Efficient Energy Conversion through Vortex Arrays in the Turbulent Magnetosheath

Turbulence is often enhanced when transmitted through a collisionless plasma shock. We investigate how the enhanced turbulent energy in the Earth's magnetosheath effectively dissipates via vortex arrays. This research topic is of great importance as it relates to particle energization at astrophysical shocks across the universe. Wave modes and intermittent coherent structures are the key candidate mechanisms for energy conversion in turbulent plasmas. Here, by comparing in-situ measurements in the Earth's magnetosheath with a theoretical model, we find the existence of vortex arrays at the transition between the downstream regions of the Earth's bow shock. Vortex arrays consist of quasi-orthogonal kinetic waves and exhibit both high volumetric filling factors and strong local energy conversion, thereby showing a greater dissipative energization than traditional waves and coherent structures. Therefore, we propose that vortex arrays are a promising mechanism for efficient energy conversion in the sheath regions downstream of astrophysical shocks

Observations of rapidly growing whistler waves in front of space plasma shock

Whistler mode wave is a fundamental perturbation of electromagnetic fields and plasmas in various environments including planetary space, laboratory and astrophysics. The origin and evolution of the waves are a long-standing question due to the limited instrumental capability in resolving highly variable plasma and electromagnetic fields. Here, we analyse data with the high time resolution from the multi-scale magnetospheric spacecraft in the weak magnetic environment (i.e., foreshock) enabling a relatively long gyro-period of whistler mode wave. Moreover, we develop a novel approach to separate the three-dimensional fluctuating electron velocity distributions from their background, and have successfully captured the coherent resonance between electrons and electromagnetic fields at high frequency, providing the resultant growth rate of unstable whistler waves. Regarding the energy origin for the waves, the ion distributions are found to also play crucial roles in determining the eigenmode disturbances of fields and electrons. The quantification of wave growth rate can significantly advance the understandings of the wave evolution and the energy conversion with particles

Knockdown of Long Non-Coding RNA XIST Inhibited Doxorubicin Resistance in Colorectal Cancer by Upregulation of miR-124 and Downregulation of SGK1

Background/Aims: Doxorubicin (DOX) is a widely used chemotherapeutic agent for colorectal cancer (CRC). However, the acquirement of DOX resistance limits its clinical application for cancer therapy. Mounting evidence has suggested that aberrantly expressed lncRNAs contribute to drug resistance of various tumors. Our study aimed to explore the role and molecular mechanisms of lncRNA X-inactive specific transcript (XIST) in chemoresistance of CRC to DOX. Methods: The expressions of XIST, miR-124, serum and glucocorticoid-inducible kinase 1 (SGK1) mRNA in DOX-resistant CRC tissues and cells were detected by qRT-PCR or western blot analysis. DOX sensitivity was assessed by detecting IC50 value of DOX, the protein levels of P-glycoprotein (P-gp) and glutathione S-transferase-π (GST-π) and apoptosis. The interactions between XIST, miR-124 and SGK1 were confirmed by luciferase reporter assay, qRT-PCR and western blot. Xenograft tumor assay was used to verify the role of XIST in DOX resistance in CRC in vivo. Results: XIST expression was upregulated and miR-124 expression was downregulated in DOX-resistant CRC tissues and cells. Knockdown of XIST inhibited DOX resistance of CRC cells, as evidenced by the reduced IC50 value of DOX, decreased P-gp and GST-π levels and enhanced apoptosis in XIST-silenced DOX-resistant CRC cells. Additionally, XIST positively regulated SGK1 expression by interacting with miR-124 in DOX-resistant CRC cells. miR-124 suppression strikingly reversed XIST-knockdown-mediated repression on DOX resistance in DOX-resistant CRC cells. Moreover, SGK1-depletion-elicited decrease of DOX resistance was greatly restored by XIST overexpression or miR-124 inhibition in DOX-resistant CRC cells. Furthermore, XIST knockdown enhanced the anti-tumor effect of DOX in CRC in vivo. Conclusion: XIST exerted regulatory function in resistance of DOX possibly through miR-124/SGK1 axis, shedding new light on developing promising therapeutic strategy to overcome chemoresistance in CRC patients

MSPoisDM: A Novel Peptide Identification Algorithm Optimized for Tandem Mass Spectra

Tandem mass spectrometry (MS/MS) plays an extremely important role in proteomics research. Thousands of spectra can be generated in modern experiments, how to interpret the LC-MS/MS is a challenging problem in tandem mass spectra analysis. Our peptide identification algorithm, MSPoisDM, is integrated the intensity information which produced by target-decoy statistics, although intensity information often undervalued. Furthermore, in order to combine the intensity information for better, we propose a novel concept scoring model which based on Poisson distribution. Compared with commonly used commercial software Mascot and Sequest at 1% FDR, the results show MSPoisDM is robust and versatile for various datasets which obtained from different instruments. We expect our algorithm MSPoisDM will be broadly applied in the proteomics studies

Research on the Influence of Small-Scale Terrain on Precipitation

Terrain plays an important role in the formation, development and distribution of local precipitation and is a major factor leading to locally abnormal weather in weather systems. Although small-scale topography has little influence on the spatial distribution of precipitation, it interferes with precipitation fitting. Due to the arbitrary combination of small, medium and large-scale terrain, complex terrain distribution is formed, and small-scale terrain cannot be clearly defined and removed. Based on the idea of bidimensional empirical mode decomposition (BEMD), this paper extracts small-scale terrain data layer by layer to smooth the terrain and constructs a macroterrain model for different scales in Central China. Based on the precipitation distribution model using multiple regression, precipitation models (B0, B1, B2 and B3) of different scales are constructed. The 18-year monthly average precipitation data of each station are compared with the precipitation simulation results under different scales of terrain and TRMM precipitation data, and the influence of different levels of small-scale terrain on the precipitation distribution is analysed. The results show that (1) in Central China, the accuracy of model B2 is much higher than that of TRMM model A and monthly precipitation model B0. The comprehensive evaluation indexes are increased by 3.31% and 1.92%, respectively. (2) The influence of different levels of small-scale terrain on the precipitation distribution is different. The first- and second-order small-scale terrain has interference effects on precipitation fitting, and the third-order small-scale terrain has an enhancement effect on precipitation. However, the effect of small-scale topography on the precipitation distribution is generally reflected as interference

Adaptive finite-time synchronization of fractional-order memristor chaotic system based on sliding-mode control

In this paper, based on sliding-mode control, a finite-time adaptive synchronization method is introduced to realize the generalized projective synchronization of fractional-order memristor chaotic systems with unknown parameters. First, a class of memristor chaotic system is extended to fractional order, and the chaos characteristics of the system are studied. Then a new fractional-order integral sliding-mode surface with faster convergence speed is designed, which can make the error system converge to zero in finite time. Next the controller with adjustable parameters and the adaptive laws are designed, and the sufficient condition for the sliding-mode surface can be reached by the synchronization error system in finite time and the unknown parameters can be identified in finite time is obtained. Finally, the numerical simulations show that the generalized projection synchronization and unknown parameter identification of fractional-order memristor chaotic system can be realized in a short time under the proposed synchronization strategy

Assessment of the Potential Adverse Events Related to Ribavirin-Interferon Combination for Novel Coronavirus Therapy

Purpose. We aimed to analyze and evaluate the safety signals of ribavirin-interferon combination through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS), so as to provide reference for the rationale use of these agents in the management of relevant toxicities emerging in patients with novel coronavirus pneumonia (COVID-19). Methods. Reports to the FAERS from 1 January 2004 to 8 March 2020 were analyzed. The proportion of report ratio (PRR), reporting odds ratio (ROR), and Bayesian confidence interval progressive neural network (BCPNN) method were used to detect the safety signals. Results. A total of 55 safety signals were detected from the top 250 adverse event reactions in 2200 reports, but 19 signals were not included in the drug labels. All the detected adverse event reactions were associated with 13 System Organ Classes (SOC), such as gastrointestinal, blood and lymph, hepatobiliary, endocrine, and various nervous systems. The most frequent adverse events were analyzed, and the results showed that females were more likely to suffer from anemia, vomiting, neutropenia, diarrhea, and insomnia. Conclusion. The ADE (adverse drug event) signal detection based on FAERS is helpful to clarify the potential adverse events related to ribavirin-interferon combination for novel coronavirus therapy; clinicians should pay attention to the adverse reactions of gastrointestinal and blood systems, closely monitor the fluctuations of the platelet count, and carry out necessary mental health interventions to avoid serious adverse events

Hybrid influence of degree and H-index in the link prediction of complex networks

Previous link prediction researchers paid more attention to the delivery ability of paths between two unlinked endpoints, but less to the influences of endpoints. In this letter, we uncover that synthesizing degree and H-index as the hybrid influences of endpoints can more reliably capture such endpoints with great and extensive maximum connected subgraph, which can more possibly attract other unlinked endpoints. In addition, the influence involving small heterogeneity of degree and H-index can further improve the accuracy of link prediction. Based on the hybrid influences of endpoints, we propose link prediction methods to explore the mechanism of link evolution. Extensive experiments on twelve real datasets suggest that the proposed methods can remarkably promote accuracy of link prediction

Accurate identification of cashmere and wool fibers based on enhanced ShuffleNetV2 and transfer learning

Abstract Recognizing cashmere and wool fibers has been a challenging problem in the textile industry due to their similar morphological structure, chemical composition, and physicochemical properties. Traditional manual methods for identifying these fibers are inefficient, labor-intensive, and inaccurate. To address these issues, we present a novel method for recognizing cashmere and wool fibers using an improved version of ShuffleNetV2 and Transfer Learning, which we implement as a new cashmere and wool classification network (CWCNet).The approach leverages depthwise separable dilated convolution to extract more feature information for fiber classification. We also introduce a new activation function that enhances the nonlinear representation of the model and allows it to more fully extract negative feature information. Experimental results demonstrate that CWCNet achieves an accuracy rate of up to 98.438% on our self-built dataset, which is a 2.084% improvement over the original ShuffleNetV2 model. Furthermore, our proposed method outperforms classical models such as EfficientNetB0, MobileNetV2, Wide-ResNet50, and ShuffleNetV2 in terms of recognition accuracy while remaining lightweight.The method is capable of extracting more information on fiber characteristics and has the potential to replace manual labor as technological advancements continue to be made. This will greatly benefit engineering applications in the textile industry by providing more efficient and accurate fiber classification