A Review on Bradykinin-Related Peptides Isolated from Amphibian Skin Secretion

Amphibian skin secretion has great potential for drug discovery and contributes hundreds of bioactive peptides including bradykinin-related peptides (BRPs). More than 50 BRPs have been reported in the last two decades arising from the skin secretion of amphibian species. They belong to the families Ascaphidae (1 species), Bombinatoridae (3 species), Hylidae (9 speices) and Ranidae (25 species). This paper presents the diversity of structural characteristics of BRPs with N-terminal, C-terminal extension and amino acid substitution. The further comparison of cDNA-encoded prepropeptides between the different species and families demonstrated that there are various forms of kininogen precursors to release BRPs and they constitute important evidence in amphibian evolution. The pharmacological activities of isolated BRPs exhibited unclear structure–function relationships, and therefore the scope for drug discovery and development is limited. However, their diversity shows new insights into biotechnological applications and, as a result, comprehensive and systematic studies of the physiological and pharmacological activities of BRPs from amphibian skin secretion are needed in the future

Experimental observation of non-Hermitian higher-order skin interface states in topological electric circuits

The study of topological states has developed rapidly in electric circuits, which permits flexible fabrications of non-Hermitian systems by introducing non-Hermitian terms. Here, nonreciprocal coupling terms are realized by utilizing a voltage follower module in non-Hermitian topological electric circuits. We report the experimental realization of one- and two- dimensional non-Hermitian skin interface states in electric circuits, where interface states induced by non-Hermitian skin effects are localized at the interface of different domains carrying different winding numbers. Our electric circuit system provides a readily accessible platform to explore non-Hermitian-induced topological phases, and paves a new road for device applications

Age as a risk factor for acute mountain sickness upon rapid ascent to 3,700 m among young adult Chinese men.

BackgroundThe aim of this study was to explore the relationship between age and acute mountain sickness (AMS) when subjects are exposed suddenly to high altitude.MethodsA total of 856 young adult men were recruited. Before and after acute altitude exposure, the Athens Insomnia Scale score (AISS) was used to evaluate the subjective sleep quality of subjects. AMS was assessed using the Lake Louise scoring system. Heart rate (HR) and arterial oxygen saturation (SaO2) were measured.ResultsResults showed that, at 500 m, AISS and insomnia prevalence were higher in older individuals. After acute exposure to altitude, the HR, AISS, and insomnia prevalence increased sharply, and the increase in older individuals was more marked. The opposite trend was observed for SaO2. At 3,700 m, the prevalence of AMS increased with age, as did severe AMS, and AMS symptoms (except gastrointestinal symptoms). Multivariate logistic regression analysis showed that age was a risk factor for AMS (adjusted odds ratio [OR] 1.07, 95% confidence interval [CI] 1.01-1.13, P<0.05), as well as AISS (adjusted OR 1.39, 95% CI 1.28-1.51, P<0.001).ConclusionThe present study is the first to demonstrate that older age is an independent risk factor for AMS upon rapid ascent to high altitude among young adult Chinese men, and pre-existing poor subjective sleep quality may be a contributor to increased AMS prevalence in older subjects

Risk factors for high-altitude headache upon acute high-altitude exposure at 3700 m in young Chinese men: a cohort study.

BackgroundThis prospective and observational study aimed to identify demographic, physiological and psychological risk factors associated with high-altitude headache (HAH) upon acute high-altitude exposure.MethodsEight hundred fifty subjects ascended by plane to 3700 m above Chengdu (500 m) over a period of two hours. Structured Case Report Form (CRF) questionnaires were used to record demographic information, physiological examinations, psychological scale, and symptoms including headache and insomnia a week before ascending and within 24 hours after arrival at 3700 m. Binary logistic regression models were used to analyze the risk factors for HAH.ResultsThe incidence of HAH was 73.3%. Age (p =0.011), physical labor intensity (PLI) (p =0.044), primary headache history (p <0.001), insomnia (p <0.001), arterial oxygen saturation (SaO2) (p =0.001), heart rate (HR) (p =0.002), the Self-Rating Anxiety Scale (SAS) (p <0.001), and the Epworth Sleepiness Scale (ESS) (p <0.001) were significantly different between HAH and non-HAH groups. Logistic regression models identified primary headache history, insomnia, low SaO2, high HR and SAS as independent risk factors for HAH.ConclusionsInsomnia, primary headache history, low SaO2, high HR, and high SAS score are the risk factors for HAH. Our findings will provide novel avenues for the study, prevention and treatment of HAH

RCLane: Relay Chain Prediction for Lane Detection

Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark improvements over time, lane detection remains a largely unsolved problem. This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution. In this paper, we present a new method for lane detection based on relay chain prediction. Specifically, our model predicts a segmentation map to classify the foreground and background region. For each pixel point in the foreground region, we go through the forward branch and backward branch to recover the whole lane. Each branch decodes a transfer map and a distance map to produce the direction moving to the next point, and how many steps to progressively predict a relay station (next point). As such, our model is able to capture the keypoints along the lanes. Despite its simplicity, our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.Comment: ECCV 202

Radiative transitions in charmonium from Nf=2N_f=2 twisted mass lattice QCD

We present a study for charmonium radiative transitions: $J/\psi\rightarrow\eta_c\gamma$, $\chi_{c0}\rightarrow J/\Psi\gamma$ and $h_c\rightarrow\eta_c\gamma$ using $N_f=2$ twisted mass lattice QCD gauge configurations. The single-quark vector form factors for $\eta_c$ and $\chi_{c0}$ are also determined. The simulation is performed at a lattice spacing of $a= 0.06666$ fm and the lattice size is $32^3\times 64$. After extrapolation of lattice data at nonzero $Q^2$ to 0, we compare our results with previous quenched lattice results and the available experimental values.Comment: typeset with revtex, 15 pages, 11 figures, 4 table

Semisupervised ranking on very large graphs with rich metadata

ABSTRACT Graph ranking plays an important role in many applications, such as page ranking on web graphs and entity ranking on social networks. In applications, besides graph structure, rich information on nodes and edges and explicit or implicit human supervision are often available. In contrast, conventional algorithms (e.g., PageRank and HITS) compute ranking scores by only resorting to graph structure information. A natural question arises here, that is, how to effectively and efficiently leverage all the information to more accurately calculate graph ranking scores than the conventional algorithms, assuming that the graph is also very large. Previous work only partially tackled the problem, and the proposed solutions are also not satisfying. This paper addresses the problem and proposes a general framework as well as an efficient algorithm for graph ranking. Specifically, we define a semi-supervised learning framework for ranking of nodes on a very large graph and derive within our proposed framework an efficient algorithm called Semi-Supervised PageRank. In the algorithm, the objective function is defined based upon a Markov random walk on the graph. The transition probability and the reset probability of the Markov model are defined as parametric models based on features on nodes and edges. By minimizing the objective function, subject to a number of constraints derived from supervision information, we simultaneously learn the optimal parameters of the model and the optimal ranking scores of the nodes. Finally, we show that it is possible to make the algorithm efficient to * This work was performed when the third author was an intern at Microsoft Research Asia. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. handle a billion-node graph by taking advantage of the sparsity of the graph and implement it in the MapReduce logic. Experiments on real data from a commercial search engine show that the proposed algorithm can outperform previous algorithms on several tasks