Objective- In response to tissue injury, the appropriate progression of events in angiogenesis is controlled by a careful balance between pro and antiangiogenic factors. We aimed to identify and ...characterize microRNAs that regulate angiogenesis in response to tissue injury. Approach and Results- We show that in response to tissue injury, microRNA-615-5p (miR-615-5p) is rapidly induced and serves as an antiangiogenic microRNA by targeting endothelial cell VEGF (vascular endothelial growth factor)-AKT (protein kinase B)/eNOS (endothelial nitric oxide synthase) signaling in vitro and in vivo. MiR-615-5p expression is increased in wounds of diabetic db/db mice, in plasma of human subjects with acute coronary syndromes, and in plasma and skin of human subjects with diabetes mellitus. Ectopic expression of miR-615-5p markedly inhibited endothelial cell proliferation, migration, network tube formation in Matrigel, and the release of nitric oxide, whereas miR-615-5p neutralization had the opposite effects. Mechanistic studies using transcriptomic profiling, bioinformatics, 3' untranslated region reporter and microribonucleoprotein immunoprecipitation assays, and small interfering RNA dependency studies demonstrate that miR-615-5p inhibits the VEGF-AKT/eNOS signaling pathway in endothelial cells by targeting IGF2 (insulin-like growth factor 2) and RASSF2 (Ras-associating domain family member 2). Local delivery of miR-615-5p inhibitors, markedly increased angiogenesis, granulation tissue thickness, and wound closure rates in db/db mice, whereas miR-615-5p mimics impaired these effects. Systemic miR-615-5p neutralization improved skeletal muscle perfusion and angiogenesis after hindlimb ischemia in db/db mice. Finally, modulation of miR-615-5p expression dynamically regulated VEGF-induced AKT signaling and angiogenesis in human skin organoids as a model of tissue injury. Conclusions- These findings establish miR-615-5p as an inhibitor of VEGF-AKT/eNOS-mediated endothelial cell angiogenic responses and that manipulating miR-615-5p expression could provide a new target for angiogenic therapy in response to tissue injury. Visual Overview- An online visual overview is available for this article.
At present, the distinctness, uniformity, and stability (DUS) testing of flue-cured tobacco (Nicotiana tabacum L.) depends on field morphological identification, which is problematic in that it is ...labor intensive, time-consuming, and susceptible to environmental impacts. In order to improve the efficiency and accuracy of tobacco DUS testing, the development of a molecular marker-based method for genetic diversity identification is urgently needed.
In total, 91 simple sequence repeats (SSR) markers with clear and polymorphic amplification bands were obtained with polymorphism information content, Nei index, and Shannon information index values of 0.3603, 0.4040, and 0.7228, respectively. Clustering analysis showed that the 33 study varieties, which are standard varieties for flue-cured tobacco DUS testing, could all be distinguished from one another. Further analysis showed that a minimum of 25 markers were required to identify the genetic diversity of these varieties. Following the principle of two markers per linkage group, 48 pairs of SSR markers were selected. Correlation analysis showed that the genetic relationships revealed by the 48 SSR markers were consistent with those found using the 91 SSR markers.
The genetic fingerprints of the 33 standard varieties of flue-cured tobacco were constructed using 48 SSR markers, and an SSR marker-based identification technique for new tobacco varieties was developed. This study provides a reliable technological approach for determining the novelty of new tobacco varieties and offers a solid technical basis for the accreditation and protection of new tobacco varieties.
RNA aptamers are synthetic oligonucleotide-based affinity molecules that utilize unique three-dimensional structures for their affinity and specificity to a target such as a protein. They hold the ...promise of numerous advantages over biologically produced antibodies; however, the binding affinity and specificity of RNA aptamers are often insufficient for successful implementation in diagnostic assays or as therapeutic agents. Strong binding affinity is important to improve the downstream applications. We report here the use of the phosphorodithioate (PS2) substitution on a single nucleotide of RNA aptamers to dramatically improve target binding affinity by ∼1000-fold (from nanomolar to picomolar). An X-ray co-crystal structure of the α-thrombin:PS2-aptamer complex reveals a localized induced-fit rearrangement of the PS2-containing nucleotide which leads to enhanced target interaction. High-level quantum mechanical calculations for model systems that mimic the PS2 moiety and phenylalanine demonstrate that an edge-on interaction between sulfur and the aromatic ring is quite favorable, and also confirm that the sulfur analogs are much more polarizable than the corresponding phosphates. This favorable interaction involving the sulfur atom is likely even more significant in the full aptamer-protein complexes than in the model systems.
Electric vehicles (EVs) in severe cold regions face the real demand for fast charging under low temperatures, but low-temperature environments with high C-rate fast charging can lead to severe ...lithium plating of the anode material, resulting in rapid degradation of the lithium-ion battery (LIB). In this paper, by constructing an electrode–thermal model coupling solid electrolyte interphase (SEI) growth and lithium plating, the competition among different factors of capacity degradation under various ambient temperatures and C-rates are systematically analyzed. In addition, the most important cause of rapid degradation of LIBs under low temperatures are investigated, which reveal the change pattern of lithium plating with temperature and C-rate. The threshold value and kinetic law of lithium plating are determined, and a method of lithium-free control under high C-rate is proposed. Finally, by studying the average aging rate of LIBs, the reasons for the abnormal attenuation of cycle life at lower C-rates are ascertained. Through the chromaticity diagram of the expected life of LIBs under various conditions, the optimal fast strategy is explored, and its practical application in EVs is also discussed. This study can provide a useful reference for the development of high-performance and high-safety battery management systems to achieve fine management.
To develop emerging electrode materials and improve the performances of batteries, the machine learning techniques can provide insights to discover, design and develop battery new materials in ...high-throughput way. In this paper, two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage, specific capacity and specific energy. The deep learning models are trained with the multilayer perceptron as the core. The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models. Based on 10 types of ion batteries, the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V, respectively. The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms. Besides, the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries. This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.
Two deep learning models, model #1 and model #2, are constructed and applied to establish a link between cathode materials and battery electrochemical performances based on the feature groups #1 and #2. The deep learning models are trained with the multilayer perceptron as the core. It’s promising that this work can contribute to a high-throughput computational method to accelerate the rational and fast discovery, design and development of emerging electrode materials. Display omitted
•Two feature groups that could be adapted to different application scenarios are constructed.•Two deep learning models are constructed to predict the electrochemical performances.•The performances prediction and screening of new materials for sodium-ion and potassium-ion batteries are executed.•The prediction times for two constructed model on thousands of cells reach 72.9 ms and 75.7 ms.
The accurate estimation of the battery state of health (SOH) is crucial for the dependability and safety of battery management systems (BMS). The generality of existing SOH estimation methods is ...limited as they tend to primarily consider information from single-source features. Therefore, a novel method for integrating multi-feature collaborative analysis with deep learning-based approaches is proposed in this research. First, several battery degradation features are obtained through differential thermal voltammetry (DTV) analysis, singular value decomposition (SVD), incremental capacity analysis (ICA), and terminal voltage characteristic (TVC) analysis. The features highly related to SOH are selected as inputs for the deep learning model based on the results of a Pearson correlation analysis. The SOH estimation is achieved by developing a deep learning framework cored by long short-term memory (LSTM) neural network (NN), which integrates multi-source features as an input. A suggested method is validated using NASA and Oxford Battery Degradation datasets. The results demonstrate that the presented model provides great SOH estimation accuracy and generality, where the maximum root mean square error (RMSE) is less than 1%. Based on a cloud computing platform, the proposed method can be applied to provide a real-time prediction of battery health, with the potential to enhance battery full lifespan management.
The accurate estimation of battery health conditions is a crucial challenge for development of battery management systems due to the degradation of cathode and anode materials. In this paper, a ...fusion of deep learning model and feature analysis methods is employed to approach accurate estimation for state of health (SOH) and remaining useful life (RUL). The differential thermal voltammetry (DTV) signal analysis is executed to pre-process the datasets from Oxford University. A deep learning model is constructed with LSTM network as the core, combined with Bayesian optimization and dropout technique. This work shows that the deep learning model could approach the SOH and RUL early estimation with the mean absolute error of RUL maintained around 0.5%. It is potential that this deep learning model, combined with DTV signal analysis methods, could approach early prediction and estimation of battery SOH and RUL, contributing to the development of the next-generation high-energy-density and highly safety commercial batteries.
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•DTV captures phase transitions characterization in electrode materials•Bayesian optimization can approach hyperparameters search of model•Deep learning model can approach accurate estimation of battery SOH and RUL•Deep learning model has excellent robustness with 40% missing data
Electrochemical energy storage; Energy Modeling; Energy materials
Although EZH2 enzymatic inhibitors have shown antitumor effects in EZH2-mutated lymphoma and ARID1A-mutated ovarian cancer, many cancers do not respond because EZH2 can promote cancer independently ...of its histone methyltransferase activity. Here we identify ZRANB1 as the EZH2 deubiquitinase. ZRANB1 binds, deubiquitinates, and stabilizes EZH2. Depletion of ZRANB1 in breast cancer cells results in EZH2 destabilization and growth inhibition. Systemic delivery of ZRANB1 small interfering RNA (siRNA) leads to marked antitumor and antimetastatic effects in preclinical models of triple-negative breast cancer (TNBC). Intriguingly, a small-molecule inhibitor of ZRANB1 destabilizes EZH2 and inhibits the viability of TNBC cells. In patients with breast cancer, ZRANB1 levels correlate with EZH2 levels and poor survival. These findings suggest the therapeutic potential for targeting the EZH2 deubiquitinase ZRANB1.
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•ZRANB1 binds, deubiquitinates, and stabilizes EZH2 protein•Depletion or inhibition of ZRANB1 causes EZH2 destabilization and anticancer effect•ZRANB1 levels correlate with EZH2 levels and poor survival in human breast cancer
Many cancer cells are sensitive to depletion of EZH2 but resistant to EZH2 inhibitors, due to EZH2’s enzyme-independent cancer-promoting function. Zhang et al. identify ZRANB1 as an EZH2 deubiquitinase and a potential anticancer target.
Chemically synthesized small interfering RNAs (siRNAs) have been widely used to identify gene function and hold great potential in providing a new class of therapeutics. Chemical modifications are ...desired for therapeutic applications to improve siRNA efficacy. Appropriately protected ribonucleoside-3′-yl S-β-(benzoylmercapto)ethylpyrrolidino-thiophosphoramidite monomers were prepared for the synthesis of siRNA containing phosphorodithioate (PS2) substitutions in which the two non-bridging oxygen atoms are replaced by sulfur atoms. A series of siRNAs containing PS2 substitutions have been strategically designed, synthesized, and evaluated for their gene silencing activities. These PS2-siRNA duplexes exhibit an A-form helical structure similar to unmodified siRNA. The effect of PS2 substitutions on gene silencing activity is position-dependent, with certain PS2-siRNAs showing activity significantly higher than that of unmodified siRNA. The relative gene silencing activities of siRNAs containing either PS2 or phosphoromonothioate (PS) linkages at identical positions are variable and depend on the sites of modification. 5′-Phosphorylation of PS2-siRNAs has little or no effect on gene silencing activity. Incorporation of PS2 substitutions into siRNA duplexes increases their serum stability. These results offer preliminary evidence of the potential value of PS2-modified siRNAs.
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