Regulation of High-Temperature Stress Response by Small RNAs

Temperature extremes constitute one of the most common environmental stresses that adversely affect the growth and development of plants. Transcriptional regulation of temperature stress responses, particularly involving protein-coding gene networks, has been intensively studied in recent years. High-throughput sequencing technologies enabled the detection of a great number of small RNAs that have been found to change during and following temperature stress. The precise molecular action of some of these has been elucidated in detail. In the present chapter, we summarize the current understanding of small RNA-mediated modulation of high- temperature stress-regulatory pathways including basal stress responses, acclimation, and thermo-memory. We gather evidence that suggests that small RNA network changes, involving multiple upregulated and downregulated small RNAs, balance the trade-off between growth/development and stress responses, in order to ensure successful adaptation. We highlight specific characteristics of small RNA-based tem- perature stress regulation in crop plants. Finally, we explore the perspectives of the use of small RNAs in breeding to improve stress tolerance, which may be relevant for agriculture in the near future

Identification of a Marine Bacillus Strain C5 and Parathion-Methyl Degradation Characteristics of the Extracellular Esterase B1

A bacterial strain C5 that can produce new type of marine esterase was isolated and screened from marine sludge. According to 16S rRNA sequence analysis and physiological and biochemical experiments, the strain was identified as Bacillus subtilis. A single isozyme with a molecular weight of 86 kDa was observed by SDS-PAGE and native-PAGE. On this basis, the mechanism of esterase B1 secreted by strain C5 degrading parathion-methyl was explored, and the effects of temperature and pH on the degradation rate were investigated. From the results, p-nitrophenol was one of the degradation products of B1 degrading parathion-methyl, and the best degradation effect could be achieved at the temperature of 40°C and the neutral pH value

Prediction of Battery SOH by CNN-BiLSTM Network Fused with Attention Mechanism

During the use and management of lead–acid batteries, it is very important to carry out prediction and study of the state of the health (SOH) of the battery. To this end, this paper proposes a SOH prediction method for lead–acid batteries based on the CNN-BiLSTM-Attention model. The model utilizes the convolutional neural network (CNN) to carry out feature extraction and data dimension reduction in the input factors of model, and then these factors are used as the input of the bidirectional long short-term memory network (BiLSTM). The BiLSTM is used to learn the temporal correlation information in the local features of input time series bidirectionally. The attention mechanism is introduced to assign more attention to key features in the input sequence with more significant influence on the output result by assigning weights to important features, and finally, multi-step prediction of the battery SOH is realized. Compared with the prediction results of battery SOH using other neural network methods, the method proposed in this study can provide higher prediction accuracy and achieve accurate multi-step prediction of battery SOH. Measured results show that most of the multi-step prediction errors of the proposed method are controlled within 3%

Prediction of Battery SOH by CNN-BiLSTM Network Fused with Attention Mechanism

During the use and management of lead–acid batteries, it is very important to carry out prediction and study of the state of the health (SOH) of the battery. To this end, this paper proposes a SOH prediction method for lead–acid batteries based on the CNN-BiLSTM-Attention model. The model utilizes the convolutional neural network (CNN) to carry out feature extraction and data dimension reduction in the input factors of model, and then these factors are used as the input of the bidirectional long short-term memory network (BiLSTM). The BiLSTM is used to learn the temporal correlation information in the local features of input time series bidirectionally. The attention mechanism is introduced to assign more attention to key features in the input sequence with more significant influence on the output result by assigning weights to important features, and finally, multi-step prediction of the battery SOH is realized. Compared with the prediction results of battery SOH using other neural network methods, the method proposed in this study can provide higher prediction accuracy and achieve accurate multi-step prediction of battery SOH. Measured results show that most of the multi-step prediction errors of the proposed method are controlled within 3%

Cloning, Overexpression and Characterization of a Catalase from a Marine Acinetobacter Bacterium

Objective: The marine catalase YS0810CAT gene was cloned and overexpressed from Acinetobacter sp. YS0810, and high stability of the recombinant enzyme was validated, which made it important for potential applications in the elimination of hydrogen peroxide from industrial process-generated streams. Methods: The gene was cloned by PCR and overexpressed in Escherichia coli. Evolutionary analyses of this enzyme were conducted with the MEGA software. Anion exchange was applied to purify the recombinant enzyme. The effects of pH and temperature on the activity and stability of YS0810CAT were measured. Results: The gene consists of 1,518 bp and belongs to Clade 3 of monofunctional catalases. The maximum protein production was obtained with 0.8 mM IPTG, a post-induction temperature of 37°C, and a post-induction time of 8 h. The recombinant protein was most active at 60°C and pH 11. Conclusion: The effects of pH and temperature on the activity and stability of the wild type and recombinant YS0810CAT are similar. The protocol for the preparation of recombinant YS0810CAT could aid enzyme crystallization; moreover, improvement in its properties may be possible through protein engineering

Test Investigation and Rule Analysis of Bearing Fault Diagnosis in Induction Motors

In this paper, a series of tests were conducted on the bearings of induction motors to investigate vibration signal analysis-based diagnosis of bearing faults, and a thorough analysis was also conducted. In the engineering field, the kurtosis coefficient of vibration acceleration and the root mean square of vibration velocity, as well as resonant demodulated spectrum analysis of vibration acceleration, have been widely used for bearing fault diagnosis. These are integrated in almost any commercially available device for diagnosing bearing faults. However, the unsuitable use of these devices results in many false diagnoses. In light of this, they were selected as research objects and were investigated experimentally. In three induction motors, faults of different severity in the bearing outer race and cage were modeled for tests, and the corresponding results were used to evaluate the performance of the selected diagnosis methods. Some vague information in engineering was clarified, and some instructive rules were outlined to improve the bearing fault diagnosis performance. Taking the kurtosis coefficient of vibration acceleration (Ku) as an example, in engineering, Ku = 4 is generally taken as the diagnostic threshold of bearing faults. This means the following rule applies: if Ku ≤ 4, the bearing is healthy; otherwise, the bearing is faulty. However, the test results in this paper show that even if Ku ≤ 4, the bearing might be faulty; if Ku > 4, the bearing is indeed faulty. Therefore, the diagnostic rule should be improved as follows: if Ku > 4, the bearing is faulty (which can be assured), and if Ku ≤ 4, the status of the bearing is still undetermined. Thus, this paper can be helpful for researchers to gain an experimental understanding of the selected diagnosis methods and provides some improved rules on their use for reducing false diagnoses

A Multilevel Terrain Rendering Method Based on Dynamic Stitching Strips

High-quality terrain rendering has been the focus of many visualization applications over recent decades. Many terrain rendering methods use the strategy of Level of Detail (LOD) to create adaptive terrain models, but the transition between different levels is usually not handled well, which may cause popping artefacts that seriously affect the reality of the terrain model. In recent years, many researchers have tried using modern Graphics Processing Unit (GPU) to complete heavy rendering tasks. By leveraging the great power of GPU, high quality terrain models with rich details can be rendered in real time, although the problem of popping artefacts still persists. In this study, we propose a real-time terrain rendering method with GPU tessellation that can effectively reduce the popping artefacts. Coupled with a view-dependent updating scheme, a multilevel terrain representation based on the flexible Dynamic Stitching Strip (DSS) is developed. During rendering, the main part of the terrain model is tessellated into appropriate levels using GPU tessellation. DSSs, generated in parallel, can seamlessly make the terrain transitions between different levels much smoother. Experiments demonstrate that the proposed method can meet the requirements of real-time rendering and achieve a better visual quality compared with other methods

Quantitative CT comparison between COVID-19 and mycoplasma pneumonia suspected as COVID-19: a longitudinal study

Objective!#!The purpose of this study was to compare imaging features between COVID-19 and mycoplasma pneumonia (MP).!##!Materials and methods!#!The data of patients with mild COVID-19 and MP who underwent chest computed tomography (CT) examination from February 1, 2020 to April 17, 2020 were retrospectively analyzed. The Pneumonia-CT-LKM-PP model based on a deep learning algorithm was used to automatically quantify the number, volume, and involved lobes of pulmonary lesions, and longitudinal changes in quantitative parameters were assessed in three CT follow-ups.!##!Results!#!A total of 10 patients with mild COVID-19 and 13 patients with MP were included in this study. There was no difference in lymphocyte counts at baseline between the two groups (1.43â±â0.45 vs. 1.44â±â0.50, pâ=â0.279). C-reactive protein levels were significantly higher in MP group than in COVID-19 group (pâ<â0.05). The number, volume, and involved lobes of pulmonary lesions reached a peak in 7-14 days in the COVID-19 group, but there was no peak or declining trend over time in the MP group (pâ<â0.05).!##!Conclusion!#!Based on the longitudinal changes of quantitative CT, pulmonary lesions peaked at 7-14 days in patients with COVID-19, and this may be useful to distinguish COVID-19 from MP and evaluate curative effects and prognosis

Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model

Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model