A Safety Control Method of Car-Following Trajectory Planning Based on LSTM

This paper focuses on the potential safety hazards of collision in car-following behaviour generated by deep learning models. Based on an intelligent LSTM model, combined with a Gipps model of safe collision avoidance, a new, Gipps-LSTM model is constructed, which can not only learn the intelligent behaviour of people but also ensure the safety of vehicles. The idea of the Gipps-LSTM model combination is as follows: the concept of a potential collision point (PCP) is introduced, and the LSTM model or Gipps model is controlled and started through a risk judgment algorithm. Dataset 1 and dataset 2 are used to train and simulate the LSTM model and Gipps-LSTM model. The simulation results show that the Gipps-LSTM can solve the problem of partial trajectory collision in the LSTM model simulation. Moreover, the risk level of all trajectories is lower than that of the LSTM model. The safety and stability of the model are verified by multi-vehicle loop simulation and multi-vehicle linear simulation. Compared with the LSTM model, the safety of the Gipps-LSTM model is improved by 42.02%, and the convergence time is reduced by 25 seconds

The binding pocket properties were fundamental to functional diversification of the GDSL-type esterases/lipases gene family in cotton

Cotton is one of the most important crops in the world. GDSL-type esterases/lipases (GELPs) are widely present in all kingdoms and play an essential role in regulating plant growth, development, and responses to abiotic and biotic stresses. However, the molecular mechanisms underlying this functional diversity remain unclear. Here, based on the identification of the GELP gene family, we applied genetic evolution and molecular simulation techniques to explore molecular mechanisms in cotton species. A total of 1502 GELP genes were identified in 10 cotton species. Segmental duplication and differences in evolutionary rates are the leading causes of the increase in the number and diversity of GELP genes during evolution for ecological adaptation. Structural analysis revealed that the GELP family has high structural diversity. Moreover, molecular simulation studies have demonstrated significant differences in the properties of the binding pockets among cotton GELPs. In the process of adapting to the environment, GELPs not only have segmental duplication but also have different evolutionary rates, resulting in gene diversity. This diversity leads to significant differences in the 3D structure and binding pocket properties and, finally, to functional diversity. These findings provide a reference for further functional analyses of plant GELPs

Conditional Random Fields for Image Labeling

With the rapid development and application of CRFs (Conditional Random Fields) in computer vision, many researchers have made some outstanding progress in this domain because CRFs solve the classical version of the label bias problem with respect to MEMMs (maximum entropy Markov models) and HMMs (hidden Markov models). This paper reviews the research development and status of object recognition with CRFs and especially introduces two main discrete optimization methods for image labeling with CRFs: graph cut and mean field approximation. This paper describes graph cut briefly while it introduces mean field approximation more detailedly which has a substantial speed of inference and is researched popularly in recent years

Is ultrasound combined with computed tomography useful for distinguishing between primary thyroid lymphoma and Hashimoto’s thyroiditis?

Introduction: The aim of the study is to investigate the usefulness of ultrasound combined with computed tomography (CT) for distinguishing between primary thyroid lymphoma (PTL) and Hashimoto’s thyroiditis (HT). Material and methods: The investigation was conducted retrospectively in 80 patients from January 2000 to July 2018. All patients underwent pathological tests to be classified into one of two groups: PTL group and HT group. The cut-off value of CT density was determined using receiver-operating characteristic (ROC) curve analysis. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of diagnosis for thyroid by CT alone, ultrasound alone, and the combination of CT plus ultrasound were calculated. Results: Of the 80 study patients, 27 patients were PTL and 53 patients were HT. Mean CT density had a sensitivity of 90.6% and a specificity of 88.9% at a cut-off value of 53.5 HU, with area under the curve (AUC) 0.88. Ultrasound combined with CT had the highest specificity, accuracy, and PPV compared with CT alone and ultrasound alone (p value < 0.05). Conclusions: Features such as extremely hypoechogenicity, enhanced posterior echo, cervical lymphadenopathy in ultrasound image, and linear high-density strand signs, and very low density in CT imaging have high sensitivity and specificity in thyroid lymphoma. Therefore, ultrasound combined with CT may be useful for distinguishing between PTL and HT.

Qubit energy tuner based on single flux quantum circuits

A device called the qubit energy tuner (QET), based on single flux quantum (SFQ) circuits, has been proposed for Z control of superconducting qubits. The QET is created by improving flux digital-to-analog converters (flux DACs). It can set the energy levels or frequencies of qubits, particularly flux-tunable transmons, and perform gate operations requiring Z control. The circuit structure of the QET is elucidated, consisting of an inductor loop and flux bias units for coarse or fine-tuning. The key feature of the QET is analyzed to understand how SFQ pulses change the inductor loop current, which provides external flux for qubits. Three simulations were performed to verify QET functionality. The first simulation verified the responses of the inductor loop current to SFQ pulses, showing a relative deviation of approximately 4.259% between the analytical solutions of the inductor loop current and the solutions from the WRSpice time-domain simulation. The second and third simulations, using QuTip, demonstrated how to perform a Z gate and an iSWAP gate using the QET, respectively, with corresponding fidelities of 99.99884% and 99.93906% for only one gate operation to specific initial states. These simulations indicate that the SFQ-based QET could act as an efficient component of SFQ-based quantum–classical interfaces for digital Z control of large-scale superconducting quantum computers

Expression of an extremely acidic β-1,4-glucanase from thermoacidophilic Alicyclobacillus sp. A4 in Pichia pastoris is improved by truncating the gene sequence

<p>Abstract</p> <p>Background</p> <p><it>Alicyclobacillus </it>sp. A4 is thermoacidophilic and produces many glycoside hydrolases. An extremely acidic β-1,4-glucanase (CelA4) has been isolated from <it>Alicyclobacillus </it>sp. A4 and purified. This glucanase with a molecular mass of 48.6 kDa decreases the viscosity of barley-soybean feed under simulated gastric conditions. Therefore, it has the potential to improve the nutrient bioavailability of pig feed. For the study reported herein, the full-length gene, <it>CelA4</it>, of this glucanase (CelA4) was identified using the sequences of six peptides and cloned from strain A4. The gene fragment (<it>CelA4</it>_<it>F</it>) encoding the mature protein was expressed in <it>Pichia pastoris</it>. Sequence truncation and glycosylation were found for recombinant CelA4_F, both of which affected the expression efficiency. The physical properties of various forms of CelA4 as they affected enzymatic activity were characterized.</p> <p>Results</p> <p>We located the full-length 2,148-bp gene for CelA4 (<it>CelA4</it>) in the genome of <it>Alicyclobacillus </it>sp. A4. <it>CelA4 </it>encodes a 715-residue polypeptide with a calculated molecular mass of 71.64 kDa, including an N-terminal signal peptide (residues 1-39), a catalytic domain (residues 39-497), and a C-terminal threonine-rich region (residues 498-715). Its deduced amino acid sequence and that of an <it>Alicyclobacillus acidocaldarius </it>endo-β-1,4-glucanase were identical at 44% of the residue positions. When the experimental molecular mass of CelA4_F--a recombinant protein designed to mimic the CelA4 sequence lacking the N-terminal signal peptide that had been expressed in <it>Pichia pastoris</it>--was compared with its hypothetical molecular mass, it was apparent that CelA4_Fwas truncated, possibly at residue 497. An artificially truncated gene fragment (<it>CelA4</it>_<it>T</it>) without C-terminal threonine-rich region was expressed in <it>P. pastoris</it>, and the expression efficiency of CelA4_Twas substantially greater than that of CelA4_F. Purified CelA4_Fand CelA4_Thad similar molecular masses (~60 kDa) and enzymatic properties (optimum pH, 3.4; optimum temperature, 60°C); they were relatively stable between pH 1.2 and 8.2 at 70°C and resistant to acidic and neutral proteases. However, their molecular masses and thermostabilities differed from those of CelA4 isolated from <it>Alicyclobacillus </it>sp. A4. A deglycosylated form of CelA4 (CelA4_D) had properties similar to that of CelA4 except that it was thermoliable at 60°C.</p> <p>Conclusions</p> <p>Truncation during expression of CelA4_For artificial truncation of its gene--both of which produced a form of CelA4 lacking a threonine-rich region that includes a putative linker--increased the level of enzyme produced in comparison with that produced by cultivation of <it>Alicyclobacillus </it>sp. A4. Glycosylation increased the thermostability of CelA4. Of the four forms of CelA4 studied, CelA4_Twas produced in highest yield and had the most favorable physical properties; therefore, it has potential for use in the feed industry.</p

A Safety Control Method of Car-Following Trajectory Planning Based on LSTM

This paper focuses on the potential safety hazards of collision in car-following behaviour generated by deep learning models. Based on an intelligent LSTM model, combined with a Gipps model of safe collision avoidance, a new, Gipps-LSTM model is constructed, which can not only learn the intelligent behaviour of people but also ensure the safety of vehicles. The idea of the Gipps-LSTM model combination is as follows: the concept of a potential collision point (PCP) is introduced, and the LSTM model or Gipps model is controlled and started through a risk judgment algorithm. Dataset 1 and dataset 2 are used to train and simulate the LSTM model and Gipps-LSTM model. The simulation results show that the Gipps-LSTM can solve the problem of partial trajectory collision in the LSTM model simulation. Moreover, the risk level of all trajectories is lower than that of the LSTM model. The safety and stability of the model are verified by multi-vehicle loop simulation and multi-vehicle linear simulation. Compared with the LSTM model, the safety of the Gipps-LSTM model is improved by 42.02%, and the convergence time is reduced by 25 seconds

Urotensin II promotes the proliferation and secretion of vascular endothelial growth factor in rat dermal papilla cells by activating the Wnt-β-catenin signaling pathway

Introduction. Urotensin II (U II) is a kind of active peptide with a variety of biological effects, such as promoting cell proliferation and endocrine effects. The aim of this study is to investigate the effect of urotensin II on the proliferation and secretion of vascular endothelial growth factor (VEGF) in cultured rat dermal papilla cells (DPCs), and to explore its molecular mechanism. Materials and Methods. We used the DPCs isolated from the thoracic aortas of Wistar-Kyoto rats to run the CCK8 and ELISA assay, RC-PCR and Western blotting techniques to identify the effect of Urotensin II on the proliferation and secretion of VEGF in DPCs, data were analyzed by one-way ANOVA or t-test. Results. U II can increase the mRNA expression of proliferation markers Ki67 and PCNA. In addition, the Wnt/β-catenin pathway was activated by U II, but Wnt inhibitor DKK1 reversed the effect of U II. Conclusions. U II promoted the proliferation and secretion of VEGF in rat DPCs through activation of the Wnt-β-catenin signaling pathway

Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases. Therefore, it is important to develop an efficient heart segmentation algorithm.Methods: A total of 275 nuclear magnetic resonance imaging (MRI) heart scans were collected, analyzed, and preprocessed from Huaqiao University Affiliated Strait Hospital, and the data were used in our improved deep learning model, which was designed based on the U-net network. The training set included 80% of the images, and the remaining 20% was the test set. Based on five time phases from end-diastole (ED) to end-systole (ES), the segmentation findings showed that it is possible to achieve improved segmentation accuracy and computational complexity by segmenting the left ventricle (LV), right ventricle (RV), and myocardium (myo).Results: We improved the Dice index of the LV to 0.965 and 0.921, and the Hausdorff index decreased to 5.4 and 6.9 in the ED and ES phases, respectively; RV Dice increased to 0.938 and 0.860, and the Hausdorff index decreased to 11.7 and 12.6 in the ED and ES, respectively; myo Dice increased to 0.889 and 0.901, and the Hausdorff index decreased to 8.3 and 9.2 in the ED and ES, respectively.Conclusion: The model obtained in the final experiment provided more accurate segmentation of the left and right ventricles, as well as the myocardium, from cardiac MRI. The data from this model facilitate the prediction of cardiovascular disease in real-time, thereby providing potential clinical utility