Crystal Structural Framework of Lithium Super‐Ionic Conductors

As technologically important materials for solid‐state batteries, Li super‐ionic conductors are a class of materials exhibiting exceptionally high ionic conductivity at room temperature. These materials have unique crystal structural frameworks hosting a highly conductive Li sublattice. However, it is not understood why certain crystal structures of the super‐ionic conductors lead to high conductivity in the Li sublattice. In this study, using topological analysis and ab initio molecular dynamics simulations, the crystal structures of all Li‐conducting oxides and sulfides are studied systematically and the key features pertaining to fast‐ion conduction are quantified. In particular, a unique feature of enlarged Li sites caused by large local spaces in the crystal structural framework is identified, promoting fast conduction in the Li‐ion sublattice. Based on these quantified features, the high‐throughput screening identifies many new structures as fast Li‐ion conductors, which are further confirmed by ab initio molecular dynamics simulations. This study provides new insights and a systematic quantitative understanding of the crystal structural frameworks of fast ion‐conductor materials and motivates future experimental and computational studies on new fast‐ion conductors.     

Preliminary analysis of geographical distribution based on cold hardiness for Evergestis extimalis (Scopoli) (Lepidoptera: Pyralidae) on Qinghai–Tibet Plateau

Evergestis extimalis  (Scopoli) is a pest insect present in spring rape fields of the Qinghai–Tibet plateau. A survey of its distribution and analysis of its physiological and biochemical variances of its overwintering larvae were conducted in this study. Prior to 2006, Evergestis extimalis Scopli appeared only sporadically at the east agricultural district of Qinghai Province at 2,100 m elevation; after 2006, there have been frequent outbreaks at 2,200 m or so height. The insect's distribution has extended continuously toward higher altitudes yearly, and the scope of its damage reached 2,800 m height in 2010. These changes indicate that the cold hardiness of E. extimalis is on the rise. Physiological and biochemical analyses were performed for the insect's overwintering larvae from November 2011 to March 2012. The supercooling point (SCP) and freezing point (FP) ranged from ?6.85°C to ?12.49°C and from ?6.23°C to ?8.17°C, respectively, and both were at their respective lowest points in January 2012; the lowest points of water and fat contents (which did not vary to any extreme degree throughout the test period) were also observed in January 2012. Glycogen content varied from 2.42 mg/g to 4.56 mg/g. Protein content increased gradually at the first two months and reached its peak in January 2012 before dropping slightly. The activity of protective enzymes POD, CAT, and SOD varied with changes in environmental temperature, and each was at its lowest point in January 2012. With the exception of protein and glycerol content, other physiological and biochemical variances were generally parallel with environmental temperature, strongly indicating that E. extimalis has indeed developed cold hardiness.     

Dysregulated expression of ACTN4 contributes to endothelial cell injury via the activation of the p38-MAPK/p53 apoptosis pathway in preeclampsia

Journal of Physiology and Biochemistry - Preeclampsia (PE) is a hypertensive disease associated with increased endothelial cell dysfunction caused by systemic oxidative stress. Alpha-actinin-4...     

Dissecting the nonlinear response of maize yield to high temperature stress with model‐data integration

Evidence suggests that global maize yield declines with a warming climate, particularly with extreme heat events. However, the degree to which important maize processes such as biomass growth rate, growing season length (GSL) and grain formation are impacted by an increase in temperature is uncertain. Such knowledge is necessary to understand yield responses and develop crop adaptation strategies under warmer climate. Here crop models, satellite observations, survey, and field data were integrated to investigate how high temperature stress influences maize yield in the U.S. Midwest. We showed that both observational evidence and crop model ensemble mean (MEM) suggests the nonlinear sensitivity in yield was driven by the intensified sensitivity of harvest index (HI), but MEM underestimated the warming effects through HI and overstated the effects through GSL. Further analysis showed that the intensified sensitivity in HI mainly results from a greater sensitivity of yield to high temperature stress during the grain filling period, which explained more than half of the yield reduction. When warming effects were decomposed into direct heat stress and indirect water stress (WS), observational data suggest that yield is more reduced by direct heat stress (?4.6 ± 1.0%/°C) than by WS (?1.7 ± 0.65%/°C), whereas MEM gives opposite results. This discrepancy implies that yield reduction by heat stress is underestimated, whereas the yield benefit of increasing atmospheric CO₂ might be overestimated in crop models, because elevated CO₂ brings yield benefit through water conservation effect but produces limited benefit over heat stress. Our analysis through integrating data and crop models suggests that future adaptation strategies should be targeted at the heat stress during grain formation and changes in agricultural management need to be better accounted for to adequately estimate the effects of heat stress.     

Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere

Climate change has substantial influences on autumn leaf senescence, that is, the end of the growing season (EOS). Relative to the impacts of temperature and precipitation on EOS, the influence of drought is not well understood, especially considering that there are apparent cumulative and lagged effects of drought on plant growth. Here, we investigated the cumulative and lagged effects of drought (in terms of the Standardized Precipitation–Evapotranspiration Index, SPEI) on EOS derived from the normalized difference vegetation index (NDVI3g) data over the Northern Hemisphere extra‐tropical ecosystems (>30°N) during 1982–2015. The cumulative effect was determined by the number of antecedent months at which SPEI showed the maximum correlation with EOS (i.e., R_max‐cml) while the lag effect was determined by a month during which the maximum correlation between 1‐month SPEI and EOS occurred (i.e., R_max‐lag). We found cumulative effect of drought on EOS for 27.2% and lagged effect for 46.2% of the vegetated land area. For the dominant time scales where the R_max‐cml and R_max‐lag occurred, we observed 1–4 accumulated months for the cumulative effect and 2–6 lagged months for the lagged effect. At the biome level, drought had stronger impacts on EOS in grasslands, savannas, and shrubs than in forests, which may be related to the different root functional traits among vegetation types. Considering hydrological conditions, the mean values of both R_max‐cml and R_max‐lag decreased along the gradients of annual SPEI and its slope, suggesting stronger cumulative and lagged effects in drier regions as well as in areas with decreasing water availability. Furthermore, the average accumulated and lagged months tended to decline along the annual SPEI gradient but increase with increasing annual SPEI. Our results revealed that drought has strong cumulative and lagged effects on autumn phenology, and considering these effects could provide valuable information on the vegetation response to a changing climate.     

Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States

Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to ?34% (?17 ± 3% on average) in the United States relative to the expected yield from the long‐term trend, comparable to the up to ?37% loss by extreme drought (?32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process‐based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.