeneration, angiogenesis, and collagen deposition in S. aureus infected wounds in vivo, achieving rapid wound healing. Thus, Gel-APB is a multifunctional hydrogel dressing with excellent antimicrobial activity and has considerable potential for application in infected skin wound healing

Thermoresponsive Self-Healing Zwitterionic Hydrogel as an In Situ Gelling Wound Dressing for Rapid Wound Healing

It is highly desired yet challenging to fabricate biocompatible injectable self-healing hydrogels with anti-bacterial adhesion properties for complex wounds that can autonomously adapt to different shapes and depths and can promote angiogenesis and dermal collagen synthesis for rapid wound healing. Herein, an injectable zwitterionic hydrogel with excellent self-healing property, good cytocompatibility, and antibacterial adhesion was developed from a thermoresponsive ABA triblock copolymer poly[(N-isopropyl acrylamide)-co-(butyl acrylate)-co-(sulfobetaine methacrylate)]-b-poly(ethylene glycol)-b-poly[(N-isopropyl acrylamide)-co-(butyl acrylate)-co-(sulfobetaine methacrylate)] (PZOPZ). The prepared PZOPZ hydrogel exhibits a distinct thermal-induced sol–gel transition around physiological temperature and could be easily applied in a sol state and in situ gelled to adapt complex wounds of different shapes and depths for complete coverage. Meanwhile, the hydrogel possesses a rapid self-healing ability and can recover autonomously from damage to maintain structural and functional integrity. In addition, the CCK-8 and 2D/3D cell culture experiments revealed that the PZOPZ hydrogel dressing shows low cytotoxicity to L929 cells and can effectively prevent the adhesion of Staphylococcus aureus and Escherichia coli. In vivo investigations verified that the PZOPZ hydrogel could increase angiogenesis and dermal collagen synthesis and shorten the transition from the inflammatory to the proliferative stage, thereby providing more favorable conditions for faster wound healing. Overall, this work provides a promising strategy to develop injectable zwitterionic hydrogel dressings with multiple functions for clinic wound management