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Terrestrial ecosystems greatly contribute to carbon (C) emission reduction targets through photosynthetic C uptake.Net primary production (NPP) represents the amount of atmospheric C fixed by plants and accumulated as biomass. The Three-North Shelterbelt Program (TNSP) zone accounts for more than 40% of China’s landmass. This zone has been the scene of several large-scale ecological restoration efforts since the late 1990s, and has witnessed significant changes in climate and human activities.Assessing the relative roles of different causal factors on NPP variability in TNSP zone is very important for establishing reasonable local policies to realize the emission reduction targets for central government. In this study, we examined the relative roles of drought and land cover conversion(LCC) on inter-annual changes of TNSP zone for 2001–2010. We applied integrated correlation and decomposition analyses to a Standardized Evapotranspiration Index (SPEI) and MODIS land cover dataset. Our results show that the 10-year average NPP within this region was about 420 Tg C. We found that about 60% of total annual NPP over the study area was significantly correlated with SPEI (p<0.05). The LCC-NPP relationship, which is especially evident for forests in the south-central area, indicates that ecological programs have a positive impact on C sequestration in the TNSP zone. Decomposition analysis generally indicated that the contributions of LCC, drought, and other Natural or Anthropogenic activities (ONA) to changes in NPP generally had a consistent distribution pattern for consecutive years. Drought and ONA contributed about 74% and 23% to the total changes in NPP, respectively, and the remaining 3% was attributed to LCC. Our results highlight the importance of rainfall supply on NPP variability in the TNSP zone. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Recently, a number of single nucleotide polymorphisms (SNPs) were identified to be associated with late-onset Alzheimer disease (LOAD) through genome-wide association study data. Identification of SNP-SNP interaction played an important role in better understanding genetic basis of LOAD. In this study, fifty-eight SNPs were screened in a cohort of 229 LOAD cases and 318 controls from mainland China, and their interaction was evaluated by a series of analysis methods. Seven risk SNPs and six protective SNPs were identified to be associated with LOAD. Risk SNPs included rs9331888 (CLU), rs6691117 (CR1), rs4938933 (MS4A), rs9349407 (CD2AP), rs1160985 (TOMM40), rs4945261 (GAB2) and rs5984894 (PCDH11X); Protective SNPs consisted of rs744373 (BIN1), rs1562990 (MS4A), rs597668 (EXOC3L2), rs9271192 (HLA-DRB5/DRB1), rs157581 and rs11556505 (TOMM40). Among positive SNPs presented above, we found the interaction between rs4938933 (risk) and rs1562990 (protective) in MS4A weakened their each effect for LOAD; for three significant SNPs in TOMM40, their cumulative interaction induced the two protective SNPs effects lost and made the risk SNP effect aggravate for LOAD. Finally, we found rs6656401-rs3865444 (CR1-CD33) pairs were significantly associated with decreasing LOAD risk, while rs28834970-rs6656401 (PTK2B-CR1), and rs28834970-rs6656401 (PTK2B-CD33) were associated with increasing LOAD risk. In a word, our study indicates that SNP-SNP interaction existed in the same gene or cross different genes, which could weaken or aggravate their initial single effects for LOAD. [ABSTRACT FROM AUTHOR]

Quantifying carbon dioxide exchange and understanding the response of key environmental factors in various ecosystems are critical to understanding regional carbon budgets and ecosystem behaviors. For this study, CO2 fluxes were measured in a variety of ecosystems with an eddy covariance observation matrix between June 2012 and September 2012 in the Zhangye oasis area of Northwest China. The results show distinct diurnal variations in the CO2 fluxes in vegetable field, orchard, wetland, and maize cropland. Diurnal variations of CO2 fluxes were not obvious, and their values approached zero in the sandy desert, desert steppe, and Gobi ecosystems. Additionally, daily variations in the Gross Primary Production (GPP), Ecosystem Respiration (Reco) and Net Ecosystem Exchange (NEE) were not obvious in the sandy desert, desert steppe, and Gobi ecosystems. In contrast, the distributions of the GPP, Reco, and NEE show significant daily variations, that are closely related to the development of vegetation in the maize, wetland, orchard, and vegetable field ecosystems. All of the ecosystems are characterized by their carbon absorption during the observation period. The ability to absorb CO2 differed significantly among the tested ecosystems. We also used the Michaelis-Menten equation and exponential curve fitting methods to analyze the impact of Photosynthetically Active Radiation (PAR) on the daytime CO2 flux and impact of air temperature on Reco at night. The results show that PAR is the dominant factor in controlling photosynthesis with limited solar radiation, and daytime CO2 assimilation increases rapidly with PAR. Additionally, the carbon assimilation rate was found to increase slowly with high solar radiation. The light response parameters changed with each growth stage for all of the vegetation types, and higher light response values were observed during months or stages when the plants grew quickly. Light saturation points are different for different species. Nighttime Reco increases exponentially with air temperature. High Q10 values were observed when the vegetation coverage was relatively low, and low Q10 values occurred when the vegetables grew vigorously.