Research on the optimization, key chemical constituents and antibacterial activity of the essential oil extraction process of Thuja koraiensis Nakai.

Thuja koraiensis Nakai is a kind of precious economic tree species with fragrance, ornamental and medicinal functions. The essential oil has the satisfactory antibacterial activity. In this paper, the essential oil from the branches and leaves of Thuja koraiensis Nakai was studied by optimization of extraction process, and the optimized parameters mainly include solid-liquid ratio, NaCl concentration, distillation time, storage conditions, etc. Which provided technical scientific basis for the development and utilization of Thuja koraiensis Nakai. The essential oil from the branches and leaves of Thuja koraiensis Nakai was extracted by steam distillation, and the single factor experiment was carried out. The extraction process of the essential oil from the branches and leaves of Thuja koraiensis Nakai was optimized by response surface methodology. The chemical constituents were analyzed by GC-MS. The antibacterial activity of the essential oil was detected by filter paper and plate coating methods. Thuja koraiensis Nakai showed that when the material-to-liquid ratio was 50 g/400 ml, the NaCl concentration was 6.0%, the distillation time was 5 h,the storage condition was dry branch, the oil content was the highest. The response surface optimization method showed that material-to-liquid ratio was 7.8804 ml/g, distillation time was 2.23 h, NaCl concentration was 6.56%, under such condition, the yield was 1.1712%. The chemical constituents of the essential oil were analyzed by GC-MS (gas chromatography-mass spectrometry), and 45 compounds were detected, accounting for 96.03% of the total number. The bacteriostatic activity was detected by filter paper method. The results showed that the essential oil of Thuja koraiensis Nakai had antibacterial effect on three strains (Staphylococcus aureus, Bacillus subtilis and Escherichia coli), among them, the diameter of bacteriostatic circle against S. aureus, B. subtilis and E. coli was 10.00 mm, 15.20 mm and 9.86 mm. The minimum inhibitory concentration (MIC) of the branches and leaves of Thuja koraiensis Nakai to S. aureus was 5 µg/ml, to B. subtilis was 0.625 µg/ml and to E. coli was 2.50 µg/ml. The highest extraction yield of essential oil from the branches and leaves of Thuja koraiensis Nakai by steam distillation was 1.30%. A total of 45 compounds were identified from the essential oils of Thuja koraiensis Nakai, among which carverol acetate was the highest. The essential oil from the branches and leaves of Thuja koraiensis Nakai has obvious antibacterial effect and great development potential, for example, making insect repell0ents, fungicides, essential oil soaps, so it is recommended to collect and use it.

Combination of UAV and Raspberry Pi 4B: Airspace detection of red imported fire ant nests using an improved YOLOv4 model.

Red imported fire ants (RIFA) are an alien invasive pest that can cause serious ecosystem damage. Timely detection, location and elimination of RIFA nests can further control the spread of RIFA. In order to accurately locate the RIFA nests, this paper proposes an improved deep learning method of YOLOv4. The specific methods were as follows: 1) We improved GhostBottleNeck (GBN) and replaced the original CSP block of YOLOv4, so as to compress the network scale and reduce the consumption of computing resources. 2) An Efficient Channel Attention (ECA) mechanism was introduced into GBN to enhance the feature extraction ability of the model. 3) We used Equalized Focal Loss to reduce the loss value of background noise. 4) We increased and improved the upsampling operation of YOLOv4 to enhance the understanding of multi-layer semantic features to the whole network. 5) CutMix was added in the model training process to improve the model's ability to identify occluded objects. The parameters of improved YOLOv4 were greatly reduced, and the abilities to locate and extract edge features were enhanced. Meanwhile, we used an unmanned aerial vehicle (UAV) to collect images of RIFA nests with different heights and scenes, and we made the RIFA nests (RIFAN) airspace dataset. On the RIFAN dataset, through qualitative analysis of the evaluation indicators, mean average precision (MAP) of the improved YOLOv4 model reaches 99.26%, which is 5.9% higher than the original algorithm. Moreover, compared with Faster R-CNN, SSD and other algorithms, improved YOLOv4 has achieved excellent results. Finally, we transplanted the model to the embedded device Raspberry Pi 4B and assembled it on the UAV, using the model's lightweight and high-efficiency features to achieve flexible and fast flight detection of RIFA nests.

Generalized Discrete-time nonlinear disturbance observer based fuzzy model predictive control for boiler-turbine systems.

The boiler-turbine system (BTS) is usually subject to the tight input constraint, the strong nonlinearity and the complex disturbance, which makes the control a challenging task To this end, a disturbance observer based fuzzy model predictive control (DOBFMPC) scheme is proposed for the BTS in this paper. The generalized discrete-time nonlinear disturbance observer (GDNDO) is first developed to estimate the higher-order disturbance by systematically extending the conventional nonlinear disturbance observer. The GDNDO exhibits a series structure of the internal states, and can precisely estimate the disturbance if its order is equal to or greater than that of the disturbance In addition, a baseline fuzzy model predictive control (FMPC) law is synthesized on the fuzzy model. With FMPC, the asymptotic stability is guaranteed, and meanwhile the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. At last, the disturbance estimate and the FMPC are applied to constitute the DOBFMPC law. With the proper design of the disturbance compensation gain, the disturbance influence is removed from the output channels by the composite DOBFMPC law at the steady state. Simulations for a 300 MW subcritical BTS well demonstrate the effectiveness of the proposed control scheme.

Disturbance-observer-based fuzzy model predictive control for nonlinear processes with disturbances and input constraints.

This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi-Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler-turbine system demonstrate the effectiveness of the proposed control scheme.

WITHDRAWN: UDE-based chattering-free sliding mode control for systems with mismatched uncertainties.

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Model-based adaptive sliding mode control of the subcritical boiler-turbine system with uncertainties.

As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction.

Modeling and sliding mode predictive control of the ultra-supercritical boiler-turbine system with uncertainties and input constraints.

The coordinated control system (CCS) serves as an important role in load regulation, efficiency optimization and pollutant reduction for coal-fired power plants. The CCS faces with tough challenges, such as the wide-range load variation, various uncertainties and constraints. This paper aims to improve the load tacking ability and robustness for boiler-turbine units under wide-range operation. To capture the key dynamics of the ultra-supercritical boiler-turbine system, a nonlinear control-oriented model is developed based on mechanism analysis and model reduction techniques, which is validated with the history operation data of a real 1000 MW unit. To simultaneously address the issues of uncertainties and input constraints, a discrete-time sliding mode predictive controller (SMPC) is designed with the dual-mode control law. Moreover, the input-to-state stability and robustness of the closed-loop system are proved. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves good tracking performance, disturbance rejection ability and compatibility to input constraints.

Simultaneously Identify Three Different Attributes of Proteins by Fusing their Three Different Modes of Chou's Pseudo Amino Acid Compositions.

Most published articles always applied a certain model or arithmetic to only a certain dataset. Considering the avalanche of biological data created in the post-genomic age, this type of research shows many shortcomings and inefficient characteristics, because it is always have difficulties to apply the same model to different datasets. So we proposed a multifunctional ensemble classifier which combines several individual classifiers. Each of them was trained in different parameter system. The final outcomes were combined through a weighted voting system. This classifier was conducted on several strictly constructed biological datasets. Based on the testing result from three different types of biological dataset, this new predictor can deal with more sweeping range of biological data, and receives more efficient and robust results in comparison with other published methods tentatively.

Modeling and parameters identification of 2-keto-L-gulonic acid fed-batch fermentation.

This article presents a modeling approach for industrial 2-keto-L-gulonic acid (2-KGA) fed-batch fermentation by the mixed culture of Ketogulonicigenium vulgare (K. vulgare) and Bacillus megaterium (B. megaterium). A macrokinetic model of K. vulgare is constructed based on the simplified metabolic pathways. The reaction rates obtained from the macrokinetic model are then coupled into a bioreactor model such that the relationship between substrate feeding rates and the main state variables, e.g., the concentrations of the biomass, substrate and product, is constructed. A differential evolution algorithm using the Lozi map as the random number generator is utilized to perform the model parameters identification, with the industrial data of 2-KGA fed-batch fermentation. Validation results demonstrate that the model simulations of substrate and product concentrations are well in coincidence with the measurements. Furthermore, the model simulations of biomass concentrations reflect principally the growth kinetics of the two microbes in the mixed culture.

Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions.

Owing to the fact that location information can indicate important functionalities of proteins, developing computational tools to predict protein subcellular localization is one of the most efficient and meaningful tasks with no doubt. The existence methods dealing with prediction of protein subchloroplast locations can only handle the case of single location site. Therefore, it is meaningful and challenging to make effort in how to deal with the proteins with multiple subchloroplast location sites instead of just excluding them. To solve this problem, new systems for predicting protein subchloroplast localization with single or multiple sites are developed and discussed in the paper. Three different editions of KNN algorithms and four different types of feature extraction were adopted to construct the prediction systems. This is the first effort to predict the proteins with multiple subchloroplast locations. The overall jackknife success rates achieved by the best combination (features+classifier) on three dataset with different levels of homology were 89.08%, 81.29% and 71.11%. The performance of the prediction models indicate that the proposed methods might be applied as a useful and efficient assistant tool for the prediction of sub-subcellular localizations.

Using radial basis function on the general form of Chou's pseudo amino acid composition and PSSM to predict subcellular locations of proteins with both single and multiple sites.

Prediction of protein subcellular location is a meaningful task which attracted much attention in recent years. A lot of protein subcellular location predictors which can only deal with the single-location proteins were developed. However, some proteins may belong to two or even more subcellular locations. It is important to develop predictors which will be able to deal with multiplex proteins, because these proteins have extremely useful implication in both basic biological research and drug discovery. Considering the circumstance that the number of methods dealing with multiplex proteins is limited, it is meaningful to explore some new methods which can predict subcellular location of proteins with both single and multiple sites. Different methods of feature extraction and different models of predict algorithms using on different benchmark datasets may receive some general results. In this paper, two different feature extraction methods and two different models of neural networks were performed on three benchmark datasets of different kinds of proteins, i.e. datasets constructed specially for Gram-positive bacterial proteins, plant proteins and virus proteins. These benchmark datasets have different number of location sites. The application result shows that RBF neural network has apparently superiorities against BP neural network on these datasets no matter which type of feature extraction is chosen.

A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.

Predicting membrane protein type is a meaningful task because this kind of information is very useful to explain the function of membrane proteins. Due to the explosion of new protein sequences discovered, it is highly desired to develop efficient computation tools for quickly and accurately predicting the membrane type for a given protein sequence. Even though several membrane predictors have been developed, they can only deal with the membrane proteins which belong to the single membrane type. The fact is that there are membrane proteins belonging to two or more than two types. To solve this problem, a system for predicting membrane protein sequences with single or multiple types is proposed. Pseudo-amino acid composition, which has proven to be a very efficient tool in representing protein sequences, and a multilabel KNN algorithm are used to compose this prediction engine. The results of this initial study are encouraging.

On enhancing productivity of bioethanol with multiple species.

The present work is initiated to investigate whether a defined culture comprising a mixture of three yeast species, Kluyveromyces marxianus, Saccharomyces cerevisiae, and Pichia stipitis can ferment a mixture of sugars to produce bioethanol at rates higher than those achieved by pure cultures of the same. For this purpose, we develop models of single species based on the hybrid cybernetic model framework, and simulate fermentations in the mixed culture by combining individual models. An underlying assumption is that the behavior of each species is determined only by the common environment independently of the presence and metabolism of other species. Model performance is thoroughly assessed using the experimental data available in the literature. The dynamic behavior of mixed cultures in mixed culture experiments are accurately predicted by the model reflecting faithfully the simultaneous/sequential uptake patterns of mixed substrates. This model is then used to investigate performance of various possible reactor configurations. With the foregoing species of organisms, mixed culture itself does not lead to a significant increase of bioethanol productivity. Rather, the model shows that substantial improvement is acquired by sequential use of different, properly chosen organisms during fermentation. Thus, the successive use of K. marxianus and P. stipitis is shown to increase bioethanol productivity up to about 58% in comparison to fermentation by single species alone.

Cybernetic modeling based on pathway analysis for Penicillium chrysogenum fed-batch fermentation.

A macrokinetic model employing cybernetic methodology is proposed to describe mycelium growth and penicillin production. Based on the primordial and complete metabolic network of Penicillium chrysogenum found in the literature, the modeling procedure is guided by metabolic flux analysis and cybernetic modeling framework. The abstracted cybernetic model describes the transients of the consumption rates of the substrates, the assimilation rates of intermediates, the biomass growth rate, as well as the penicillin formation rate. Combined with the bioreactor model, these reaction rates are linked with the most important state variables, i.e., mycelium, substrate and product concentrations. Simplex method is used to estimate the sensitive parameters of the model. Finally, validation of the model is carried out with 20 batches of industrial-scale penicillin cultivation.

A two pathway model for tonic suppressed-by-contrast cells in the cat retina.

A two pathway spatiotemporal model is proposed to describe the function of tonic suppressed-by-contrast cells of the cat retina. The model is able to describe the experimentally determined responses of such neurons to drifting sinusoidal gratings. It is also able to predict their responses to alternating sinusoidal gratings and flashing or moving spots of light, and these predictions resemble experimental observations, at least qualitatively. The model is physiologically plausible, it can be used to summarize the dynamic responses of the tonic suppressed-by-contrast cells of the cat and potentially to account for the responses of the suppressed-by-contrast cells of other species.

A simple kinetic model for myeloma cell culture with consideration of lysine limitation.

A simple kinetic model is developed to describe the dynamic behavior of myeloma cell growth and cell metabolism. Glucose, glutamine as well as lysine are considered as growth limiting substrates. The cell growth was restricted as soon as the extracellular lysine is exhausted and then intracellular lysine becomes a growth limiting substrate. In addition, a metabolic regulator model together with the Monod model is used to deal with the growth lag phase after inoculation or feeding. By using these models, concentrations of substrates and metabolites, as well as densities of viable and dead cells are quantitatively described. One batch cultivation and two fed-batch cultivations with pulse feeding of nutrients are used to validate the model.

Spike sorting based on multi-class support vector machine with superposition resolution.

A new spike sorting method based on the support vector machine (SVM) is proposed to resolve the superposition problem. The spike superposition is generally resolved by the template matching. Previous template matching methods separate the spikes through linear classifiers. The classification performance is severely influenced by the background noise included in spike trains. The nonlinear classifiers with high generation ability are required to deal with the task. A multi-class SVM classifier is therefore applied to separate the spikes, which contains several binary SVM classifiers. Every binary SVM classifier corresponding to one spike class is used to identify the single and superposition spikes. The superposition spikes are decomposed through template extraction. The experimental results on the simulated and real data demonstrate the utility of the proposed method.

A population balance model describing the cell cycle dynamics of myeloma cell cultivation.

A multi-staged population balance model is proposed to describe the cell cycle dynamics of myeloma cell cultivation. In this model, the cell cycle is divided into three stages, i.e., G1, S, and G2M phases. Both DNA content and cell volume are used to differentiate each cell from other cells of the population. The probabilities of transition from G1 to S and division of G2M are assumed to be dependent on cell volume, and transition probability from S to G2M is determined by DNA content. The model can be used to simulate the dynamics of DNA content and cell volume distributions, phase fractions, and substrate and byproduct concentrations, as well as cell densities. Measurements from myeloma cell cultivations, especially the FACS data with respect to DNA distribution and cell fractions in different stages, are employed for model validation.

A macrokinetic model for myeloma cell culture based on stoichiometric balance.

A macrokinetic model for a myeloma cell line is proposed. The model describes the dynamic balances of lactate, alanine, ATP and NADH during the metabolsim of glucose, glutamine and other amino acids. The metabolic pathways mainly include glycolysis, glutaminolysis, the trcicarboxylic acid cycle, the formation and utilization of amino acids, the respiratory chain, cell growth and cell death. The metabolic shift of glucose is especially considered because of a change in the rate of glycolysis. Thus the model functions in three modes to describe the behaviour of the myeloma cell line. On the basis of this model the macrokinetic bioreaction rates such as the specific substrate consumption rate, the specific growth rate, the specific acetyl-CoA formation rate, as well as the specific oxygen uptake rate, are estimated. The specific substrate consumption rate and the specific growth rate are then coupled into a bioreactor model such that the key variables, i.e., the cell density, the substrate and metabolite concentrations, are obtained. Experiments with batch and fed-batch cultures of a myeloma cell line (X63-Ag8.653) were used to validate the model. The prediction of the model was simulated by the rolling prediction approach.

Oxygen transfer model in recombinant Pichia pastoris and its application in biomass estimation.

An oxygen transfer model was established for Pichia pastoris growing on glycerol and methanol in a stirred tank bioreactor and expressing a recombinant human serum albumin (rHSA). This was based on pseudo-steady state mass balance, where the volumetric O(2) transfer coefficient, k (L) a, was estimated as a function of power input per unit volume and aeration rate. Under pseudo-steady state, the O(2) transfer rate model matched the O(2) uptake rate obtained from a previous macrokinetic model. This procedure was also applied to estimate biomass concentration by using the on-line rolling identification approach.