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This paper presents a new model for ionospheric total electron content (TEC) over China. The new model is developed using a hybrid method composed of the particle swarm optimization (PSO) and artificial neural network and long‐term observations from 257 ground‐based global navigation satellite systems (GNSS) stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) during the 14‐year period of 2008–2021. The PSO algorithm is used to optimize the traditional back‐propagation neural network (BP‐NN) model by reducing the effects of the local minimum problem. The new model is validated using out‐of‐sample data, and its results are compared to the BP‐NN, IRI‐2016 model, and global ionospheric maps provided by the International GNSS Service. Results show that TEC predicted from the new model agrees better with the reference TEC than the BP‐NN and IRI‐2016 models. The improvements made by the new model over the BP‐NN and IRI‐2016 models in the equinox, summer, and winter seasons of the solar maximum year (2015) are 4%–20%/20%–36%, 9%–21%/26%–42%, and 6%–22%/21%–43%, respectively, and their corresponding results in the solar minimum year (2019) are 12%–24%/41%–59%, 9%–24%/28%–56%, and 10%–26%/53%–72%. Furthermore, the new model well captures the diurnal evolution, seasonal variation, and variations in the ionospheric TEC under different solar activity levels. It also well captures the mid‐latitude summer nighttime anomaly over China, and the diurnal anomaly is more pronounced in the solar minimum year (2019) than in the solar maximum year (2015) in terms of the nighttime‐to‐noontime ratio and the range of months it lasts in a year. Plain Language Summary: With the continuous expansion of ground‐based and space‐borne global navigation satellite systems (GNSS) data sets, more and more high‐resolution, high‐accuracy, and long‐term measurements are available. These data can be used for characterizing, modeling, and forecasting the ionosphere. However, due to the limitation of data analysis tools and modeling techniques, only a fraction of the huge data set has been used. As proven by previous research, the back‐propagation neural network (BP‐NN) is an efficient approach in exploring hidden patterns from a large data set. Nevertheless, BP‐NN has shortcomings, for example, the possible local minimum and slow convergence rate problem. Therefore, this study uses the particle swarm optimization neural network and long‐term observations obtained from 257 ground‐based GNSS stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) to develop a new model of ionospheric total electron content (TEC) over China. The new model captures the TEC diurnal evolution, seasonal variation, and variations under different solar activity levels. Moreover, the new model also well captures the mid‐latitude summer nighttime anomalies over China. This new model offers more opportunities for the investigation of large‐scale ionospheric phenomena over China, like the distribution of the mid‐latitude summer nighttime anomaly and its month‐to‐month variations. Key Points: A particle swarm optimization neural network based total electron content (TEC) model over China is developed using observations of ground‐based global positioning system, COSMIC, and Fengyun during 2008–2021The new TEC model performs better than the IRI‐2016 model and back‐propagation neural network model under high and low solar activity levelsMid‐latitude summer nighttime anomaly is well captured by the new model, and the diurnal anomaly is more pronounced in 2019 than in 2015 [ABSTRACT FROM AUTHOR]

The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model's 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation. Plain Language Summary: The ionospheric total electron content (TEC) is the total number of free electrons along a signal propagation path in a cross section of one square meter, and the ionospheric delay error of the signal from the global navigation satellite system (GNSS) is proportional to the value of TEC. The variation of the ionospheric TEC can also be used to study the variations in space weather. In this study, a new ionospheric TEC model over China based on the bidirectional long short‐term memory (bi‐LSTM) method and long‐term ground‐based observations at 257 GNSS stations from the Crustal Movement Observation Network of China was developed and subsequently validated. The main reason for the selection of the bi‐LSTM model is for its consideration of the sequence information in both forward and backward directions. The prediction results of the new model show that in comparison with that of three other models, including the artificial neural network, LSTM, and traditional IRI‐2016 models, the new model is the best performer. Furthermore, the bi‐LSTM model also successfully captures the diurnal and seasonal variations of the ionospheric TEC and the TEC variations under geomagnetic storm conditions. Key Points: The bidirectional long short‐term memory (bi‐LSTM) method is adopted to predict the ionospheric total electron content maps over China using long‐term ground‐based global positioning system observations from the Crustal Movement Observation Network of ChinaThe bi‐LSTM‐based model outperforms the IRI‐2016, artificial neural network and long short‐term memory‐based modelsThe bi‐LSTM‐based model can predict the ionospheric response during geomagnetic storm periods, such as the 4–7 November 2021 G3 storm period [ABSTRACT FROM AUTHOR]

The adverse effect of the ionospheric scintillation on Global Navigation Satellite System (GNSS) requires scintillation monitoring on a global scale. Ionospheric Scintillation Monitoring Receivers (ISMR) are usually adopted to monitor scintillation, while they are not suitable for global monitoring due to the 50 Hz data collecting rate, which restricts the distribution. This paper proposes a new method to extract the phase scintillation index from each GNSS carrier with 1s‐sampling‐interval, mainly based on the cycle slip detection, the geodetic detrending and the wavelet transform, in which the optimal symmetry parameter and the time‐bandwidth product are determined with trial calculation. Taken the σϕ ${\sigma }_{\phi }$ index provided by ISMR as the reference, 1‐year observations are utilized to evaluate the scintillation monitoring performance of the extracted index regarding the correlation of the magnitude in each observation arc, the detected daily scintillation occurrence rate, the diurnal variation pattern of the ionospheric scintillation, the correlation between the scintillation occurrence rate and the space weather parameter, and the complementary cumulative distribution of the magnitudes. Compared to the performance of Rate of Total electron content Index, a higher consistency can be achieved between the extracted index and the σϕ ${\sigma }_{\phi }$ index, indicating the rationality of applying the proposed method in monitoring scintillations. The extracted scintillation index can be expected to introduce geodetic receivers operating at 1s‐sampling‐interval into the field of ionospheric scintillation monitoring on a global scale. Plain Language Summary: Small scale structures in the ionosphere will interfere with the amplitude and phase of the Global Navigation Satellite System (GNSS) signals passing through them. This phenomenon is called ionospheric scintillation, which can affect the positioning navigation and timing services of GNSS, calling for the need of monitoring ionospheric scintillations on a global scale. A special type of receiver, called Ionospheric Scintillation Monitoring Receiver (ISMR), is usually needed in ionosphere monitoring. However, due to the high sampling rate and price, the distribution of ISMR is limited, making it impossible to realize large‐scale ionospheric scintillation monitoring using only ISMR. In order to solve this problem, this paper proposes a new method to extract the scintillation index from the widely distributed geodetic receivers. Although the existing Rate of Total electron content Index (ROTI) can also use geodetic receivers for ionospheric scintillation monitoring, experiments based on long‐period observations collected in the Arctic region of the North America show that the extracted scintillation index can be more accurate than ROTI and the scintillation information on each carrier can be provided. The extracted scintillation index provides a basis for realizing global ionospheric scintillation monitoring and modeling in the future. Key Points: A new method is proposed to extract the scintillation index by applying cycle slip detection, geodetic detrending and wavelet transformThe scintillation index can be extracted from each carrier of the 1s‐sampling‐interval Global Navigation Satellite System observationsThe extracted scintillation index can provide a consistent performance in monitoring scintillation as that given by Ionospheric Scintillation Monitoring Receiver [ABSTRACT FROM AUTHOR]

Geomagnetic storms on 7–8 September 2017 triggered severe ionospheric disturbances that had a serious effect on satellite navigation and radio communication. Multiple observations derived from Global Navigation Satellite System receivers, Earth's Magnetic Field and Environment Explorers (SWARM) and the Thermosphere‐Ionosphere ‐Electrodynamics General Circulation Model's simulations are utilized to investigate the spatial‐temporal ionospheric behaviors under storm conditions. The results indicate that the electron density in the Asia‐Australia, Europe‐Africa and America sectors suddenly changed with the Bz southward excursion, and the ionosphere over low‐middle latitudes under the sunlit hemisphere is easily affected by the disturbed magnetic field. The SWARM observations verified the remarkable double‐peak structure of plasma enhancements over the equator and middle latitudes. The physical mechanism of low‐middle plasma disturbances can be explained by a combination effect of equatorial electrojets, vertical E × B drifts, meridional wind and thermospheric O/N2 change. Besides, the severe storms triggered strong Polar plasma disturbances on both dayside and nightside hemispheres, and the Polar disturbances had a latitudinal excursion associated with the offset of geomagnetic field. Remarkable plasma enhancements at the altitudes of 100–160 km were also observed in the auroral zone and middle latitudes (>47.5°N/S). The topside polar ionospheric plasma enhancements were dominated by the O+ ions. Furthermore, the TIE‐GCM's simulations indicate that the enhanced vertical E × B drifts, cross polar cap potential and Joule heating play an important role in generating the topside plasma perturbations. Key Points: Positive disturbances over low‐middle latitudes only appear on the dayside, but polar disturbances occur on both dayside and nightsideStorms trigger bottomside plasma enhancements over middle‐high latitudes (>47.5 N/S) that have a latitudinal offset with geomagnetic fieldA combination of equatorial electrojet, E × B drifts, meridional wind and O/N2 change is responsible for low‐middle latitudinal disturbances [ABSTRACT FROM AUTHOR]

The inhomogeneities, the so‐called changepoints, inevitably occur in the precipitable water vapor (PWV) time series derived from Global Navigation Satellite Systems (GNSS‐PWV). Currently, the two predominant types of homogenization methods, that is, absolute and relative methods, are limited in the poor performance and dependency on reference time series. In this study, a new absolute approach named adaptive absolute homogenization test (AAHT) by combining Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) is proposed. The performance of the new approach was validated using Monte Carlo simulations. Results showed the success rate and false alarm rate were better than 74.9% and 12.2%; the detection accuracy of the changepoint epochs and the offset magnitudes were 8.0 days and 0.153 mm. AAHT was also applied to homogenizing the real GNSS‐PWV time series over 91 International GNSS Service stations, from which 63 time series were identified as inhomogeneous containing 126 changepoints. Based on a comparison with the PWV time series from the fifth‐generation European center for medium‐range weather forecasts (ECMWF) reanalysis (ERA5), 43 changepoints were connected to climatic drivers, leaving 83 artificial changepoints at 48 stations. The offset magnitudes at these artificial changepoints were estimated and corrected in the inhomogeneous time series. The long‐term trends of those homogenized time series were compared with that of the PWV time series derived from ERA5 data sets (ERA‐PWV) and the correlation coefficient between the two sets of trends was 0.75, which was significantly larger than 0.23 before homogenization. Plain Language Summary: The precipitable water vapor time series derived from Global Navigation Satellite Systems (GNSS‐PWV) can be contaminated by inhomogeneities. This study aims to propose a new reliable method to homogenize the global GNSS‐PWV time series. This method, named adaptive absolute homogenization test (AAHT), combines Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) with an overall success rate and a false alarm rate better than 74.9% and 12.2%, respectively. An application on the GNSS‐PWV over 91 GNSS stations showed that homogenization using AAHT significantly improved the trend correlation between GNSS‐PWV and PWV time series retrieved from ERA5 reanalysis. Key Points: A new absolute method is proposed to homogenize a precipitable water vapor (PWV) time series derived from the Global Navigation Satellite Systems (GNSS)The success rate and false alarm rate were better than 74.4% and 12.2%; the accuracy of epochs and magnitudes were 8.3 days and 0.144 mmPWV time series at 91 global GNSS stations were homogenized, and the impact of the homogenization was also investigated [ABSTRACT FROM AUTHOR]

Root‐associated microbes are critical for plant growth and nutrient acquisition. However, scant information exists on optimizing communities of beneficial root‐associated microbes or the mechanisms underlying their interactions with host plants. In this report, we demonstrate that root‐associated microbes are critical influencers of host plant growth and nutrient acquisition. Three synthetic communities (SynComs) were constructed based on functional screening of 1,893 microbial strains isolated from root‐associated compartments of soybean plants. Functional assemblage of SynComs promoted significant plant growth and nutrient acquisition under both N/P nutrient deficiency and sufficiency conditions. Field trials further revealed that application of SynComs stably and significantly promoted plant growth, facilitated N and P acquisition, and subsequently increased soybean yield. Among the tested communities, SynCom1 exhibited the greatest promotion effect, with yield increases of up to 36.1% observed in two field sites. Further RNA‐seq implied that SynCom application systemically regulates N and P signaling networks at the transcriptional level, which leads to increased representation of important growth pathways, especially those related to auxin responses. Overall, this study details a promising strategy for constructing SynComs based on functional screening, which are capable of enhancing nutrient acquisition and crop yield through the activities of beneficial root‐associated microbes. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive comparison of the impact of solar activity on forecasting the upper atmosphere through assimilation of radio occultation (RO)-derived electron density (Ne) into a physics-based model (TIE-GCM) using an ensemble Kalman filter (KF). Globally abundant RO-derived Ne offers one of the most promising means to test the effect of assimilation on the model forecasted state on a global scale. This study emphasizes the importance of understanding how the assimilation results vary with solar activity, which is one of the main drivers of thermosphere-ionosphere dynamics. This study validates the forecast states with independent RO-derived GRACE (Gravity Recovery and Climate Experiment mission) Ne data. The principal result of the study is that the agreement between forecast Ne and data is better during solar minimum than solar maximum. The results also show that the agreement between data and forecast is mostly better than that of the standalone TIE-GCM driven with observed geophysical indices. The results emphasize that TIE-GCM significantly underestimate Ne in altitudes below 250 km and the assimilation of Ne is not as effective in these lower altitudes as it is in higher altitudes. The results demonstrate that assimilation of Ne significantly impacts the neutral mass density estimates via the KF state vector--the impact is larger during solar maximum than solar minimum relative to a control case that does not assimilate Ne. The results are useful to explain the inherent model bias, to understand the limitations of the data, and to demonstrate the capability of the assimilation technique. [ABSTRACT FROM AUTHOR]

Summary: Legume crops contribute a great portion of clean nitrogen (N) to agro‐ecosystems through symbiotic N2 fixation in the nodule; however, the nodulation is always inhibited by high N availability which is known as the N inhibitory effect through largely unknown mechanisms.We functionally investigated miR169c‐GmNFYA‐C‐GmENOD40 under multiple N conditions in soybean (Glycine max) (ENOD, Early Nodulin; NFYA, Nuclear Factor‐Y Subunit A). We elucidated their regulatory roles in soybean nodulation through analyzing expression patterns, micro–messenger RNA (miRNA–mRNA) interactions, phenotypes of transgenic soybean plants and genetic interactions.We found that miR169c expression was induced by high N, whereas its target GmNFYA‐C was preferentially expressed in nodules and induced by rhizobium inoculation. Overexpression of miR169c inhibited nodulation through targeting 3′‐UTR of GmNFYA‐C, whereas knockout miR169c through CRISPR‐cas9 promoted nodulation. However, overexpression of GmNFYA‐C promoted soybean nodulation through facilitating rhizobium infection and increasing the expression of symbiotic signaling gene GmENOD40. Besides, GmNFYA‐C directly induced the expression of GmENOD40. In addition, overexpression of GmNFYA‐C without the target site of miR169c partially attenuated the inhibitory effect of high N on soybean nodulation.We discovered a new regulatory pathway involving the miR169c‐NFYA‐C‐ENOD40 module that regulates soybean nodulation in response to N availability. This pathway provides substantial new insights into the mechanisms underlying the N inhibitory effect on nodulation. [ABSTRACT FROM AUTHOR]

The ionosphere plays an important role in satellite navigation, radio communication, and space weather prediction. However, it is still a challenging mission to develop a model with high predictability that captures the horizontal‐vertical features of ionospheric electrodynamics. In this study, multiple observations during 2005–2019 from space‐borne global navigation satellite system (GNSS) radio occultation (RO) systems (COSMIC and FY‐3C) and the Digisonde Global Ionosphere Radio Observatory are utilized to develop a completely global ionospheric three‐dimensional electron density model based on an artificial neural network, namely ANN‐TDD. The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root‐mean‐square error of the predicted residuals is 7.8 × 104 el/cm3. Under quiet space weather, the predicted accuracy of the ANN‐TDD is 30%–60% higher than the IRI‐2016 at the Millstone Hill and Jicamarca incoherent scatter radars. However, the ANN‐TDD is less capable of predicting ionospheric dynamic evolution under severe geomagnetic storms compared to the IRI‐2016 with the STORM option activated. Additionally, the ANN‐TDD successfully reproduces the large‐scale horizontal‐vertical ionospheric electrodynamic features, including seasonal variation and hemispheric asymmetries. These features agree well with the structure revealed by the RO profiles derived from the FORMOSAT/COSMIC‐2 mission. Furthermore, the ANN‐TDD successfully captures the prominent regional ionospheric patterns, including the equatorial ionization anomaly, Weddell Sea anomaly and mid‐latitude summer nighttime anomaly. The new model is expected to play an important role in the application of GNSS navigation and in the explanation of the physical mechanisms involved.Key Points: An artificial neural network‐based electron density model is developed using the observations obtained from COSMIC, FY‐3C, and DigisondesANN‐TDD has a strong predicting capability in high‐low solar activities and quiet‐disturbed space conditions compared with ISR and IRI‐2016ANN‐TDD model successes to reproduce ionospheric seasonal variations and spatial prominent patterns well including the EIA, WSA, and MSNA [ABSTRACT FROM AUTHOR]

The goal of this study is to present a new model for global topside electron temperature (Te) using a deep neural network (DNN) that is trained using measurements from incoherent scatter radars (ISRs). This study is also an investigation into whether this model can be used to generate the electron temperature in the topside ionosphere using GNSS ionospheric radio occultation (GNSS‐IRO) data as the input. ISR is one of the most reliable and long‐term sources to measure topside ionospheric electron density and plasma temperature information simultaneously. However, a drawback of ISR databases is the relatively poor spatial coverage due to the low number of ISR stations around the world. In contrast, GNSS‐IRO can be used to measure the global distributed electron density, but Te information is not directly detected. The relationship between the electron density and the electron temperature has been investigated by many researchers, but these studies have not explicitly considered the parameters that are known to influence the electron temperature, such as solar and geomagnetic activity level, and the features of electron density profile (hmF2, NmF2, and scale height). This study uses a DNN technique to create a new global topside electron temperature model from three submodels that have been trained using data from three ISR stations: Arecibo (low latitude), Millstone Hill (midlatitude), and Poker Flat (high latitude). This global model is trained using electron density profile information (e.g., vertical scale height [VSH], hmF2, and NmF2) and solar and geomagnetic activity (F10.7 and Kp, respectively) in addition to traditional spatial and temporal variables (e.g., local time, month, and latitude) as the independent variables. After the Te model is developed, Te information can be generated from the GNSS‐IRO electron density profiles using a newly created Ne−Te model. This model's outputs are assessed with regard to out‐of‐sample ISR data and compared to the latest International Reference Ionosphere model. It is found that the electron temperature profiles from the DNN have a root‐mean‐square deviation of 259 K in the low‐latitude region (i.e., against Arecibo data), 254 K in the midlatitude region (i.e., against Millstone Hill data), and 314 K in high‐latitude region (i.e., against Poker Flat data), and all of them are smaller than the root‐mean‐square deviation from International Reference Ionosphere. An additional comparison between the model results versus the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs is also conducted. A statistical analysis of the diurnal electron temperature profiles obtained from GNSS‐IRO is shown to agree with the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs. The full codes and outputs can be found at the Zenodo (10.5281/zenodo.3637617). Key Points: A new global topside Te model using deep neural network from incoherent scatter radar (ISR) with newly added Ne‐related variables is proposedThe new model agrees better with the out‐of‐sample ISR measurements than IRI‐2016The Te results predicted from the proposed model and ionospheric radio occultation measurements agree well with TIEGCM in the region with sufficient IRO data [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) method for the modeling of global topside ionospheric vertical scale height (VSH) using electron density profiles retrieved from Global Navigation Satellite Systems radio occultation (RO) data is proposed in this study. The data for this study are 80,124 VSHs derived from the events randomly selected from 9 years of Constellation Observing System for Meteorology, Ionosphere, and Climate RO measurements and 144,530 VSHs derived from the events randomly selected from 16 years of topside sounder measurements of both Aluoette‐1/2 and ISIS‐1/2 satellites during 1962–1978 are used for comparison. VSHs from the International Reference Ionosphere are also used for the comparison. Results showed that: (1) the median of the relative residuals of the new ANN regression approach/model (which was based on RO measurements) was 8.5% less than that of the traditional approach/model (which was based on the topside sounder data); (2) the median of the relative residuals of the ANN model when longitude was used as a variable was 1.1% less than the one without longitude; and substantial error in the polar region was shown to be mitigated by taking the variable longitude into consideration; (3) compared to International Reference Ionosphere, the accuracy of the new ANN model was improved by around 14%; (4) the new ANN model outperforms the traditional base vector‐based least squares model by around 10% when incoherent scatter radar measurements are used as a reference; and (5) the characteristics of global VSHs generated from the new model during geomagnetic storms better agree with measurements than that of the base vector‐based least squares. Key Points: A new approach for vertical scale height modeling from ionospheric radio occultation data using artificial neural network is proposedThe new approach significantly outperforms the traditional model in accuracyThe new approach agrees with the characteristic of VSH in equatorial region better than traditional approach during geomagnetic storm times [ABSTRACT FROM AUTHOR]

Aims: Elamipretide (ELAM), an aromatic–cationic tetrapeptide, interacts with cardiolipin and normalizes dysfunctional mitochondria of cardiomyocytes. This study examined the effects of ELAM on skeletal muscle mitochondria function in dogs with chronic heart failure (HF). Methods and results: Studies were performed in skeletal muscle biopsy specimens obtained from normal dogs (n = 7) and dogs with chronic intracoronary microembolization‐induced HF (n = 14) treated with subcutaneous ELAM 0.5 mg/kg (HF + ELAM, n = 7) or vehicle (normal saline control, HF‐CON, n = 7). After 3 months of therapy, triceps skeletal muscle samples were obtained from all dogs, and the proportion of type 1 and type 2 fibres was assessed. Mitochondria isolated from myofibrils of the vastus lateralis skeletal muscle exposed in vitro to ELAM for 1 h were used to assess mitochondrial function. The proportion of skeletal muscle type 1 fibres was lower in HF‐CON dogs compared with normal dogs (23 ± 4 vs. 32 ± 5%, P < 0.05). Treatment with ELAM restored a near‐normal fibre‐type composition (31 ± 7%, P < 0.05 vs. HF‐CON). Skeletal muscle mitochondria showed significantly lower levels of adenosine diphosphate‐dependent mitochondrial respiration (100 ± 9 vs. 164 ± 15 natom O/min/mg protein, P < 0.05), mitochondrial membrane potential (0.17 ± 0.03 vs. 0.53 ± 0.03 red/green fluorescence ratio, P < 0.05), mitochondrial permeability transition pore (38 ± 3 vs. 62 ± 2 relative light units, P < 0.05), maximum rate of adenosine triphosphate synthesis (3284 ± 418 vs. 8835 ± 423 RLU/μg protein, P < 0.05), and cytochrome c oxidase activity (1390 ± 108 vs. 2459 ± 210 natom O/min/mg protein, P < 0.05) compared with normal dogs. Exposure of skeletal muscle myofibrillar mitochondria from HF dogs to ELAM showed a dose‐dependent improvement/normalization of all measures of mitochondrial function. In mitochondria from skeletal muscle of HF dogs exposed to 0.10 μM ELAM, adenosine diphosphate‐dependent mitochondrial respiration increased to 183 ± 18 natom O/min/mg protein, membrane potential increased to 0.30 ± 0.03 red/green fluorescence ratio, mitochondrial permeability transition pore increased to 54 ± 4 RLU, maximum rate of adenosine triphosphate synthesis increased to 4423 ± 414, and cytochrome c oxidase activity increased to 2033 ± 191 natom O/min/mg protein. Exposure of skeletal muscle myofibrillar mitochondria from normal dogs to ELAM had no effect on mitochondrial function parameters. Conclusions: The results indicate that ELAM, previously shown to positively influence mitochondrial function of the failing heart, can also positively impact mitochondrial function of skeletal muscle and potentially help restore skeletal muscle function and improve exercise tolerance. [ABSTRACT FROM AUTHOR]

The paper presents a detailed analysis of the density‐temperature (ρ‐T) synchrony in the thermosphere using a hydrostatic general circulation model. The numerical models in general offer not only great potential for forecasting the transient response of the thermosphere but also are excellent tools for understanding the driving mechanisms of various thermospheric trends and features. This study investigates and isolates the dependency of the ρ‐T synchrony on the season, altitude, space weather, high‐latitude electrodynamics, and the lower atmospheric tidal spectrum. The results demonstrate that the previously reported ρ‐T synchrony begins around 300‐km (350‐km) altitude at the equator (high latitudes). The effect of the lower atmospheric tidal spectrum on the ρ‐T synchrony patterns seems to be only marginal and more noticeable during the equinox months. The study demonstrates that the ρ‐T phase lag is larger in the high latitudes of the summer hemisphere and evolves through the day and is attributable to ion drag and temperature fluctuations via soft particle precipitation. The study provides physical insights into how the winds contribute to the ρ‐T synchrony. In addition, the results show that geomagnetic activity contributes significantly to the ρ‐T synchrony; the underlying mechanism may be related to temperature enhancements in the high latitudes via Joule heating and associated nonlinear interactions. While the ρ‐T phase lags attributable to different solar activity levels are modest, the solar heating is the primary source that maintains the ρ‐T synchrony in the low/middle latitudes via upward propagating thermal tides. Key Points: A detailed analysis of the neutral density‐temperature (ρ‐T) synchrony in the thermosphere is presentedThe ρ‐T synchrony begins around 300 km near the equator and varies significantly with latitude, height, and seasonThe ion drag and temperature fluctuations via electron precipitation alters the ρ‐T synchrony in the high latitudes [ABSTRACT FROM AUTHOR]

Two solar flares X2.2 and X9.3 erupted over the active region 2673 on 6 September 2017, and the second flare is the strongest since 2005. In order to investigate the ionospheric and thermospheric responses to the two solar flares, the global total electron content and the critical frequency of F2 layer obtained from GPS stations and ionosondes are used. The results indicate that the ionosphere in the sunlit hemisphere increased significantly with magnitudes of 0.1 and 0.5 total electron content units for the X2.2 and X9.3 solar flares, respectively. The electron density, thermospheric neutral density, and neutral temperature simulated by the Thermosphere‐Ionosphere Electrodynamics Global Circulation Model show that the behavior of ionospheric and thermospheric responses is different. The ionospheric disturbances occurred at the altitude ranges of 150−300 km, and the thermospheric responses occurred at the altitudes of 250−400 km are caused by solar extreme ultraviolet and ultraviolet photons, respectively. Both ionospheric and thermospheric responses are proportional to the height within their corresponding altitude ranges. Observations and simulations reveal that the ionospheric and thermospheric responses are nonlinearly dependent on the solar zenith angle. The disturbances caused by the X2.2 solar flare are symmetric, but the X9.3 solar flare are not. The O/N2 density ratio simulated by Thermosphere‐Ionosphere Electrodynamics Global Circulation Model increases from lev0 to lev5.0 pressure surface with a magnitude of 0.1–1.8, while the ratio decreases in the American sector with a magnitude of −0.6 to −0.3. The longitudinal asymmetry of O/N2 density ratio is a major contributor to the longitudinal asymmetry of ionospheric and thermospheric responses. Key Points: The behaviors of ionospheric and thermospheric responses to solar flares are found to be contrastingThe ionospheric and thermospheric responses are dependent on solar zenith angle nonlinearlyThe longitudinal asymmetry of responses is attributed to the distribution of O/N2 ratio [ABSTRACT FROM AUTHOR]

The first systematic comparison between Swarm‐C accelerometer‐derived thermospheric density and both empirical and physics‐based model results using multiple model performance metrics is presented. This comparison is performed at the satellite's high temporal 10‐s resolution, which provides a meaningful evaluation of the models' fidelity for orbit prediction and other space weather forecasting applications. The comparison against the physical model is influenced by the specification of the lower atmospheric forcing, the high‐latitude ionospheric plasma convection, and solar activity. Some insights into the model response to thermosphere‐driving mechanisms are obtained through a machine learning exercise. The results of this analysis show that the short‐timescale variations observed by Swarm‐C during periods of high solar and geomagnetic activity were better captured by the physics‐based model than the empirical models. It is concluded that Swarm‐C data agree well with the climatologies inherent within the models and are, therefore, a useful data set for further model validation and scientific research. Plain Language Summary: The first systematic comparison of the thermospheric densities between Swarm‐C accelerometer‐derived densities is presented. The data are compared to two latest versions of a physics‐based general circulation model and arguably the most widely used empirical models using multiple model performance metrics. The comparison at the satellite's temporal resolution provides a useful evaluation of the models' fidelity for orbit prediction and pertinent space weather forecasting applications. The results of this study show that the short‐timescale variations observed by Swarm‐C during periods of high solar and geomagnetic activities were better captured by the physics‐based model than the empirical model. The results show the complex interconnectedness of solar activity and geomagnetic activity on model performance in terms of estimating density. The study also uses a machine learning exercise to demonstrate characteristics inherent to each model run. Swarm‐C data are consistent with the models. It is concluded that Swarm‐C data are suitable for further model validation and scientific research. Key Points: Presented is the first analysis of Swarm‐C accelerometer‐derived densities using both physical and empirical modelsSwarm‐C data are consistent with models, and TIE‐GCM significantly outperforms both DTM‐2013 and NRLMSISE‐00Implications of various model‐forcing parameters are clarified for neutral density along the Swarm‐C orbit [ABSTRACT FROM AUTHOR]

ABSTRACT Radiosonde observations provide a good data source for examining the long-term trend of atmospheric water vapour, temperature and cold point tropopause ( CPT). In this article, precipitable water vapour ( PWV) and surface temperature from the Hong Kong radiosonde station over the period of 1973-2012 are analysed. We find that the atmospheric water vapour in Hong Kong in the layers from the surface to approximately 1181 m, from ∼1181 to ∼2509 m, from ∼2509 to ∼5126 m and from ∼5126 to ∼8093 m accounts for 50, 25, 20 and 5% of the PWV, respectively. The atmosphere is almost completely dry above approximately 8000 m. Surface temperature has increased at a rate of 0.16° decade−1 over the past 40 years. On a seasonal timescale, the largest rate of increase is 0.23° decade−1 in winter and the smallest rate is 0.09° decade−1 in spring. The CPT height is located at approximately 17.5 km above mean sea level over the period of this study. This CPT height is estimated to have risen at a rate of 87.3 m decade−1 over 1983-2012, which is 1.36 times the global average. The CPT temperature is observed to decrease at a rate of 0.84° decade−1, which is 2.05 times the global average. Taking advantage of the periodicity of PWV and surface temperature over the past 40 years, Fourier series analysis models have been developed. The models are evaluated using 1 year (2012) of radiosonde data. It was found that the modelled PWV data can achieve root mean square error accuracy of 9.23 mm and the modelled surface temperature can achieve a standard deviation of 2.34° with a bias of −0.19°. [ABSTRACT FROM AUTHOR]

Many thermospheric mass density (TMD) variations have been recognized in observations and physical simulations; however, their impact on the low‐Earth‐orbit satellites has not been fully evaluated. The present study investigates the quantitative impact of periodic spatiotemporal TMD variations modulated by the empirical DTM2013 model. Also considered are two small‐scale variations, that is, the equatorial mass anomaly and the midnight density maximum, which are reproduced by the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model. This investigation is performed through a 1‐day orbit prediction (OP) simulation for a 400‐km circular orbit. The results show that the impact of TMD variations during solar maximum is 1 order of magnitude larger than that during solar minimum. The dominant impact has been found in the along‐track direction. Semiannual and semidiurnal variations in TMD exert the most significant impact on OP among the intra‐annual and intradiurnal variations, respectively. The zero mean periodic variations in TMD may not significantly affect the predicted orbit but increase the orbital uncertainty if their periods are shorter than the time span of OP. Additionally, the equatorial mass anomaly creates a mean orbit difference of 50 m (5 m) with a standard deviation of 30 m (3 m) in 1‐day OP during high (low) solar activity. The midnight density maximum exhibits a stronger impact in the order of 150±30 and 15±6 m during solar maximum and solar minimum, respectively. This study makes clear that careful selection of TMD variations is of great importance to balance the trade‐off between efficiency and accuracy in OP problems. Key Points: The impact of thermospheric mass density on the orbital dynamics of low Earth orbit has been comprehensively investigatedThe impact of periodic thermospheric mass density variations in time has also been characterizedThe EMA and MDM during different solar conditions are reproduced and analyzed using TIE‐GCM [ABSTRACT FROM AUTHOR]