Soil salinity is one of several major abiotic stresses that constrain maize productivity worldwide. An improved understanding of salt-tolerance mechanisms will thus enhance the breeding of ...salt-tolerant maize and boost productivity. Previous studies have indicated that the maintenance of leaf Na+ concentration is essential for maize salt tolerance, and the difference in leaf Na+ exclusion has previously been associated with variation in salt tolerance between maize varieties.
Here, we report the identification and functional characterization of a maize salt-tolerance quantitative trait locus (QTL), Zea mays Na
+
Content1 (ZmNC1), which encodes an HKT-type transporter (designated as ZmHKT1).
We show that a natural ZmHKT1 loss-of-function allele containing a retrotransposon insertion confers increased accumulation of Na+ in leaves, and salt hypersensitivity. We next show that ZmHKT1 encodes a plasma membrane-localized Na+-selective transporter, and is preferentially expressed in root stele (including the parenchyma cells surrounding the xylem vessels). We also show that loss of ZmHKT1 function increases xylem sap Na+ concentration and causes increased root-to-shoot Na+ delivery, indicating that ZmHKT1 promotes leaf Na+ exclusion and salt tolerance by withdrawing Na+ from the xylem sap.
We conclude that ZmHKT1 is a major salt-tolerance QTL and identifies an important new gene target in breeding for improved maize salt tolerance.
Inspired by the exquisite helices in Nature, fabrication of helical materials with controlled handedness has attracted considerable attention. Herein, we report on precis synthesis of single left‐ ...and right‐handed helical polyisocyanides through living polymerization of achiral monomers using chiral palladium catalysts under helix‐sense‐selective manner. Mechanism study revealed that the yielded helices with opposite handedness showed different activity of the living chain end. The helix with unfavored handedness was self‐terminated, while the one with favored handedness showed high activity and could undergo chain propagation to form a high molecular weight polymer with maintained single‐handed helicity.
Living polymerization of achiral isocyanide by chiral PdII‐catalyst yielded a mixture of right‐ and left‐handed helices with great bias. The helix with preferred handedness has an active chain end and can undergo chain propagation. While the opposite handed helix was self‐terminated due to the inactive chain end. The mechanism study will pave an avenue for the development of new catalysts for synthesis of chiral materials.
Abstract
Gravitational-wave astronomy provides a unique new way to study the expansion history of the universe. In this work, we investigate the impact future gravitational-wave observatories will ...have on cosmology. Third-generation observatories like the Einstein Telescope and Cosmic Explorer will be sensitive to essentially all of the binary black hole coalescence events in the universe. Recent work by Farr et al. points out that features in the stellar-mass black hole population break the mass–redshift degeneracy, facilitating precise determination of the Hubble parameter without electromagnetic counterparts or host galaxy catalogs. Using a hierarchical Bayesian inference model, we show that with one year of observations by the Einstein Telescope, the Hubble constant will be measured to ≲1%. We also show that this method can be used to perform Bayesian model selection between cosmological models. As an illustrative example, we find that a decisive statement can be made comparing the ΛCDM and RHCT cosmological models using two weeks of data from the Einstein Telescope.
Monolayer Schottky barrier (SB) field-effect transistors based on the in-plane heterojunction of 1T/1T′-phase (metallic) and 2H-phase (semiconducting) transition-metal dichalcogenides (TMDs) have ...been proposed following the recent experimental synthesis of such devices. By using density functional theory and ab initio simulations, intrinsic device performance, sub-10 nm scaling, and performance boosting of MoSe2, MoTe2, WSe2, and WTe2, SB field-effect transistors are systematically investigated. We find that the Schottky barrier heights (SBHs) of these in-plane 1T(1T′)/2H contacts are proportional to their band gaps: the bigger band gap corresponds to bigger SBH. For four TMDs, the SBH of 1T/2H contact is always smaller than that of 1T′/2H contact. The WTe2 SB field-effect transistor can provide the best performance and satisfy the requirement of the high-performance transistor outlined by the International Technology Roadmap for Semiconductors down to a 6 nm gate length. In addition, the replacement of suitable 1T-TMD on the source/drain regions can modulate conduction band SB, leading to the 8.8 nm WSe2 SB field-effect transistor also satisfying the requirement. Moreover, the introduction of the underlap can increase the effective channel length and reduce the coupling between the source/drain and the channel, leading to the 5.1 nm WTe2 SB field-effect transistor also satisfying the International Technology Roadmap for Semiconductors high-performance requirement. The underlying physical mechanisms are discussed, and it is concluded that the in-plane SB engineering is the key point to optimize such two-dimensional devices.
We investigate the interconnectedness and systemic risk of China's financial institutions by constructing dynamic tail-event driven networks (TENETs) at 1% risk level based on weekly returns of 24 ...publicly-listed financial institutions from 2008 to 2016. Total connectedness reaches a peak when the system exhibits stress, especially during the recent period from mid-2014 to end-2016. Large commercial banks and insurers usually exhibit systemic importance, but some small firms are systemically important due to their high level of incoming (outgoing) connectedness. Our results provide useful information to regulators when they assess systemic risk of financial institutions and formulate macroprudential supervision policy.
•Dynamic TENETs for 24 Chinese financial institutions are built for measuring interconnectedness and systemic risk.•Total connectedness of the TENET reaches a record high during the recent period from mid-2014 to end-2016.•Dynamic indicators (e.g., SRR and SRE indices) provide an early-warning function for identifying systemic risk.•One potential reason for the high level of systemic risk is the increasing size of China's shadow banking system.•Some small firms are systemically important due to their high level of incoming or outgoing connectedness.
•Monthly runoff is divided into several subseries by variational mode decomposition.•Support vector machine models the input-output relationships of all the subseries.•Quantum-behaved particle swarm ...algorithm searches for suitable model parameters.•The hybrid method obtains satisfying results in the real-world simulations.
Accurate monthly runoff prediction plays an important role in the planning and management of water resources. However, owing to climate changes and human activities, natural runoff often contains a variety of frequency components, and existing monthly runoff estimation techniques may fail to capture potential change processes effectively. To overcome this problem, we have developed a hybrid model for monthly runoff prediction. First, observed runoff is decomposed into several subcomponents via variational mode decomposition. Second, support vector machine models based on quantum-behaved particle swarm optimization are adopted to identify the input-output relationships hidden in each subcomponent. Finally, the total output of all submodules is treated as the final forecasting result for the original runoff. Three quantitative indexes are considered to test the performance of the developed models. The monthly streamflow of two reservoirs in China’s Yangtze Valley is considered as the survey target. This area contains the world’s largest hydropower project (Three Gorges Reservoir) and the waterhead of the middle line of Asia's largest inter-basin water transfer project (Danjiangkou Reservoir). Test results indicate that the hybrid model provides better forecasting accuracy compared to several traditional methods (artificial neural networks and extreme learning machines), making it an effective tool for the scientific operation of hydropower reservoirs.
A novel Ebola virus (EBOV) first identified in March 2014 has infected more than 25,000 people in West Africa, resulting in more than 10,000 deaths. Preliminary analyses of genome sequences of 81 ...EBOV collected from March to June 2014 from Guinea and Sierra Leone suggest that the 2014 EBOV originated from an independent transmission event from its natural reservoir followed by sustained human-to-human infections. It has been reported that the EBOV genome variation might have an effect on the efficacy of sequence-based virus detection and candidate therapeutics. However, only limited viral information has been available since July 2014, when the outbreak entered a rapid growth phase. Here we describe 175 full-length EBOV genome sequences from five severely stricken districts in Sierra Leone from 28 September to 11 November 2014. We found that the 2014 EBOV has become more phylogenetically and genetically diverse from July to November 2014, characterized by the emergence of multiple novel lineages. The substitution rate for the 2014 EBOV was estimated to be 1.23 × 10(-3) substitutions per site per year (95% highest posterior density interval, 1.04 × 10(-3) to 1.41 × 10(-3) substitutions per site per year), approximating to that observed between previous EBOV outbreaks. The sharp increase in genetic diversity of the 2014 EBOV warrants extensive EBOV surveillance in Sierra Leone, Guinea and Liberia to better understand the viral evolution and transmission dynamics of the ongoing outbreak. These data will facilitate the international efforts to develop vaccines and therapeutics.
Determining the long-term impact of COVID-19 on cognition is important to inform immediate steps in COVID-19 research and health policy.
To investigate the 1-year trajectory of cognitive changes in ...older COVID-19 survivors.
This cohort study recruited 3233 COVID-19 survivors 60 years and older who were discharged from 3 COVID-19-designated hospitals in Wuhan, China, from February 10 to April 10, 2020. Their uninfected spouses (N = 466) were recruited as a control population. Participants with preinfection cognitive impairment, a concomitant neurological disorder, or a family history of dementia were excluded, as well as those with severe cardiac, hepatic, or kidney disease or any kind of tumor. Follow-up monitoring cognitive functioning and decline took place at 6 and 12 months. A total of 1438 COVID-19 survivors and 438 control individuals were included in the final follow-up. COVID-19 was categorized as severe or nonsevere following the American Thoracic Society guidelines.
The main outcome was change in cognition 1 year after patient discharge. Cognitive changes during the first and second 6-month follow-up periods were assessed using the Informant Questionnaire on Cognitive Decline in the Elderly and the Telephone Interview of Cognitive Status-40, respectively. Based on the cognitive changes observed during the 2 periods, cognitive trajectories were classified into 4 categories: stable cognition, early-onset cognitive decline, late-onset cognitive decline, and progressive cognitive decline. Multinomial and conditional logistical regression models were used to identify factors associated with risk of cognitive decline.
Among the 3233 COVID-19 survivors and 1317 uninfected spouses screened, 1438 participants who were treated for COVID-19 (691 male 48.05% and 747 female 51.95%; median IQR age, 69 66-74 years) and 438 uninfected control individuals (222 male 50.68% and 216 female 49.32%; median IQR age, 67 66-74 years) completed the 12-month follow-up. The incidence of cognitive impairment in survivors 12 months after discharge was 12.45%. Individuals with severe cases had lower Telephone Interview of Cognitive Status-40 scores than those with nonsevere cases and control individuals at 12 months (median IQR: severe, 22.50 16.00-28.00; nonsevere, 30.00 26.00-33.00; control, 31.00 26.00-33.00). Severe COVID-19 was associated with a higher risk of early-onset cognitive decline (odds ratio OR, 4.87; 95% CI, 3.30-7.20), late-onset cognitive decline (OR, 7.58; 95% CI, 3.58-16.03), and progressive cognitive decline (OR, 19.00; 95% CI, 9.14-39.51), while nonsevere COVID-19 was associated with a higher risk of early-onset cognitive decline (OR, 1.71; 95% CI, 1.30-2.27) when adjusting for age, sex, education level, body mass index, and comorbidities.
In this cohort study, COVID-19 survival was associated with an increase in risk of longitudinal cognitive decline, highlighting the importance of immediate measures to deal with this challenge.
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•A novel FUNP analysis on QFPD function.•QFPD act on COVID-19 via anti-viral, anti-inflammatory and metabolic programming.•9 QFPD ingredients presented good molecular docking score ...for 2019-nCov.•SGMH, MXSG and Others are the top 3 efficient formula for COVID-19.
Qingfei Paidu decoction (QFPD), a multi-component herbal formula, has been widely used to treat COVID-19 in China. However, its active compounds and mechanisms of action are still unknown. Firstly, we divided QFPD into five functional units (FUs) according to the compatibility theory of traditional Chinese medicine. The corresponding common targets of the five FUs were all significantly enriched in Go Ontology (oxidoreductase activity, lipid metabolic process, homeostatic process, etc.), KEGG pathways (steroid biosynthesis, PPAR signaling pathway, adipocytokine signaling pathway, etc.), TTD diseases (chronic inflammatory diseases, asthma, chronic obstructive pulmonary Disease, etc.), miRNA (MIR183), kinase (CDK7) and TF (LXR). QFPD contained 257 specific targets in addition to HCoV, pneumonia and ACE2 co-expression proteins. Then, network topology analysis of the five components-target-pathway-disease networks yielded 67 active ingredients. In addition, ADMET estimations showed that 20 compounds passed the stringent lead-like criteria and in silico drug-likeness test with high gastrointestinal absorption and the median lethal dose (LD50 > 1600 mg/kg). Moreover, 4 specific ingredients (M3, S1, X2 and O2) and 5 common ingredients (MS1, MX16, SX1, WO1 and XO1) of QFPD presented good molecular docking score for 2019-nCov structure and non-structure proteins. Finally, drug perturbation of COVID-19 network robustness showed that all five FUs may protect COVID-19 independently, and target 8 specifically expressed drug-attacked nodes which were related to the bacterial and viral responses, immune system, signaling transduction, etc. In conclusion, our new FUNP analysis showed that QFPD had a protection effect on COVID-19 by regulating a complex molecular network with safety and efficacy. Part of the mechanism was associated with the regulation of anti-viral, anti-inflammatory activity and metabolic programming.
The dynamic correlation is examined between government’s policy uncertainty and Chinese stock market returns in the period from January 1995 to December 2014. We find that the stock market is ...significantly correlated to policy uncertainty based on the results of the Vector Auto Regression (VAR) and Structural Vector Auto Regression (SVAR) models. In contrast, the results of the Dynamic Conditional Correlation Generalized Multivariate Autoregressive Conditional Heteroscedasticity (DCC-MGARCH) model surprisingly show a low dynamic correlation coefficient between policy uncertainty and market returns, suggesting that the fluctuations of each variable are greatly influenced by their values in the preceding period. Our analysis highlights the understanding of the dynamical relationship between stock market and fiscal and monetary policy.
•We examine the dynamic correlation between policy uncertainty and stock returns.•We use a VAR model, SVAR model, and DCC-MGARCH model to capture the correlation.•We find that the stock market is significantly related to policy uncertainty.
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