Circular RNAs (circRNAs) are emerging as important regulators in human disease. The expression profile and mechanism of circRNAs in postmenopausal osteoporosis remains largely unknown. Bone ...morphogenetic protein 2 (BMP2) is known to play important role in inducing osteogenic differentiation. MiR-98 was reported to regulate osteogenic differentiation of human bone mesenchymal stromal cells by targeting BMP2.
We aimed to analyze circRNA expression profiles in osteoporosis and explore the molecular mechanism of circRNA_0016624 and interaction between circRNA_0016624, miR-98 and BMP2 during osteogenic differentiation.
RNA-seq and bioinformatics analysis was performed in postmenopausal osteoporosis patients to screen for differentially expressed circRNAs. MiRanda and TargetScan were used to detect miR-98 binding sites of circRNA_0016624 and the target relationship was confirmed by dual luciferase assay. Expression level of circRNA_0016624, miR-98 and BMP2 were measured by qRT-PCR or Western blot. ARS staining was used to observe the level of osteogenic differentiation after transfection.
There were 387 circRNAs were differentially expressed in osteoporosis (|fold change| > 2 and P-value < 0.01). circRNA_0016624 and BMP2 were down-regulated in osteoporosis. CircRNA_0016624 could sponge miR-98 and regulate miR-98 expression. Overexpression of circRNA_0016624 promoted the expression of BMP2 and prevented osteoporosis.
Conclusion: circRNA_0016624 could sponge miR-98 and enhance BMP2 expression, thus circRNA_0016624 prevents osteoporosis and may provide a novel therapeutic strategy.
•There were 387 circRNAs were differentially expressed in osteoporosis (.|fold change| > 2 and P-value < 0.01).•circRNA_0016624 and BMP2 were down-regulated in osteoporosis.•circRNA_0016624 could sponge miR-98 and regulate miR-98 expression.•Overexpression of circRNA_0016624 promoted the expression of BMP2 and prevented osteoporosis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A sea level model which is normally used to calculate sea level changes during glacial-interglacial cycles is modified to solve the approximate nonlinear Liouvelle equation, in order to calculate ...‘large’ true polar wander (TPW) that may be induced by ice sheet loading. The purpose is to understand when the TPW will be too large to be solved properly by the linear model, and to properly calculate the TPW induced by snowball Earth events. It is found that the relative error for TPW calculated with a linear model will be >10% when the TPW exceeds ∼2° for ice sheets that develop near the poles, but remains <10% when the TPW exceeds 20° for ice sheets centered at between 45° and 60° latitude. To ensure the relative error for TPW speed to be <10%, the TPW should not exceed 1° and 10° for ice sheets near the poles and 50° latitude, respectively. Because the hypothesized ice sheets during the Neoproterozoic snowball Earth events were located near 55°S for one of the continental configuration, and not much lower than 45°S for the other, the TPW calculated with the linear model in a previous study is overall correct even though its magnitude could be >10° for certain viscosity profiles of the Earth.
•Nonlinear effect is large when true polar wander is >2° for ice sheet near the poles.•Nonlinear effect is small even when true polar wander is 20° for ice sheet at 55°N(S).•Nonlinear effect is small for true polar wander caused by snowball Earth events.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Accurate state of health estimation for lithium-ion batteries is crucial to ensure the safety and reliability of electric vehicles. This study presents an accurate state of health estimation method ...based on temperature prediction and gated recurrent unit neural network. First, the extreme learning machine method is leveraged to forecast the entire temperature variation during the constant current charging process based on randomly discontinuous short-term charging data. Next, a finite difference method is employed to calculate the raw differential temperature variation, which is then smoothed by the Kalman filter. On this basis, multi-dimensional health features are extracted from the differential temperature curves to reflect battery degradation from multiple perspectives, and six strong correlated features are selected by the Pearson correlation coefficient method. After preparing all the related health features, the gated recurrent unit neural network is exploited to predict state of health. The feasibility of the developed method is verified by comparing with other classic approaches in terms of accuracy and reliability. The experimental results demonstrate that the proposed method can effectively lead to the error of state of health within 2.28% based on only partial random and discontinuous charging data, justifying its anticipated prediction performance.
•A temperature prediction model is developed to compensate incomplete information.•Extreme learning machine is exploited to predict the missing temperature curve.•Six health features are extracted from the built temperature curves.•State of health model is estimated based on gated recurrent unit neural network.•The proposed method is validated with high accuracy for state of health estimation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Synthesizing and designing of the hydrogen evolution reaction (HER) electrode composed of earth-abundance elements is the current research topics. Among the many discovered catalysts, cobalt ...disulfide has outstanding performance for HER due to its catalytic activity cobalt sites. Here, phosphorus-doped cobalt disulfide (P doped CoS2) samples are successfully synthesized via simple one-step hydrothermal method. By using them as the electrocatalysts, the most efficient electrode we obtained shows the excellent electrocatalytic activity, with low overpotential of 53 mV to achieve a 10 mA/cm2 current density, a small Tafel slope of 57 mV/dec and a high stability after 10000 cycles. First-principle calculations results indicate that phosphorus dopants could improve the electrocatalytic activity of hydrogen evolution by lowing the free energy for atomic hydrogen adsorption (ΔGH*) at the Co sites. Moreover, the metallic P doped CoS2 could promote electron transfer and offers faster HER kinetics, thereby leading to better HER activity. This finding demonstrates an effective way to synthesis nonmetal doped CoS2 catalysts via one-step hydrothermal method and further improve the HER performance of CoS2 under the guidance of theoretical calculations.
•P doped CoS2 catalysts are prepared by a simple one-step hydrothermal method.•P dopants promote electron transfer and activate more HER catalytic sites in CoS2.•The ΔGH* at the Co site after P doped (0.15 eV) is lower than that of pure CoS2 (0.41 eV).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Ocean ventilation is an important regulator for atmospheric CO2 level (pCO2) by affecting the relative proportion of carbon stored in the atmosphere and deep ocean. Expansion of sea ice during ...glacial periods slows down ocean ventilation and its effect is expected to be the largest during the Neoproterozoic pre‐snowball stage. Here, our Community Earth System Model version 1.2.2 simulations demonstrate that averaged deep ocean ventilation age almost triples when the climate cools from a warm state with negligible sea ice to one in which the global sea‐ice coverage reaches ∼50% when pCO2 is lowered to 280 ppmv. Further cooling by reducing pCO2 from 280 to 70 ppmv increases the ventilation age from 1900 to 2300 years. This latter small increase in deep‐ocean ventilation age can reduce pCO2 by 48 ppmv, assuming Neoproterozoic organic production was comparable to present level. Therefore, the weakened ocean ventilation constitutes a significant positive feedback to the Late Neoproterozoic climate cooling.
Plain Language Summary
Ocean ventilation is important for the carbon exchange between the atmosphere and the ocean. When the ventilation is slow, due to for example, sluggish ocean overturning circulation or sea‐ice coverage, more carbon tends to be stored in the deep ocean and atmospheric CO2 level (pCO2) decreases. During the Late Neoproterozoic (∼800–541 Ma), climate evolved toward an extremely cold state called snowball Earth due to the imbalance of CO2 sources and sinks; the sea ice extended toward the tropics. Here we show, using an Earth system model, that the ocean ventilation slowed down significantly as the climate cooled. Our quantitative estimate shows that if pCO2 is reduced from 280 ppmv to 70 ppmv, the slowdown of ocean ventilation will further draw down pCO2 by 48 ppmv. This is a strong positive feedback that could have promoted snowball Earth initiation. The slowdown of ocean ventilation not only affects pCO2 but also is expected to affect the ratio of carbon isotopes in the ocean. Our work indicates that the Late Neoproterozoic ocean biogeochemical cycle was critical for climate stability.
Key Points
Ocean ventilation is estimated using Community Earth System Model version 1.2.2 for the Neoproterozoic pre‐snowball stage during which sea ice was extensive
Compared to an almost ice‐free warm climate, the deep ocean ventilation age would triple when sea‐ice edges extend to the tropics
Weakened ocean ventilation would induce a reduction of atmospheric CO2 level, promoting Neoproterozoic snowball Earth initiation
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Autonomous driving is a crucial issue of the automobile industry, and research on lane change is its significant part. Previous works on the autonomous vehicle lane change mainly focused on lane ...change path planning and path tracking, but autonomous vehicle lane change decision making is rarely mentioned. Therefore, this paper establishes an autonomous lane change decision-making model based on benefit, safety, and tolerance by analyzing the factors of the autonomous vehicle lane change. Then, because of the multi-parameter and non-linearity of the autonomous lane change decision-making process, a support vector machine (SVM) algorithm with the Bayesian parameters optimization is adopted to solve this problem. Finally, we compare a lane change model based on rules with the proposed SVM model in the test set, and results illustrate that the SVM model performs better than the rule-based lane change model. Moreover, the real car experiment is carried out to verify the effectiveness of the decision model.
In this paper, a stochastic model predictive control (MPC) method based on reinforcement learning is proposed for energy management of plug-in hybrid electric vehicles (PHEVs). Firstly, the power ...transfer of each component in a power-split PHEV is described in detail. Then an effective and convergent reinforcement learning controller is trained by the Q-learning algorithm according to the driving power distribution under multiple driving cycles. By constructing a multi-step Markov velocity prediction model, the reinforcement learning controller is embedded into the stochastic MPC controller to determine the optimal battery power in predicted time domain. Numerical simulation results verify that the proposed method achieves superior fuel economy that is close to that by stochastic dynamic programming method. In addition, the effective state of charge tracking in terms of different reference trajectories highlight that the proposed method is effective for online application requiring a fast calculation speed.
•Stochastic model predictive control is achieved based on reinforcement learning.•The Q-learning algorithm is employed to build the reinforcement learning controller.•A multi-step Markov velocity prediction model is embedded into the controller.•The proposed method achieves superior fuel economy with fast calculation speed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Fuel economy of hybrid vehicles is affected by their powertrain configurations, powertrain parameters, and energy management strategies. It is most beneficial to optimizing all the three factors ...simultaneously. However, when the design search space is large, an exhaustive, optimal control strategy, such as dynamic programming (DP), is too computationally expensive. Hence, a faster optimization method with higher computational efficiency and acceptable accuracy is required. Based on the DP approach, an approximate optimization method, called rapid dynamic programming (Rapid-DP), is developed and discussed in this paper. This method effectively reduces the decision-making time (the time can be reduced by a factor of 700, compared to the DP approach) for optimal control. The optimization processes and results are described and then compared with the original DP and PEARS + methods under two different driving cycles: FTP72 and HWFET. In conjunction with particle swarm optimization (PSO), the rapid-DP is leveraged, for the first time, to optimize key powertrain parameters for power split hybrid electric vehicles. Based on two power-split hybrids: Toyota Prius and Prius++, the joint optimization approach is exploited to examine vehicular fuel savings attributed to synergistic parameters optimization and operating-mode increase. The multi-mode configuration with optimal component parameters is demonstrated to be most fuel-efficient, with 6.56% and 3.15% fuel reductions under FTP72 and HWFET cycles, respectively, with respect to the original Prius 2010.
•Dynamics of power split hybrid electric vehicles (PS-HEVs) are modeled.•Rapid dynamic programming (Rapid-DP) is introduced.•A joint energy management and component-parameter optimization is made.•Fuel economy of power split hybrid electric vehicles (PS-HEVs) is optimized.•Synergy of operating-mode increase and system optimization is examined.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
While the impact of dust on climate and Atlantic Meridional Overturning Circulation (AMOC) during the interglacial period such as the mid‐Holocene has been studied extensively, its impact during the ...glacial period is unclear. Here we investigate how the climate and AMOC would change if there had been no dust during the Last Glacial Maximum. Model simulations show that the dust removal leads to a global cooling of over 2.4°C and a weakening of AMOC by ∼30%. Such temperature change is opposite in sign to that for the MH. The cooling is attributed to the increase of snow and ice albedo and weakening of AMOC when dust is removed, and is amplified through a positive feedback between sea ice and AMOC. Our results indicate that the climate and AMOC are more sensitive to dust change during the glacial than the interglacial period.
Plain Language Summary
Dust in the atmosphere reflects and absorbs sunlight, reducing the shortwave radiation reaching the surface, while the dust deposited on snow and ice reduces the surface albedo and increases the shortwave radiation received at the surface. Our previous work (Zhang et al., 2021) showed dust reduction during the interglacial period (e.g., mid‐Holocene; MH) would cause a global warming of 0.1°C and a weakening of Atlantic Meridional Overturning Circulation (AMOC) by 6.2%. This warming was due to more sunlight received at the surface when atmospheric dust is removed. Here we show that if dust was removed during the Last Glacial Maximum (LGM), climate will be cooled significantly rather than warmed. The major reason is that snow on land was much more extensive during LGM than MH, such that the increase of snow albedo after dust removal has a dominating effect on climate. Result suggests that the global climate and AMOC in the glacial period are more sensitive to dust change than those in the interglacial period.
Key Points
If there were no dust during the Last Glacial Maximum (LGM), global surface temperature would have been lower by 2.4 °C
Atlantic Meridional Overturning Circulation would have been 30% weaker if there were no dust during the LGM
The ocean and sea‐ice dynamics amplify the climate impact of dust by a factor of 2
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Glacier growth affects the local climate, and in turn, can either promote or prohibit its own growth. Such feedback has not been considered in modeling the glaciers of the Tibetan Plateau and its ...surroundings (TPS) during the Last Glacial Maximum (LGM; ∼28–23 ka). We find that the volume/area of the glaciers simulated by a coupled glacier‐climate model is 20%/10% less than that by a standalone glacier model forced with fixed climate fields; glaciers advance toward their western rims and yet decrease in the interior of TPS. Such changes in spatial patterns improve model‐data comparison. Moreover, the expansion of glaciers warms the winter surface temperature of the eastern TPS and decreases precipitation almost everywhere. These effects are primarily due to the added surface elevation, which blocks the water vapor brought by westerlies and south‐westerlies, reducing precipitation and increasing surface temperatures to the east and northeast of the newly grown glaciers.
Plain Language Summary
Depending on their area and volume, glaciers could impact regional atmospheric circulations, temperatures, precipitation, surface properties, etc. Changes in these climate variables would in turn feedback on the glacier growth and retreat. The glacial area over the Tibetan Plateau and its surroundings (TPS) should be much broader during the Last Glacial Maximum (LGM) than today, but glacier‐climate feedback was largely ignored in previous numerical studies. We show that the feedback significantly alters the simulated glacier area and distribution in LGM glaciers, and helps improve model‐data comparison. When glaciers grow, the surface elevation increases, which intercepts water vapor. Hence, more glaciers will grow along the western rim of TPS and less over the interior when the glacier‐climate feedback is considered. In the meantime, the winter surface temperature of the eastern Tibetan Plateau increases by more than 2 K and the precipitation over most of TPS decreases.
Key Points
Coupled modeling of the Last Glacial Maximum glaciers and climate over the Tibetan Plateau and its surroundings is necessary
The elevated glaciers blocked water vapor transport, reduced precipitation over most of TPS, and increased winter temperature in eastern TPS
The coupled glacial modeling decreases glacial area, and with changes in spatial distribution better matches the reconstructions
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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