Analysis of pedestrian accident severity by considering temporal instability and heterogeneity.

The aim of this study was to investigate the effects of temporal instability and possible heterogeneity on pedestrian accident severity, 48786 accident data from 2018 to 2021 in the UK STATS database were used as the study object, and accident severity was used as the dependent variable, and 49 accident characteristics were selected as independent variables from 6 characteristics of accident pedestrian, driver, vehicle, road, environment and time to construct the pedestrian accident mean heterogeneity random-parameter logit model and examined its temporal stability. The results of model estimation and likelihood ratio tests indicate that the variables affecting pedestrian injury severity are highly variable and not stable over the years. And further demonstrates the potential of models that address unobserved heterogeneity for significant relationships in pedestrian accident severity analyses.

The response of sediment transport and morphological evolution to storms with different characteristics.

Strong wave-current interaction under the impact of storm events can induce a series of complex sedimentary processes of sediment resuspension and transport and morphology changes, significantly changing the topography of coastal zones. However, coastal sedimentary processes during storm events have not been fully understood. In this study, we developed a wave-current-sediment coupled model to investigate the response of dynamical processes to extreme storm events. The model was first validated against the observed data for both storm conditions during the 2007 Typhoon Wipha and fair-weather conditions in 2016 in the Haizhou Bay (HZB) of the Yellow Sea. The simulated results indicated that the longshore sediment transport was dominated originally by tidal effects which were significantly enhanced by wind-induced waves during the passage of the Typhoon Wipha. Storms with different characteristics correspond to two typical sedimentary dynamic response modes based on a series of numerical experiments. The tidal pumping effect (T3 + T4 + T5) and gravitational circulation term (T6) controlled the total storm-induced sediment flux, and T6 played a crucial and special role, typically in the opposite direction of the dominant wind of the storm. The strong wind could lead to the stratification of the water column, causing the down-slope or up-slope cross-shore sediment transport, resulting in coastal seabed erosion/deposition. In addition, the onshore wind was found to have a stronger impact on the sedimentary process. The methodology and findings of this study provide a scientific basis for understanding the response mechanism of sediment transport during storm events in coastal areas.

Inverse kinematics solution and control method of 6-degree-of-freedom manipulator based on deep reinforcement learning.

The advent of Industry 4.0 has significantly promoted the field of intelligent manufacturing, which is facilitated by the development of new technologies are emerging. Robot technology and robot intelligence methods have rapidly developed and been widely applied. Manipulators are widely used in industry, and their control is a crucial research topic. The inverse kinematics solution of manipulators is an important part of manipulator control, which calculates the joint angles required for the end effector to reach a desired position and posture. Traditional inverse kinematics solution algorithms often face the problem of insufficient generalization, and iterative methods have challenges such as large computation and long solution time. This paper proposes a reinforcement learning-based inverse kinematics solution algorithm, called the MAPPO-IK algorithm. The algorithm trains the manipulator agent using the MAPPO algorithm and calculates the difference between the end effector state of the manipulator and the target posture in real-time by designing a reward mechanism, while considering Gaussian distance and cosine distance. Through experimental comparative analysis, the feasibility, computational efficiency, and superiority of this reinforcement learning algorithm are verified. Compared with traditional inverse kinematics solution algorithms, this method has good generalization and supports real-time computation, and the obtained result is a unique solution. Reinforcement learning algorithms have better adaptability to complex environments and can handle different sudden situations in different environments. This algorithm also has the advantages of path planning, intelligent obstacle avoidance, and other advantages in dynamically processing complex environmental scenes.

A novel PDIA3/FTO/USP20 positive feedback regulatory loop induces osteogenic differentiation of preosteoblast in osteoporosis.

Osteoporosis is a chronic skeletal disease and the major source of risk for fractures in aged people. It is urgent to investigate the mechanism regulating osteoporosis for developing potential treatment and prevention strategies. Osteogenic differentiation of preosteoblast enhances bone formation, which might be a promising strategy for treatment and prevention of osteoporosis. Protein disulfide isomerase family A, member 3 (PDIA3) could induce bone formation, yet the role of PDIA3 in osteogenic differentiation of preosteoblast remains unknown. In this study, m6 A RNA methylation was detected by methylated RNA immunoprecipitation (MeRIP), while mRNA stability was identified by RNA decay assay. Besides, protein-protein interaction and protein phosphorylation were determined using co-immunoprecipitation (Co-IP). Herein, results revealed that PDIA3 promoted osteogenic differentiation of preosteoblast MC3T3-E1. Besides, PDIA3 mRNA methylation was suppressed by FTO alpha-ketoglutarate dependent dioxygenase (FTO) as RNA methylation reduced PDIA3 mRNA stability during osteogenic differentiation of MC3T3-E1 cells. Moreover, ubiquitin specific peptidase 20 (USP20) improved FTO level through inhibiting FTO degradation while PDIA3 increased FTO level by enhancing USP20 phosphorylation during osteogenic differentiation of MC3T3-E1 cells, suggesting a positive feedback regulatory loop between PDIA3 and FTO. In summary, these findings indicated the mechanism of PDIA3 regulating osteogenic differentiation of preosteoblast and provided potential therapeutic targets for osteoporosis.

Identification and assessment of the drift velocity of green tides using the maximum cross-correlation method in the Yellow Sea.

The green tides outbreak events seriously threaten the ecological balance of the coastal areas. Quickly and accurately obtaining the spatial distribution and drift state of green tides is key to early warning. Based on Landsat 8 (L8) and Sentinel-2 (S2) image pair, the green tides drift velocity was extracted using the maximum cross-correlation (MCC) method, and windage was calculated by combining ocean current and wind data. The results of the MCC method were validated. Ulva's drift in the Yellow Sea is shaped by both ocean currents and wind, closely aligning with the direction of the currents. Notably, the northward drift velocity of Ulva exhibits a clear boundary around 34°40'N. Windage shows similar characteristics with the Ulva drift velocity, as its values vary with time and space. This study will enhance our comprehension of the dynamic mechanism of green tides drift.

A Comparative Study of the Typing Performance of Two Mid-Air Text Input Methods in Virtual Environments.

Inputting text is a prevalent requirement among various virtual reality (VR) applications, including VR-based remote collaboration. In order to eliminate the need for complex rules and handheld devices for typing within virtual environments, researchers have proposed two mid-air input methods-the trace and tap methods. However, the specific impact of these input methods on performance in VR remains unknown. In this study, typing tasks were used to compare the performance, subjective report, and cognitive load of two mid-air input methods in VR. While the trace input method was more efficient and novel, it also entailed greater frustration and cognitive workload. Fortunately, the levels of frustration and cognitive load associated with the trace input method could be reduced to the same level as those of the tap input method via familiarity with VR. These findings could aid the design of virtual input methods, particularly for VR applications with varying text input demands.

Combining Deep Learning and Hydrological Analysis for Identifying Check Dam Systems from Remote Sensing Images and DEMs in the Yellow River Basin.

Identifying and extracting check dams is of great significance for soil and water conservation, agricultural management, and ecological assessment. In the Yellow River Basin, the check dam, as a system, generally comprises dam locations and dam-controlled areas. Previous research, however, has focused on dam-controlled areas and has not yet identified all elements of check dam systems. This paper presents a method for automatically identifying check dam systems from digital elevation model (DEM) and remote sensing images. We integrated deep learning and object-based image analysis (OBIA) methods to extract the dam-controlled area's boundaries, and then extracted the location of the check dam using the hydrological analysis method. A case study in the Jiuyuangou watershed shows that the precision and recall of the proposed dam-controlled area extraction approach are 98.56% and 82.40%, respectively, and the F1 score value is 89.76%. The completeness of the extracted dam locations is 94.51%, and the correctness is 80.77%. The results show that the proposed method performs well in identifying check dam systems and can provide important basic data for the analysis of spatial layout optimization and soil and water loss assessment.

Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval.

Near-field high-resolution synthetic aperture radar (SAR) imaging is mostly accompanied by a large number of data acquisition processes, which increases the system complexity and device cost. According to extensive reports, reducing the number of sampling points of a radar in space can greatly reduce the amount of data. However, when spatial sparse sampling is carried out, a ghost normally appears in the imaging results due to the high side lobes generated in the azimuth. To address this issue, a technique is introduced in this paper to recover the blank data through amplitude and phase compensation based on the correlation between sparse array sampling through adjacent points. Firstly, the data sampled by the sparse array is compressed in the range direction to obtain the expected data slices in the same range direction. Then, the blank element of the slice is compensated for with amplitude and phase to obtain full aperture data. Finally, the matched filter method is used to aid in the image reconstruction. The simulation results verified that the method proposed in this paper can effectively reconstruct the image under two kinds of sparse sampling conditions. Thus, a simple single-input single-output (SISO) synthetic aperture radar imaging test bench is established. Compared with the results of a 1 mm (1/4 λ) sampling interval, the quality of the reconstructed image under the condition of a 4 mm (1 λ) sampling interval still stands using our proposed method. Demonstrated by the experiment, the normalized root-mean-square error(NMSE) is 5.75%. Additionally, when the spatial sampling points are sampled randomly with 30% of the full sampling condition, this method can also restore and reconstruct the image with high quality. Due to the decrease of sampling points, the data volume can be reduced, which is beneficial for improving the scanning speed and alleviating the pressure of data transmission for near-field high resolution SAR imaging systems.

Sensitivity analysis of greenhouse gas emissions at farm level: case study of grain and cash crops.

Sensitivity analysis is useful to downgrade/upgrade the number of inputs to limit greenhouse emissions and enhance crop yield. The primary data from the 300 rice (grain crop) and 300 cotton (cash crop) farmers were gathered in face-to-face interviews by applying a multistage random sampling technique using a well-structured pretested questionnaire. Energy use efficiency was estimated with data envelopment analysis (DEA) model, and a second-stage regression analysis was conducted by applying Cobb-Douglas production function to evaluate the influencing factors affecting. The results exhibit that chemical fertilizers, diesel fuel and water for irrigation are the major energy inputs that are accounted to be 15,721.55, 10,787.50 and 6411.08 MJ ha-1 for rice production, while for cotton diesel fuel, chemical fertilizer and water for irrigation were calculated to be 13,860.94, 12,691.10 and 4456.34 MJ ha-1, respectively. Total GHGs emissions were found to be 920.69 and 954.71 kg CO2eq ha-1 from rice and cotton productions, respectively. Energy use efficiency (1.33 and 1.53), specific energy (11.03 and 7.69 MJ ha-1), energy productivity (0.09 and 0.13 kg MJ-1) and energy gained (14,497.85 and 20,047.56 MJ ha-1) for rice and cotton crop, respectively. Moreover, the results obtained through the second-stage regression analysis revealed that excessive application of fertilizer had a negative impact on the yield of rice and cotton, while farm machinery, diesel fuel and biocides had a positive effect. We hope that these findings could help in the management of the energy budget that we believe will reduce the high emissions of GHGs to address the growing environmental hazards.

Identifying a novel KLF2/lncRNA SNHG12/miR-494-3p/RAD23B axis in Spare Nerve Injury-induced neuropathic pain.

Spinal cord injury (SCI) is a devastating condition for patients, affecting nearly 2.5 million people globally. Multiple side effects of SCI have resulted in a terrible life experience for SCI patients, of which neuropathic pain has attracted the most scientific interest. Even though many efforts have been made to attenuate or eliminate neuropathic pain induced by SCI, the outcomes for patients are still poor. Therefore, identifying novel diagnosis or therapeutic targets of SCI-induced neuropathic pain is urgently needed. Recently, multiple functions of long non-coding RNA (lncRNA) have been elucidated, including those in SCI-induced neuropathic pain. In this study, lncRNA small nucleolar RNA host gene 12 (SNHG12) was found to be upregulated in the dorsal root ganglion (DRGs) of rats with spare nerve injury (SNI). By constructing SCI rat models, we found that lncRNA SNHG12 expression was increased in the DRGs, and mainly distributed in the cytoplasm of PC12 cells. Paw withdrawal threshold (PWT), paw withdrawal latency (PWL), and enzyme linked immunosorbent assay (ELISA) results indicated that lncRNA SNHG12 knockdown attenuated SNI-induced neuropathic pain, and decreased the expression levels of interleukin (IL)-1ß, IL-6, and tumour necrosis factor α (TNF-α) in the DRGs. Bioinformatics analysis, RNA pull-down, chromatin immunoprecipitation (ChIP), and luciferase reporter gene assays showed that lncRNA SNHG12 regulates the RAD23 homologue B, nucleotide excision repair protein (RAD23B) expression, through targeting micro RNA (miR)-494-3p. Furthermore, the study indicated that Kruppel-Like Factor 2 (KLF2) could regulate lncRNA SNHG12 expression in PC12 cells. This study identified a novel KLF2/lncRNA SNHG12/miR-494-3p/RAD23B axis in SNI-induced neuropathic pain, which might provide a new insight for developing novel diagnosis, or therapeutic targets of SCI-induced neuropathic pain in the future.

How do precipitation events modify the stable isotope ratios in leaf water at Lhasa on the southern Tibetan Plateau?

Serving as a medium between source water and cellulose, leaf water contributes to the isotope ratios (δ18O, δ2H) of plant organic matter, which can be used for paleoclimate reconstruction. This study is the first to examine the diurnal variations in the δ18O and δ2H of leaf water on the southern Tibetan Plateau. The δ18O and δ2H of leaf water were relatively low when precipitation events occurred. In particular, 18O and 2H of leaf water became extremely depleted 5 h after the precipitation event. Our findings demonstrate that precipitation can modify the isotope ratios of leaf water from external and internal causes. First, precipitation events affect meteorological elements, lead to decreases in leaf transpiration, and immediately weaken the isotope enrichment of leaf water ('rapid effect' of precipitation). Second, precipitation events affect the internal plant-soil water cycle process, causing the plant to preferentially use deeper soil water, and the corresponding isotope ratios of leaf water exhibit extremely low values 5 h after precipitation events ('delay effect' of precipitation). This study suggests that researchers need to be cautious in separating the signals of precipitation and hydrological processes when interpreting isotope records preserved in tree-ring cellulose archives from the Tibetan Plateau.

TRIM21 inhibits the osteogenic differentiation of mesenchymal stem cells by facilitating K48 ubiquitination-mediated degradation of Akt.

Tripartite motif containing 21 (TRIM21) is a member of the TRIM protein family with E3 ubiquitin ligase activity. Recent studies have demonstrated that TRIM21 widely contributes to physiological and pathological processes by ubiquitylating critical proteins in many kinds of cells. Additionally, multiple studies have shown that TRIM21 plays an important role in multiple cell differentiation processes. However, whether TRIM21 modulates the osteogenic differentiation process of mesenchymal stem cells (MSCs) remains unclear. In this study, we demonstrated that the expression of TRIM21 was decreased during the osteogenic process of MSCs and that TRIM21 negatively regulated the osteogenic capacity of MSCs both in vitro and in vivo. Moreover, we further demonstrated that TRIM21 modulated the osteogenic process of MSCs by acting as an E3 ubiquitin ligase to mediate the K48-linked ubiquitination of Akt and cause degradation. In summary, these results emphasize the critical role of TRIM21 in bone formation and TRIM21 may be a promising target to improve the clinical use of MSCs in tissue engineering.

Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia.

With the progress of urbanization, atmospheric pollution and physical health issues caused by the increase of aerosol optical depth (AOD) become more and more prominent. Hence, population exposure risk to AOD becomes a research hotspot. The arid Central Asia (ACA) has a generally high AOD and is a major source area for dust aerosols in the world. Only few studies have discussed population exposure risk to AOD in ACA. Based on multisource remote sensing data, and used population exposure risk model, this study evaluated population exposure risk to AOD in six ecological zones (Northern steppe region of ACA (NSCA), Aral Sea desert area (ASDA), Tianshan Mountains (TSMT), Junggar Basin desert area (JBDA), Tarim Basin desert area (TBDA) and Hexi corridor desert area (HCDA)). Generally, AOD in ACA was kept increasing from 2000 to 2015, and it increased mostly in HCDA and areas near the Aral Sea (p < 0.001). With respect to seasonal variations, the maximum AOD was observed in spring and autumn, and the minimum was in winter. Considering land use changes, AOD was mainly manifested by the reduction of water bodies and expansion of construction lands. This was the mostly significant in NSCA and ASDA (p < 0.01). The population exposure risk to AOD in ACA was increasing continuously from 2000 to 2015, and high-value regions (>9) concentrated in oases, specifically, in the Aral Sea basin and Tarim River basin.The Aral Sea basin became the major AOD source region in ACA due to the shrinking water area after unreasonable development and utilization of water resources. These further increase population exposure risk to AOD in the Aral Sea area. Hence, ecological restoration in terminal lakes of ACA will become the key to lower population exposure risk to AOD practically.

Impacts of climate change on vegetation phenology and net primary productivity in arid Central Asia.

Vegetation is highly sensitive to climate changes in arid regions. The relationship between vegetation and climate changes can be effectively characterized by vegetation phenology. However, few studies have examined the vegetation phenology and productivity changes in arid Central Asia (ACA). The vegetation phenological information of ACA was extracted using MODIS NDVI (Normalized Difference Vegetation Index) data, and the dynamics of vegetation phenological changes under spatiotemporal variations were quantitatively assessed. Moreover, the impacts of climate change on vegetation phenology and net primary productivity were analyzed by combining meteorological data with that of MODIS NPP (Net Primary Productivity) during the same period. The results demonstrated that the start of the season (SOS) of vegetation in the study was concentrated from mid-February to mid-April, while the end of the season (EOS) was concentrated from early October to mid-December. The length of growing season (LOS) ranged from 6 to 10 months. The SOS of vegetation was gradually postponed at a rate of 0.16 d·year-1. The EOS advanced at a rate of 0.69 d·year-1. The LOS was gradually shortened at a rate of 0.89 d·year-1. For each per 1000 m increase in elevation, the SOS of vegetation was postponed by 12.40 d; the EOS advanced by 0.40 d, and the LOS was shortened by 11.70 d. For the impacts of climate changes on vegetation phenology and NPP, the SOS of vegetation phenology negatively correlated with temperature but positively correlated with precipitation and NPP. The EOS and LOS positively correlated with temperature but negatively with precipitation and NPP. Results indicated that the SOS was not moved ahead but was delayed, while the EOS advanced rather than being postponed under climate change. These results can offer new insights on the phenological response to climate change in arid regions and on non-systematic changes in phenology under global warming.

Projections of desertification trends in Central Asia under global warming scenarios.

Central Asia (CA) is a core area of global desertification, but the effect of the intensifying "global greening" policy on the desertification process under global warming scenarios in CA remains unclear. Based on multi-source remote sensing data and Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) 2b climate data, this study investigated desertification in CA using actual evapotranspiration (ETa), temperature and precipitation as driving factors. Coupling with the CA-Markov model, the inversion method of desertification was improved, and the evolution normal form of desertification in CA was proposed. Finally, spatio-temporal variations of desertification in CA were quantified. The results indicate that temperature, precipitation, and normalized difference vegetation index (NDVI) in CA increased during the historical period (1980-2015), with sudden changes in 1994. In contrast, although ETa exhibited fluctuating increases (7.41 mm/10 yr) during this period, no sudden changes were observed in 1994. In the future (2006-2099), the climate of CA will become warmer and wetter. With reference to 1980-2005, precipitation under global warming of 2.0 °C (GW2.0) will be higher than that under global warming of 1.5 °C (GW1.5) by 10.3 mm, and ETa will increase by 20.88 mm and 27.54 mm under GW1.5 and GW2.0, respectively. Although the area of desert lands has decreased (5.94 × 104 km2/10 yr), the area of potential desert lands has increased (0.17 × 104 km2/10 yr). With global warming, this situation will continue to intensify, mainly in Xinjiang of China, and Kazakhstan. The Aral Sea plays an important role in the desertification of CA. The potential increase in desert land under GW2.0 is equivalent to the current water area of the Aral Sea. The findings could provide policy support for combating desertification in CA and promoting the achievement of the Sustainable Development Goals.

Distribution pattern of Tugai forests species diversity and their relationship to environmental factors in an arid area of China.

Ecological restoration of degraded riparian Tugai forests is a key driver to combat desertification in arid regions. Previous studies have focused mainly on changes in groundwater as the underlying mechanisms of Tugai forest's decline. We evaluated species composition and diversity of Tugai forest and their relationship to groundwater, soil salinity, and soil nutrient. Using 73 quadrats (100 m × 100 m) from 13 transects located perpendicularly to river in the upper reaches of the Tarim River. Eighteen plant species belonging to sixteen genera and eight families were recorded, and the dominant species included Populus euphratica, Phragmites communis, and Tamarix ramosissima. Three P. euphratica stand ages were detected: young stand, mature stand, and old stand. There were significant differences in species diversity, groundwater depth, groundwater salinity, distance from the quadrat to the river channel, soil moisture content, pH, electrical conductivity, total salt, Cl-, SO42-, Ca2-, Mg2+, Na+, K+, soil organic carbon, and soil organic matter across the stand ages. Seven species were identified as indicators of the three stand ages. Redundancy analysis indicated that the Tugai forest diversity indices were negatively correlated with groundwater depth, groundwater salinity, and distance from the river, and positively associated with electrical conductivity, total salt, pH, Cl-, SO42-, CO32-, soil organic matter, soil organic carbon, and soil moisture content. Plant diversity was the highest at 3-6 m groundwater depth, followed by 0-3 m and then 6-9 m, with the lowest recorded at > 9 m. The appropriate groundwater depth for herbs was about 1-4 m, whereas the depth for trees and shrubs was about 3-6 m. The groundwater depth < 6 m was deemed suitable for the growth of desert riparian forests. This results provide a scientific reference for the ecological restoration and protection for Tugai forests in arid areas.

Impact of groundwater depth and soil salinity on riparian plant diversity and distribution in an arid area of China.

Riparian plant diversity in arid regions is sensitive to changes in groundwater. Although it is well known that groundwater has a significant influence on plant diversity, there have been few studies on how groundwater and soil salinity impact plant community in desert riparian ecosystems. Therefore, we surveyed 77 quadrats (100 m × 100 m) to examine the relationship between groundwater depth, groundwater salinity, soil salinity and plant community in the upper reaches of the Tarim River. Data were analyzed with two-way indicator species analysis (TWINSPAN), detrended canonical correspondence analysis (DCCA) and principal component analysis (PCA). The results indicated that Populus euphratica, Tamarix ramosissima, and Phragmites australis were the dominant plants among trees, shrubs and herbs, respectively. Five plant community types were classified. There were significant differences in species diversity, soil moisture, soil salinity, groundwater depth and groundwater salinity across the community types. The composition and distribution of plant community are significantly influenced by groundwater depth, groundwater salinity, soil moisture, distances from the river to the quadrats, soil pH, electrical conductivity, total salt, CO32-, Cl-, SO42-, Ca2+, Mg2+, Na+ and K+. Shallow groundwater depth, low groundwater salinity, and high soil moisture and soil salinity were associated with higher plant diversity.

Influences of 1.5 °C and 2.0 °C global warming scenarios on water use efficiency dynamics in the sandy areas of northern China.

Water use efficiency (WUE) is an important variable used in hydrometeorology study to reveal the links between carbon-water cycles in sandy ecosystems which are highly sensitive to climate change and can readily reflect the effects of it. In light of the Paris Agreement, it is essential to identify the regional impacts of 0.5 °C of additional global warming to inform climate adaptation and mitigation strategies. Using the modified Carnegie-Ames-Stanford Approach (CASA) and Advection-Aridity (AA) models with global warming values of 1.5 °C and 2.0 °C above preindustrial levels from Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) datasets, we conducted a new set of climate simulations to assess the effects of climate on WUE (the ratio of net primary productivity (NPP) to actual evapotranspiration (ETa)) in different sandy land types (mobile sandy land, MSL; semimobile/semifixed sandy land, SMSF; and fixed sandy land, FSL) during the period of baseline (1986-2005) and future (2006-2100). The spatiotemporal patterns of ETa, NPP, and WUE mostly showed increasing trends; the value of WUE decreased (6.40%) only in MSL with an additional 0.5 °C of warming. Meteorological and vegetation factors determined the variations in WUE. With warming, only the correlation between precipitation and WUE decreased in the three sandy land types, and the leaf area index (LAI) increased with an additional 0.5 °C of warming. The desertification degree comprehensively reflects the linkages among the standardized precipitation evapotranspiration index (SPEI), LAI and WUE. Simulation results indicated the sandy area extent could potential increase by 20 × 104 km2 per decade on average during 2016-2047 and that the increase could be gradual (2.60 × 104 km2 per decade) after 2050 (2050-2100). These results highlight the benefits of limiting the global mean temperature change to 1.5 °C above preindustrial levels and can help identify the risk of desertification with an additional 0.5 °C of warming.

Projections of actual evapotranspiration under the 1.5 °C and 2.0 °C global warming scenarios in sandy areas in northern China.

Actual evapotranspiration (ETa) is an essential component of Earth's global energy balance and water cycle. The Paris Agreement aspires to limit global mean surface warming to <2 °C and no >1.5 °C relative to preindustrial levels. However, it is uncertain how this global level will impact the shifts in the extents of sandy areas caused by global desertification. Using Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) datasets and advection-aridity models, we investigated the spatiotemporal features of ETa in sandy areas in northern China under global warming scenarios of 1.5 °C and 2.0 °C. The four climate models indicated significant increases in ETa in arid areas across northwestern China. Over time, the ETa value under only the representative concentration pathway 2.6 (RCP2.6) emission scenario increased towards a plateau and significantly increased in the other three emission scenarios (P < 0.01) under global warming of 1.5 °C and 2.0 °C. In terms of the spatial variations, ETa showed an increasing trend in all seasons except winter. The maximum ETa was 84.61 mm, and high values were mainly located in the southeast of the study area. Precipitation and the normalized difference vegetation index (NDVI) showed good correlations with ETa in the sandy areas in northern China. The sandy areas in northern China showed decreasing trends (0.45 km2/a) from 1980 to 2015. Under global warming of 2.0 °C (2040-2059) relative to that of 1.5 °C (2020-2039), the area of sandy land will increase at a rate of 27.04 km2 per decade (P < 0.01); after this period, the sandy land area in northern China may gradually stabilize, with a trend of 0.02 km2/a (2047-2100). Early efforts to achieve the 1.5 °C temperature goal could therefore markedly reduce the likelihood that large regions will face substantial global desertification and the related impacts.

Characterization of Chondrogenic Gene Expression and Cartilage Phenotype Differentiation in Human Breast Adipose-Derived Stem Cells Promoted by Ginsenoside Rg1 In Vitro.

BACKGROUND/AIMS: Investigating and understanding chondrogenic gene expression during the differentiation of human breast adipose-derived stem cells (HBASCs) into chondrogenic cells is a prerequisite for the application of this approach for cartilage repair and regeneration. In this study, we aim to characterize HBASCs and to examine chondrogenic gene expression in chondrogenic inductive culture medium containing ginsenoside Rg1. METHODS: Human breast adipose-derived stem cells at passage 3 were evaluated based on specific cell markers and their multilineage differentiation capacity. Cultured HBASCs were treated either with basic chondrogenic inductive conditioned medium alone (group A, control) or with basic chondrogenic inductive medium plus 10 µg/ml (group B), 50 µg/ml (group C), or 100µg/ml ginsenoside Rg1 (group D). Cell proliferation was assessed using the CCK-8 assay for a period of 9 days. Two weeks after induction, the expression of chondrogenic genes (collagen type II, collagen type XI, ACP, COMP and ELASTIN) was determined using real-time PCR in all groups. RESULTS: The different concentrations of ginsenoside Rg1 that were added to the basic chondrogenic inductive culture medium promoted the proliferation of HBASCs at earlier stages (groups B, C, and D) but resulted in chondrogenic phenotype differentiation and higher mRNA expression of collagen type II (CO-II), collagen type XI (CO-XI), acid phosphatase (ACP), cartilage oligomeric matrix protein (COMP) and ELASTIN compared with the control (group A) at later stages. The results reveal an obvious positive dose-effect relationship between ginsenoside Rg1 and the proliferation and chondrogenic phenotype differentiation of HBASCs in vitro. CONCLUSIONS: Human breast adipose-derived stem cells retain stem cell characteristics after expansion in culture through passage 3 and serve as a feasible source of cells for cartilage regeneration in vitro. Chondrogenesis in HBASCs was found to be prominent after chondrogenic induction in conditions containing ginsenoside Rg1.