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The functionality of Moloney murine leukemia virus reverse transcriptase (MMLV RT) will increase with the improvement of its solubility and thermal stability. Introduce directed mutation at specific positions of the MMLV RT sequence and codon optimization is needed to achieve these properties. The two RT coding sequences with (rRT-K) and without directed mutations (rRT-L) were versatility optimized and expressed to analyze the ribonuclease H (RNase H) inactivity and thermostable polymerase activity. For this purpose, the five-point mutations (438–442aa) and three-point mutations (530, 568, and 659 aa) were done at the RT connection domain and RNase H active site, respectively. High expression levels of rRT-L and rRT-K were obtained in E. coli BL21(DE3) and BL21(shuffle) strains, 0.5 mM IPTG concentration at 37 °C, and 8 hours' post-induction condition. Then, recombinant enzymes were purified and verified by Ni-NTA resin and western blotting. Insilico analysis (IUpred 3.0) showed that the directed mutation in the RNase H domain caused the formation of disorder regions or instability in the RNase H domain of rRT-K compared to rRT-L. The modified RT-PCR and the RT-LAMP reactions proved the RNase H inactivity of rRT-K. In addition, increasing of thermostability of rRT-K compared to rRT-L and commercial RT was evaluated by the RT-PCR and RT-LAMP reactions. The results showed that rRT-K could successfully tolerate 60 ºC in the two methods. This study revealed that the directed mutations and the versatile sequence optimization can promise to produce thermostable commercial enzymes to decrease non-specific one-step RT-PCR and RT-LAMP products. Key points: • The versatile sequence optimization to improve the yield of the recombinant enzyme. • Directed specific mutations can promise to produce thermostable and soluble RT commercial enzymes. • Predicted hot mutations in the active site of the RNase H domain of recombinant RT without any effect on its structure. [ABSTRACT FROM AUTHOR]

Abstract The functionality of Moloney murine leukemia virus reverse transcriptase (MMLV RT) will increase with the improvement of its solubility and thermal stability. Introduce directed mutation at specific positions of the MMLV RT sequence and codon optimization is needed to achieve these properties. The two RT coding sequences with (rRT-K) and without directed mutations (rRT-L) were versatility optimized and expressed to analyze the ribonuclease H (RNase H) inactivity and thermostable polymerase activity. For this purpose, the five-point mutations (438–442aa) and three-point mutations (530, 568, and 659 aa) were done at the RT connection domain and RNase H active site, respectively. High expression levels of rRT-L and rRT-K were obtained in E. coli BL21(DE3) and BL21(shuffle) strains, 0.5 mM IPTG concentration at 37 °C, and 8 hours’ post-induction condition. Then, recombinant enzymes were purified and verified by Ni-NTA resin and western blotting. Insilico analysis (IUpred 3.0) showed that the directed mutation in the RNase H domain caused the formation of disorder regions or instability in the RNase H domain of rRT-K compared to rRT-L. The modified RT-PCR and the RT-LAMP reactions proved the RNase H inactivity of rRT-K. In addition, increasing of thermostability of rRT-K compared to rRT-L and commercial RT was evaluated by the RT-PCR and RT-LAMP reactions. The results showed that rRT-K could successfully tolerate 60 ºC in the two methods. This study revealed that the directed mutations and the versatile sequence optimization can promise to produce thermostable commercial enzymes to decrease non-specific one-step RT-PCR and RT-LAMP products.

This study was designed to evaluate the effectiveness of recombinant polypeptide-p derived from Momordica charantia on diabetic rats. In this research, the optimized sequence of polypeptide-p gene fused to a secretion signal tag was cloned into the expression vector and transformed into probiotic Saccharomyces boulardii. The production of recombinant secretion protein was verified by western blotting, HPLC, and mass spectrometry. To assay recombinant yeast bioactivity in the gut, diabetic rats were orally fed wild-type and recombinant S. boulardii, in short SB and rSB, respectively, at two low and high doses as well as glibenclamide as a reference drug. In untreated diabetic and treated diabetic + SB rats (low and high doses), the blood glucose increased from 461, 481, and 455 (mg/dl), respectively, to higher than 600 mg/dl on the 21st day. Whereas glibenclamide and rSB treatments showed a significant reduction in the blood glucose level. The result of this study promised a safe plant-source supplement for diabetes through probiotic orchestration. [ABSTRACT FROM AUTHOR]

Aptamers as a kind of biological recognition element have shown great potential in monitoring and the rapid quantification of organophosphorus pesticides (OPPs). However, molecules of OPPs are structurally similar and original aptamers selected by systematic evolution of ligands by exponential enrichment are usually long-chain bases, which hamper the further application under OPPs-aptamer recognition. The aim of the research was to develop a new strategy to design oligonucleotide sequences for binding OPPs by combination of experimental and molecular modeling methods. 3D models of aptamers binding OPPs were constructed, and binding energy and the most probable binding site for the OPPs were then determined by molecular docking, and the binding sites were further confirmed by the results of 2-AP replaced experiments. Based on the docking results, a new aptamer for detection 4 representative OPPs with only 29 bases was designed by reasonable truncation and mutation of the reported aptamer (named S4-29). The interaction between this new aptamer and OPPs were analyzed by molecular docking, microscale thermophoresis, circular dichroism and fluorometric analysis. The results revealed that the new aptamer exhibit more superior recognition performance to OPPs, which can be promote the monitoring ability of OPPs contaminations in food.

Most studies assume the ramp to be a single lane regardless of the possibility of multiple lanes on a freeway ramp. This assumption limits the application of previous ramp metering strategies in the real world. Therefore, this paper proposes a strategy for a centralized controller to efficiently merge connected automated vehicles from a multiple-lane ramp. The proposed strategy aims to allow vehicles from different lanes to pass through the conflict point with the objective of minimum delay and fuel consumption. Numerical experiments are carried out to compare the proposed strategy with first-in-first-out (FIFO) and Vissim built-in strategies. Simulation results indicate that the proposed strategy can reduce more delay than FIFO and Vissim-based strategies. Furthermore, the proposed strategy is also found to be the most reliable in various scenarios with different traffic demand splits, safe headways, and numbers of lanes. [ABSTRACT FROM AUTHOR]

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Deep brain stimulation (DBS) is an effective treatment of various neurological disorders. Due to higher intrinsic signal, 7 T MRI can potentially improve delineation of DBS targets. However, the severe RF transmit field (B1+) inhomogeneity at 7 T can compromise the image contrast of traditional single-contrast sequences for DBS targeting, leading to sub-optimal target visualization. The Magnetization Prepared 2 Rapid Acquisition Gradient Echo (MP2RAGE)-based T1 mapping provides an alternative to the traditional single-contrast techniques by allowing retrospective synthesis of images at arbitrary inversion times to aid in visualization of various DBS targets. With this approach, optimization of sequence parameters to create T1 maps with low noise and low quantification bias is critical, as these characteristics directly affect the noise and uniformity of the synthetic images. In this work, we perform sequence optimization for MP2RAGE-based T1 mapping using a radial view-ordering technique to improve image quality, and demonstrate the clinical utility of T1 mapping approach for DBS targeting. We first introduce a systematic sequence optimization framework for 7 T MP2RAGE T1 mapping by formulating it into a constrained, multi-dimensional optimization process considering the effect of B1+ inhomogeneity on image noise, T1 quantification bias, and image blurring. With this framework, we investigate the use of radial view-order approach for T1 mapping, in lieu of the conventional linear view-ordering. Bloch's equation-based simulations were performed to compare the T1 maps generated using different approaches. Images of healthy volunteer and patients were acquired on a clinical 7 T MRI scanner for validation and to demonstrate the utility of T1 mapping for DBS targeting. Numerical experiments demonstrated that the proposed framework allowed optimization of image SNR in T1 maps while controlling the quantification bias and image blurring, therefore facilitating the selection of optimal sequence parameters for visualizing DBS targets. The optimized sequence using radial view-ordering offered 40–60% noise reduction compared to the linear view-ordering. The improvement of SNR was confirmed in the in vivo examples. Clinical images showed that the synthetic images generated from the optimized T1 maps allowed clear visualization of DBS targets. We demonstrated the optimization of MP2RAGE T1 mapping with radial view-ordering technique for DBS targeting at 7 T and showed that the optimized sequence allows retrospective generation of synthetic inversion time images commonly utilized in DBS targeting, such as fast gray matter acquisition T1 inversion recovery (FGATIR) and edge-enhancing gradient echo (EDGE) sequences. [ABSTRACT FROM AUTHOR]

Abstract High-frequency mutations in tumor genomes could be exploited as an asset for developing tumor vaccines. In recent years, with the tremendous breakthrough in genomics, intelligence algorithm, and in-depth insight of tumor immunology, it has become possible to rapidly target genomic alterations in tumor cell and rationally select vaccine targets. Among a variety of candidate vaccine platforms, the early application of mRNA was limited by instability low efficiency and excessive immunogenicity until the successful development of mRNA vaccines against SARS-COV-2 broken of technical bottleneck in vaccine preparation, allowing tumor mRNA vaccines to be prepared rapidly in an economical way with good performance of stability and efficiency. In this review, we systematically summarized the classification and characteristics of tumor antigens, the general process and methods for screening neoantigens, the strategies of vaccine preparations and advances in clinical trials, as well as presented the main challenges in the current mRNA tumor vaccine development.

The L1 cell adhesion molecule (L1CAM) plays important roles in the development and plasticity of the nervous system as well as in tumor formation, progression, and metastasis. New ligands are necessary tools for biomedical research and the detection of L1CAM. Here, DNA aptamer yly12 against L1CAM was optimized to have much stronger binding affinity (10–24 fold) at room temperature and 37 °C via sequence mutation and extension. This interaction study revealed that the optimized aptamers (yly20 and yly21) adopted a hairpin structure containing two loops and two stems. The key nucleotides for aptamer binding mainly located in loop I and its adjacent area. Stem I mainly played the role of stabilizing the binding structure. The yly-series aptamers were demonstrated to bind the Ig6 domain of L1CAM. This study reveals a detailed molecular mechanism for the interaction between yly-series aptamers and L1CAM and provides guidance for drug development and detection probe design against L1CAM. [ABSTRACT FROM AUTHOR]

Magnetic resonance fingerprinting (MRF) is a novel quantitative MR technique that simultaneously provides multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions for direct measurement of quantitative errors will make the optimization results more practical and robust. However, optimizing such cost function is computationally expensive and impractical for MRF optimization with tens of thousands of iterations. Here, we introduce a fast MRF simulator to simulate aliased images from actual scan scenarios including undersampling and system imperfections, which substantially reduces computational time and allows for direct error estimation of the quantitative maps and efficient sequence optimization. We evaluate the performance and computational speed of the proposed approach by simulations and in vivo experiments. The simulations from the proposed method closely approximate the signals and MRF maps from in vivo scans, with 158 times shorter processing time than the conventional simulation method using Non-uniform Fourier transform. We also demonstrate the power of applying the fast MRF simulator in MRF sequence optimization. The optimized sequences are validated with in vivo scans to assess the image quality and accuracy. The optimized sequences produce artifact-free T1 and T2 maps in 2D and 3D scans with equivalent mapping accuracy as the human-designed sequence but at shorter scan times. Incorporating the proposed simulator in the MRF optimization framework makes direct estimation of undersampling errors during the optimization process feasible, and provide optimized MRF sequences that are robust against undersampling artifacts and field inhomogeneity. [ABSTRACT FROM AUTHOR]

Purpose: The diffusion‐weighted SPLICE (split acquisition of fast spin‐echo signals) sequence employs split‐echo rapid acquisition with relaxation enhancement (RARE) readout to provide images almost free of geometric distortions. However, due to the varying T2$$ {}_2 $$‐weighting during k‐space traversal, SPLICE suffers from blurring. This work extends a method for controlling the spatial point spread function (PSF) while optimizing the signal‐to‐noise ratio (SNR) achieved by adjusting the flip angles in the refocusing pulse train of SPLICE. Methods: An algorithm based on extended phase graph (EPG) simulations optimizes the flip angles by maximizing SNR for a flexibly chosen predefined target PSF that describes the desired k‐space density weighting and spatial resolution. An optimized flip angle scheme and a corresponding post‐processing correction filter which together achieve the target PSF was tested by healthy subject brain imaging using a clinical 1.5 T scanner. Results: Brain images showed a clear and consistent improvement over those obtained with a standard constant flip angle scheme. SNR was increased and apparent diffusion coefficient estimates were more accurate. For a modified Hann k‐space weighting example, considerable benefits resulted from acquisition weighting by flip angle control. Conclusion: The presented flexible method for optimizing SPLICE flip angle schemes offers improved MR image quality of geometrically accurate diffusion‐weighted images that makes the sequence a strong candidate for radiotherapy planning or stereotactic surgery. [ABSTRACT FROM AUTHOR]

The method used and results found by the team "ASTL-NUAA" from NUAA for the problem of GTOC 11 are presented in the paper. The problem is mainly decomposed into three parts: multiple targets flyby sequence optimization for the Mother Ships, station-building database construction problem of asteroid transfer arcs, and station-building mission planning. Therefore, a three-step optimization framework is proposed to solve the problem. After analyzing the data of all the asteroids and selecting according to the orbital elements and mass of the asteroids, an asteroid candidate set RS1 is chosen as candidate targets for the Mother Ships. A two-level optimization algorithm based on a genetic principle is applied to solve the two-impulse asteroid flyby problem and rapidly determine the asteroid sequence of each Mother Ship. Subsequently, a method of filtering input data for the time-optimal rendezvous problem by the optimal flight time and the corresponding optimal relative phase is proposed to construct a station-building database with less computation burden. The optimal relative phase is obtained by solving free final-phase continuous low-thrust time-optimal problem. The station-building database is composed of the results of continuous low-thrust time-optimal rendezvous problems. At last, a branch-and-bound algorithm based on the greedy strategy is adopted to rapidly get the result by maximizing the minimal mass among the stations. The final score of our solution is 4315.6663, which is obtained after the competition. • The method used and results found by NUAA for the GTOC 11 is presented. • The flyby sequence of asteroids is obtained by the two-level optimization algorithm. • For less computation burden, a filter strategy is proposed to filter the input of time-optimal problem. • To assign asteroids, the branch-and-bound algorithm is applied. [ABSTRACT FROM AUTHOR]

Abstract Messenger RNA (mRNA) vaccines are third‐generation nucleic acid vaccines developed after first‐generation inactivated and live‐attenuated vaccines and second‐generation subunit and viral vector vaccines, characterized by a rapid response to pathogen mutation, simple production process, and high production capacity. The basic mechanism through which mRNA vaccines provide immune protection is the introduction into the body of mRNA expressing a target antigen through a specific delivery system and expression of the corresponding protein in vivo, which stimulates a specific immunological response. Multiple mRNA vaccine platforms against infectious diseases and cancers have shown encouraging results in both animal models and human subjects; in particular, mRNA vaccines against COVID‐19 have been widely adopted around the world. However, the development of mRNA vaccines has not been straightforward. The rapid progress of mRNA vaccines would not have been possible without major recent advances in innate immune sensing and in vivo delivery strategies. Creative research in mRNA design, lipid/polymer/novel nanocarrier development, and coupling to wearable/implantable electrostimulation medical devices may drive the evolution of mRNA vaccines.

In modulated wideband converter (MWC), all signals are aliased and sampled indiscriminately, which reduces the reconstruction accuracy of target signal. The focused compressed sampling uses optimized mixing sequence to increase the mixing gain of the target signal. To establish the sequence library with selective characteristics, this brief analyzes the signal components aliased into baseband, then proposes a sequence optimization method based on the greedy algorithm. Simulation and experiments show that the optimized sequence possesses a high gain for a desired frequency band signal. In the three-carrier signal test, as the approximate desired signal power ratio (DSR) is raised to 0.99 in baseband, the normalized mean square error (NMSE) of the reconstructed desired signal is improved by nearly 10 dB compared to using pseudorandom sequences. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has spotlighted the potential of in vitro transcribed (IVT) mRNA vaccines with their demonstrated efficacy, safety, cost-effectiveness, and rapid manufacturing. Numerous IVT mRNA vaccines are now under clinical trials for a range of targets, including infectious diseases, cancers, and genetic disorders. Despite their promise, IVT mRNA vaccines face hurdles such as limited expression levels, nonspecific targeting beyond the liver, rapid degradation, and unintended immune activation. Overcoming these challenges is crucial to harnessing the full therapeutic potential of IVT mRNA vaccines for global health advancement. This review provides a comprehensive overview of the latest research progress and optimization strategies for IVT mRNA molecules and delivery systems, including the application of artificial intelligence (AI) models and deep learning techniques for IVT mRNA structure optimization and mRNA delivery formulation design. We also discuss recent development of the delivery platforms, such as lipid nanoparticles (LNPs), polymers, and exosomes, which aim to address challenges related to IVT mRNA protection, cellular uptake, and targeted delivery. Lastly, we offer insights into future directions for improving IVT mRNA vaccines, with the hope to spur further progress in IVT mRNA vaccine research and development.

To improve the efficiency of complex assemblies in large-scale assembly sequence planning, an intelligent sequence planning method for constructing an assembly hybrid G-diagram model to realize the hierarchy of assembly structures is proposed. The assembly hybrid G-diagram model is constructed according to the assembly relationship semantics, and the assembly relationship semantics can be transformed into the corresponding assembly connection matrix and assembly priority matrix. Subassembly discriminant conditions are given to realize subassembly and isolated parts extraction, and the assembly structure is divided into part-level and subassembly-level. According to the assembly hybrid G-diagram, all feasible assembly sequences of part-level (within subassembly) and subassembly-level (subassembly as a whole) are solved, respectively. The particle swarm algorithm is used to optimize the assembly sequence with the goal of aggregation and redirection. The optimal sequence of part-level and subassembly-level is obtained, respectively. The sequence information is integrated to obtain the complete assembly sequence with the highest assembly efficiency under parallel planning. The feasibility and effectiveness of the assembly sequence optimization method are verified using a V-type dual-cylinder engine as an example. This planning method can greatly reduce the search space and avoid infeasible sequences when solving assembly sequences. In parallel planning, the sequence optimization process can be greatly shortened to ensure the assembly efficiency. [ABSTRACT FROM AUTHOR]

Purpose: To introduce a computationally efficient approach to optimizing the data acquisition parameters of MR Fingerprinting experiments with the Cramér–Rao bound. Methods: This paper presents a new approach to the optimal experimental design (OED) problem for MR Fingerprinting, which leverages an early observation that the optimized data acquisition parameters of MR Fingerprinting experiments are highly structured. Specifically, the proposed approach captures the desired structure by representing the sequences of data acquisition parameters with a special class of piecewise polynomials known as B‐splines. This incorporates low‐dimensional spline subspace constraints into the OED problem, which significantly reduces the search space of the problem, thereby improving the computational efficiency. With the rich B‐spline representations, the proposed approach also allows for incorporating prior knowledge on the structure of different acquisition parameters, which facilitates the experimental design. Results: The effectiveness of the proposed approach was evaluated using numerical simulations, phantom experiments, and in vivo experiments. The proposed approach achieves a two‐order‐of‐magnitude improvement of the computational efficiency over the state‐of‐the‐art approaches, while providing a comparable signal‐to‐noise ratio efficiency benefit. It enables an optimal experimental design problem for MR Fingerprinting with a typical acquisition length to be solved in approximately 1 min. Conclusions: The proposed approach significantly improves the computational efficiency of the optimal experimental design for MR Fingerprinting, which enhances its practical utility for a variety of quantitative MRI applications. [ABSTRACT FROM AUTHOR]

The turbine rotor is the key component of the turbine, which has a great impact on the construction cost and power generation efficiency of an entire hydropower station. Receiving the torque of the runner transmission and completing the specified power generation is its main function. There are many uncertain factors in the design, manufacture, and operation environment of a turbine rotor. Therefore, it is necessary to optimize the mechanism on the premise of ensuring that the mechanical system meets high reliability and high safety levels. This article uses the multidisciplinary reliability analysis and optimization method under random and interval uncertainty to quantitatively analyze the uncertainty factors, and then optimally solves the RBMDO problem of the turbine rotor mechanism. Through the finite element simulation analysis of the optimized design scheme, the rationality and feasibility of the obtained results are further verified. [ABSTRACT FROM AUTHOR]

The redundancy allocation problem (RAP) is an intriguing area in the field of reliability optimization to which a lot of research has been devoted in recent years. In this paper, a bi-objective model is developed for RAP with a heterogeneous backup scheme and a mixed redundancy strategy. Elimination of the lower bound estimation and the exact calculation of the reliability of the mixed strategy forms one of the most striking features of the proposed model. Investigating the optimal sequence of components in each subsystem, the study modifies a non-dominated sorting genetic algorithm (NSGA-II), as a powerful multi-objective evolutionary one, to solve the proposed bi-objective model. Two numerical examples will then be used to verify the efficiency of the model in achieving enhanced system reliability. Finally, the results of Pareto optimal set are used to demonstrate that the assumptions made enable the proposed model to improve system reliability by offering various structures while simultaneously considering system limitations. [ABSTRACT FROM AUTHOR]

Purpose: To identify a reliable metric for predicting the encoding capability of CEST MR fingerprinting acquisition schedules for pH quantification, which may facilitate CEST MR fingerprinting protocol optimization. Methods: Numerical simulations and Cr phantom MRI experiments were conducted at 3 Tesla under representative CEST MR fingerprinting sampling scenarios, including the pseudorandomization of imaging parameters (e.g., saturation power B1, saturation frequency offset, saturation time, and relaxation time), and variation of the maximum saturation power B1max, B1 number, and sampling pattern. The CEST effect at 2 ppm was measured using asymmetry analysis and matched to a predefined dictionary to determine the pH. The pH quantification error was assessed using RMSE. Three metrics, namely the Cramer‐Rao bound, dot product, and Euclidean distance, were calculated for each sampling scenario, and their relationships with the pH RMSE were investigated to examine their effectiveness for predicting the encoding capability of sampling schedules for pH quantification. Results: Both simulation and phantom studies revealed that the Cramer‐Rao bound metric consistently exhibited superior performance for predicting the pH quantification error. Although dot product exhibited good encoding capability prediction in most sampling scenarios, it failed in the scenario with varied B1 numbers. In contrast, Euclidean distance exhibited the worst performance among the 3 metrics in all scenarios. Conclusion: Superior over dot product and Euclidean distance, the Cramer‐Rao bound metric may reliably predicting the encoding capability of CEST MR fingerprinting sampling strategies and may be useful for guiding CEST MRI protocol optimization. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) is an increasingly utilized treatment for multiple neurological disorders. Continued improvements in DBS outcome are, in part, related to increasing ability to directly visualize stimulation targets by MRI. However, it is challenging to image DBS targets with conventional MRI techniques due to limited contrast. Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR) is a commonly used MRI sequence that improves visualization of several key DBS targets by suppressing white matter (WM) signal to better reveal deep-brain gray matter (GM) structures. Due to increased signal level at high field strength, application of FGATIR on 7T MRI may allow higher spatial resolution and better DBS targeting accuracy. However, successful utilization of FGATIR requires meticulous sequence optimization involving multiple parameters to maximize GM signal while suppressing WM. This is further complicated by the transmit RF field (B1+) inhomogeneity on 7T, which can cause severe contrast degradation. In this work, we introduce a systematic approach to optimize FGATIR and to improve visualization of thalamic DBS targets on 7T. FGATIR optimization is cast into a constrained optimization problem whose objective function and constraints are designed to maximize the GM-WM contrast-to-noise ratio (CNR) while accounting for B1+ inhomogeneity. This approach allows a systematic search for optimal parameters across the multi-dimensional parametric space while limiting the negative effect of B1+ variation. Bloch equation simulations were performed to solve the proposed optimization problem and to compare the sequence derived from this method against the sequence optimized without considering B1+ inhomogeneity. The results showed that this approach can improve GM-WM CNR in the presence of B1+ inhomogeneity, especially in some high relative B1+ areas where several key thalamic DBS targets are located. Additionally, in vivo images were acquired on a clinical 7T MRI to further validate this approach. Severe contrast degradation in the thalamus was observed when B1+ effect was not considered in sequence optimization, while the proposed approach yielded improved image contrast in the thalamus with key DBS targets well-defined. These results demonstrated that the proposed method allowed optimization of FGATIR on 7T to better visualize thalamic DBS targets, which may lead to improved DBS targeting accuracy as well as treatment outcome.

The sorting operation of the production line in mechanical manufacturing has the double complexity of the problem and data. To optimize the sorting operation and improve production efficiency, this paper designed a method for data representation and an evolutionary algorithm based on population optimization. At the same time, this paper arranged and disclosed a real industrial data set. The method for data representation abstracted the original job data by referring to the bag-of-words model. The evolutionary algorithm used deep reinforcement learning to initialize the population in the genetic algorithm and introduced the elite retention strategy, which improved the optimization ability of the algorithm. Finally, it compared the proposed algorithm with other algorithms on the real industrial data set and travelling salesman problem data set. The results show that the proposed algorithm can find a better sorting sequence and the access path, which verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Protein engineering or candidate therapeutic peptide optimization are processes in which the identification of relevant sequence variants is critical. Starting from one amino-acid sequence, the choice of the substitutions must meet the objective of not disrupting the structure of the protein, not impacting the main functional properties of the starting entity, while also meeting the condition to enhance some expected property such as thermal stability, resistance to degradation, … Here, we introduce a new approach of sequence evolution that focuses on the objective of not disrupting the structure of the initial protein by embedding a point to point control on the preservation of the local structure at each position in the sequence. For 6 mini-proteins, we find that, starting from a single sequence, our simple approach intrinsically contains information about site-specific rate heterogeneity of substitution, and that it is able to reproduce sequence diversity as can be observed in the sequences available in the Uniref repository. We show that our approach is able to provide information about positions not to substitute and about substitutions not to perform at a given position to maintain structure integrity. Overall, our results demonstrate that point to point preservation of the local structure along a sequence is an important determinant of sequence evolution.

[Display omitted] Optimization of discrete structures aims at generating a new structure with the better property given an existing one, which is a fundamental problem in machine learning. Different from the continuous optimization, the realistic applications of discrete optimization (e.g., text generation) are very challenging due to the complex and long-range constraints, including both syntax and semantics, in discrete structures. In this work, we present SAGS, a novel Simulated Annealing framework for Graph and Sequence optimization. The key idea is to integrate powerful neural networks into metaheuristics (e.g., simulated annealing, SA) to restrict the search space in discrete optimization. We start by defining a sophisticated objective function, involving the property of interest and pre-defined constraints (e.g., grammar validity). SAGS searches from the discrete space towards this objective by performing a sequence of local edits, where deep generative neural networks propose the editing content and thus can control the quality of editing. We evaluate SAGS on paraphrase generation and molecule generation for sequence optimization and graph optimization, respectively. Extensive results show that our approach achieves state-of-the-art performance compared with existing paraphrase generation methods in terms of both automatic and human evaluations. Further, SAGS also significantly outperforms all the previous methods in molecule generation. [ABSTRACT FROM AUTHOR]

The irregular shapes and gravitational fields of asteroids challenge the design of collision-free asteroid surface hopping trajectories. A novel collision-free long-distance transfer trajectory design algorithm based on the convex optimization is presented in this article. A combinatorial keepout zone consisting of three spheres is defined to avoid the spacecraft colliding with the asteroid surface during the transfer. The boundary of the keepout zone is close to the surface of the target asteroid. With the Taylor expansion approximation, the nonconvex keepout zone constraint becomes convex. Two convex glide-slope constraints are employed for avoiding colliding during the ascent and descent. Thereafter, a convex optimization problem for solving a collision-free long-distance transfer trajectory between arbitrary two surface points is formulated. Moreover, an ant colony optimization algorithm combined with the proposed method is used to solve the optimal hopping sequence problem for exploring multiple points on the asteroid surface. Finally, several numerical simulation examples for the asteroid 433 Eros validate the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Rotavirus is the important etiological agents of infectious diarrhea among children under 5 years old. Rotaviruses are divided into 10 serogroups (A–J) and each group is based on genetic properties of major structural protein VP6. We designed a novel VP6 sequence optimization to increase the expression level of this protein. Numerous factors such as codon adaptation index, codon pair bias, and guanine‐cytosine content were adapted based on Escherichiacoli codon usage. In addition, the ribosome binding site (RBS) of pET‐15b was redesigned by the RBS calculator and the secondary structure of VP6 messenger RNA was optimized in the whole length of the coding sequence. Various factors including isopropyl beta‐ d‐thiogalactoside (IPTG) concentration, temperature, and induction time were analyzed for the optimization of the best expression in E. coli by sodium dodecyl sulfate‐polyacrylamide gel electrophoresis and western blotting. The recombinant VP6 (rVP6) protein was purified by the Ni‐sepharose and then the hyperimmune sera were generated against rVP6 in rabbits. Among three different temperatures, IPTG concentrations, and postinductions, the level of rVP6 was higher at 37°C, 1 mM of IPTG, and 8 h, respectively. Also, the high expression level of rVP6 was obtained in the insoluble aggregate form (43.8 g/L). After purification, the yield of rVP6 was 10.83 g/L. The rVP6 specific antiserum was confirmed by both immunofluorescent and western blotting. The versatile sequence optimization was the reason to produce a high level of rVP6 compared to other reports and can potentially apply to produce cheaper commercial kits to diagnose serological tests and new rotavirus vaccines. Highlights: The versatile sequence optimization was the reason to produce a high level of rVP6.The high yield of rVP6 can apply to produce cheaper commercial kit.Polyclonal antibody was raised against rVP6.Immunoblotting and immunofluorescence assays showed the potency of VP6 antibody to detect RV. [ABSTRACT FROM AUTHOR]

The turbine rotor is the key component of the turbine, which has a great impact on the construction cost and power generation efficiency of an entire hydropower station. Receiving the torque of the runner transmission and completing the specified power generation is its main function. There are many uncertain factors in the design, manufacture, and operation environment of a turbine rotor. Therefore, it is necessary to optimize the mechanism on the premise of ensuring that the mechanical system meets high reliability and high safety levels. This article uses the multidisciplinary reliability analysis and optimization method under random and interval uncertainty to quantitatively analyze the uncertainty factors, and then optimally solves the RBMDO problem of the turbine rotor mechanism. Through the finite element simulation analysis of the optimized design scheme, the rationality and feasibility of the obtained results are further verified.

The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case. Here, we show that by backpropagating gradients through the transform-restrained Rosetta (trRosetta) structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures in a single calculation. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single-point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by conformational landscape optimization with the standard energy-based sequence design methodology in Rosetta and show that the former can result in energy landscapes with fewer alternative energy minima. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low-resolution trRosetta model serves to disfavor alternative states, and the high-resolution Rosetta model serves to create a deep energy minimum at the design target structure. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) potentially serve as ideal antimicrobial agents for the treatment of polymicrobial abdominal infections due to their broad-spectrum antimicrobial activity and excellent biocompatibility. However, the balance of chain length, positive charges, and hydrophobicity on the antimicrobial activity of AMPs are still far from being optimal. Herein, a series of AMPs ([KX] n -NH 2 , X = Ile, Leu or Phe, n = 3, 4, 5, or 6) with varied charges and hydrophobicity for the treatment of polymicrobial abdominal infections are designed. Specifically, [KI] 4 -NH 2 peptide exhibits the best in vitro antimicrobial activity against Gram-positive and -negative bacteria, as well as fungal strains. Based on the good cell biocompatibility, [KI] 4 -NH 2 peptide is found to have negligible in vivo toxicity at the dosage of up to 28 mg kg -1 . Furthermore, great in vivo therapeutic efficacy of [KI] 4 -NH 2 peptide against S. typhimurium is demonstrated in the mice abdominal infection model. The design of short sequence of antimicrobial peptides with a charge/hydrophobicity balanced structures provides a simple and efficient strategy for potential clinical applications of antimicrobial peptide-based biomaterials in a variety of bacterial infection diseases., (© 2023 Wiley‐VCH GmbH.)

Abnormal metabolism γ-aminobutyric acid (GABA) is involved in many brain diseases, and in vivo GABA detection is important for the diagnosis of these diseases. This study optimized a commonly used spectral editing method for in vivo GABA detection, MEGA-PRESS. We used high-performance Shinnar-Le Roux (SLR) pulses to replace the commonly used Gaussian pulses. Single-band pulse and dual-band pulse were combined to excite the resonances of given metabolites. An optimized MEGA-PRESS sequence was implemented on the uMR780 magnetic resonance imaging system manufactured by United Imaging Healthcare (UIH). The results showed that the SLR pulse could reduce the interference of macromolecule (MM) on GABA signal. Compared with the traditional methods, combining the single-band and dual-band pulses could better suppress contamination of residual water signal to the GABA* signal. Volunteer experiments demonstrated that the optimized MEGA-PRESS sequence improved in vivo measurement of GABA* concentration.

BACKGROUND: At present, DWI, DTI, T1-mapping, T2-mapping, and T2*-mapping are commonly used in cartilage functional imaging sequences in both scientific research and clinic. T2-mapping is often used for the quantitative analysis of cartilage, but it was obviously limited due to the long scanning time and poor experience of the patient. OBJECTIVE: To quantitatively evaluate the talar cartilage injury degree of chronic lateral ankle instability patients by sequence optimized T2-mapping quantitative imaging technique. METHODS: Totally 53 cases of chronic ankle instability (case group) and 46 healthy subjects (control group) were from the Second Affiliated Hospital of Inner Mongolia Medical University. They underwent conventional Magnetic Resonance Imaging and optimal T2-mapping quantitative imaging scanning. The total talar cartilage was divided into six compartments: internal anterior, internal medial, internal posterior, lateral anterior, lateral center and lateral posterior. Six T2 values were totally taken as each partition had one T2 value. The experiment was approved by the Ethics Committee of Inner Mongolia Medical University. RESULTS AND CONCLUSION: The T2 values of internal anterior, internal medial, internal posterior areas of case group were higher than those of control group (P < 0.05). The T2 values of lateral anterior, lateral center and lateral posterior areas had no significant difference between the two groups (P > 0.05). The results showed that the talus cartilage injury mainly occurred in the internal talus in patients with chronic lateral ankle instability. The cartilage damage degree could be quantitatively evaluated by magnetic resonance T2-mapping imaging technique. It is possible that T2-mapping based on sequence optimization can be used in functional image routine scan. [ABSTRACT FROM AUTHOR]

The utilization of structure-switching aptamers (SSAs) has enabled the development of novel sensing platforms for the sensitive and continuous detection of molecules. De novo development of SSAs, however, is complex and laborious. Here we describe a rational approach to SSA optimization that simultaneously improves aptamer binding affinity and introduces target-dependent conformation-switching for compatibility with real-world biosensor applications. Key structural features identified from NMR and computational modeling were used to optimize conformational switching in the presence of target, while large-scale, microarray-based mutation analysis was used to map regions of the aptamer permissive to mutation and identify combinations of mutations with stronger binding affinity. Optimizations were carried out in a relevant biofluid to ensure a seamless transition of the aptamer to a biosensing platform. Initial proof-of-concept for this approach is demonstrated with a cortisol binding aptamer but can easily be translated to other relevant aptamers. Cortisol is a hormone correlated with the stress response that has been associated with various medical conditions and is present at quantifiable levels in accessible biofluids. The ability to continuously track levels of stress in real-time via cortisol monitoring, which can be enabled by the aptamers reported here, is crucial for assessing human health and performance.

Background: Evaluating heterogeneity in tumor vascularization through texture analysis could improve predictions of patients' outcome and response evaluation.Purpose: To investigate the influence of temporal parameters on texture features extracted from dynamic contrast-enhanced (DCE)-MRI parametric maps.Study Type: Prospective cross-sectional study.Subjects: Twenty-five adults with soft-tissue sarcoma (STS), median age: 68 years.Field Strength/sequence: DCE-MRI acquisition using a CAIPIRINHA-Dixon-TWIST-VIBE sequence at 1.5T (temporal resolutions: 2 sec, duration: 5 min).Assessment: The area under time-intensity curve (AUC) and Ktrans maps were generated for several temporal resolution (dt = 2 sec, 4 sec, 6 sec, 8 sec, 10 sec, 12 sec, 20 sec) and scan durations (T = 3 min, 4 min, 5 min for a 6-sec sampling) by downsampling and truncating the initial DCE-MRI sequence. Tumor volume was manually segmented and propagated on all parametric maps. Thirty-two first- and second order-texture features were extracted per map to quantify the intratumoral heterogeneity.Statistical Tests: The influence of temporal parameters on texture features was studied with repeated-measures analysis of variance (or nonparametric equivalent). The dispersion of each texture feature depending on temporal parameters was estimated with coefficients of variation (CVs). The performances of multivariate models to predict the response to chemotherapy (ie, binary logistic regression based on the baseline texture features) were compared.Results: The temporal resolution had a significant influence on 12/32 (37.5%) and 14/32 (43.8%) texture features evaluated on AUC and Ktrans maps, respectively (range of P < 0.0001-0.0395). Scan duration had a significant influence on 23/32 (71.9%) texture features from Ktrans map (range of P < 0.0001-0.0321). Dispersion was high (mean CV >0.5) with sampling for 2/32 (6.3%) and 10/32 (31.3%) features from AUC and Ktrans maps, respectively; and with truncating for 6/32 (18.8%) features from Ktrans map. The area under the receiver operating characteristics curve of predictive models ranged from 0.77 (95% confidence interval [CI] = [0.54-1.00], with dt = 6 sec T = 4 min) to 0.90 (95% CI = [0.74-1.00], with dt = 6 sec T = 5 min).Data Conclusion: The values of texture features extracted from DCE-MRI parametric maps can be influenced by temporal parameters, which can lead to variations in performance of predictive models.Level Of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1773-1788. [ABSTRACT FROM AUTHOR]

Purpose: To optimize a steady‐state imaging sequence for maximizing the amide proton transfer effects in pulsed‐CEST (pCEST) imaging. Method: The steady‐state pCEST (SS‐pCEST) sequence is a fast CEST imaging scheme that applies repetitive short RF pulses for generating CEST and acquiring MR imaging signal alternately. To maximize the obtainable amide proton transfer effects, the SS‐pCEST scheme is analyzed and optimized with respect to not only the imaging parameters but also the imaging schemes of the signal acquisition part. Three imaging parameters such as the flip angle and RF power for saturation and the flip angle for imaging are selected as factors affecting the obtainable CEST effects; and 2 imaging schemes, namely, SSFP and spoiled gradient echo sequences, are analyzed and compared for numerical simulations and MRI experiments at 3 tesla. Results: SS‐pCEST combined with SSFP could provide higher amide proton transfer effects than that with spoiled gradient echo. Furthermore, in the proposed SS‐pCEST imaging with SSFP, 3 imaging parameters can be independently optimized so that the optimization complexities can be reduced. Conclusion: We optimized the SS‐pCEST imaging method with SSFP to maximize the amide proton transfer effects. In addition, our analysis showed the SSFP sequence was more efficient than the spoiled gradient echo sequence for SS‐pCEST imaging. [ABSTRACT FROM AUTHOR]

Radiation therapy is among the top three cancer treatments in current medical services. The novel noncoplanar radiation therapy which claimed the best characteristics in almost all dosimetric properties encountered the challenges of the potential collision and the long time delivering. In this paper, we proposed a brand new scheme which uses a combined method of the collision avoidance path planning based on an improved probability roadmap method (PRM) and the delivery sequence optimization based on a modified genetic algorithm (GA) to solve the problems in noncoplanar radiation therapy. A uniform sampling strategy, an improved connection strategy, and an efficient local planner are introduced to optimize the roadmap result and accelerate the roadmap construction. The GA is improved by the elitist selection, the local search strategy, and the similar substitution strategy to achieve a better performance both in convergence rate and optimal solution. Experiments are carried out on the simulation platform with typical therapy system models. The results show that our proposed methods work well with the radiation therapy system in a compact working area. Collision is avoided and time consumption is reduced. We believe that our proposed algorithms could solve the problems in current radiation therapy and promote their clinic applications. [ABSTRACT FROM AUTHOR]

• Plant-based electroporation models were analyzed by morphologic and quantitative MRI. • Potato was suitable for MRI, showing distinct contrast changes post electroporation. • FLAIR yielded highest image contrast in potatoes enabling semi-automatic segmentation. • T1 relaxation time was the main driver for MRI contrast changes in potatoes. • Our findings help to further optimize MRI of electroporated plant tissues. Plant-based models can reduce the number of animal studies for electroporation research in medical cancer treatment modalities like irreversible electroporation. Magnetic resonance imaging (MRI) provides volumetric visualisation of electroporated animal or plant tissues; however, contrast behaviour is complex, depending on tissue and sequence parameters. This study numerically analysed contrast between electroporated and non-electroporated tissue at 1.5 T in various MRI sequences (DWI, T1W, T2W, T2*W, PDW, FLAIR) performed 4 h after electroporation in apples (N = 4) and potatoes (N = 8). Sequence parameters (inversion time [TI], echo time [TE], b-value) for optimal contrast and electroporation-mediated changes in T1 and T2 relaxation times and apparent diffusion coefficient (ADC) were determined for potato (N = 4) using quantitative parameter mapping. FLAIR showed the electroporated zone in potatoes with best contrast, whereas no sequence yielded clear visibility in apples. After electroporation, T1 and T2 in potato decreased by 29% ([1245 ± 54 to 886 ± 119] ms) and 12% ([249 ± 17 to 217 ± 12] ms), respectively. ADC increased by 11% ([1303 ± 25 to 1449 ± 28] × 10–6 mm2/s). Optimal contrast was found for TI = 1000 ms, low TE and high b-value. T1 was most sensitive to EP-mediated tissue changes. Future research could use this methodology and findings to obtain high-contrast MR images of electroporated and non-electroporated biological tissues. [ABSTRACT FROM AUTHOR]

This study was designed to evaluate the effectiveness of recombinant polypeptide-p derived from Momordica charantia on diabetic rats. In this research, the optimized sequence of polypeptide-p gene fused to a secretion signal tag was cloned into the expression vector and transformed into probiotic Saccharomyces boulardii. The production of recombinant secretion protein was verified by western blotting, HPLC, and mass spectrometry. To assay recombinant yeast bioactivity in the gut, diabetic rats were orally fed wild-type and recombinant S. boulardii, in short SB and rSB, respectively, at two low and high doses as well as glibenclamide as a reference drug. In untreated diabetic and treated diabetic + SB rats (low and high doses), the blood glucose increased from 461, 481, and 455 (mg/dl), respectively, to higher than 600 mg/dl on the 21st day. Whereas glibenclamide and rSB treatments showed a significant reduction in the blood glucose level. The result of this study promised a safe plant-source supplement for diabetes through probiotic orchestration., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Highlights • An MR phantom, simulating neonatal brain parenchyma at 1.5 T was fabricated. • The phantom exhibited stability and reproducibility at successive relaxometry tests. • T1w, T2w sequences' parameters were optimized for maximum relative tissue contrast. Abstract Purpose Tο fabricate a tissue-mimicking phantom simulating the MR relaxation times of neonatal gray and white matter at 1.5 T, for the optimization of clinical Τ1 weighted (T1w) and T2 weighted (T2w) sequences. Methods Numerous agarose gel solutions, doped with paramagnetic Gadopentetic acid (Gd-DTPA) ions, underwent quantitative relaxometry with a Turbo-Inversion-Recovery Spin-Echo (TIRSE) sequence and a Car-Purcell-Meiboom-Gill (CPMG) sequence for T1 and T2 measurements, respectively. Twenty samples which simulated the spectrum of relaxation times of neonatal brain parenchyma were selected. Reproducibility was tested by refabrication and relaxometry of the relevant samples while stability was tested by six sets of quantitative relaxometry scans during a 12-month period. Results "Neonatal gray matter equivalent"(0.6%w/v agarose-0.10 mM Gd-DTPA), accurately mimicked relaxation times of neonatal gray matter: T1 = (1134 ± 7)ms, T2 = (200 ± 7)ms. "Neonatal white matter equivalent"(0.3%w/v agarose-0.03 mM Gd-DTPA), accurately mimicked relaxation times of neonatal white matter: T1 = (1654 ± 9)ms, T2 = (376 ± 4)ms. Coefficient of variation of T1 and T2 relaxation times measurements remained less than 5% during 12 months. Sequences were modified according to maximum relative contrast (RC) between neonatal gray and white matter equivalents. Optimized T2wTSE and T1wTSE parameters were TR/TE = 9500 ms/280 ms and TR/TE = 1200 ms/10 ms, respectively for a MAGNETOM Vision/Sonata Hybrid 1.5 T system. Quantitative relaxometry at different 1.5 T MR systems resulted in inter-system T1, T2 measurement deviations of 12% and 3%, respectively. Conclusion A precise, stable and reproducible phantom for the neonatal brain was fabricated. Subsequent optimization of clinical T1w and T2w sequences based on maximum RC between neonatal gray and white matter equivalents was scientifically supported with robust relaxometry. The procedure was applicable in different 1.5 T systems. Highlight TR & TE optimization of neonatal brain at 1.5 T was based on relaxometry of a stable, reproducible phantom. [ABSTRACT FROM AUTHOR]

Abstract The finished geometry of a welded structure is greatly affected by heat-induced distortion from the welding process. This often requires correction by straightening and top-level machining to ensure manufacturing quality. Distortion is sensitive to weld sequencing and manufacturing environment. Optimal weld sequencing can significantly reduce distortion and manufacturing cost. However, an optimal weld sequence is not always intuitively obvious, especially for large structures with many welds. Virtual Fabrication Technology (VFT) is Caterpillar's proprietary software for the simulation of welding physics via finite element analysis. Powered by the highly successful VFT platform, a combinatorial genetic algorithm based optimization approach combined with an uncertainty quantification (UQ) module for evaluating robustness has been developed at Caterpillar for weld sequence optimization (WSO). WSO has been successfully applied to more than 50 structures, resulting in the minimization of straightening operations. The combined VFT and robust WSO approach results in an accelerated manufacturing process development schedule. We will present a summary of our WSO approach and some results that demonstrate how WSO has proven to be a transformative manufacturing planning technology at Caterpillar. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed strategy is aimed to solve the peak charging power demand issue of pure electric bus fast-charging station. It is effective for the charging power suppression of stations in the area with extremely limited distribution capacity. In order to reduce the recharging time of electric vehicles, the charging power and voltage are becoming higher, which has led to a huge distribution capacity demand and load fluctuation, especially in pure electric buses (PEBs) with large onboard batteries. Based on one actual direct current (DC) fast-charging station, a two-step strategy for the suppression of the peak charging power was developed in this paper, which combined charging optimization and a battery energy storage system (BESS) configuration. A novel charging strategy was proposed, with the PEBs fast-charging during operating hours and normal charging at night, based on a new charging topology. Then, a charging sequence optimization model was established, according to the operation characteristics analysis of the DC fast-charging station. The particle swarm optimization (PSO) algorithm is applied to optimize the charging sequence, which is disordered at present. Linear programming is used to configure the battery energy storage system in order to further decrease the peak charging power and satisfy the distribution capacity constraint. The two-step strategy was simulated by the dataset from the real station. The results show that the distribution capacity demand, charging load fluctuation, electricity cost, and size of the BESS were significantly decreased. [ABSTRACT FROM AUTHOR]

Purpose Clinical use of magnetic resonance electrical impedance tomography (MREIT) still requires significant sensitivity improvements. Here, the measurement of the current-induced magnetic field (ΔBz,c) is improved using systematic efficiency analyses and optimization of multi-echo spin echo (MESE) and steady-state free precession free induction decay (SSFP-FID) sequences. Theory and Methods Considering T1, T2, and [ABSTRACT FROM AUTHOR]