Genetic construction and biochemical analysis of thermostability mutants of glucoamylase from Aspergillus awamori

To study the molecular basis of Aspergillus awamori glucoamylase (GA) thermostability, eighteen mutants were constructed by site-directed mutagenesis and expressed in Saccharomyces cerevisiae based on thermostability theories. Four lysine residues, K61, K279, K352 and K404, were replaced with arginine, with all but K404 well exposed to the solvent and far away from the enzyme activity site. Mutations K61F/D65E and H254W/E326Q were made to fill a packing void around the inner set of six [alpha]-helices of GA and to displace water molecules inside the void. Five residues (A27, A393, A435, Ser436 and Ser460) were replaced with proline. Two additional disulfide bonds and combined thermostable mutations were engineered at position 20,27 and 72,471, respectively. Finally, The disulfide bond mutant A27C/N20C was combined with two other mutants which have been shown to increase thermostability, G137A (Chen et al., Protein Eng., 9, 499-505) and S436P. The mutants K61R, K352R, K404R, K61F/D65E, A27P, A393P, A435P, S436P, A27C/N20C, A471C/T72C, A27C/N20C/S436P, G137A/S436P and A27C/N20C/G137A had the similar specific activities with the wild-type (WT) GA, while mutants K279R, H254W/E326Q, S460P, A27C and N20C had decreased specific activity. The mutant H254W/E326Q affected the correct folding of GA as demonstrated by circular dichroism (CD) spectrum. Mutants S436P, A27C/N20C, A27C/N20C/G137A, G137A/S436P were more thermostable than WT GA, while mutants K352R and A27C were slightly more thermostable than WT, mutants K279R, K404R, A435P and A472C/T72C had the similar thermostability with WT, and mutants K61R, K61F/D65E, A27P, A393P, S460P and N20C were less stable than WT. Mutants A435P and S436P were more resistant to chemical guanidine hydrochloride (Gdn·HCl) unfolding than WT, whereas mutants A27P, A393P and S460P were more susceptible to chemical unfolding caused by Gdn·HCl than WT GA, as demonstrated by CD spectra. The mutants A27C/N20C and A471C/T72C increased the optimal temperature of catalysis by ~1.5°C over WT GA, while mutant A27C/N20C/G 137A increased it by ~2.0°C. The thermostability of two of the combined mutations, G137A/S436P and A27C/N20C/G137A is shown to be additive

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system

Satellite altimetry for hydrological purpose

As a new spatial measuring technic developed in 1970s, altimetry was designed to determine the sea surface height based on spatial technology, electronic technology and microwave technology. It also plays an important role in geodesy and oceanography; meanwhile, it can provide all-weather and repetitious measurements in global region for the studying of variation of SSH, earth gravity field, ocean circulation as well as submarine topography. In addition, real-time data can also be provided for the field of weather forecast, ocean circulation forecast and wave forecast in globally. Satellite radar altimetry, well known as TOPEX/POSEIDON, JASON, ENVISAT, which have been originally designed to measure global ocean surface height, nowadays, also demonstrated with great potential for applications of inland water body studies. Therefore, the main task of this study is to analyze and summarize the relevant theory and technology of altimetry waveform, waveform retracking methods based on the investigative research up to now. And above all, data used in this study is Topex geophysical data and sensor data from 1992 until 2002 provided by NASA (http://podaac.jpl.nasa.gov/). The main content of this study are listed as follows: - Discuss the theory and process of altimeter waveform, and distinguish the real waveforms from ideal ones. - Waveform classification based on different shapes. - Automatic waveform filter is designed to move out noisy data. - The most popular retracking methods (OCOG, Threshold, 5β) are compared and evaluated with respect to different groups of waveform. And finally an optimal Lake level height is to be generated by sufficient combing all the above retracking methods. - Generate time series of Lake level height (from 10Hz data) in selected water bodies; evaluate the result by comparison with in-situ gauge data

Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions

Visual language navigation (VLN) is an embodied task demanding a wide range of skills encompassing understanding, perception, and planning. For such a multifaceted challenge, previous VLN methods totally rely on one model's own thinking to make predictions within one round. However, existing models, even the most advanced large language model GPT4, still struggle with dealing with multiple tasks by single-round self-thinking. In this work, drawing inspiration from the expert consultation meeting, we introduce a novel zero-shot VLN framework. Within this framework, large models possessing distinct abilities are served as domain experts. Our proposed navigation agent, namely DiscussNav, can actively discuss with these experts to collect essential information before moving at every step. These discussions cover critical navigation subtasks like instruction understanding, environment perception, and completion estimation. Through comprehensive experiments, we demonstrate that discussions with domain experts can effectively facilitate navigation by perceiving instruction-relevant information, correcting inadvertent errors, and sifting through in-consistent movement decisions. The performances on the representative VLN task R2R show that our method surpasses the leading zero-shot VLN model by a large margin on all metrics. Additionally, real-robot experiments display the obvious advantages of our method over single-round self-thinking.Comment: Submitted to ICRA 202