Modelling phenological development, yield and quality of lucerne (Medicago sativa L.) using APSIM next generation : A thesis submitted in partial fulfillment of the requirement for the Degree of Doctor of Philosophy at Lincoln University

This research integrated knowledge of lucerne crop physiology into the Agricultural Production Systems sIMulator (APSIM) next generation (APSIM NextGen) model framework to develop and verify a comprehensive lucerne simulation model (APSIM NextGen lucerne model). The model was developed to simulate the growth, development and quality of lucerne cultivars grown under different defoliation management and growth conditions. One of the major challenges for developing a lucerne crop simulation model is to capture the seasonality of perennial reserves and their effect on shoot regrowth in response to different defoliation regimes. In this thesis, model development and testing was based on long-term field datasets with multiple defoliation regimes (28 day: S; 42 day: L; and 84 day: H) and three genotypes of fall dormancy (FD; FD2, FD5 and FD10) under irrigated conditions. The APSIM Plant Modelling Framework (PMF) was used to simulate generic organs (leaf, stem and root) and represent key crop physiological processes, including crop phenological development, canopy expansion, dry matter and N accumulation, remobilization and partitioning. Development was parameterized based on thermal time (Tt) targets and a photoperiod (Pp) response. Seedling crops required a juvenile phase (Ttjuv of 215 to 547 ˚Cd). For both seedling and regrowth crops, the Tt to reach 50% buds visible (Tt0-bv) increased as Pp shortened in autumn, a minimum of 278 ˚Cd for the basic vegetative (TtBVP) period was required at Pp >14h for regrowth crops to reach buds visible stage. After crops reached buds visible stage, another 310 ˚Cd of Tt (Ttbv-fl) was required to reach flowering. Lucerne biomass supply was parameterized as the product of accumulated intercepted total radiation, and radiation use efficiency (RUEtotal, g DM MJ-1 total radiation). The intercepted total radiation was calculated by LAI and an extinction coefficient (k) of 0.81. LAI was parameterized as leaf area expansion rate (LAER) and Pp response. LAER declined as the Pp decrease, being 0.018 m2 m-2 ˚Cd-1 at 16.5 h and 0.008 m2 m-2 ˚Cd-1 at 10 h. However, a Pp response was not observed in seedling crops and regrowth crops in increasing Pp conditions. The RUEtotal was 1.1±0.31 g DM MJ−1 at 18 ˚C for both seedling and regrowth crops. Biomass supply was then allocated based on the relative demand of each organ. Leaf and stem biomass demand were parameterized as positive power functions. Root biomass showed a seasonal pattern. The APSIM NextGen lucerne model provided a mechanistic framework to model root biomass dynamics with structural and storage components. Structural root biomass was defined and estimated as the amount of root biomass (~2500 kg ha-1) that had no root maintenance respiration loss in winter. The ratio of storage to structural root differed among development stages and FD classes. In an increasing Pp, there was no storage root demand. The decrease of root biomass during this period was due to remobilization from root to shoots and root maintenance respiration. A remobilization coefficient value and a regrowth coefficient function were used to calculate root remobilization. A remobilization coefficient value was defined as the percentage of storage root biomass per day (5 for FD5, 1 for FD2 and FD10). The regrowth coefficient function includes two parameters (remobilization duration and remobilization rate). Remobilization duration was defined as Tt since harvest, whereas remobilization rate is an adjusted value for the current remobilization coefficient value (ranging from 0 to 1.5). The regrowth coefficient function represents remobilization started at the maximum remobilization rate (1.5) from the beginning of each regrowth cycle (0 ˚Cd). This remained constant until 300 ˚Cd for FD5 (250 ˚Cd for FD2 and 500 ˚Cd for FD10), and then declined to 0 at 350 ˚Cd for FD5 (300 ˚Cd for FD2 and 550 ˚Cd for FD10). In a decreasing Pp, the increasing root biomass was caused by carbon partitioning. Thus, the model was parameterized to have a maximal root demand with no remobilization. A constant root maintenance respiration coefficient (Rm_root_day) of 0.0005 g g-1.day-1 was applied to model root storage maintenance loss. The model had good prediction on shoot biomass and fair prediction on root biomass for 42 and 84 day defoliation treatments. However, the model did not accurately predict root biomass under a 28 day frequent defoliation (SS) probably due to a limitation of root N reserves. The N module was linked with DM in the PMF. The N supply was estimated as 2.5% of total biomass, whereas N demand was built as N threshold functions for each organ. Root N showed a similar seasonal pattern as root biomass. A root N remobilization coefficient value (% storage root N per day; 2 for FD5, 0.5 for FD2 and FD10) was used for remobilization calculations in an increasing Pp. Applying the N module improved biomass prediction, especially for the 28 day defoliation treatment (SS). Simulation results showed good agreement for predicting phenological development stages (NSE of 0.77 for buds visible and 0.67 for flowering stage), good agreement for canopy expansion (overall NSE = 0.61), good agreement for shoot and root biomass (NSE of 0.68 and 0.53). However, there was fair to poor agreement for leaf N (NSE of 0.16 to -0.14), stem N (NSE of 0.51 to -4.61) and root N (NSE of 0.16 to 0.29) for all three FD classes under different defoliation regimes. This was because leaf biomass was used to parameterize leaf N thresholds which resulted in systemic bias. There was a lack of measured N concentration data for the model testing for most treatments. Thus, additional measurement and a more effective approach for parameterizing N demand are required to improve the model. Overall, these results indicate that the APSIM NextGen lucerne model was successfully created to predict growth and development of crops grown under unlimited environmental conditions. Model validation is required under different climate conditions

NQO1 targeting prodrug triggers innate sensing to overcome checkpoint blockade resistance

Lack of proper innate sensing inside tumor microenvironment (TME) limits T cell-targeted immunotherapy. NAD(P)H:quinone oxidoreductase 1 (NQO1) is highly enriched in multiple tumor types and has emerged as a promising target for direct tumor-killing. Here, we demonstrate that NQO1-targeting prodrug β-lapachone triggers tumor-selective innate sensing leading to T cell-dependent tumor control. β-Lapachone is catalyzed and bioactivated by NQO1 to generate ROS in NQO1high tumor cells triggering oxidative stress and release of the damage signals for innate sensing. β-Lapachone-induced high mobility group box 1 (HMGB1) release activates the host TLR4/MyD88/type I interferon pathway and Batf3 dendritic cell-dependent cross-priming to bridge innate and adaptive immune responses against the tumor. Furthermore, targeting NQO1 is very potent to trigger innate sensing for T cell re-activation to overcome checkpoint blockade resistance in well-established tumors. Our study reveals that targeting NQO1 potently triggers innate sensing within TME that synergizes with immunotherapy to overcome adaptive resistance

The Construction of a Clinical Decision Support System Based on Knowledge Base

Part 7: e-Health, the New Frontier of Service Science InnovationInternational audienceBased on a review of domestic and foreign research, application status, classification, composition, and the main problem of a clinical decision support system, this paper proposed a CDSS mode based on a knowledge base. On KB-CDSS mode, this paper discussed the architecture, principle, process, construction of the knowledge base, system design, and application value, then introduced the application WanFang Data Clinical Diagnosis and Treatment Knowledge Base

Clean development mechanism in China: Regional distribution and prospects

Late in 2012, when the first commitment period of the Kyoto Protocol came to an end, it was further extended to December 2020. This was based on the strong realisation that the clean development mechanism (CDM) projects have been playing an important role globally, particularly in promoting clean development in China. Based on a review of international and domestic sources, the paper analyses the progress in the development of CDM projects both globally and in China.China has attracted the lion share of CDM investment in terms of projects located in this country and the global annual certified emission reductions under this mechanism. Due to the relative easiness of implementation, the main area of investment is new and renewable energy. China's fast economic development, open door investment policy, political stability, high educational and technological standards and reliable infrastructure are all encouraging the interest of foreign investors seeking to reduce their domestic carbon footprint. In order to facilitate the location of CDM projects, the Chinese government formulated a series of policies and regulations as well as established national coordinating groups for climate change, CDM Designated National Authorities and projects auditing boards, which are responsible for projects application, auditing and management.There are however large differences in the regional distribution of Chinese CDM projects. Provinces, such as Yunnan, Sichuan, Inner Mongolia, Hunan and Gansu (located in central and western China) are attracting more projects because of their rich hydro and wind resources while there are very few projects in the eastern already developed parts of the country. This trend is consistent with the CDM's main goal to assist less developed regions to achieve a more sustainable development

An optimized fractional order PID controller for suppressing vibration of AC motor

Fractional order Proportional-Integral-Derivative (PID) controller is composed of a number of integer order PID controllers. It is more accurate to control the complex system than the traditional integer order PID controller. The values of parameters of the fractional order PID controller play a decisive role for the control effect. Because the fractional order PID controller added two adjustable parameters than the traditional PID controller, it is very difficult to tune parameters. So the Back Propagation (BP) neural network is selected to optimize the parameters of the fractional order PID controller in order to obtain the high performance. Then the optimized fractional order PID controller and the traditional PID controller are used to control AC motor speed governing system. And the vibration spectrum and stator current spectrum under different rotating speeds are compared and analyzed in detail. The results show that the optimized fractional order PID controller has better vibration suppression performance than the traditional PID controller. The reason is that the optimized fractional order PID controller changed the stator current component, and further changed the frequency components and the amplitude of the vibration signal of the motor

Artificial Neural Network Analysis of Xinhui Pericarpium Citri Reticulatae Using Gas Chromatography - Mass Spectrometer - Automated Mass Spectral Deconvolution and Identification System

Purpose: To develop an effective analytical method to distinguish old peels of Xinhui Pericarpium citri reticulatae (XPCR) stored for > 3 years from new peels stored for < 3 years.Methods: Artificial neural networks (ANN) models, including general regression neural network (GRNN) and multi-layer feedforward neural network (MLFN), were used to analyze the Gas Chromatography - Mass Spectrometer - Automated Mass Spectral Deconvolution and Identification System (GC-MSAMDIS) data of the essential oils of the XPCR. The Root Mean Square (RMS) errors of each ANN model was obtained through judging the characteristic of old peels and new peels.Results: The Root Mean Square (RMS) error of GRNN was 0.22, less than the error MLFN at different levels, indicating that GRNN model is more reliable and accurate for judging the characteristics of old peels and new ones.Conclusion: The general regression neural network model is established to reliably distinguish between old peels and new peels.Keywords: Artificial neural networks, Xinhui, Pericarpium, Citri reticulatae, Gas Chromatography, Automated Mass Spectral Deconvolution and Identification System, Peel

General survey of Fructus Psoraleae from the different origins and chemical identification of the roasted from raw Fructus Psoraleae

Fructus Psoraleae, a traditional Chinese medicine, is widely used for preventing and treating various diseases such as vitiligo, osteoporosis and psoriasis. Coumarin, such as psoralenoside, isopsoralenoside, psoralen and isopsoralen, are important compounds in Fructus Psoraleae. In our study, ultra performance liquid chromatography coupled with diode array detector was employed for an excellent method validation for simultaneous quantification of psoralenoside, isopsoralenoside, psoralen and isopsoralen, which was further applied in performing general survey of Fructus Psoraleae from the different origins and chemical identification of the roasted from raw Fructus Psoraleae in the light of illuminating the transformed rule of psoralenoside and isopsoralenoside. There is a reciprocal relationship between (iso)psoralenoside and (iso)psoralen, and the total content remains balance in Fructus Psoraleae from the different origins. In addition, we found that (iso)psoralenoside in the powder of the raw Fructus Psoraleae could be easily transformed into (iso)psoralen in methanol aqueous solution, especially above 50% water, rather than the roasted one. Thus, we proposed a hypothesis that transformation between (iso)psoralenoside and (iso)psoralen was hindered by inactivation of β-glucosidase in the process of roasting Fructus Psoraleae, which was further verified by observing transformation of (iso)psoralenoside under the different conditions, such as temperature, pH and β-glucosidase. Therefore, we developed a feasible method to distinguish the roasted from raw Fructus Psoraleae by observing conversion from (iso)psoralenoside to (iso)psoralen in 50% methanol aqueous solution. In summary, these results pave the way for elevating quality standard for Fructus Psoraleae and distinguishing the salt-processed from raw Fructus Psoraleae