Human serum albumin (HSA) is widely used in clinical and cell culture applications. Conventional production of HSA from human blood is limited by the availability of blood donation and the high risk of viral transmission from donors. Here, we report the production of Oryza sativa recombinant HSA (OsrHSA) from transgenic rice seeds. The level of OsrHSA reached 10.58% of the total soluble protein of the rice grain. Large-scale production of OsrHSA generated protein with a purity >99% and a productivity rate of 2.75 g/kg brown rice. Physical and biochemical characterization of OsrHSA revealed it to be equivalent to plasma-derived HSA (pHSA). The efficiency of OsrHSA in promoting cell growth and treating liver cirrhosis in rats was similar to that of pHSA. Furthermore, OsrHSA displays similar in vitro and in vivo immunogenicity as pHSA. Our results suggest that a rice seed bioreactor produces cost-effective recombinant HSA that is safe and can help to satisfy an increasing worldwide demand for human serum albumin.

No abstract

Puerarin is a widely used compound in Chinese traditional medicine and exhibits many pharmacological activities. Binding of puerarin to human serum albumin (HSA) was investigated by ultraviolet absorbance, fluorescence, circular dichroism and molecular docking. Puerarin caused a static quenching of intrinsic fluorescence of HSA, the quenching data was analyzed by Stern-Volmer equation. There was one primary puerarin binding site on HSA with a binding constant of 4.12 x 10(4) M(-1) at 298 K. Thermodynamic analysis by Van Hoff equation found enthalpy change (DeltaH(0)) and entropy change (DeltaS(0)) were -28.01 kJ/mol and -5.63 J/mol K respectively, which indicated the hydrogen bond and Van der Waas interaction were the predominant forces in the binding process. Competitive experiments showed a displacement of warfarin by puerarin, which revealed that the binding site was located at the drug site I. Puerarin was about 2.22 nm far from the tryptophan according to the observed fluorescence resonance energy transfer between HSA and puerarin. Molecular docking suggested the hydrophobic residues such as tyrosine (Tyr) 150, Tyr 148, Tyr 149 and polar residues such as lysine (Lys) 199, Lys 195, arginine 257 and histidine 242 played an important role in the binding reaction.

No abstract

We introduce Approximate Agglomerative Clustering (AAC), an efficient, easily parallelizable algorithm for generating high-quality bounding volume hierarchies using agglomerative clustering. The main idea of AAC is to compute an approximation to the true greedy agglomerative clustering solution by restricting the set of candidates inspected when identifying neighboring geometry in the scene. The result is a simple algorithm that often produces higher quality hierarchies (in terms of subsequent ray tracing cost) than a full sweep SAH build yet executes in less time than the widely used top-down, approximate SAH build algorithm based on binning.

No abstract

Short-term traffic prediction is vital for intelligent traffic systems and influenced by neighboring traffic condition. Gradient boosting decision trees (GBDT), an ensemble learning method, is proposed to make short-term traffic prediction based on the traffic volume data collected by loop detectors on the freeway. Each new simple decision tree is sequentially added and trained with the error of the previous whole ensemble model at each iteration. The relative importance of variables can be quantified in the training process of GBDT, indicating the interaction between input variables and response. The influence of neighboring traffic condition on prediction performance is identified through combining the traffic volume data collected by different upstream and downstream detectors as the input, which can also improve prediction performance. The relative importance of input variables for 15 GBDT models is different, and the impact of upstream traffic condition is not balanced with that of downstream. The prediction accuracy of GBDT is generally higher than SVM and BPNN for different steps ahead, and the accuracy of multi-step-ahead models is lower than 1-step-ahead models. For 1-step-ahead models, the prediction errors of GBDT are smaller than SVM and BPNN for both peak and nonpeak hours.

No abstract