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Abstract Different from traditional organic luminescent materials based on covalent delocalization, clusteroluminescence from nonconjugated luminogens relies on noncovalent through-space conjugation of electrons. However, such spatial electron delocalization is usually weak, resulting in low luminescent efficiency and board emission peak due to multiple vibrational energy levels. Herein, several nonconjugated luminogens are constructed by employing biphenyl as the building unit to reveal the structure-property relationship and solve current challenges. The intramolecular through-space conjugation can be gradually strengthened by introducing building units and stabilized by rigid molecular skeleton and multiple intermolecular interactions. Surprisingly, narrowband clusteroluminescence with full width at half-maximum of 40 nm and 100% efficiency is successfully achieved via an asymmetric conformation, exhibiting comparable performance to the traditional conjugated luminogens. This work realizes highly efficient and narrowband clusteroluminescence from nonconjugated luminogens and highlights the essential role of structural conformation in manipulating the photophysical properties of unconventional luminescent materials.

Urban growth has accelerated significantly in recent decades, categorized into two spatial patterns: adjacent and outlying growth. Traditional Cellular Automata (CA)-based models excel at simulating adjacent growth but exhibit limitations in modeling outlying growth, which can be summarized into two issues: 1) the overestimation of neighborhood effects caused by models’ excessive dependence on neighboring cell states; and 2) the ignorance of spatial heterogeneity in the relative importance of land suitability and neighborhood effects on urban growth. To address these problems, a novel CA model with Separate Extraction and Adaptive Fusion of land suitability and neighborhood effects (SEAF-CA) is proposed. In this model, a dual-path convolution structure is employed to extract spatial features from driving factors and cell states; geographical coordinates of each cell then input into a multilayer perceptron to derive spatially varying weights for feature fusion. Finally, the derived conversion probability is integrated with CA to simulate urban growth. Land use data collected from 2000 to 2020 in Wuhan are selected to evaluate the proposed model. Experimental results illustrate that SEAF-CA outperforms three typical CA models, achieving the closest outlying growth proportion to reality and the highest simulation accuracy. In addition, the source code of SEAF-CA is now available at GitHub (https://github.com/ohXu/SEAF-CA).

Few-shot human action recognition, a prominent area in computer vision, has garnered increasing attention and broader use in real-life scenarios. Extracting spatio-temporal skeletal information from human movement videos offers interpretable and data-efficient features. However, existing spatio-temporal feature encoders face challenges such as handling sequence boundaries and coping with noise. In order to solve the above problems, this paper proposes a temporal complement method to optimize the Dynamic Time Warping (DTW) algorithm based on the feature representation of the human skeleton sequence. DTW helps to find optimal alignment between sequences by warping them in the time domain. This is quite useful specially in scenarios where training data is limited. However, DTW has the drawback that the optimal alignment path is highly sensitive to errors in the time series distance matrix. Therefore, we apply a Virtual Adversarial Training method to improve the anti-noise capability of the algorithm. Here, We introduce adversarial perturbations in the training phase to the time series distance matrix, thus incentivizing the model to be resilient to such noise. Our method achieves highest accuracy among protonet, DTW and DASTM methods for the 5-way-1-shot setting for the NTU-S (77.7%), and Kinetics (41.2%) datasets. For the 5-way-5-shot setting, our method achieves highest accuracy of 51.8% for Kinetics dataset when compared with the other approaches.

Abstract Background Myocardial ischemia (MI) can cause angina, myocardial infarction, and even death. Angiogenesis is beneficial for ensuring oxygen and blood supply to ischemic tissue, promoting tissue repair, and reducing cell damage. In this study, we evaluated the effects of Salvianolic acid B (Sal B) against myocardial ischemia and explored its underlying mechanism on autophagy. Methods The anti-apoptosis effect of Sal B was conducted by staining Annexin V-FITC/PI and Hoechst as well as evaluating apoptosis bio-markers at protein level in H9c2 cells at glucose deprivation condition. HUVECs were co-cultured with H9c2, and the tube formation assay was used to monitor Sal B’s impact on angiogenesis. The MI model of mice was induced by intraperitoneal injection of isoproterenol (ISO). The effect of Sal B on MI mice was evaluated by HE, Masson, immunohistochemistry, WB and kits. In addition, Atg5 siRNA was applied to verify whether the protective effect of Sal B was regulated to autophagy. Results In H9c2, Sal B reduced the levels of lactate dehydrogenase (LDH), malondialdehyde (MDA) and reactive oxygen species (ROS), improved the levels of superoxide dismutase (SOD) and mitochondrial membrane potential, downregulated the expressions of Bax and cleaved-Caspase3, upregulated the expression of Bcl-2. Therefore, Sal B could significantly inhibit the damage of H9c2 caused by glucose deprivation. In the co-culture system of H9c2 and HUVECs, vascular endothelial growth factor (VEGF) level in the supernatant was dramatically raised by Sal B. Sal B upregulated the expressions of VEGF, platelet derived growth factor (PDGF) and endothelial marker CD31. It implied that Sal B exerted a significant pro-angiogenic effect. Moreover, Sal B increased the expression of LC3, Atg5, and Beclin1, while reducing the level of P62. When the expression of Atg5 was inhibited, the protective effects of Sal B on apoptosis and angiogenesis was reversed. Conclusions Sal B inhibited cardiomyocyte apoptosis and promoted angiogenesis by regulating autophagy, thereby improving MI.

Abstract Background trans-4-Hydroxyproline (T-4-HYP) is a promising intermediate in the synthesis of antibiotic drugs. However, its industrial production remains challenging due to the low production efficiency of T-4-HYP. This study focused on designing the key nodes of anabolic pathway to enhance carbon flux and minimize carbon loss, thereby maximizing the production potential of microbial cell factories. Results First, a basic strain, HYP-1, was developed by releasing feedback inhibitors and expressing heterologous genes for the production of trans-4-hydroxyproline. Subsequently, the biosynthetic pathway was strengthened while branching pathways were disrupted, resulting in increased metabolic flow of α-ketoglutarate in the Tricarboxylic acid cycle. The introduction of the NOG (non-oxidative glycolysis) pathway rearranged the central carbon metabolism, redirecting glucose towards acetyl-CoA. Furthermore, the supply of NADPH was enhanced to improve the acid production capacity of the strain. Finally, the fermentation process of T-4-HYP was optimized using a continuous feeding method. The rate of sugar supplementation controlled the dissolved oxygen concentrations during fermentation, and Fe2+ was continuously fed to supplement the reduced iron for hydroxylation. These modifications ensured an effective supply of proline hydroxylase cofactors (O2 and Fe2+), enabling efficient production of T-4-HYP in the microbial cell factory system. The strain HYP-10 produced 89.4 g/L of T-4-HYP in a 5 L fermenter, with a total yield of 0.34 g/g, the highest values reported by microbial fermentation, the yield increased by 63.1% compared with the highest existing reported yield. Conclusion This study presents a strategy for establishing a microbial cell factory capable of producing T-4-HYP at high levels, making it suitable for large-scale industrial production. Additionally, this study provides valuable insights into regulating synthesis of other compounds with α-ketoglutaric acid as precursor.

Minerals, metallurgy, and other production activities will cause a large number of heavy metal elements to leak into the natural environment. A large number of heavy metal elements have been found in the farmland soil, where the adsorption of plants enhances the enrichment. Here, we have selected a region with three terraces to conduct a whole-area soil sample collection and satellite hyperspectral data processing study to explore the role of terrain in this enrichment process. Five spectral transformation methods and four feature enhancement algorithms were designed, and the content extraction model was established to quantitatively retrieve eight heavy metal elements. The results indicates that the three terraces are the source state, transition state, and stable state of heavy metals respectively with the decrease of elevation; The correlation coefficient of various heavy metal elements exceeds 0.92, and the enrichment pattern is consistent although slope and aspect have no significant correlation with the enrichment of heavy metal elements; Local Cd exceeds 30.00%, Hg exceeds 10 times, and As exceeds 48.30% according to the indicator provisions of Chinese national standard (GB 15618-2018). Such knowledge extends our understanding of the abundance, migration, and enrichment of heavy metals from the perspective of topography, which is crucial for pollution assessment and soil remediation.

ObjectiveTo determine the effectiveness and safety of 5-aminolevulinic acid mediated photodynamic therapy (5-ALA PDT) in HR-HPV infected patients with cervical low-grade squamous intraepithelial lesions (LSIL) and to explore possible factors affecting treatment outcomes.MethodsThis retrospective study included 96 patients with histologically confirmed cervical LSIL and high-risk human papillomavirus (HR-HPV) infection. They received 5-ALA PDT treatment once a week for a total of 3 courses. All patients were evaluated by cytology tests, HPV DNA assay, colposcopy, and biopsy at 2 weeks, 3 months, and 6 months checkpoint. The chi-square test were used to evaluate the differences in various clinical data, and a p value

Early cancer diagnosis increases therapy efficiency and saves huge medical costs. Traditional blood-based cancer markers and endoscopy procedures demonstrate limited capability in the diagnosis. Reliable, non-invasive, and cost-effective methods are in high demand across the world. Worm-based diagnosis, utilizing the chemosensory neuronal system of C. elegans, emerges as a non-invasive approach for early cancer diagnosis with high sensitivity. It facilitates effectiveness in large-scale cancer screening for the foreseeable future. Here, we review the progress of a unique route of early cancer diagnosis based on the chemosensory neuronal system of C. elegans. We first introduce the basic procedures of the chemotaxis assay of C. elegans: synchronization, behavior assay, immobilization, and counting. Then, we review the progress of each procedure and the various cancer types for which this method has achieved early diagnosis. For each procedure, we list examples of microfluidics technologies that have improved the automation, throughput, and efficiency of each step or module. Finally, we envision that microfluidics technologies combined with the chemotaxis assay of C. elegans can lead to an automated, cost-effective, non-invasive early cancer screening technology, with the development of more mature microfluidic modules as well as systematic integration of functional modules.

Different land utilization types have unignorable impacts on adjacent aquifers, so studying the effects of varying land utilization types on groundwater balance and groundwater table in arid and semi-arid areas is crucial to facilitate the rational development of territorial space and groundwater resource management. This study investigated the relationship between land utilization type variations and groundwater dynamics from spatial and temporal scales in Huocheng plain, Xinjiang, China, via taking advantage of different land utilization maps provided by remote sensing techniques in 1990, 2000, and 2015. Based on the analysis of results, the findings can be summarized as follows. First, the cultivated and construction land has expanded significantly in the past 25 years. The migration of the regional center of cultivated land was insignificant, as it is still located almost in the center of the entire study area. However, the transfer span of the location center of construction land was more prominent, and its moving direction was mainly from south to northeast, directly affecting the groundwater balance state and groundwater table. Second, the total amount of groundwater storage exhibited a remarkable reduction, from a recharge in 1990 to a continuous discharge in 2000–2015, during which the total recharge and discharge had both continuously declined. Meanwhile, recharging and discharging elements were also changed profoundly as less groundwater recharges from ambient aqueous environments and more groundwater extraction. Thus, human activities were the fundamental driving terms causing the changes in groundwater sources. Third, the groundwater table gradually and continuously dropped from south to northeast in the study area over time. The drawdown of spatially averaged groundwater tables was positively correlated with construction and cultivated area extension. In addition, compared to the former literature, this study offers an alternative approach for groundwater protection and optimization of territorial space development and utilization in arid and semi-arid areas.

The deep convolutional neural network performs well in current computer vision tasks. However, most of these models are trained on an aforehand complete dataset. New application scenario data sets should be added to the original training data set for model retraining when application scenarios change significantly. When the scenario changes only slightly, the transfer learning can be used for network training by a small data set of new scenarios to adapt it to the new scenario. In actual application, we hope that our model has bio-like intelligence and can adaptively learn new knowledge. This paper proposes a pretrained adaptive resonance network (PAN) based on the CNN and an intra-node back propagation ART network, which can adaptively learn new knowledge using prior information. The PAN network explores the difference between the new data and the stored information and learns this difference to realize the adaptive growth of the network. The model is testified on the MNIST and Omniglot data set, which show the effectiveness of PAN in adaptive incremental learning and its competitive classification accuracy.

Abstract Speech interaction is an important means of robot interaction. With the rapid development of deep learning, end‐to‐end speech synthesis methods based on this technique have gradually become mainstream. Chinese deep learning‐based speech synthesis techniques suffer from problems such as unstable synthesised speech, poor naturalness and poor personalised speech synthesis, which do not satisfy some practical application scenarios. Hence, an F‐MelGAN model is adopted to improve the performance of Chinese speech synthesis. A post‐processing network is used to refine the Mel‐spectrum predicted by the decoder and alleviate the Mel‐spectrum distortion phenomenon. A phoneme‐level and sentence‐level combined module is proposed to model the personalised style of speakers. A combination of an acoustic conditioning network, speaker encoder network GCNet and feedback‐constrained training is proposed to solve the problem of poor personalised speech synthesis and achieve personalised speech customisation in Chinese. Experimental results show that the whole model can generate high‐quality speech with high speaker similarity for both speakers that appear in the training process and speakers that never appear in the training process.

Based on dimensional memory mechanism and adaptive elite population, this paper proposes a satisfactory and efficient artificial bee colony algorithm (DMABC_elite) to solve optimization problems and train artificial neural networks (ANN). DMABC_elite proposes the concept of adaptive elite population that changes dynamically with the search process, and modifies the search equations for employed and onlooker bee phases on this basis. In addition, a dimensional memory mechanism has been introduced that allows multi-dimensional updates, which improves exploitation and speeds up convergence. Next, a new selection strategy and a Lévy flight-based solution-generating method are introduced in the scout bee phase to enhance the global search ability. Finally, the performance of DMABC_elite on two different problem groups is analyzed experimentally. On the one hand, DMABC_elite is evaluated using 22 classical benchmark functions with different dimensions and CEC 2013 test functions. Compared with basic ABC and nine state-of-the-art ABC variants, DMABC_elite achieved better results, ranking first in all 10-, 30- and 100-dimensional tests across 22 classical benchmark functions and 30-dimensional tests across CEC 2013 test functions. On the other hand, DMABC_elite is compared with traditional backpropagation-based algorithms and other ABC variants when training seven different ANNs. The results show that DMABC_elite is efficient and competitive in training ANNs.

Abstract Background 5-hydroxytryptophan (5-HTP), the direct biosynthetic precursor of the neurotransmitter 5-hydroxytryptamine, has been shown to have unique efficacy in the treatment of a variety of disorders, including depression, insomnia, and chronic headaches, and is one of the most commercially valuable amino acid derivatives. However, microbial fermentation for 5-HTP production continues to face many challenges, including low titer/yield and the presence of the intermediate L-tryptophan (L-Trp), owing to the complexity and low activity of heterologous expression in prokaryotes. Therefore, there is a need to construct an efficient microbial cell factory for 5-HTP production. Results We describe the systematic modular engineering of wild-type Escherichia coli for the efficient fermentation of 5-HTP from glucose. First, a xylose-induced T7 RNA polymerase-P T7 promoter system was constructed to ensure the efficient expression of each key heterologous pathway in E. coli. Next, a new tryptophan hydroxylase mutant was used to construct an efficient tryptophan hydroxylation module, and the cofactor tetrahydrobiopterin synthesis and regeneration pathway was expressed in combination. The L-Trp synthesis module was constructed by modifying the key metabolic nodes of tryptophan biosynthesis, and the heterologous synthesis of 5-HTP was achieved. Finally, the NAD(P)H regeneration module was constructed by the moderate expression of the heterologous GDH esi pathway, which successfully reduced the surplus of the intermediate L-Trp. The final engineered strain HTP11 was able to produce 8.58 g/L 5-HTP in a 5-L bioreactor with a yield of 0.095 g/g glucose and a maximum real-time productivity of 0.48 g/L/h, the highest values reported by microbial fermentation. Conclusion In this study, we demonstrate the successful design of a cell factory for high-level 5-HTP production, combined with simple processes that have potential for use in industrial applications in the future. Thus, this study provides a reference for the production of high-value amino acid derivatives using a systematic modular engineering strategy and a basis for an efficient engineered strain development of 5-HTP high-value derivatives.

Introduction: L-lysine is a bulk product. In industrial production using high-biomass fermentation, the high density of bacteria and the intensity of production require sufficient cellular respiratory metabolism for support. Conventional bioreactors often have difficulty meeting the oxygen supply conditions for this fermentation process, which is not conducive to improving the sugar-amino acid conversion rate. In this study, we designed and developed an oxygen-enhanced bioreactor to address this problem.Methods: This bioreactor optimizes the aeration mix using an internal liquid flow guide and multiple propellers.Results: Compared with a conventional bioreactor, it improved the kLa from 367.57 to 875.64 h-1, an increase of 238.22%. The results show that the oxygen supply capacity of the oxygen-enhanced bioreactor is better than that of the conventional bioreactor. Its oxygenating effect increased the dissolved oxygen in the middle and late stages of fermentation by an average of 20%. The increased viability of Corynebacterium glutamicum LS260 in the mid to late stages of growth resulted in a yield of 185.3 g/L of L-lysine, 74.57% conversion of lysine from glucose, and productivity of 2.57 g/L/h, an increase of 11.0%, 6.01%, and 8.2%, respectively, over a conventional bioreactor. Oxygen vectors can further improve the production performance of lysine strains by increasing the oxygen uptake capacity of microorganisms. We compared the effects of different oxygen vectors on the production of L-lysine from LS260 fermentation and concluded that n-dodecane was the most suitable. Bacterial growth was smoother under these conditions, with a 2.78% increase in bacterial volume, a 6.53% increase in lysine production, and a 5.83% increase in conversion. The different addition times of the oxygen vectors also affected the final yield and conversion, with the addition of oxygen vectors at 0 h, 8 h, 16 h, and 24 h of fermentation increasing the yield by 6.31%, 12.44%, 9.93%, and 7.39%, respectively, compared to fermentation without the addition of oxygen vectors. The conversion rates increased by 5.83%, 8.73%, 7.13%, and 6.13%, respectively. The best results were achieved by adding oxygen vehicles at the 8th hour of fermentation, with a lysine yield of 208.36 g/L and a conversion rate of 83.3%. In addition, n-dodecane significantly reduced the amount of foam produced during fermentation, which is beneficial for fermentation control and equipment.Conclusion: The new oxygen-enhanced bioreactor improves oxygen transfer efficiency, and oxygen vectors enhance the ability of cells to take up oxygen, which effectively solves the problem of insufficient oxygen supply during lysine fermentation. This study provides a new bioreactor and production solution for lysine fermentation.

Corynebacterium glutamicum is an important industrial production strain that is widely used in amino acid fermentation, biopharmaceuticals, and other fields. It is particularly important to develop efficient genome editing methods for the targeted modification of C. glutamicum production strains. Currently, the gene editing system of C. glutamicum is inefficient and time-consuming. In this paper, we reported on a Francisella novicida (Fn) CRISPR-Cpf1-based system for genome editing. The system used linear DNA detached from the plasmid, and, with the assistance of the recombinase RecET, gene deletion was achieved by simultaneous electrotransformation of linear DNA with a plasmid carrying the FnCpf1 and crRNA expression cassette for double-strand breaks of the genome. Compared with previous all-in-one plasmids, this system reduced the time for one round of constructing recombinant plasmids and shortened the editing cycle by about 24 h. Finally, we successfully constructed an engineered strain (X−2) with high L-valine production by using the linear DNA-mediated gene deletion system. This method is of great importance for accelerating the process of metabolic engineering modification of C. glutamicum and its further application in high value-added products.

Abstract With the rapid development of artificial intelligence (AI), the application of this technology in the medical field is becoming increasingly extensive, along with a gradual increase in the amount of intelligent equipment in hospitals. Service robots can save human resources and replace nursing staff to achieve some work. In view of the phenomenon of mobile service robots' grabbing and distribution of patients' drugs in hospitals, a real‐time object detection and positioning system based on image and text information is proposed, which realizes the precise positioning and tracking of the grabbing objects and completes the grasping of a specific object (medicine bottle). The lightweight object detection model NanoDet is used to learn the features of the grasping objects and the object category, and bounding boxes are regressed. Then, the images in the bounding boxes are enhanced to overcome unfavourable factors, such as a small object region. The text detection and recognition model PP‐OCR is used to detect and recognise the enhanced images and extract the text information. The object information provided by the two models is fused, and the text recognition result is matched with the object detection box to achieve the precise positioning of the grasping object. The kernel correlation filter (KCF) tracking algorithm is introduced to achieve real‐time tracking of specific objects to precisely control the robot's grasping. Both deep learning models adopt lightweight networks to facilitate direct deployment. The experiments show that the proposed robot grasping detection system has high reliability, accuracy and real‐time performance.

Zearalenone and its metabolites are mycotoxins generated by Fusarium species while crops are growing and can typically be found in various foods, posing a risk to human health. Governments have implemented stricter regulations concerning the permissible levels of zearalenone in food products to safeguard public health. Stricter regulations on zearalenone levels in food have been implemented. However, detecting zearalenone and its metabolites remains challenging due to sample complexity and interference. Surprisingly few reviews of sample preparation methods for zearalenone in food have appeared in the past decade. In this overview, we outline the most recent developments in the sample pre-treatment technology of zearalenone and its metabolites in food samples based on chromatography–mass spectrometry methods since 2012. This review covers some prominent technologies, such as liquid–liquid extraction-based methods, solid-phase extraction-based methods, and QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction, providing valuable insights into their advantages and limitations for potential applications. The assessment of the methods discussed, along with an overview of current challenges and prospects, will guide researchers in advancing the field and ensuring safer food quality for consumers worldwide.

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. Firstly, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. This paper completes the simulation and physical experiments, respectively, and the performance of the proposed method is evaluated using three commonly used performance indexes: the average deviation of food, the energy efficiency, and the number of obstacle avoidance events. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in all three indexes.

In order to solve the problems of high complexity, many by-products, high pollution and difficult extraction of the existing adenine production process, in this study, ceramic membrane-coupled mixed cell fermentation was used to produce adenine while reducing the synthesis of by-products and simplifying the production process of adenine. Nucleoside hydrolase (encoded by the rihC gene) was used to produce adenine by coordinated fermentation with the adenosine-producing bacterium Bacillus Subtilis XGL. The adenosine hydrolase (AdHy)-expressing strain Escherichia coli BL21-AdHy was successfully employed and the highest activity of the crude enzyme solution was found by orthogonal experiments at 170 W power, 42% duty cycle, and 8 min of sonication. The highest AdHy activity was found after 18 h of induction incubation. E. coli BL21-AdHy was induced for 18 h and sonicated under the above ultrasonic conditions and the resulting crude enzyme solution was used for co-fermentation of the strain and enzyme. Moreover, 15% (v/v) of the AdHy crude enzyme solution was added to fermentation of B. subtilis XGL after 35 h. Finally, the whole fermentation system was dialyzed using coupled ceramic membranes for 45 and 75 h, followed by the addition of fresh medium. In contrast, the AdHy crude enzyme solution was added after 35, 65, and 90 h of B. subtilis fermentation, with three additions of 15, 15, and 10% of the B. subtilis XGL fermentation system. The process was validated in a 5 L fermenter and 14 ± 0.25 g/L of adenine was obtained, with no accumulation of adenosine and d-ribose as by-products. The enzymatic activity of the AdHy crude solution treated with ultrasound was greatly improved. It also reduced the cellular activity of E. coli BL21-AdHy and reduced effects on bacterial co-fermentation. Membrane-coupled dialysis solved the problem of decreased yield due to poor bacterial survival and decreased viability, and eliminated inhibition of the product synthesis pathway by adenosine. The batch addition of crude enzyme broth allowed the continuous conversion of adenosine to adenine. This production method provides the highest yield of biologically produced adenine reported to date, reduces the cost of adenine production, and has positive implications for the industrial production of adenine by fermentation. And it provides a reference for producing other high-value-added products made by fermentation.

AI technology has developed so fast, and it has been applied to the commercial area. In order to predict the customer preference and adjust the placement of product or advertisement, etc., the intelligent surveillance video analysis technique has been proposed to gather the sufficient customer information and realize crowd counting and density map drawing. In this paper, a series of deep learning techniques are adopted to realize surveillance video analysis. This work covers different subproblems such as object detection, tracking and human identification. A skeleton recognition algorithm is adopted instead of object detection algorithm to overcome the severe occlusion problem. A multiple human tracking algorithm combing the human re-identification technology is adopted to realize the human tracking and counting. Finally, the density map and statistics information are obtained which can be used to evaluate and adjust the current business plan. A real store surveillance video is analyzed by the algorithm, and the results show the advantage of the algorithm.

Recently, the learning-based LiDAR odometry has obtained robust estimation results in the field of mobile robot localization, but most of them are constructed based on the idea of supervised learning. In the network training stage, these supervised learning-based methods rely heavily on real pose labels, which is defective in practical applications. Different from these methods, a novel self-supervised LiDAR odometry, namely SSLO, is proposed in this article. The proposed SSLO only uses unlabeled point cloud data to train the three-view pose network to complete the robot localization task. Specifically, first, due to the sparseness and disorder of the original LiDAR point cloud, it is difficult to use deep convolutional neural networks for feature extraction. In this article, the spherical projection of the point cloud is used to convert the original point cloud into a regular vertex map. Then the vertex map obtained by the projection is used as the input of the neural network. Second, in the network training phase, SSLO uses multiple geometric losses for different situations of matching point clouds and introduces uncertainty weights when calculating the losses to reduce the interference of noise or moving objects in the scene. Last but not least, the proposed method is not only used in the simulation experiments based on the KITTI dataset and Apollo-SouthBay dataset but also applied to a real-world wheeled robot SLAM task. Extensive experimental results show that the proposed method has good performance in different environments.

A temperature-sensitive mutant of Corynebacterium glutamicum is widely used as the fermentation strain in L-glutamate industrial production. Rapid synthesis of L-glutamate in this type of strain can be triggered by raising the culture temperature. However, the molecular mechanism of the temperature-sensitivity trait and L-glutamate excretion has not been clarified. In this study, we compared the genome sequence of an industrial L-glutamate-producing strain C. glutamicum TCCC11822 with the wild-type strain C. glutamicum ATCC13032. Seven mutant genes related to cell membrane synthesis were screened and separately integrated into the genome of C. glutamticum ATCC13032 to replace the corresponding genes. All the recombinant strains showed temperature-sensitivity and increased L-glutamate excretion. Electron microscopy showed that there was no significant change in cell morphology of recombinant strains at different temperatures. Genotypes found in this article can be applied to the construction of temperature-sensitive engineering strains and the regulation of cell permeability.

We study the relationships between the real-time psychophysiological activity of professional traders, their financial transactions, and market fluctuations. We collected multiple physiological signals such as heart rate, blood volume pulse, and electrodermal activity of 55 traders at a leading global financial institution during their normal working hours over a five-day period. Using their physiological measurements, we implemented a novel metric of trader’s “psychophysiological activation” to capture affect such as excitement, stress and irritation. We find statistically significant relations between traders’ psychophysiological activation levels and such as their financial transactions, market fluctuations, the type of financial products they traded, and their trading experience. We conducted post-measurement interviews with traders who participated in this study to obtain additional insights in the key factors driving their psychophysiological activation during financial risk processing. Our work illustrates that psychophysiological activation plays a prominent role in financial risk processing for professional traders.

Grey wolf optimization (GWO) is a meta-heuristic algorithm inspired by the hierarchy and hunting behavior of grey wolves. GWO has the superiorities of simpler concept and fewer adjustment parameters, and has been widely used in different fields. However, there are some disadvantages in avoiding prematurity and falling into local optimum. This paper presents an improved grey wolf optimization (IGWO) to ameliorate these drawbacks. Firstly, a modified position update mechanism for pursuing high quality solutions is developed. By designing an ameliorative position update formula, a proper balance between the exploration and exploitation is achieved. Moreover, the leadership hierarchy is strengthened by proposing adaptive weights of α, β and δ. Then, a dynamic local optimum escape strategy is proposed to reinforce the ability of the algorithm to escape from the local stagnations. Finally, some individuals are repositioned with the aid of the positions of the leaders. These individuals are pulled to new positions near the leaders, helping to accelerate the convergence of the algorithm. To verify the effectiveness of IGWO, a series of contrast experiments are conducted. On the one hand, IGWO is compared with some state-of-the-art GWO variants and several promising meta-heuristic algorithms on 20 benchmark functions. Experimental results indicate that IGWO performs better than other competitors. On the other hand, the applicability of IGWO is verified by a robot global path planning problem, and simulation results demonstrate that IGWO can plan shorter and safer paths. Therefore, IGWO is successfully applied to the path planning as a new method.

Summary Corynebacterium glutamicum is an important industrial microorganism, but the availability of tools for its genetic modification has lagged compared to other model microorganisms such as Escherichia coli. Despite great progress in CRISPR‐based technologies, the most feasible genome editing method in C. glutamicum is suicide plasmid‐mediated, the editing efficiency of which is low due to high false‐positive rates of sacB counter selection, and the requirement for tedious two‐round selection and verification of rare double‐cross‐over events. In this study, an rpsL mutant conferring streptomycin resistance was harnessed for counter selection, significantly increasing the positive selection rate. More importantly, with the aid of high selection efficiencies through the use of antibiotics, namely kanamycin and streptomycin, the two‐step verification strategy can be simplified to just one‐step verification of the final edited strain. As proof of concept, a 2.5‐kb DNA fragment comprising aroGfbrpheAfbr expressing cassettes was integrated into the genome of C. glutamicum, with an efficiency of 20% out of the theoretical 50%. The resulting strain produced 110 mg l−1 l‐tyrosine in shake‐flask fermentation. This updated suicide plasmid‐mediated genome editing system will greatly facilitate genetic manipulations including single nucleotide mutation, gene deletion and gene insertion in C. glutamicum and can be easily applied to other microbes.

In the practical scene, object detection faces a very complicated situation. The occlusion problem always occurs in actual scene, which may affect the accuracy of object detection, especially for the occluded objects. For the deep models, a larger dataset with sufficient occlusion samples will improve the performance of the object detection models. However, the sample with occlusion problem is too hard to obtain. Therefore, a global average pooling(GAP) based adversarial Faster-RCNN is proposed to generate the hard samples and enhance the performance of object detection algorithm. Sufficient hard samples can be generated with the help of this model. Therefore, the object detection model can be trained adequately for the occluded objects. The hard sample generation is carried out in the space of image feature instead of image generation directly. The class-dependent part is obtained by the GAP network, and it is obscured to generate the feature map of hard sample for model reinforcement training. Therefore, the better object detection model can be trained using a conventional dataset. The Faster-RCNN is adopted as the baseline. The Faster-RCNN and GAP have a joint training to improve the performance of the proposed model. The simulation results exhibit the validation of the proposed algorithm.

Acetic acid, an important by-product of L-tryptophan production by Escherichia coli, is detrimental to cell growth and tryptophan accumulation. To reduce acetic acid accumulation and further improve the production of L-tryptophan, an acetyl-coenzyme A (acetyl-CoA) synthase (ACS) with high synthetic activity is needed. In this study, E. coli ACS was found to have a superior catalytic capability compared to that of other bacterial strains. To reduce the accumulation of acetic acid by metabolic overflow, a higher expression level of ACS was obtained by promoter engineering and copy number optimisation. Furthermore, reconstruction of the tri-carboxylic acid cycle (TCA) not only enhanced the supply of phosphoenolpyruvate (PEP), which is one of the precursors required for tryptophan synthesis, but also improved bacterial growth. The final engineered strain T16 accumulated 54.6 g/L of L-tryptophan, 11.4% more than the original strain T03, with an accumulation of only 1.4 g/L acetic acid after fermentation, which reduced by 74.5% compared to T03.

L-theanine is the most abundant free amino acid in tea that offers various favorable physiological and pharmacological effects. Bacterial enzyme of γ-glutamylmethylamide synthetase (GMAS) can catalyze the synthesis of theanine from glutamate, ethylamine and ATP, but the manufacturing cost is uncompetitive due to the expensive substrates and complex processes. In this study, we described pathway engineering of wild-type Escherichia coli for one-step fermentative production of theanine from sugars and ethylamine. First, the synthetic pathway of theanine was conducted by heterologous introduction of a novel GMAS from Paracoccus aminovorans. A xylose-induced T7 RNA polymerase-PT7 promoter system was used to enhance and control gmas gene expression. Next, the precursor glutamate pool was increased by overexpression of native citrate synthase and introduction of glutamate dehydrogenase from Corynebacterium glutamicum. Then, in order to push more carbon flux towards theanine synthesis, the tricarboxylic acid cycle was interrupted and pyruvate carboxylase from C. glutamicum was introduced as a bypath supplying oxaloacetate from pyruvate. Finally, an energy-conserving phosphoenolpyruvate carboxykinase from Mannheimia succiniciproducens was introduced to increase ATP yield for theanine synthesis. After optimizing the addition time and concentration of ethylamine hydrochloride in the fed-batch fermentation, the recombinant strain TH11 produced 70.6 ​g/L theanine in a 5-L bioreactor with a yield and productivity of 0.42 ​g/g glucose and 2.72 ​g/L/h, respectively. To our knowledge, this is the first report regarding the pathway engineering of E. coli for fermentative production of theanine. The high production capacity of recombinant strain, combined with the easy processes, will hold attractive industrial application potential for the future.

To balance the diagonal gait of a quadruped robot, a dynamic balance control method is presented to improve the stability of the quadruped robot by adjusting its foot position. We set up a trunk-based coordinate system and a hip-based local coordinate system for the quadruped robot, established the kinematics equation of the robot, and designed a reasonable initial diagonal gait through the spring inverted pendulum model. The current trunk posture of the quadruped robot is obtained by collecting the data of its pitch and roll angle, and the foot position is predicted according to the current posture and initial gait of the quadruped robot. To reduce the impact of one leg landing on the ground and increase the stability of the quadruped robot, we adjust the landing point of the robot according to the landing time difference between the diagonal legs. The proposed method can adjust the body in such scenarios as planar walking and lateral impact resistance. It can reduce the disturbance during the robot motion and make the robot move smoothly. The validity of this method is verified by simulation experiments.

We generated an induced human pluripotent stem cell line from a child with microcephaly carrying compound heterozygous mutations of TYW1 inherited from healthy parents. This iPS cell line showed typical embryonic stem cell-like morphology, expressed pluripotent markers that comparable to human embryonic stem cells. Moreover, these cells have the ability to differentiate into three germ layers and maintain a normal karyotype.

Background: During l-tryptophan production by Escherichia coli, the by-products, acetic acid and NH4+, accumulate in the fermentation broth, resulting in inhibited cell growth and activity and decreased l-tryptophan production. To improve the l-tryptophan yield and glucose conversion rate, acetic acid and NH4+ were removed under low-temperature vacuum conditions by vacuum scraper concentrator evaporation; the fermentation broth after evaporation was pressed into another fermenter to continue fermentation. To increase the volatilisation rate of acetic acid and NH4+ and reduce damage to bacteria during evaporation, different vacuum evaporation conditions were studied. Results: The optimum operating conditions were as follows: vacuum degree, 720 mm Hg; concentration ratio, 10%; temperature, 60°C; and feeding rate, 300 mL/min. The biomass yield of the control fermentation (CF) and fermentation by vacuum evaporation (VEF) broths was 55.1 g/L and 58.3 g/L at 38 h, respectively, (an increase of 5.8%); the living biomass yield increased from 8.9 (CF) to 10.2 pF (VEF; an increase of 14.6%). l-tryptophan production increased from 50.2 g/L (CF) to 60.2 g/L (VEF) (an increase of 19.9%), and glucose conversion increased from 18.2% (CF) to 19.5% (VEF; an increase of 7.1%). The acetic acid concentrations were 2.74 g/L and 6.70 g/L, and the NH4+ concentrations were 85.3 mmol/L and 130.9 mmol/L in VEF and CF broths, respectively. Conclusions: The acetic acid and NH4+ in the fermentation broth were quickly removed using the vacuum scraper concentrator, which reduced bacterial inhibition, enhanced bacterial activity, and improved the production of l-tryptophan and glucose conversion rate.How to cite: Xu Q, Bai F, Chen N, et al. Removing the by-products acetic acid and NH4+ from the l-tryptophan broth by vacuum thin film evaporation during l-tryptophan production. Electron J Biotechnol 2018; 33. https://doi.org/10.1016/j.ejbt.2018.04.003. Keywords: Acetic acid, Amino acid, Concentration, Escherichia coli, Fermentation, Glucose conversion rate, l-tryptophan production, NH4+, Vacuum thin film evaporation, Waste product

Metastatic disease is the major reason for breast cancer-related deaths; therefore, a better understanding of this process and its players is needed. Here the authors report the role of SIRT7 in inhibiting SMAD4-mediated breast cancer metastasis providing a possible therapeutic avenue.

Systems metabolic engineering is a multidisciplinary area that integrates systems biology, synthetic biology and evolutionary engineering. It is an efficient approach for strain improvement and process optimization, and has been successfully applied in the microbial production of various chemicals including amino acids. In this review, systems metabolic engineering strategies including pathway-focused approaches, systems biology-based approaches, evolutionary approaches and their applications in two major amino acid producing microorganisms: Corynebacterium glutamicum and Escherichia coli, are summarized.

Collaborative control of a dual-arm robot refers to collision avoidance and working together to accomplish a task. To prevent the collision of two arms, the control strategy of a robot arm needs to avoid competition and to cooperate with the other one during motion planning. In this paper, a dual-arm deep deterministic policy gradient (DADDPG) algorithm is proposed based on deep reinforcement learning of multi-agent cooperation. Firstly, the construction method of a replay buffer in a hindsight experience replay algorithm is introduced. The modeling and training method of the multi-agent deep deterministic policy gradient algorithm is explained. Secondly, a control strategy is assigned to each robotic arm. The arms share their observations and actions. The dual-arm robot is trained based on a mechanism of “rewarding cooperation and punishing competition”. Finally, the effectiveness of the algorithm is verified in the Reach, Push, and Pick up simulation environment built in this study. The experiment results show that the robot trained by the DADDPG algorithm can achieve cooperative tasks. The algorithm can make the robots explore the action space autonomously and reduce the level of competition with each other. The collaborative robots have better adaptability to coordination tasks.

In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the shuffle layer improves the composition of features in each channel which can promote the utilization of useful information. The Histogram of Oriented Gradients (HOG) spatial-temporal features of the object are extracted for training, which can significantly improve the ability of human action expression and reduce the calculation of feature extraction. The I3D-shufflenet is testified on the UCF101 dataset, and compared with other models. The final result shows that the I3D-shufflenet has higher accuracy than the original I3D with an accuracy of 96.4%.

Recently, the information analysis technology of underwater has developed rapidly, which is beneficial to underwater resource exploration, underwater aquaculture, etc. Dangerous and laborious manual work is replaced by deep learning-based computer vision technology, which has gradually become the mainstream. The binocular cameras based visual analysis method can not only collect seabed images but also construct the 3D scene information. The parallax of the binocular image was used to calculate the depth information of the underwater object. A binocular camera based refined analysis method for underwater creature body length estimation was constructed. A fully convolutional network (FCN) was used to segment the corresponding underwater object in the image to obtain the object position. A fish’s body direction estimation algorithm is proposed according to the segmentation image. The semi-global block matching (SGBM) algorithm was used to calculate the depth of the object region and estimate the object body length according to the left and right views of the object. The algorithm has certain advantages in time and accuracy for interest object analysis by the combination of FCN and SGBM. Experiment results show that this method effectively reduces unnecessary information, improves efficiency and accuracy compared to the original SGBM algorithm.

We show that the predicted probability distributions for any N-parameter statistical model taking the form of an exponential family can be explicitly and analytically embedded isometrically in a N+N-dimensional Minkowski space. That is, the model predictions can be visualized as control parameters are varied, preserving the natural distance between probability distributions. All pairwise distances between model instances are given by the symmetrized Kullback-Leibler divergence. We give formulas for these intensive symmetrized Kullback-Leibler (isKL) coordinate embeddings, and illustrate the resulting visualizations with the Bernoulli (coin-toss) problem, the ideal gas, n-sided die, the nonlinear least-squares fit, and the Gaussian fit. We highlight how isKL can be used to determine the minimum number of parameters needed to describe probabilistic data, and conclude by visualizing the prediction space of the two-dimensional Ising model, where we examine the manifold behavior near its critical point.

Deep learning based intelligent fault diagnosis methods have become a research hotspot in the fields of fault diagnosis and the health management of rolling bearings in recent years. To effectively identify incipient faults in rotating machinery, this paper proposes a novel hybrid intelligent fault diagnosis framework based on a convolutional neural network and support vector machine (SVM). First, an improved one-dimensional convolutional neural network (1DCNN) was adopted to extract fault features, and the state information and intrinsic properties of the raw vibration signals were mined. Second, the extracted features were used to train the SVM, which was applied to classify the fault category. The proposed hybrid framework combined the excellent classification performance of the SVM for small samples and the strong feature-learning ability of CNN network. In order to tune the parameters of the SVM, an improved novel particle swarm optimization algorithm (INPSO) which combined the Tent map and Lévy flight strategy was proposed. Numerical experimental results indicated that the proposed PSO variant had a better performance in searching accuracy and convergence speed. At last, multiple groups of rolling bearing fault diagnosis experiments were carried out and experimental results showed that, with the proposed 1DCNN-INPSO-SVM model, the hybrid framework was capable of diagnosing with high precision for rolling bearings and superior to some traditional fault diagnosis methods.

Recently, ship collision avoidance has become essential due to the emergence of special vessels like chemical tankers and VLCCs (very large crude carriers), etc. The information needed for safe navigation is obtained by combining electrical equipment with real-time visual information. However, misjudgements and human errors are the major cause of ship collisions according to research data. The decision support system of Collision avoidance is an advantageous facility to make up for this. Collision risk evaluation is one of the most important problems in collision avoidance decision supporting system. A review is presented of different approaches to evaluate the collision risk in maritime transportation. In such a context, the basic concepts and definitions of collision risk and their evaluation are described. The review focuses on three categories of numerical models of collision risk calculation: methods based on traffic flow theory, ship domain and methods based on dCPA and tCPA.

The implementation of a novel cell recycling technology based on a special disk centrifuge during microbial fermentation process can continuously separate the product and harmful intermediates, while maintaining the cell viability owing to the installed cooling system. Acetate accumulation is an often encountered problem in L-tryptophan fermentation by Escherichia coli. To extend our previous studies, the current study deleted the key genes underlying acetate biosynthesis to improve l-tryptophan production. The deletion of the phosphotransacetylase (pta)-acetate kinase (ackA) pathway in a gltB (encoding glutamate synthase) mutant of E. coli TRTHB, led to the highest production of l-tryptophan (47.18 g/L) and glucose conversion rate (17.83%), with a marked reduction in acetate accumulation (1.22 g/L). This strain, TRTHBPA, was then used to investigate the effects of the cell recycling process on L-tryptophan fermentation. Four different strategies were developed concerning two issues, the volume ratio of the concentrated cell solution and clear solution and the cell recycling period. With strategy I (concentrated cell solution: clear solution, 1: 1; cell recycling within 24-30 h), L-tryptophan production and the glucose conversion rate increased to 55.12 g/L and 19.75%, respectively, 17.55% and 10.77% higher than those without the cell recycling. In addition, the biomass increased by 13.52% and the fermentation period was shortened from 40 h to 32 h. These results indicated that the cell recycling technology significantly improved L-tryptophan production by E. coli.

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

PID controller is used in most of the current-speed closed-loop control of permanent magnet synchronous motors (PMSM) servo system. However, Kp, Ki, and Kd of PID are difficult to tune due to the multiple objectives. In order to obtain the optimal PID parameters, we adopt a NSGA-II to optimize the PID parameters in this paper. According to the practical requirement, several objective functions are defined. NSGA-II can search the optimal parameters according to the objective functions with better robustness. This approach provides a more theoretical basis for the optimization of PID parameters than the aggregation function method. The simulation results indicate that the system is valid, and the NSGA-II can obtain the Pareto front of PID parameters.