PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST

We describe PSR J1926-0652, a pulsar recently discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive single-pulse detections from FAST and long-term timing observations from the Parkes 64-m radio telescope, we probed phenomena on both long and short time scales. The FAST observations covered a wide frequency range from 270 to 800 MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at least four profile components, short-term nulling lasting from 4 to 450 pulses, complex subpulse drifting behaviours and intermittency on scales of tens of minutes. While the average band spacing P3 is relatively constant across different bursts and components, significant variations in the separation of adjacent bands are seen, especially near the beginning and end of a burst. Band shapes and slopes are quite variable, especially for the trailing components and for the shorter bursts. We show that for each burst the last detectable pulse prior to emission ceasing has different properties compared to other pulses. These complexities pose challenges for the classic carousel-type models.Comment: 13pages with 12 figure

GPX8 regulates pan-apoptosis in gliomas to promote microglial migration and mediate immunotherapy responses

IntroductionGliomas have emerged as the predominant brain tumor type in recent decades, yet the exploration of non-apoptotic cell death regulated by the pan-optosome complex, known as pan-apoptosis, remains largely unexplored in this context. This study aims to illuminate the molecular properties of pan-apoptosis-related genes in glioma patients, classifying them and developing a signature using machine learning techniques.MethodsThe prognostic significance, mutation features, immunological characteristics, and pharmaceutical prediction performance of this signature were comprehensively investigated. Furthermore, GPX8, a gene of interest, was extensively examined for its prognostic value, immunological characteristics, medication prediction performance, and immunotherapy prediction potential. ResultsExperimental techniques such as CCK-8, Transwell, and EdU investigations revealed that GPX8 acts as a tumor accelerator in gliomas. At the single-cell RNA sequencing level, GPX8 appeared to facilitate cell contact between tumor cells and macrophages, potentially enhancing microglial migration. ConclusionsThe incorporation of pan-apoptosis-related features shows promising potential for clinical applications in predicting tumor progression and advancing immunotherapeutic strategies. However, further in vitro and in vivo investigations are necessary to validate the tumorigenic and immunogenic processes associated with GPX8 in gliomas

Taxonomy of the order Mononegavirales: update 2017.

In 2017, the order Mononegavirales was expanded by the inclusion of a total of 69 novel species. Five new rhabdovirus genera and one new nyamivirus genus were established to harbor 41 of these species, whereas the remaining new species were assigned to already established genera. Furthermore, non-Latinized binomial species names replaced all paramyxovirus and pneumovirus species names, thereby accomplishing application of binomial species names throughout the entire order. This article presents the updated taxonomy of the order Mononegavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV)

Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point Selection

Landslide susceptibility prediction has the disadvantages of being challenging to apply to expanding landslide samples and the low accuracy of a subjective random selection of non-landslide samples. Taking Fu’an City, Fujian Province, as an example, a model based on a semi-supervised framework using particle swarm optimization to optimize extreme learning machines (SS-PSO-ELM) is proposed. Based on the landslide samples, a semi-supervised learning framework is constructed through Density Peak Clustering (DPC), Frequency Ratio (FR), and Random Forest (RF) models to expand and divide the landslide sample data. The landslide susceptibility was predicted using high-trust sample data as the input variables of the data-driven model. The results show that the area under the curve (AUC) valued at the SS-PSO-ELM model for landslide susceptibility prediction is 0.893 and the root means square error (RMSE) is 0.370, which is better than ELM and PSO-ELM models without the semi-supervised framework. It shows that the SS-PSO-ELM model is more effective in landslide susceptibility. Thus, it provides a new research idea for predicting landslide susceptibility

A spatiotemporal model for PM2.5 prediction based on the K‐Core idea and label distribution

Abstract With the increasingly severe problem of PM2.5 environmental pollution, the threat to human health is gradually increasing. Therefore, accurate prediction of PM2.5 concentration is of great significance to the healthy life of human beings. To make up for the deficiencies of previous studies on PM2.5 concentration prediction, a spatiotemporal model (Spatiotemporal prediction model of label distribution, LDSPM) for PM2.5 concentration prediction based on the K‐Core algorithm concept and label distribution learning was proposed. Leveraging K‐Core ideas and the label distribution support vector regression model of the label distribution paradigm, the influence weight of each meteorological factor on PM2.5 concentration in each piece of data was obtained with the decomposition of meteorological factors using the complete ensemble empirical mode decomposition of adaptive noise. Using a long short‐term memory neural network to predict each decomposed signal and obtain the forecast data of meteorological factors. Finally, according to the expected weight and meteorological factor data, a particle swarm optimization extreme learning machine is used to train the prediction, and the predicted value of PM2.5 is obtained. The experimental results show that the forecasting model performs better than other combined and single forecasting models. It provides new directions and ideas for PM2.5 concentration prediction

Application of Beetle Colony Optimization Based on Improvement of Rebellious Growth Characteristics in PM2.5 Concentration Prediction

Aiming at the shortcomings of the beetle swarm algorithm, namely its low accuracy, easy fall into local optima, and slow convergence speed, a rebellious growth personality–beetle swarm optimization (RGP–BSO) model based on rebellious growth personality is proposed. Firstly, the growth and rebellious characters were added to the beetle swarm optimization algorithm to dynamically adjust the beetle’s judgment of the optimal position. Secondly, the adaptive iterative selection strategy is introduced to balance the beetles’ global search and local search capabilities, preventing the algorithm from falling into a locally optimal solution. Finally, two dynamic factors are introduced to promote the maturity of the character and further improve the algorithm’s optimization ability and convergence accuracy. The twelve standard test function simulation experiments show that RGP–BSO has a faster convergence speed and higher accuracy than other optimization algorithms. In the practical problem of PM2.5 concentration prediction, the ELM model optimized by RGP–BSO has more prominent accuracy and stability and has obvious advantages

Estimating LAI for Cotton Using Multisource UAV Data and a Modified Universal Model

Leaf area index(LAI) is an important indicator of crop growth and water status. With the continuous development of precision agriculture, estimating LAI using an unmanned aerial vehicle (UAV) remote sensing has received extensive attention due to its low cost, high throughput and accuracy. In this study, multispectral and light detection and ranging (LiDAR) sensors carried by a UAV were used to obtain multisource data of a cotton field. The method to accurately relate ground measured data with UAV data was built using empirical statistical regression models and machine learning algorithm models (RFR, SVR and ANN). In addition to the traditional spectral parameters, it is also feasible to estimate LAI using UAVs with LiDAR to obtain structural parameters. Machine learning models, especially the RFR model (R2 = 0.950, RMSE = 0.332), can estimate cotton LAI more accurately than empirical statistical regression models. Different plots and years of cotton datasets were used to test the model robustness and generality; although the accuracy of the machine learning model decreased overall, the estimation accuracy based on structural and multisources was still acceptable. However, selecting appropriate input parameters for different canopy opening and closing statuses can alleviate the degradation of accuracy, where input parameters select multisource parameters before canopy closure while structural parameters are selected after canopy closure. Finally, we propose a gap fraction model based on a LAImax threshold at various periods of cotton growth that can estimate cotton LAI with high accuracy, particularly when the calculation grid is 20 cm (R2 = 0.952, NRMSE = 12.6%). This method does not require much data modeling and has strong universality. It can be widely used in cotton LAI prediction in a variety of environments

Improved electrochemical oxidation kinetics of La0.5Ba0.5FeO3-δ anode for solid oxide fuel cells with fluorine doping

Funding Information: The financial support from National Natural Science Foundation of China under contract number 22075205 and the support of Tianjin Municipal Science and Technology Commission under contract number 19JCYBJC21700 are gratefully acknowledged. The work has been also supported by the Program of Introducing Talents to the University Disciplines under file number B06006 , and the Program for Changjiang Scholars and Innovative Research Teams in Universities under file number IRT 0641 . Publisher Copyright: © 2021 Elsevier B.V.Sluggish anode kinetics and serious carbon deposition are two major obstacles to developing hydrocarbon fueled solid oxide fuel cells. A highly active and stable perovskite La0.5Ba0.5FeO3-δ anode material is studied in this work. The oxygen surface exchange and charge transfer steps are the rate-determining steps of the anode process, and the former is accelerated with fluorine doping on the anion sites due to the lowering of metal-oxygen bond energy. The oxygen surface exchange coefficients of La0.5Ba0.5FeO3-δ and La0.5Ba0.5FeO2.9-δF0.1 at 850 °C are 1.4 × 10−4 and 2.8 × 10−4 cm s−1, respectively. A single cell supported by a 300 μm-thick La0.8Sr0.2Ga0.8Mg0.2O3-δ electrolyte layer with La0.5Ba0.5FeO3-δ anode shows maximum power densities of 1446 and 691 mW cm−2 at 850 °C with wet hydrogen and methane fuels, respectively, which increase to 1860 and 809 mW cm−2 respectively when La0.5Ba0.5FeO2.9-δF0.1 is used as the anode. The cell exhibits a short-term durability of 40 h using wet methane as fuel without carbon deposition on the anode.Peer reviewe

De novo characterization of venom apparatus transcriptome of Pardosa pseudoannulata and analysis of its gene expression in response to Bt protein

Abstract Background Pardosa pseudoannulata is a prevailing spider species, and has been regarded as an important bio-control agent of insect pests in farmland of China. However, the available genomic and transcriptomic databases of P. pseudoannulata and their venom are limited, which severely hampers functional genomic analysis of P. pseudoannulata. Recently high-throughput sequencing technology has been proved to be an efficient tool for profiling the transcriptome of relevant non-target organisms exposed to Bacillus thuringiensis (Bt) protein through food webs. Results In this study, the transcriptome of the venom apparatus was analyzed. A total of 113,358 non-redundant unigenes were yielded, among which 34,041 unigenes with complete or various length encoding regions were assigned biological function annotations and annotated with gene ontology and karyotic orthologous group terms. In addition, 3726 unigenes involved in response to stimulus and 720 unigenes associated with immune-response pathways were identified. Furthermore, we investigated transcriptomic changes in the venom apparatus using tag-based DGE technique. A total of 1724 differentially expressed genes (DEGs) were detected, while 75 and 372 DEGs were functionally annotated with KEGG pathways and GO terms, respectively. qPCR analyses were performed to verify the DEGs directly or indirectly related to immune and stress responses, including genes encoding heat shock protein, toll-like receptor, GST and NADH dehydrogenase. Conclusion This is the first study conducted to specifically investigate the venom apparatus of P. pseudoannulata in response to Bt protein exposure through tritrophic chain. A substantial fraction of transcript sequences was generated by high-throughput sequencing of the venom apparatus of P. pseudoannulata. Then a comparative transcriptome analysis showing a large number of candidate genes involved in immune response were identified by the tag-based DGE technology. This transcriptome dataset will provide a comprehensive sequence resource for furture molecular genetic research of the venom apparatus of P. pseudoannulata