Construction of an Integrated mCherry Red Fluorescent Protein Expression System for Labeling and Tracing in Lactiplantibacillus plantarum WCFS1

Thorough intestinal adhesion and colonization greatly promote the probiotic properties of lactic acid bacteria (LAB). Labeling and tracing with fluorescent proteins are effective and reliable for studying the in vivo physiological activities of LAB including localization, adhesion, and colonization. Lactiplantibacillus plantarum WCFS1 was successfully traced with a red fluorescent protein (RFP), which was expressed by the bacteria-carrying recombinant plasmids. In this study, we aimed to construct a stable RFP mCherry expression system, whose encoding gene was integrated into the bacterial chromosome via double-crossed homologous recombination, and use it for labeling WCFS1 with the goal of avoiding the potential loss of non-chromosomal plasmids along with intestinal growth. First, the constitutive expression of the mCherry protein was improved after adjusting the length of the spacer between the promoter and the gene start codon. Then, the optimized mCherry gene expression cassette was integrated into the chromosome of WCFS1. The resulting strain had normal unimpaired growth and strong fluorescent signals, even after 100 generations, indicating its stability. Furthermore, quantitative polymerase chain reaction (PCR) results revealed a strong positive correlation between the fluorescence intensity of the strain and the number of viable cells, demonstrating its potential usage for the quantification of in vivo WCFS1 cells. Finally, the increased adhesion ability of WCFS1 due to the recombinant expression of the bsh gene was visualized and evaluated using fluorescence intensity, the results of which were consistent with those obtained using the previously established quantification methods. These results suggest that the chromosomal-integrated mCherry labeling system can be extensively used to examine the distribution, colonization, and survival of LAB in vivo in order to determine the mechanism of its probiotic function

A calibration method of USBL installation error based on attitude determination

The Ultra-short baseline (USBL) positioning system has important application in the positioning of underwater vehicles. The installation error angle of the USBL positioning system has an important influence on the positioning accuracy of USBL system. The traditional calibration methods have limited estimation accuracy for installation error angles and have high route requirements. To solve the above problems, a calibration method of installation error angle based on attitude determination is proposed in this paper. When strapdown inertial navigation system (SINS) and USBL are fixed together in the application process, the installation error angle of USBL is fixed and unchanged. Then the calibration of installation error angle can be accomplished with the idea of attitude determination. The vector observation model based on the installation error angle matrix is established first. Observation vectors are obtained by the relative position of transponders in the USBL coordinate frame. The reference vector is calculated by position of transponder, position and attitude of SINS and lever arm between SINS and USBL. By constructing the observation vectors and the reference vectors, the proposed method can calibrate the installation error angle of SINS and USBL in real time. The advantages of the proposed method are that it has no specific requirements for the calibration route and can calibrate the installation error angle in real time with high accuracy. In order to verify the performance of the proposed algorithm, simulation experiment and field experiment are carried out in this paper. The results of simulation experiment and field experiment show that the proposed method can give the estimated installation error angle of USBL in real time, and the estimated result is the best among several methods. The proposed method can not only achieve the calibration of the installation error angle in circular trajectory, but also in straight trajectory

Optimization of production of PLA microbubble ultrasound contrast agents for Hydroxycamptothecin delivery

In this paper, ultrasound contrast agents based on a high molecular polymer poly lactic acid (PLA) and loaded with Hydroxycamptothecin (HCPT) were prepared by combining ultrasound method and a Shirasu Porous Glass (SPG) membrane emulsification technique. A special focus was on the optimization of production of RCPT-PLA microbubbles. Different factors, such as the power and the time of ultrasonic action, the ratio of inner aqueous phase against outer oil phase, and the concentration of PLA were evaluated, and the average size of HCPT-PLA microbubbles, the drug carrying efficiency, as well as the acoustically-triggered drug release at 3kHz ultrasound were determined. The study showed that the HCPT-PLA microbubbles prepared using our optimized conditions, were sphere-like in shape with a mean diameter of 1-7 mu m. The drug loading efficiency reached up to 56.48%. In vitro, the drug release of HCPT-PLA microbubbles increased significantly at 3kHz ultrasound for 30s compared with that of ultrasound free condition. In conclusion, the HCPT-PLA microbubbles has the characteristics desirable for an intravenously administered ultrasound contrast agent for further clinical use

Biological Characteristics of Severe Combined Immunodeficient Mice Produced by CRISPR/Cas9-Mediated Rag2 and IL2rg Mutation

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas)9 is a novel and convenient gene editing system that can be used to construct genetically modified animals. Recombination activating gene 2 (Rag2) is a core component that is involved in the initiation of V(D)J recombination during T- and B-cells maturation. Separately, the interleukin-2 receptor gamma chain gene (IL2rg) encoded the protein-regulated activity of natural killer (NK) cells and shared common receptors of some cytokines. Rag2 and IL2rg mutations cause immune system disorders associated with T-, B-, and NK cell function and some cytokine activities. In the present study, 2 single-guide RNAs (sgRNAs) targeted on Rag2 and IL2rg genes were microinjected into the zygotes of BALB/c mice with Cas9 messenger RNA (mRNA) to create Rag2/IL2rg-/- double knockout mice, and the biological characteristics of the mutated mice were subsequently analyzed. The results showed that CRISPR/Cas9-induced indel mutation displaced the frameshift of Rag2 and IL2rg genes, resulting in a decrease in the number of T-, B-, and NK cells and the destruction of immune-related tissues like the thymus and spleen. Mycobacterium tuberculosis 85B antigen could not induce cellular and humoral immune response in mice. However, this aberrant immune activity compromised the growth of several tumor heterogenous grafts in the mutated mice, including orthotopic and subcutaneous transplantation tumors. Thus, Rag2/IL2rg-/- knockout mice possessed features of severe combined immunodeficiency (SCID), which is an ideal model for human xenograft

Ballasted Track Behaviour Induced by Absent Sleeper Support and its Detection Based on a Convolutional Neural Network Using Track Data

Abstract With development of the heavy-haul railway, the increased axle load and traction weight bring a significant challenge for the service performance and safety maintenance of the railway track. Conducting defect recognition on concrete sleepers and ballast using big data is vital. This paper focused on the detection of absent sleeper support in a ballasted track with an emphasis on the integration of model-based and data-driven methods. To this end, a mathematical model consisting of the wagon, track and wheel–rail contact subsystems was first established to acquire the necessary raw data for the data-driven method, in which the wagon was regarded as a 47-degree-of-freedom multi-body subsystem, and the track was treated as a multi-layer discrete-elastic support beam subsystem with absent sleeper support. Then, an architectural hierarchy of a three-layer  convolutional neural network (TLCNN) was developed, which includes three convolutional layers and two pooling layers, and a method for reconstructing one-dimensional sleeper vertical displacement to a two-dimensional time–space matrix was also proposed. Thirdly, verification was carried out by comparing the simulation and experimental results to illustrate the accuracy and reliability of the mathematical model, and the dynamic behaviour of the track with absent sleeper support was investigated. Lastly, the established TLCNN was used to train the raw data of the sleeper vertical displacement and detect the existence of absent sleeper support. Results show that the integration of model-based and data-driven methods was a reliable and effective approach for the detection of absent sleeper support. The proposed TLCNN can acquire and extract robust characteristics in a noisy environment. To handle more complex recognition tasks and further improve performance, deeper CNN models and larger sample sizes should be preferentially considered in practical applications

Crystal structure of (Z)-2-hydroxy-N′-(4-oxo-1,3-thiazolidin-2-ylidene)benzohydrazide

In the title compound, C10H9N3O3S, the five-membered ring adopts a slightly twisted conformation about the Cm—S (m = methylene) bond. The dihedral angle between this ring and the benzene ring is 7.99 (9)°. A bifurcated intramolecular N—H...(O,S) hydrogen bond helps to establish the near planar conformation of the molecule. In the crystal, molecules are linked by N—H...O and O—H...O hydrogen bonds to generate (001) sheets

Long-term high temperature stress decreases the photosynthetic capacity and induces irreversible damage in chrysanthemum seedlings

To study the effects of long-term and short-term high temperature stress and recovery on the physiological functions and appearance quality of chrysanthemums, a controlled experiment with chrysanthemums was conducted. The treatments were 25 °C for 3 days (T1D3), 25 °C for 9 days (T1D9), 41 °C for 3 days (T2D3) and 41°C for 9 days (T2D9). The results indicated that there is no significant difference between the T1D3 and T1D9 groups. Conversely, the total chlorophyll content (Chl), net photosynthetic rate (PN), and maximum quantum yield of Photosystem II (PSII) (FV/FM) under T2D3 and T2D9 decreased by 27.07%, 43.30%, 5.62%, and 44.85%, 68.22%, 8.29%, respectively. The JIP-test results showed that the T2D9-stressed plants had a lower efficiency and functional antenna size, and a higher activity of the reaction centre than T2D3. The contents of malondialdehyde, soluble protein and proline increased by 3.67 nmol/g FM, 298.75 μg/g, and 192.99 μg/g, and the antioxidant enzymes activities were inhibited significantly under T2D9. After the stress was relieved, Chl, PN, and FV/FM under T2D3 recovered to the same level as T1D3, while T2D9 did not. Furthermore, the diameter of the flowers in T2D3 showed no significant difference with the chrysanthemums under T1D3. However, the plants in T2D9 recovered poorly. Both the diameter of the flowers and the anthocyanin under T2D9 reduced significantly comparing with T1D9, indicating that the damage in the chrysanthemum seedlings caused by long-term high temperature was irreversible

Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’ power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, as well as the structural characteristics and advantages and disadvantages of lithium battery SOC estimation in deep learning methods. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Second, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of three types of deep learning methods were obtained using the structure of the neural network used for training as the classification criterion; further, the selection of the deep learning structure for SOC estimation was discussed. Finally, the challenges and future development directions of lithium battery SOC estimation using the deep learning method were explained. Over all, this review provides insights into deep learning for EVs’ Li-ion battery SOC estimation in the future