Advances in chemical syntheses have led to the formation of various kinds of nanoparticles (NPs) with more rational control of size, shape, composition, structure and catalysis. This review ...highlights recent efforts in the development of Pt and non‐Pt based NPs into advanced nanocatalysts for efficient oxygen reduction reaction (ORR) under fuel‐cell reaction conditions. It first outlines the shape controlled synthesis of Pt NPs and their shape‐dependent ORR. Then it summarizes the studies of alloy and core–shell NPs with controlled electronic (alloying) and strain (geometric) effects for tuning ORR catalysis. It further provides a brief overview of ORR catalytic enhancement with Pt‐based NPs supported on graphene and coated with an ionic liquid. The review finally introduces some non‐Pt NPs as a new generation of catalysts for ORR. The reported new syntheses with NP parameter‐tuning capability should pave the way for future development of highly efficient catalysts for applications in fuel cells, metal‐air batteries, and even in other important chemical reactions.
Efforts in searching for efficient nanoparticle catalysts for the oxygen reduction reaction (ORR) in fuel cells have led to various nanoparticle (NP) systems with precise control of size, shape, composition, and structure. Whereas the traditional Pt‐based catalysts are still under heavy investigation, recent studies have led to the emergence of non‐Pt systems. This Review highlights the recent efforts in developing Pt‐ and non‐Pt‐based NPs into advanced nanocatalysts for the ORR.
Due to the natural nonlinearity and unique memory characteristics, memristors are promising candidates for the construction of multiscroll attractors having better application potential in the field ...of information encryption than the traditional double-scroll attractors. This article proposes a novel memristive multidouble-scroll Chua's system (MMDSCS) via coupling a nonideal flux-controlled memristor with multipiecewise-linear memductance function in Chua's system directly. Specially, any number of multidouble-scroll chaotic attractors can be generated through adjusting the internal parameters of the memristor conveniently and without changing the original system's nonlinearity. Moreover, the amount of double scrolls is also closely related to the strength of the memristive coupling. Another striking highlight is that infinite initial offset-boosted coexisting Chua's double-scroll attractors with the same shape are produced with the variation of the memristor initial conditions, indicating the emergence of an intriguing phenomenon of homogeneous extreme multistability. This unique property and its formation mechanism are investigated in detail using phase portraits, bifurcation diagrams, Lyapunov exponents, time series, and attraction basins. Furthermore, hardware experiments based on the field-programmable gate array are carried out to confirm the numerical simulations. Finally, an image encryption scheme is designed based on the memristor initial offset boosting dynamics from a perspective of engineering application. In comparison with the existing memristive Chua's systems, the proposed MMDSCS has many merits, such as multidouble-scroll attractors, memristor initial-controlled chaotic sequences with controllability, good robustness, and high security performance, which is more practical in applications involving information confidential communication.
Enormous studies have corroborated that long non-coding RNAs (lncRNAs) extensively participate in crucial physiological processes such as metabolism and immunity, and are closely related to the ...occurrence and development of tumors, cardiovascular diseases, nervous system disorders, nephropathy, and other diseases. The application of lncRNAs as biomarkers or intervention targets can provide new insights into the diagnosis and treatment of diseases. This paper has focused on the emerging research into lncRNAs as pharmacological targets and has reviewed the transition of lncRNAs from the role of disease coding to acting as drug candidates, including the current status and progress in preclinical research. Cutting-edge strategies for lncRNA modulation have been summarized, including the sources of lncRNA-related drugs, such as genetic technology and small-molecule compounds, and related delivery methods. The current progress of clinical trials of lncRNA-targeting drugs is also discussed. This information will form a latest updated reference for research and development of lncRNA-based drugs.
This review summarizes the current knowledge on pre- and clinical transformation of lncRNAs-based drugs, covering latest strategies to target pathogenic lncRNAs, indispensable delivery systems, arising clinical trials, future directions and challenges. Display omitted
Abstract
In this paper, we report 591 high-velocity star candidates (HiVelSCs) selected from over 10 million spectra of Data Release 7 (DR7) of the Large Sky Area Multi-object Fiber Spectroscopic ...Telescope and the second Gaia data release, with three-dimensional velocities in the Galactic rest frame larger than 445 km s
−1
. We show that at least 43 HiVelSCs are unbound to the Galaxy with escape probabilities larger than 50%, and this number decreases to eight if the possible parallax zero-point error is corrected. Most of these HiVelSCs are metal-poor and slightly
α
-enhanced inner halo stars. Only 14% of them have Fe/H > −1, which may be the metal-rich “in situ” stars in the halo formed in the initial collapse of the Milky Way or metal-rich stars formed in the disk or bulge but kinematically heated. The low ratio of 14% implies that the bulk of the stellar halo was formed from the accretion and tidal disruption of satellite galaxies. In addition, HiVelSCs on retrograde orbits have slightly lower metallicities on average compared with those on prograde orbits; meanwhile, metal-poor HiVelSCs with Fe/H < −1 have an even faster mean retrograde velocity compared with metal-rich HiVelSCs. To investigate the origins of HiVelSCs, we perform orbit integrations and divide them into four types, i.e., hypervelocity stars, hyper-runaway stars, runaway stars and fast halo stars. A catalog for these 591 HiVelSCs, including radial velocities, atmospheric parameters, Gaia astrometric parameters, spatial positions, and velocities, etc., is available in the China-VO PaperData Repository at doi:
10.12149/101038
.
N6-methyladenosine (m6A) is the most prevalent RNA epigenetic regulation in eukaryotic cells. However, understanding of m6A in colorectal cancer (CRC) is very limited. We designed this study to ...investigate the role of m6A in CRC.
Expression level of METTL14 was extracted from public database and tissue array to investigate the clinical relevance of METTL14 in CRC. Next, gain/loss of function experiment was used to define the role of METTL14 in the progression of CRC. Moreover, transcriptomic sequencing (RNA-seq) was applied to screen the potential targets of METTL14. The specific binding between METTL14 and presumed target was verified by RNA pull-down and RNA immunoprecipitation (RIP) assay. Furthermore, rescue experiment and methylated RNA immunoprecipitation (Me-RIP) were performed to uncover the mechanism.
Clinically, loss of METTL14 correlated with unfavorable prognosis of CRC patients. Functionally, knockdown of METTL14 drastically enhanced proliferative and invasive ability of CRC cells in vitro and promoted tumorigenicity and metastasis in vivo. Mechanically, RNA-seq and Me-RIP identified lncRNA XIST as the downstream target of METTL14. Knockdown of METTL14 substantially abolished m6A level of XIST and augmented XIST expression. Moreover, we found that m6A-methylated XIST was recognized by YTHDF2, a m6A reader protein, to mediate the degradation of XIST. Consistently, XIST expression negatively correlated with METTL14 and YTHDF2 in CRC tissues.
Our findings highlight the function and prognostic value of METTL14 in CRC and extend the understanding of the importance of RNA epigenetics in cancer biology.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Good support: A solution‐phase self‐assembly approach leads to Co/CoO core/shell nanoparticles deposited on graphene (G–Co/CoO NPs). Their catalytic activity for the oxygen reduction reaction in ...O2‐saturated KOH solution depends on the thickness of the CoO shell (green in picture). The optimized G–Co/CoO NPs have a comparative activity and better stability than the commercial Pt NP catalyst supported on carbon (C–Pt).
Energy circulation in geospace lies at the heart of space weather research. In the inner magnetosphere, the steep plasmapause boundary separates the cold dense plasmasphere, which corotates with the ...planet, from the hot ring current/plasma sheet outside. Theoretical studies suggested that plasmapause surface waves related to the sharp inhomogeneity exist and act as a source of geomagnetic pulsations, but direct evidence of the waves and their role in magnetospheric dynamics have not yet been detected. Here, we show direct observations of a plasmapause surface wave and its impacts during a geomagnetic storm using multi-satellite and ground-based measurements. The wave oscillates the plasmapause in the afternoon-dusk sector, triggers sawtooth auroral displays, and drives outward-propagating ultra-low frequency waves. We also show that the surface-wave-driven sawtooth auroras occurred in more than 90% of geomagnetic storms during 2014-2018, indicating that they are a systematic and crucial process in driving space energy dissipation.
The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis ...under different working conditions is mainly based on vibration signals. However, vibration signal acquisition is limited by its requirement for contact measurement, while vibration signal analysis methods relies heavily on diagnostic expertise and prior knowledge of signal processing technology. To solve this problem, a novel acoustic-based diagnosis (ABD) method for gear fault diagnosis under different working conditions based on a multi-scale convolutional learning structure and attention mechanism is proposed in this paper. The multi-scale convolutional learning structure was designed to automatically mine multiple scale features using different filter banks from raw acoustic signals. Subsequently, the novel attention mechanism, which was based on a multi-scale convolutional learning structure, was established to adaptively allow the multi-scale network to focus on relevant fault pattern information under different working conditions. Finally, a stacked convolutional neural network (CNN) model was proposed to detect the fault mode of gears. The experimental results show that our method achieved much better performance in acoustic based gear fault diagnosis under different working conditions compared with a standard CNN model (without an attention mechanism), an end-to-end CNN model based on time and frequency domain signals, and other traditional fault diagnosis methods involving feature engineering.
•Uncertain process is adopted for degradation modeling accounting for epistemic uncertainty.•A novel similarity based-uncertain weighted least squares estimation method is proposed.•A denoising ...method is proposed to deal with the noises caused by recovery phenomenon.•The proposed framework is applied to real lithium-ion battery degradation dataset for demonstration.
Remaining useful life prediction based on degradation modeling is of great importance to condition-based maintenance, for which epistemic uncertainty due to the lack of sufficient knowledge needs to be characterized. For certain components, such as the batteries, the recovery phenomenon during degradation has to be considered, and the epistemic uncertainty associated with it is inevitable. This paper proposes a systematic method for degradation modeling and remaining useful life prediction based on uncertain process for degradation with recovery phenomenon. First, uncertain process is adopted for degradation modeling accounting for epistemic uncertainty. Then, a novel similarity based-uncertain weighted least squares estimation method is proposed to update the model parameters with real-time monitoring data. Afterwards, a denoising method is used to deal with the noises caused by recovery phenomenon. Finally, remaining useful life is calculated by uncertain simulation. A case study on real lithium-ion battery degradation dataset is performed to illustrate the effectiveness of the proposed method in comparison with traditional stochastic process.
The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG ...classification based on faster regions with a convolutional neural network (Faster R-CNN) algorithm. The original one-dimensional ECG signals contain the preprocessed patient ECG signals and some ECG recordings from the MIT-BIH database in this experiment. Each ECG beat of one-dimensional ECG signals was transformed into a two-dimensional image for experimental training sets and test sets. As a result, we classified the ECG beats into five categories with an average accuracy of 99.21%. In addition, we did a comparative experiment using the one versus rest support vector machine (OVR SVM) algorithm, and the classification accuracy of the proposed Faster R-CNN was shown to be 2.59% higher.
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