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• Three novel highly active phthalimide based ionic liquids are synthesized. • 98.7 % yield is achieved under the atmospheric CO 2 pressure and 30 °C. • Contributions of each active components are analyzed by DFT calculations. • Inert cation and moderate basicity of ionic liquid are crucial items. The carboxylative coupling of carbon dioxide (CO 2) with propargyl alcohols is an important pathway to accomplish the reusability of CO 2. Moreover, the α-methylene cyclic carbonates are the critical intermediate to generate the fine chemicals and pharmaceuticals. However, the robust catalyst is still limited to perform this reaction under the mild conditions, especially to obtain the equilibrium among several reaction conditions including reaction time, reaction temperature, and catalyst amount. The novel ionic liquid, [Pth][BTMA], is synthesized in this work to be catalyst for the title reaction along with AgOAc as the co-catalyst. To explore the influence of cation, anion, and Ag salt, other four ionic liquids are utilized to catalyze the reaction along with five different Ag salts. Under the atmospheric pressure and 30 °C, the 98.7 % product yield is achieved by [Pth][BTMA]/AgOAc within 9 h, which are better than most of catalysts in literature. More important, the role of cation and anion in the catalysis is elucidated by the 1H NMR, 13C NMR, and density functional theory (DFT) calculations. Both the moderate basicity and inert cation of [Pth][BTMA] contribute the excellent catalytic activity. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • This study theoretically reveals the impact of rigidly fused π-bridge on the photovoltaic efficiency of organic dyes. • Introducing strong electron-withdrawing group can enhance the photovoltaic performance of the dye molecule. • This work provides a useful guidance for designing highly-efficient dye sensitizers by molecular engineering. The influence of different types of π-bridges in triphenylamine-based organic sensitizers on their photophysical and photovoltaic properties is investigated by using sophisticated first-principles calculations and reliable theoretical models. Two well-designed strategies, namely the cyclization between separate benzothiadiazole and bithiophene as well as the insertion of electron-rich/deficient groups between the rigidly fused π-bridge and anchor moieties are proposed. It is found that the calculated photophysical and photovoltaic parameters of the dye with an unfused π-bridge reproduce very well the available experimental data, ensuring the rationality of present theoretical methods and models. The designed dye featuring a rigidly fused π-bridge shows a remarkable decrease of power conversion efficiency (PCE), with respect to the dye with an unfused π-spacer (6.36% vs. 11.18%), which implies that simply cyclizing separate aromatic heterocyclic groups is unfavorable for improving the photoelectrical performance. However, the PCE can be increased to 14.20% when an electron-rich thiophene group is introduced between the fused π-bridge and the anchor units. More importantly, replacing the thiophene moiety with an electron-deficient benzothiadiazole group can further improve the photovoltaic performance, and consequently a PCE as high as 23.81% is achieved, which is a significant theoretical improvement for triphenylamine-based organic dyes. To sum up, this study theoretically elucidates the effect of unfused and fused π-bridges as well as the introduction of electron-rich/deficient groups in organic dyes on their photophysical and photoelectric properties and provides a useful guidance for further designing highly-efficient dye sensitizers by molecular engineering. [ABSTRACT FROM AUTHOR]

Hydrogen storage is an essential technology for the development of a sustainable energy system. Magnesium (Mg) and its alloys have been identified as promising materials for hydrogen storage due to their high hydrogen storage capacity, low cost, and abundance. However, the use of Mg alloys in hydrogen storage materials requires a thorough understanding of the material properties and their behavior under different conditions. In this study, we established a database of Mg alloys properties and their hydrogen storage performance, which was then used to train various machine learning (ML) regression models with maximum hydrogen storage (Ab_max) and maximum hydrogen release (De_max) as outputs. These models possess excellent prediction accuracy. The GBR model for Ab_max and MLP model for De_max present highest accuracy with the R2 of the test set reaching 0.947 and 0.922, respectively. The Shapley additive explanations (SHAP) algorithm was then used to interpret the best ML models, and the critical values of important descriptors were obtained to guide the design of hydrogen storage materials. Finally, based on the best ML models, the Ab_max and De_max were predicted for the Mg-based binary alloys with other 16 metal elements and Mg–Ni-based ternary alloys with other 15 metal elements, respectively. Among them, 96Mg-4Sm and 95Mg–1Ni-4Sm have higher Ab_max and De_max of 6.31 wt% and 5.69 wt%, 6.64 wt% and 5.63 wt%, respectively, meeting the requirement of high Ab_max/De_max at the operating temperature below 300 °C. [Display omitted] • A database of Mg-based hydrogen storage materials was constructed. • High-precision machine learning models of maximum hydrogen storage/release were established. • The critical factors affecting hydrogen storage property of Mg alloys were revealed. • Potential excellent binary and ternary Mg alloys for hydrogen storage were predicted. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning was used to predict the yields of ionic liquids catalyzed CO 2 cyclization reaction for the first time. • Density function theory calculations were conducted to obtain reaction descriptors with high-precision. • The strategy of refining the database quality improves the predictive ability of RF model significantly. • The ML model was interpreted well by the shapely additive explanation method. Ionic liquids are one of the excellent catalysts for the CO 2 cycloaddition reaction, which is an effective means to realize CO 2 utilization and alleviate environmental problems. However, the design of ionic liquids catalysts has great blindness due to the diversity of their anion and cation structures. Herein, we collected a database of 866 samples for CO 2 cyclization with ionic liquids as catalysts and established the yield prediction model with various machine learning regression algorithms. Together with density function theory (DFT) calculated molecular electronic properties and the experimental conditions as the input descriptors, random forest (RF) model has better prediction accuracy than support vector regression (SVR) and multilayer perceptron (MLP) for the whole data. When the dataset was subdivided into different subsets based on substrates and ionic liquids, the prediction accuracy of the model was also improved. For the imidazole subset with no additional solvent or additive added, the RF model achieves good accuracy with the R2 of 0.80 for the test data. Moreover, the shapely additive explanation (SHAP) method was used to interpret the ML models. The strategy of refining the descriptors and dataset used in our work provides guidance for establishing highly reliable machine learning models in the chemistry reactions. [ABSTRACT FROM AUTHOR]

• Migratory corridors and core stopover sites were identified to guide conservation. • Movement patterns and utilization intensity en route have seasonal differences. • Seasonal migratory differences affected the efficiency of protection systems. • High selection in artificial surfaces calls for eco-friendly land use mode. • Satellite tracking and remote sensing facilitate the study of movement ecology. Migratory species interact with different ecosystems in different regions during migration, making them more environmentally sensitive and therefore more vulnerable to extinction. Long migration routes and limited conservation resources desire clear identification of conservation priorities to improve the allocation efficiency of conservation resources. Clarifying the spatio-temporal heterogeneity of the utilization intensity during migration is an effective way to guide the conservation areas and priority. 12 Oriental White Storks (Ciconia boyciana), listed as an "endangered" species by the IUCN, were equipped with satellite-tracking loggers to record their hourly location throughout the year. Then, combined with remote sensing and dynamic Brownian Bridge Movement Model (dBBMM), characteristics and differences between spring and autumn migration were identified and compared. Our findings revealed that: (1) the Bohai Rim has always been the core stopover area for the Storks' spring and autumn migration, but the utilization intensity has spatial differences; (2) differences in habitat selection resulted in differences in the Storks' spatial distribution, thus affecting the efficiency of existing conservation systems; (3) the shift of habitat from natural wetlands to artificial surfaces calls for the development of eco-friendly land use mode; (4) the development of satellite tracking, remote sensing, and advanced data analysis methods have greatly facilitated movement ecology, even though they are still under development. [ABSTRACT FROM AUTHOR]

Concentrating limited resources to protect as many species as possible is the most effective way to improve conservation efficiency, of which identifying the clustering characteristics of species in urgent need of conservation is a prerequisite. In this study, selectivity was used to characterize the taxonomic and geographic clustering characteristics of extinction risk for threatened spermatophytes in China. Results showed that the number of threatened species was highly clustered in taxonomy, floristic regions, and at the county scale. Clustering of threatened species was detected in 18 orders, 53 families, and 201 genera, account for 15.0 %–32.8 % of the total and 52.5 %–71.3 % of the threatened spermatophyte species. 18 out of 57 floristic regions and subregions were over-threatened. 298 counties (10.1 % of China's territory) were over-threatened, covering 83.4 %, 79.3 % and 80.3 % of the total species, threatened species and endemic species, respectively. Counties with the top 10.0 % species richness (SR), threatened species richness (TR) and endemic species richness (ER) covered 79.5 %, 70.3 % and 75.0 % of the total species, threatened species and endemic species, respectively, while covering only 5.7 % of the area of China. More than half of the counties with the top 10.0 % SR, TR and ER, as well as those over-threatened, were outside the existing conservation strategy and action plan. Areas most at risk were also found to be moderately affected by human influence (evaluated by human influence index (HII)) in China. This study provides a new perspective for the study of conservation priority in taxonomic groups and geographical areas. [ABSTRACT FROM AUTHOR]

Desertification is treated as an important and critical environmental hazard. In the face of increasingly serious grassland desertification, China has made great efforts to combat desertification and a series of key national ecological projects has been launched. However, accurate, timely, and effective monitoring and assessment are required to determine whether these projects work well. The Horqin sandy land represents the largest area of sandy land in China. In this article, the Naiman and Ongniud Banners were studied because these contain the main desertified grassland in Horqin. Next, a desertification classification and grading system was designed for the Horqin sandy land after conducting fieldwork. Based on spectral mixture analysis (SMA) and decision-tree methods, we interpreted Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images of the study area from four years: 1985, 1992, 2001, and 2013. Overall, the following results were obtained. The total area of desertified grassland in the Naiman and Ongniud Banners increased from 5979 km2in 1985 to 9195 km2in 1992 (an increase of 53.79%) and then decreased to 7828 km2in 2001 and to 6023 km2in 2013. The changes in the areas of desertified grassland with various degrees of desertification displayed the same trends as that of the total desertified grassland area. The severely desertified grassland expanded from 1872 km2in 1985 to 3723 km2in 1992 before shrinking to 2189 km2in 2013. The annual rates of expansion of desertified grassland during the periods 1985–1992, 1992–2001, and 2001–2013 were 7.68%, −1.65%, and −1.92%, respectively, and the corresponding expansion rates of severely desertified grassland were 14.12%, −3.48%, and −1.19%, respectively. Both the desertified grasslands and the areas with various degrees of desertification displayed significant expansion during the period 1985–1992. Since 1992, this trend has reduced. During the study period, the changes in temperature and precipitation did not benefit the reversal of grassland sandy desertification. Furthermore, the growing population and expansion of livestock production and farming inhibited such reversal. However, the results presented in this article suggest that a reversal in grassland sandy desertification has been occurring since 1992. The results indicate that ecological engineering measures have helped reverse desertification and have promoted the restoration of grassland vegetation. [ABSTRACT FROM PUBLISHER]

Abstract: State and local governments in China have implemented a series of grassland protection policies to address the problem of grassland degradation. In 2003, Ningxia was the first province to implement a province-wide grazing ban. The effect of this ban is contentious at all levels of government and has become a topic of public concern. Grassland desertification is the most direct indicator of the effect of the grazing ban. We selected 14 counties and cities in north-central Ningxia as the study area. A desertification classification and grading system for Ningxia's grassland was then designed based on fieldwork and expert review. Using the Spectral Mixture Analysis (SMA) and decision-tree methods, we interpreted Landsat TM/ETM+ images of the study area during four years: 1993, 2000, 2006 and 2011. The following results were obtained: from 1993 to 2011, the area of desertified grassland in north-central Ningxia decreased gradually from 8702km2 in 1993 to 7485km2 in 2011, a decrease of 13.98%; the degree of desertification gradually decreased from 3573km2 of severely desertified grassland in 1993 to 1450km2 in 2011, a decrease of 59.41%; desertified grassland vegetation was restored rapidly during 2000–2006 and 2006–2011, reducing the total area of desertified grassland annually by 1.87 and 0.61%, respectively; finally, the area of severely desertified grassland decreased annually by 5.78 and 6.28% during 2000–2006 and 2006–2011, respectively. These results show that the region-wide grazing ban, together with other ecological engineering measures, has helped reverse desertification and promote the restoration of grassland vegetation. [Copyright &y& Elsevier]

Clarifying species-environment relationships is crucial for the development of efficient conservation and restoration strategies. However, this work is often complicated by a lack of detailed information on species distribution and habitat features and tends to ignore the impact of scale and landscape features. Here, we tracked 11 Oriental White Storks (Ciconia boyciana) with GPS loggers during their wintering period at Poyang Lake and divided the tracking data into two parts (foraging and roosting states) according to the distribution of activity over the course of a day. Then, a three-step multiscale and multistate approach was employed to model habitat selection characteristics: (1) first, we minimized the search range of the scale for these two states based on daily movement characteristics; (2) second, we identified the optimized scale of each candidate variable; and (3) third, we fit a multiscale, multivariable habitat selection model in relation to natural features, human disturbance and especially landscape composition and configuration. Our findings reveal that habitat selection of the storks varied with spatial scale and that these scaling relationships were not consistent across different habitat requirements (foraging or roosting) and environmental features. Landscape configuration was a more powerful predictor for storks' foraging habitat selection, while roosting was more sensitive to landscape composition. Incorporating high-precision spatiotemporal satellite tracking data and landscape features derived from satellite images from the same periods into a multiscale habitat selection model can greatly improve the understanding of species-environmental relationships and guide efficient recovery planning and legislation. [ABSTRACT FROM AUTHOR]

• The mechanism of title reaction catalyzed by AgNO 3 /[C 2 C 1 mim][OAc] is elucidated. • The contributions of anions and cations are discussed in detail. • The differences between different catalysts are discussed in depth. The chemical fixation of CO 2 has attracted elevated attentions since it is a promising pathway to solve the serious environmental pollution and fuel shortage. The three-component reaction of CO 2 , propargyl alcohols, and 2-aminoethanols is one of the most attractive candidates due to the wide applications and extreme importance of products. However, the reaction mechanism is desired to explore due to the complicated catalytic components and substrates. In addition, contribution of each catalytic component and actual catalytic component are still not clear. The mechanism of title reaction catalyzed by AgNO 3 /[C 2 C 1 mim][OAc] is elucidated by the density functional theory (DFT). In addition, the catalytic routes of AgNO 3 /[C 4 C 1 mim][OAc], AgNO 3 /[DBUH][OAc], AgNO 3 /[C 2 C 1 mim][NO 3 ], and AgNO 3 /[C 2 C 1 mim][ClO 4 ] are also considered to unfold the contribution of cation and anion. The atoms in molecule (AIM) and noncovalent interactions (NCI) are calculated for some important configurations to deeply understand the difference among various catalysts. To elucidate these issues is favorable to explore the more robust catalysts for this reaction. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Suitable transition metals are selected for CO 2 RR on the defective g-C 3 N 4. • Machine learning builds a bridge between catalytic activity and various features. • Selected transition metals are coordinated on other carbon supporter. • The 25% doped amount of uncoordinated N is suitable. Electrocatalysis of CO 2 reduction reaction (CO 2 RR) has attracted elevated attentions since it is favorable to prompt the carbon cycle in a sustainable pathway. However, the applications of this technology is obstructed by the low activity and selectivity due to the inert of CO 2 and favored hydrogen evolution reaction. Development of robust catalysts is the most feasible method to decrease the overpotential and suppress the competitive side reaction. Single-atom-catalysts (SACs) is one of the most promising catalysts with the satisfied activity. However, the clear and popular rules are still absence to explore the SACs with the desired properties due to the complicated items related with the overall performance. The suitable transition metals (TMs) are screened out based on the defective g-C 3 N 4 (V N -C 3 N 4) supporter by the first principle. Among 24 screened transition metals, the Ni-V N -C 3 N 4 exhibits the better performance as compared to others. Later, the machine learning (ML) algorithm builds a bridge between catalytic activity and intrinsic structural and atomic features. Then, the same coordination environment is constructed on the graphene to explore the possibility to replace the V N -C 3 N 4 with other carbon substrate. When the doped amount of uncoordinated N reaches 25 wt%, the catalytic activity of Ni N C SAC based on the graphene is close to that based on the V N -C 3 N 4. This work introduces a method to construct the SACs by regulating the N -doped amount on the supporter. [ABSTRACT FROM AUTHOR]

Abstract The uncertain threat status of species inevitably influences their focus on conservation. Just as in extinction risk, the non-randomness phenomenon related to uncertainty (also referred to as selectivity), which is a certain character cluster in some groupings, also exists in data deficiency of species' knowledge. In order to illustrate this kind of non-random phenomenon and explain the uncertainties it caused, we performed a hypergeometric test on taxonomic and geographic groupings of China's spermatophyte species and quantified two factors— frequency of collections and spatial accessibility — to indicate the primary causes of spatial knowledge deficiencies. We found that selectivity in data deficiency exists both taxonomically and geographically. Fifteen of the families were more deficient than expected, which included 30.0% of species and 56.3% ranked data deficient (DD). Among these, eight families were statistically highly significant with p < 0.001 and included 25.2% of species and 50.0% ranked DD. Forty-six families were less deficient than expected. With respect to floristic division, four of 29 floristic regions and subregions were more deficient than expected, and seven were less deficient than expected. Spatial autocorrelation analysis on DD species suggested an aggregated pattern of data deficiency in China (Moran's I = 0.58, z-score = 27.0, p < 0.001), and these areas that contained the highest numbers of DD species also contained the highest number of species (Spearman's R2 = 0.879, p < 0.001). However, the largest DD ratio had a low correlation with the richest DD spatial diversity. Moreover, we found the larger the DD ratio was, the lower the frequency of collections and the poorer the spatial accessibility would be. In the research, we showed that the uncertainties associated with DD species would alter the non-randomness in the selectivity of data deficiency and further affect the focus of conservation. Only with a full understanding of the process and mechanisms of data deficiency can we determine where and what kind of actions are necessary to improve the knowledge of plant diversity. [ABSTRACT FROM AUTHOR]

[Display omitted] • The catalyzed mechanism of propargylic alcohol and H 2 O is investigated. • CO 2 could be absorbed by the [Bu 4 P+] cation to form [Bu 4 P -H -CO 2 ] complex. • A novel mechanism is proposed with the [Bu 4 P -H -CO 2 ][Im +H ] complex employed as the actual catalytic species. Hydration of propargylic alcohols is one of the most important pathways to synthesize α-hydroxy ketones. However, its mechanism is still obscure in the presence of multiple catalytic species. The mechanism of catalysis for the hydration of propargylic alcohols in the presence of [Bu 4 P+][Im-] is investigated along with the carbon dioxide (CO 2) as the co-catalyst by the density functional theory (DFT) with the aim to elucidate the role of [Bu 4 P+] cation and [Im-] anion. The CO 2 could be absorbed by the [Bu 4 P+] cation to form [Bu 4 P -H -CO 2 ] complex after the hydrogen atom in [Bu 4 P+] cation transfers to the [Im-] anion. Then, both [Bu 4 P -H -CO 2 ] and [Im +H ] are employed as the actual catalytic species to construct a new pathway. It is the more favorable as compared with the previously reported pathways. In previous reported pathway, the [Im-] anion is regarded as the real catalytic species, while [Bu 4 P+] cation plays the auxiliary role to stabilize the substrate by the hydrogen bond. The mechanism of the same reaction catalyzed by other four different ionic liquids is also investigated to further testify the contribution of [Bu 4 P+] cation and [Im-] anion in the catalysis. This work not only provides a new insight into the cation-regulated pathway for the hydration of propargylic alcohols but also points out a new strategy to design the ionic liquids achieving the improved catalytic efficiency. [ABSTRACT FROM AUTHOR]

Grassland vegetation growth directly reflects plant growth conditions, and growth processes are an important component of ecological status assessments of grasslands and can provide timely guidance for agricultural production. In this study, the Xilingol Grassland in Inner Mongolia was used as the study area, and a monitoring indicator system for the remote sensing of grassland vegetation growth was established based on 16 days of Moderate Resolution Imaging Spectroradiometer (MODIS) data and ground sampling data, which were used to assess the suitability of the indicator system. A monitoring indicator system for the remote sensing of grassland vegetation that included the modified growth index (MGI)-normalized difference vegetation index (NDVI), MGI-enhanced vegetation index (EVI), growth index (GI)-NDVI, and GI-EVI was established by using the year 2000 as the base year, two vegetation indices and difference and normalized difference methods. A model for estimating the ground growth (g) was then constructed by using expert opinion scoring and the Analytic Hierarchy Process (AHP) to determine the weights of vegetation coverage (c), height (h), and yield (y) in the ground plots, with the model calculated as follows:g = 0.2543c + 0.1848 h + 0.5609y. Additionally, the ground growth value was calculated according to the ground growth model, and the values obtained from the remote-sensing indicators in the corresponding region were subjected to a correlation analysis based on this partition. The remote-sensing growth indices suitable for temperate steppe, meadow steppe, and desert steppe regions were GI-EVI, MGI-NDVI, and GI-NDVI, respectively. Finally, vegetation growth in the Xilingol Grassland was evaluated using the optimal remote-sensing GI for each area, and the results indicated that the areas with the greatest growth improvement occurred in the temperate steppe region followed by the meadow steppe region, whereas vegetation growth improvement was insignificant in the desert steppe region. The results of this study have important significance for the economic development and ecological environmental improvement of pastoral and semi-pastoral regions and can be used to determine the optimal use and scientific management of grasslands. [ABSTRACT FROM PUBLISHER]

Grassland is not only an important landscape of the Beijing–Tianjin sandstorm source control project area, but also a significant object of the Beijing–Tianjin sandstorm source control project. By taking the situation in 2000 as the base of comparison and using the established grassland vegetation growth model, the monitoring and evaluation of grassland vegetation dynamic variation in the project area from 2000 to 2010 was conducted based on MODIS 16 days NDVI data. The conclusions are as follows: (1) The comparative result of average growth between each year from 2001 to 2010 and the base year was on the good side in general; the grassland growth was good both in the early and later periods of grassland growth peak season than in the first years of the project implementation, indicating that the implementation of the Beijing–Tianjin sandstorm source project has significantly improved the growth conditions of grassland vegetation; (2) With regard to the annual dynamic variation of grassland growth, the area proportions of the grasslands, of which the average grassland growth was on the good side, fluctuated and increased slightly with the time changes in general. The area proportions of the grasslands, of which the average grassland growth was on the bad side, fluctuated and decreased in general. The area proportions of the grasslands with normal growth showed an increasing overall trend; (3) From the regional perceptive on four zones, including the northern arid grassland desertification control zone, Hunshandake sandy land control zone, the farming-pastoral area of desertified land control zone, and the water conservation zone of Yanshan hills and mountains, except that the grassland growth in the farming-pastoral area of desertified land control zone was bad, the average growth of other three zones was good each year from 2001 to 2010 compared with the base year. (4) In respect of space, the regions with big grassland growth variation in the project area were concentrated in the western and eastern sections of the northern arid grassland desertification control zone and the western section of Hunshandake sandy land control zone. The grassland growth variation in the water conservation zone of Yanshan hills and mountains and the farming-pastoral area of desertified land control zone were relatively stable. On one hand, the conclusions of this paper can evaluate the effectiveness of the project control, on the other hand, it can also provide scientific basics to grassland management departments, facilitate the rational utilization of grassland, and preserve the regional ecological balance. [ABSTRACT FROM AUTHOR]

It is critical to understanding grassland biomass and its dynamics to study regional carbon cycles and the sustainable use of grassland resources. In this study, we estimated aboveground biomass (AGB) and its spatio-temporal pattern for Inner Mongolia’s grassland between 2001 and 2011 using field samples, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS-NDVI) time series data, and statistical models based on the relationship between NDVI and AGB. We also explored possible relationships between the spatio-temporal pattern of AGB and climatic factors. The following results were obtained: (1) AGB averaged 19.1 Tg C (1 Tg = 1012 g) over a total area of 66.01 × 104 km2between 2001 and 2011 and experienced a general fluctuation (coefficient of variation = 9.43%), with no significant trend over time (R2 = 0.05,p > 0.05). (2) The mean AGB density was 28.9 g C m−2 over the whole study area during the 11 year period, and it decreased from the northeastern part of the grassland to the southwestern part, exhibiting large spatial heterogeneity. (3) The AGB variation over the 11 year period was closely coupled with the pattern of precipitation from January to July, but we did not find a significant relationship between AGB and the corresponding temperature changes. Precipitation was also an important factor in the spatial pattern of AGB over the study area (R2 = 0.41,p< 0.001), while temperature seemed to be a minor factor (R2 = 0.14,p< 0.001). A moisture index that combined the effects of precipitation and temperature explained more variation in AGB than did precipitation alone (R2 = 0.45,p< 0.001). Our findings suggest that establishing separate statistical models for different vegetation conditions may reduce the uncertainty of AGB estimation on a large spatial scale. This study provides support for grassland administration for livestock production and the assessment of carbon storage in Inner Mongolia. [ABSTRACT FROM PUBLISHER]

N-myc plays an important role in early cerebellar development; however, the role of N-myc in postnatal cerebellar development is still unknown. In this study, inducible and reversible N-myc mouse models (NmycTRE/TRE:tTS and NmycEGFP/TRE:tTS) are used to regulate and track the expression of endogenous N-myc in vivo. Loss of N-myc at the neonatal stage results in reduced proliferation of granule cell precursors (GCPs) and reduced cerebellar volume/mass. Restoration of N-myc expression no later than postnatal day 4 can rescue the cerebellar developmental defect caused by the absence of N-myc after birth. During cerebellar postnatal development, N-myc acts as a key switch, regulating the proliferation cycle of postnatal granule cell progenitors. Loss of N-myc significantly impairs the Sonic hedgehog signalling pathway, and disrupts the expression of cell cycle effectors with a significant reduction of Ccnd2. More importantly, N-myc negatively regulates the expression of microRNA-9 during postnatal cerebellar development. Our findings demonstrate that over-expression of miR-9 can inhibit the proliferation of GCPs. The regulation of these factors by N-myc is at least partly responsible for the switch role of N-myc in the proliferation cycle of GCPs. [ABSTRACT FROM AUTHOR]

Knowledge about grassland biomass and its dynamics is critical for studying regional carbon cycles and for the sustainable use of grassland resources. In this study, we investigated the spatio-temporal variation of biomass in the Xilingol grasslands of northern China. Field-based biomass samples and MODIS time series data sets were used to establish two empirical models based on the relationship of the normalized difference vegetation index (NDVI) with above-ground biomass (AGB) as well as that of AGB with below-ground biomass (BGB). We further explored the climatic controls of these variations. Our results showed that the biomass averaged 99.01 Tg (1 Tg=1012 g) over a total area of 19.6×104 km2 and fluctuated with no significant trend from 2001 to 2012. The mean biomass density was 505.4 g/m2, with 62.6 g/m2 in AGB and 442.8 g/m2 in BGB, which generally decreased from northeast to southwest and exhibited a large spatial heterogeneity. The year-to-year AGB pattern was generally consistent with the inter-annual variation in the growing season precipitation (GSP), showing a robust positive correlation (R2=0.82, P<0.001), but an opposite coupled pattern was observed with the growing season temperature (GST) (R2=0.61, P=0.003). Climatic factors also affected the spatial distribution of AGB, which increased progressively with the GSP gradient (R2=0.76, P<0.0001) but decreased with an increasing GST (R2=0.70, P<0.0001). An improved moisture index that combined the effects of GST and GSP explained more variation in AGB than did precipitation alone (R2=0.81, P<0.0001). The relationship between AGB and GSP could be fit by a power function. This increasing slope of the GSP–AGB relationships along the GSP gradient may be partly explained by the GST–GSP spatial pattern in Xilingol. Our findings suggest that the relationships between climatic factors and AGB may be scale-dependent and that multi-scale studies and sufficient long-term field data are needed to examine the relationships between AGB and climatic factors. [ABSTRACT FROM AUTHOR]