SUMMARY
Cryptotaenia japonica, a traditional medicinal and edible vegetable crops, is well‐known for its attractive flavors and health care functions. As a member of the Apiaceae family, the ...evolutionary trajectory and biological properties of C. japonica are not clearly understood. Here, we first reported a high‐quality genome of C. japonica with a total length of 427 Mb and N50 length 50.76 Mb, was anchored into 10 chromosomes, which confirmed by chromosome (cytogenetic) analysis. Comparative genomic analysis revealed C. japonica exhibited low genetic redundancy, contained a higher percentage of single‐cope gene families. The homoeologous blocks, Ks, and collinearity were analyzed among Apiaceae species contributed to the evidence that C. japonica lacked recent species‐specific WGD. Through comparative genomic and transcriptomic analyses of Apiaceae species, we revealed the genetic basis of the production of anthocyanins. Several structural genes encoding enzymes and transcription factor genes of the anthocyanin biosynthesis pathway in different species were also identified. The CjANSa, CjDFRb, and CjF3H gene might be the target of Cjaponica_2.2062 (bHLH) and Cjaponica_1.3743 (MYB). Our findings provided a high‐quality reference genome of C. japonica and offered new insights into Apiaceae evolution and biology.
Significance Statement
Our results indicated how the genome of C. japonica provided a useful model for studying evolutionary trajectory and biological properties in Apiaceae species. Our findings are helpful for enriching genome research of Apiaceae family.
Energy‐storage technology is moving beyond lithium batteries to sodium as a result of its high abundance and low cost. However, this sensible transition requires the discovery of high‐rate and ...long‐lifespan anode materials, which remains a significant challenge. Here, the facile synthesis of an amorphous Sn2P2O7/reduced graphene oxide nanocomposite and its sodium storage performance between 0.01 and 3.0 V are reported for the first time. This hybrid electrode delivers a high specific capacity of 480 mA h g−1 at a current density of 50 mA g−1 and superior rate performance of 250 and 165 mA h g−1 at 2 and 10 A g−1, respectively. Strikingly, this anode can sustain 15 000 cycles while retaining over 70% of the initial capacity. Quantitative kinetic analysis reveals that the sodium storage is governed by pseudocapacitance, particularly at high current rates. A full cell with sodium super ionic conductor (NASICON)‐structured Na3V2(PO4)2F3 and Na3V2(PO4)3 as cathodes exhibits a high energy density of over 140 W h kg−1 and a power density of nearly 9000 W kg−1 as well as stability over 1000 cycles. This exceptional performance suggests that the present system is a promising power source for promoting the substantial use of low‐cost energy storage systems.
The illustration of amorphous tin‐based composite oxide (ATCO) as a high‐performance anode for a sodium ion battery is shown with a high rate of performance of 180 mA h g−1 at current of 2 A g−1 for 15 000 cycles. Quantitative kinetic analysis reveals that the sodium storage is governed by pseudocapacitance, particularly at high current rates.
A purely data‐driven and transformer‐based model with a novel self‐attention mechanism (3D‐Geoformer) is used to make predictions by adopting a rolling predictive manner similar to that in dynamical ...coupled models. The 3D‐Geoformer yields a successful prediction of the 2021 second‐year cooling conditions that followed the 2020 La Niña event, including covarying anomalies of surface wind stress and three‐dimensional (3D) upper‐ocean temperature, the reoccurrence of negative subsurface temperature anomalies in the eastern equatorial Pacific and a corresponding turning point of sea surface temperature (SST) evolution in mid‐2021. The reasons for the successful prediction with interpretability are explored comprehensively by performing sensitivity experiments with modulating effects on SST due to wind and subsurface thermal forcings being separately considered in the input predictors for prediction. A comparison is also conducted with physics‐based modeling, illustrating the suitability and effectiveness of 3D‐Geoformer as a new platform for El Niño and Southern Oscillation studies.
Plain Language Summary
The tropical Pacific experienced the prolonged cooling conditions during 2020–2022 (often called a triple La Niña), which exerted great impacts on the weather and climate globally. However, physics‐derived coupled models still have difficulty in accurately making long‐lead real‐time predictions for sea surface temperature (SST) evolution in the tropical Pacific. With the rapid development of deep learning‐based modeling, purely data‐driven models provide an innovative way for SST predictions. Here, a transformer‐based deep learning model is used to evaluate its performance in predicting the evolution of SST in the tropical Pacific during 2020–2022 and explore process representations that are important for SST evolution during 2021, including subsurface thermal effect and surface wind forcing on SST, the crucial factors determining the second‐year prolonged La Niña conditions and turning point of SST evolution. A comparison is made between the completely differently constructed physics‐derived dynamical coupled model and the pure‐data driven deep learning model, showing they both can be used for predictions of SST evolution in the 2021 second‐year cooling conditions. This indicates that it is necessary to adequately represent the thermocline feedback in predictive models, either in dynamical coupled models or purely data‐driven models, so that El Niño and Southern Oscillation predictions can be improved.
Key Points
A transformer‐based deep learning model is used for El Niño‐Southern Oscillation multivariate prediction in a rolling predictive manner
The purely data‐driven model successfully predicts the 2021 second‐year La Niña and turning point of temperature evolution in mid‐2021
Applications of purely data‐driven model for process representations and understanding are demonstrated as in dynamical coupled models
Sudden stratospheric warming (SSW) is a large‐scale meteorological process in the winter hemisphere lasting several days or weeks. The Incoherent Scatter World Day campaign conducted on January ...17–February 1, 2008 was arranged during a minor SSW event and focuses on studies of thermospheric and ionospheric response to stratospheric changes. We analyze ion temperature observations at 100–300 km height obtained by the Millstone Hill incoherent scatter radar (42.6°N, 288.5°E). Alternating regions of warming in the lower thermosphere and cooling above 150km altitude were observed by the radar. We use National Center for Environmental Prediction (NCEP) temperature data at 10hPa (∼30km) level and the F10.7 and Ap indices to identify any cause‐effect relationship between observed variations in the temperature and stratospheric warming event. We conclude that the seasonal trend, solar flux and geomagnetic activity cannot account for the observed warming and cooling temperature variation and suggest that this variation is associated with stratospheric warming. This study demonstrates a link between the lower atmosphere and the ionosphere which has not been considered before and indicates that ionospheric variability as part of space weather should be considered in conjunction with stratospheric changes.
With the assistance of microwave irradiation, greenish‐yellow luminescent graphene quantum dots (gGQDs) with a quantum yield (QY) up to 11.7% are successfully prepared via cleaving graphene oxide ...(GO) under acid conditions. The cleaving and reduction processes are accomplished simultaneously using microwave treatment without additional reducing agent. When the gGQDs are further reduced with NaBH4, bright blue luminescent graphene quantum dots (bGQDs) are obtained with a QY as high as 22.9%. Both GQDs show well‐known excitation‐dependent PL behavior, which could be ascribed to the transition from the lowest unoccupied molecular orbital (LUMO) to the highest occupied molecular orbital (HOMO) with a carbene‐like triplet ground state. Electrochemiluminescence (ECL) is observed from the graphene quantum dots for the first time, suggesting promising applications in ECL biosensing and imaging. The ECL mechanism is investigated in detail. Furthermore, a novel sensor for Cd2+ is proposed based on Cd2+ induced ECL quenching with cysteine (Cys) as the masking agent.
Two‐color graphene quantum dots are prepared using a facile microwave‐assisted approach to have fluorescent quantum yields as high as 22.9%. The graphene quantum dots are demonstrated to be electrochemiluminescent. A novel electrochemiluminescence sensor for Cd2+ is proposed based on the competitive coordination between cysteine and graphene quantum dots for metal ions.
Mitophagy plays pivotal roles in the selective disposal of unwanted mitochondria, and accumulation of damaged mitochondria has been linked to aging-related diseases. However, definitive proof that ...mitophagy regulates mitochondrial quality in vivo is lacking. It is also largely unclear whether damaged mitochondria are the cause or just the consequence of these diseases. We previously showed that FUNDC1 is a mitophagy receptor that interacts with LC3 to mediate mitophagy in response to hypoxia in cultured cells. We established Fundc1 knockout mouse models and used genetic and biochemical approaches, including a synthetic peptide that blocks the FUNDC1-LC3 interaction, to demonstrate that mitophagy regulates both mitochondrial quantity and quality in vivo in response to hypoxia or hypoxic conditions caused by ischemia-reperfusion (I/R) heart injury. We found that hypoxic mitophagy regulates platelet activities. Furthermore, we found that hypoxic preconditioning induces FUNDC1-dependent mitophagy in platelets and reduces I/R-induced heart injury, suggesting a new strategy to protect cardiac function and fight cardiovascular diseases.
Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the ...infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation ( JCS ) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation ( COVID-CS ) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS .
El Niño-Southern Oscillation (ENSO) can be currently predicted reasonably well six months and longer, but large biases and uncertainties remain in its real-time prediction. Various approaches have ...been taken to improve understanding of ENSO processes, and different models for ENSO predictions have been developed, including linear statistical models based on principal oscillation pattern (POP) analyses, convolutional neural networks (CNNs), and so on. Here, we develop a novel hybrid model, named as POP-Net, by combining the POP analysis procedure with CNN-long short-term memory (LSTM) algorithm to predict the Niño-3.4 sea surface temperature (SST) index. ENSO predictions are compared with each other from the corresponding three models: POP model, CNN-LSTM model, and POP-Net, respectively. The POP-based pre-processing acts to enhance ENSO-related signals of interest while filtering unrelated noise. Consequently, an improved prediction is achieved in the POP-Net relative to others. The POP-Net shows a high-correlation skill for 17-month lead time prediction (correlation coefficients exceeding 0.5) during the 1994–2020 validation period. The POP-Net also alleviates the spring predictability barrier (SPB). It is concluded that value-added artificial neural networks for improved ENSO predictions are possible by including the process-oriented analyses to enhance signal representations.
Abstract
Available satellite data reveal a decreasing trend in surface chlorophyll (SChl) over the entire tropical ocean until 2020. Where contributions by internal variability and external forcing ...remain unclear. Here, state-of-the-art climate model simulations are analyzed to show that external forcing significantly contributes to the decreasing SChl trend. In contrast, internal variability plays a weak or even offsetting role. As for the underlying processes, anthropogenic greenhouse emissions lead to a remarkable reduction in SChl over the tropical oceans, whereas industrial aerosol load facilitates a considerable increase in SChl in the western tropical Pacific. In addition, the negative phase of the interdecadal Pacific variability during 1998–2020 contributes to an increase in SChl, while the impact from the Atlantic multidecadal variability is relatively weak in facilitating a decrease in SChl. Overall, these results imply that the impact of anthropogenic forcing has emerged as indicated in the tropical marine ecosystem.
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