An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
The main aim of the present work was to optimize mead production using Response Surface Methodology. The effects of pH (X1:4–6), diammonium phosphate concentration (X2: 90–150g/hL), and temperature(X3: 24–32°C) on mead quality, concerning the final ethanol, was studied. The results showed that regression equation fit well with experimental data and the optimum extraction conditions determined in order to maximize the combined responses were pH value of 6.5, diammonium phosphate concentration of 150g/hL, temperature of 28°C. The mead produced under these conditions had the following characteristics: ethanol concentration of 9.3% and good flavor.
The magnitude of engine shaking is chosen to evaluate the vehicle performance. The engine shaking is evaluated by the vehicle vibration. Based on the laser triangulation, the vehicle vibration is measured by detecting the distance variation between the bodywork and road surface. The results represent the magnitude of engine shaking. The principle and configuration of the laser triangulation is also introduced in this paper.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Summary In this report we describe the 1500‐fold purification and characterization of the haemolytic phospholipase C (PLC) of Pseudomonas aeruginosa , the paradigm member of a novel PLC/phosphatase superfamily. Members include proteins from Mycobacterium tuberculosis, Bordetella spp., Francisella tularensis and Burkholderia pseudomallei . Purification involved overexpression of the plcHR 1,2 operon, ion exchange chromatography and native preparative polyacrylamide gel electrophoresis. Matrix‐assisted laser desorption ionization time‐of‐flight (MALDI‐TOF) mass spectrometry confirmed the presence of two proteins in the purified sample with sizes of 17 117.2 Da (PlcR 2 ) and 78 417 Da (PlcH). Additionally, liquid chromatography electrospray mass spectrometry (LCMS) revealed that PlcH and PlcR 2 are at a stoichiometry of 1 : 1. Western blot analysis demonstrated that the enzyme purifies as a heterodimeric complex, PlcHR 2 . PlcHR 2 is only active on choline‐containing phospholipids. It is equally active on phosphatidylcholine (PC) and sphingomyelin (SM) and is able to hydrolyse plasmenylcholine phospholipids (plasmalogens). Neither PlcHR 2 nor the M. tuberculosis homologues are inhibited by D609 a widely used, competitive inhibitor of the Bacillus cereus PLC. PlcH, PlcR 2 , and the PlcHR 2 complex bind calcium. While calcium has no detectable effect on enzymatic activity, it inhibits the haemolytic activity of PlcHR 2 . In addition to being required for the secretion of PlcH, the chaperone PlcR 2 affects both the enzymatic and haemolytic properties of PlcH. Inclusive in these data is the con‐clusion that the members of this PC‐PLC and phosphatase family possess a novel mechanism for the recognition and hydrolysis of their respective substrates.
Learning from multimodal data is an important research topic in machine learning, which has the potential to obtain better representations. In this work, we propose a novel approach to generative modeling of multimodal data based on generative adversarial networks. To learn a coherent multimodal generative model, we show that it is necessary to align different encoder distributions with the joint decoder distribution simultaneously. To this end, we construct a specific form of the discriminator to enable our model to utilize data efficiently, which can be trained constrastively. By taking advantage of contrastive learning through factorizing the discriminator, we train our model on unimodal data. We have conducted experiments on the benchmark datasets, whose promising results show that our proposed approach outperforms the-state-of-the-art methods on a variety of metrics. The source code will be made publicly available.
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