Advancing Agro-Based Research

Taking the next sums up Universiti Putra Malaysia (UPM) approach to research. The university now aims to create an environment that inspires innovative research following its selection as a research university by the Higher Education Ministry in November 2006

Genomic Characterization Provides New Insights Into the Biosynthesis of the Secondary Metabolite Huperzine a in the Endophyte Colletotrichum gloeosporioides Cg01

A reliable source of Huperzine A (HupA) meets an urgent need due to its wide use in Alzheimer's disease treatment. In this study, we sequenced and characterized the whole genomes of two HupA-producing endophytes, Penicillium polonicum hy4 and Colletotrichum gloeosporioides Cg01, to clarify the mechanism of HupA biosynthesis. The whole genomes of hy4 and Cg01 were 33.92 and 55.77 Mb, respectively. We compared the differentially expressed genes (DEGs) between the induced group (with added extracts of Huperzia serrata) and a control group. We focused on DEGs with similar expression patterns in hy4 and Cg01. The DEGs identified in GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were primarily located in carbon and nitrogen metabolism and nucleolus, ribosome, and rRNA processing. Furthermore, we analyzed the gene expression for HupA biosynthesis genes proposed in plants, which include lysine decarboxylase (LDC), copper amine oxidase (CAO), polyketides synthases (PKS), etc. Two LDCs, one CAO, and three PKSs in Cg01 were selected as prime candidates for further validation. We found that single candidate biosynthesis-gene knock-out did not influence the HupA production, while both LDC gene knock-out led to increased HupA production. These results reveal that HupA biosynthesis in endophytes might differ from that proposed in plants, and imply that the HupA-biosynthesis genes in endophytic fungi might co-evolve with the plant machinery rather than being acquired through horizontal gene transfer (HGT). Moreover, we analyzed the function of the differentially expressed epigenetic modification genes. HupA production of the histone acetyltransferase (HAT) deletion mutant ΔCgSAS-2 was not changed, while that of the histone methyltransferase (HMT) and histone deacetylase (HDAC) deletion mutants ΔCgClr4, ΔCgClr3, and ΔCgSir2-6 was reduced. Recovery of HupA-biosynthetic ability can be achieved by retro-complementation, demonstrating that HMT and HDACs associated with histone modification are involved in the regulation of HupA biosynthesis in endophytic fungi. This is the first report on epigenetic modification in high value secondary metabolite- producing endophytes. These findings shed new light on HupA biosynthesis and regulation in HupA-producing endophytes and are crucial for industrial production of HupA from fungi

Aerodynamic Optimization Design of a 150 kW High Performance Supercritical Carbon Dioxide Centrifugal Compressor without a High Speed Requirement

Supercritical carbon dioxide (S-CO2) Brayton cycle technology has the advantages of excellent energy density and heat transfer. The compressor is the most critical and complex component of the cycle. Especially, in order to make the system more reliable and economical, the design method of a high efficiency compressor without a high speed requirement is particularly important. In this paper, thermodynamic design software of a S-CO2 centrifugal compressor is developed. It is used to design the 150 kW grade S-CO2 compressor at the speed of 40,000 rpm. The performance of the initial design is carried out by a 3-D aerodynamic analysis. The aerodynamic optimization includes three aspects: numerical calculation, design software and the flow part geometry parameters. The aerodynamic performance and the off-design performance of the optimal design are obtained. The results show that the total static efficiency of the compressor is 79.54%. The total pressure ratio is up to 1.9. The performance is excellent, and it can operate normally within the mass flow rate range of 5.97 kg/s to 11.05 kg/s. This research provides an intelligent and efficient design method for S-CO2 centrifugal compressors with a low flow rate and low speed, but high pressure ratio

Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

The reliable design of the supercritical carbon dioxide (S-CO2) turbine is the core of the advanced S-CO2 power generation technology. However, the traditional computational fluid dynamics (CFD) method is usually applied in the S-CO2 turbine design-optimization, which is a high computational cost, high memory requirement, and long time-consuming solver. In this research, a flexible end-to-end deep learning approach is presented for the off-design performance prediction of the S-CO2 turbine based on physical fields reconstruction. Our approach consists of three steps: firstly, an optimal design of a 60,000 rpm S-CO2 turbine is established. Secondly, five design variables for off-design analysis are selected to reconstruct the temperature and pressure fields on the blade surface through a deconvolutional neural network. Finally, the power and efficiency of the turbine is predicted by a convolutional neural network according to reconstruction fields. The results show that the prediction approach not only outperforms five classical machine learning models but also focused on the physical mechanism of turbine design. In addition, once the deep model is well-trained, the calculation with graphics processing unit (GPU)-accelerated can quickly predict the physical fields and performance. This prediction approach requires less human intervention and has the advantages of being universal, flexible, and easy to implement

Aerodynamic Design and Off-design Performance Analysis of a Multi-Stage S-CO₂ Axial Turbine Based on Solar Power Generation System

Solar energy is an inexhaustible source of clean energy. Meanwhile, supercritical carbon dioxide has excellent characteristics such as easy access to critical conditions, high density, and low viscosity, making it one of the most popular circulating working fluids in solar power generation technology. However, solar power generation systems are severely affected by geographical distribution, seasonal variations and day-night cycles. Therefore, efficient and adaptable turbine design is the key to realize supercritical carbon dioxide solar power generation technology. In this paper, the initial thermodynamic design of 10 MW S-CO2 three-stage axial turbine is completed by self-developed thermodynamic design software, and the key thermodynamic and structural parameters are obtained. The optimal design of turbine and its aerodynamic performance at rated operating conditions are obtained by using a three-dimensional aerodynamic numerical analysis and optimization method. At last, nine off-design conditions are analyzed in detail. The results show that the designed turbine output power is 10.37 MW and the total-total efficiency is 91.60%. It can operate efficiently and steadily in the range of output power from 16.2% to 155.9%. It can adapt to the variable operating conditions of solar power generation system and meet the design requirements

Flow and Heat Transfer Characteristics of the Turbine Blade Variable Cross-Section Internal Cooling Channel with Turning Vane

The gas turbine blades are scoured by high temperature gas sustainedly and long-term in harsh environment. It is of great significance to explore effective cooling methods to lower the turbine blade temperature so as to ensure safe and stable operation of the gas turbine. However, there are few studies on the cooling channel considering the turning vane, variable cross-section characteristics, and rotation effect. In this paper, five kinds of serpentine cooling channel models with variable cross-section properties and different thickness guide vanes are constructed. The effects of different thickness guide vanes on the overall performance of the channel under stationary and rotating conditions are discussed and compared by numerical method. The result shows that when stationary (Re = 10,000–50,000), the turning vane with suitable thickness can increase the Nu/Nu0 by 56.5%. The f/f0 is decreased by 14.2%, and the comprehensive thermal performance is increased by 4.5%. When rotating (Re = 10,000, Ro = 0–0.5), the turning vane with suitable thickness can increase the Nuup/Nu0 and Nuall/Nu0 by 33.0% and 4.0%, respectively. The comprehensive performance of the variable cross-section serpentine channel can be greatly improved by arranging the turning vane structure with appropriate thickness

Investigation on Unsteady Flow Characteristics of a SCO2 Centrifugal Compressor

Supercritical carbon dioxide (SCO2) is a vital working fluid in the application of power units and its high density helps to achieve a compact mechanical structure. Centrifugal compressors are of vital use in various kinds of equipment. In this paper, a SCO2 centrifugal compressor of large input power and mass flow rate is designed and numerically investigated. A thorough numerical analysis of the unsteady flow field in the centrifugal compressor is performed in ANSYS-CFX. The computation adopts hexahedral mesh, finite volume method, and the RNG k-ε two-equation turbulence model. Streamlines, temperature, pressure, and Mach number distributions at different time steps in one revolution period are covered to present the unsteady effect of turbomachinery. Meanwhile, the force on a single rotor blade is monitored to investigate the frequency components of exciting force, thus providing the foundation for vibration analysis. Moreover, the torque, output power, pressure ratio, and isentropic efficiency in the steady and the unsteady time-averaged condition are calculated and compared with the design condition to measure the validity of the design. In summary, the unsteady computation result reveals that the unsteady flow characteristics are prominent in the designed compressor and the design of impeller and diffuser meet the requirement

CNNcon: improved protein contact maps prediction using cascaded neural networks.

BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence) alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective prediction of long length proteins could be possible by the CNNcon