Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

[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]

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]

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]

• 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]

Sequence optimization and appropriate sequence selection is still an unmet need in magnetic resonance fingerprinting (MRF). The main challenge in MRF sequence design is the lack of an appropriate measure of the sequence's encoding capability. To find such a measure, three different candidates for judging the encoding capability have been investigated: local and global dot-product-based measures judging dictionary entry similarity as well as a Monte Carlo method that evaluates the noise propagation properties of an MRF sequence. Consistency of these measures for different sequence lengths as well as the capability to predict actual sequence performance in both phantom and in vivo measurements was analyzed. While the dot-product-based measures yielded inconsistent results for different sequence lengths, the Monte Carlo method was in a good agreement with phantom experiments. In particular, the Monte Carlo method could accurately predict the performance of different flip angle patterns in actual measurements. The proposed Monte Carlo method provides an appropriate measure of MRF sequence encoding capability and may be used for sequence optimization. [ABSTRACT FROM AUTHOR]

In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice. [Display omitted] • We here further develop an evolutionary protocol for binding affinity optimisation. • We implement a replica exchange scheme for antibody fragments optimisation. • We show that an initial hit is not needed. • Similar solutions can be found by either optimizing a known weak binder or a non-binder. [ABSTRACT FROM AUTHOR]

• Oyster-derived xanthine oxidase inhibitory peptides were first reported. • Three novel xanthine oxidase inhibitory peptides were obtained. • Reveal the mechanism of action of peptides with xanthine oxidase. • A series of designed xanthine oxidase inhibitory peptides were obtained. • Through rational design, the activity of peptides is greatly improved. Xanthine oxidase (XO) inhibitory peptides are secure and efficient functional food ingredients for treating or alleviating hyperuricemia (HUA). In this study, ultrafiltration and molecular exclusion chromatography were used to separate and purify enzymatic lysate of oyster protein, three novel peptides (ALSGSW, GGYGIF and MAIGLW) with strong XO inhibitory activity were screened by LC-MS/MS identification and molecular docking technology. Then, the mechanism of action between peptides and the XO was revealed by molecular docking technology, and the structure of the peptide was rationally designed on this basis. The results showed that replacing N -terminal Gly of peptide GGYGIF with Trp can significantly improve its XO inhibition rate, and peptides formed by the simple structure amino acid was connected to the aromatic amino acids has preferable inhibitory activity. These results showed that oyster was a good source of XO peptide inhibitors, molecular docking technology was an effective tool for obtaining highly active peptides. [ABSTRACT FROM AUTHOR]

Microcystinase C (MlrC), one key hydrolase of the microcystinase family, plays an important role in linearized microsystin (L-MC) degradation. However, the three-dimensional structure and structural features of MlrC are still unclear. This study obtained high specific activity and high purity of MlrC by heterologous expression, and revealed that MlrC derived from Sphingomonas sp. ACM-3962 (ACM-MlrC) can degrade linearized products of MC-LR, MC-RR and MC-YR to product 3-amino-9-methoxy-2,6,8-trimethyl-10-phenyldeca-4,6-dienoic acid (Adda), indicating the degradation function and significance in MC-detoxification. More importantly, this study reported the crystal structure of ACM-MlrC at 2.6 Å resolution for the first time, which provides a basis for further understanding the structural characteristics and functions of MlrC. MlrC had a dual-domain feature, namely N and C terminal domain respectively. The N -terminal domain contained a Glutamate-Aspartate-Histidine-Histidine catalytic quadruplex coordinated with zinc ion in each monomer. The importance of zinc ions and their coordinated residues was analyzed by dialysis and site-directed mutagenesis methods. Moreover, the important influence of the N/C-terminal flexible regions of ACM-MlrC was also analyzed by sequence truncation, and then the higher yield and total activity of variants were obtained, which was beneficial to study the better function and application of MlrC. [Display omitted] • The first crystal structure of MlrC was solved at 2.6 Å resolution. • ACM-MlrC degraded linearized products of microcystin-LR/RR/YR into Adda. • The important influence of N/C terminal flexible regions for MlrC was analyzed. • The key role of zinc ion and its coordinated residues were studied in detail. • The higher yield, total activity and stability of ACM-MlrC variants were obtained. [ABSTRACT FROM AUTHOR]

Background: Codon usage plays a crucial role when recombinant proteins are expressed in different organisms. This is especially the case if the codon usage frequency of the organism of origin and the target host organism differ significantly, for example when a human gene is expressed in E. coli. Therefore, to enable or enhance efficient gene expression it is of great importance to identify rare codons in any given DNA sequence and subsequently mutate these to codons which are more frequently used in the expression host. Results: We describe an open-source web-based application, ATGme, which can in a first step identify rare and highly rare codons from most organisms, and secondly gives the user the possibility to optimize the sequence. Conclusions: This application provides a simple user-friendly interface utilizing three optimization strategies: 1. one-click optimization, 2. bulk optimization (by codon-type), 3. individualized custom (codon-by-codon) optimization. ATGme is an open-source application which is freely available at: http://atgme.org [ABSTRACT FROM AUTHOR]

To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a “fuzzy”-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics. [ABSTRACT FROM AUTHOR]

Abstract: The computational design of proteins that bind small molecule ligands is one of the unsolved challenges in protein engineering. It is complicated by the relatively small size of the ligand which limits the number of intermolecular interactions. Furthermore, near-perfect geometries between interacting partners are required to achieve high binding affinities. For apolar, rigid small molecules the interactions are dominated by short-range van der Waals forces. As the number of polar groups in the ligand increases, hydrogen bonds, salt bridges, cation–π, and π–π interactions gain importance. These partial covalent interactions are longer ranged, and additionally, their strength depends on the environment (e.g. solvent exposure). To assess the current state of protein-small molecule interface design, we benchmark the popular computer algorithm Rosetta on a diverse set of 43 protein–ligand complexes. On average, we achieve sequence recoveries in the binding site of 59% when the ligand is allowed limited reorientation, and 48% when the ligand is allowed full reorientation. When simulating the redesign of a protein binding site, sequence recovery among residues that contribute most to binding was 52% when slight ligand reorientation was allowed, and 27% when full ligand reorientation was allowed. As expected, sequence recovery correlates with ligand displacement. [Copyright &y& Elsevier]

• Application of FGATIR on 7T may allow higher resolution and better DBS targeting. • However, intensive sequence optimization is needed on 7T due to B1 inhomogeneity. • A systematic optimization framework was developed to optimize FGATIR on 7T MRI. • Our results showed that this approach improved visualization of key DBS targets. 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. [ABSTRACT FROM AUTHOR]

The disassembly process of obsolete products can suffer from much uncertainty, and then the disassembly profit of the whole disassembly activeness should be taken into consideration during the target disassembly. After getting the feasible disassembly sequences of the target component(s) by the disassembly network graph, combining the uncertainty in the quality of the components, the probability of incurring damage, the randomness of the basic disassembly time and other factors, this paper built the model of the disassembly profit under uncertain environment, while satisfying both the constraints of the probability of incurring damage and the disassembly time, optimized the sequences with the help of disassembly profit probability, then adapted a solution algorithm based on stochastic simulation. Finally, it presented an example to reflect the changes of sequence optimization results with the variation of the decision-maker's requests. And it demonstrated the feasibly of the proposed model. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this study was to compare 3T and 7T signal-to-noise and contrast-to noise ratios of clinical sequences for imaging of the ankles with optimized sequences and dedicated coils. Ten healthy volunteers were examined consecutively on both systems with three clinical sequences: (1) 3D gradient-echo, T1-weighted; (2) 2D fast spin-echo, PD-weighted; and (3) 2D spin-echo, T1-weighted. SNR was calculated for six regions: cartilage; bone; muscle; synovial fluid; Achilles tendon; and Kager''s fat-pad. CNR was obtained for cartilage/bone, cartilage/fluid, cartilage/muscle, and muscle/fat-pad, and compared by a one-way ANOVA test for repeated measures. Mean SNR significantly increased at 7T compared to 3T for 3D GRE, and 2D TSE was 60.9% and 86.7%, respectively. In contrast, an average SNR decrease of almost 25% was observed in the 2D SE sequence. A CNR increase was observed in 2D TSE images, and in most 3D GRE images. There was a substantial benefit from ultra high-field MR imaging of ankles with routine clinical sequences at 7T compared to 3T. Higher SNR and CNR at ultra-high field MR scanners may be useful in clinical practice for ankle imaging. However, carefully optimized protocols and dedicated extremity coils are necessary to obtain optimal results. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) algorithm is a well-known optimization approach to deal with discrete problems. There are two models proposed for the operators of PSO algorithm, one is based on value exchange and the other on order exchange, accordingly two versions of PSO algorithms are formed. A new version of PSO algorithm based on order exchange has been presented in our studies, which is capable of converging on the global optimization solution, with the method of generating the stop evolution particle over again. In this paper, we propose another version of PSO algorithm based on value exchange with the same method. There exist, thus, totally four versions of PSO algorithms, which is given a brief introduction individually and the performance of which are compared in solving sequence optimization problems through fifty runs. The performance comparison show that the PSO algorithm with global convergence characteristics based on order exchange outperforms the other versions of PSO in solving sequence optimization problem. [Copyright &y& Elsevier]

BHP Billiton's Illawarra Coal operates several longwall coal extraction systems in the Bulli and Wongawilli coal seams in the Southern Coalfields of the Sydney Basin, Australia. IC has applied a proprietary mixed integer linear programming open pit strategic mine planning tool called Blasor to rapidly evaluate, for a number of longwall mining domains, the jointly optimal underground development strategy and mining sequences necessary. For each development scenario, the Blasor optimizer maximizes the discounted operating cash flow as the objective function, subject to mining, processing, and transportation capacity constraints and product blend constraints. When applied to the underground longwall domain sequence optimization problem, Blasor evaluates a set of carefully considered scenarios, each describing a feasible underground development and transport strategy. In this application, Blasor plays the role of a fair valuation tool for each major scenario, by determining the most valuable extraction schedule for each development scenario. This establishes a basis for comparing the economic merits of competing scenarios that are structurally different. [ABSTRACT FROM AUTHOR]

Gene therapy has proven to be of potential value for the correction of inherited hematopoietic disorders. However, the occurrence of severe side effects in some of the clinical trials has questioned the safety of this approach and has hampered the use of long terminal repeat-driven vectors for the treatment of a large number of patients. The development of self-inactivating (SIN) vectors with reduced genotoxicity provides an alternative to the currently used vectors. Our initial attempts to use SIN vectors for the correction of a myeloid disorder, chronic granulomatous disease, failed due to low vector titers and poor transgene expression. The optimization of the transgene cDNA (gp91phox) resulted in substantially increased titers and transgene expression. Most notably, transgene optimization significantly improved expression of a second cistron located downstream of gp91phox. Thus, optimization of the transgene sequence results in higher expression levels and increased therapeutic index allowing the use of low vector copy numbers per transduced cell and weaker internal promoters.Gene Therapy (2009) 16, 111–118; doi:10.1038/gt.2008.143; published online 11 September 2008 [ABSTRACT FROM AUTHOR]

Abstract: When delivering and presenting a multimedia document through a high-delay network, presentation lag is a major and critical factor affecting the service quality of the presentation. A dynamically generated multimedia document from a digital library often includes sets of both unconstrained objects and temporally-constrained objects. The order of the objects in a prefetch-enabled presentation environment has an impact on the overall presentation lag. This study explores techniques for optimizing the object sequence for an auto-assembled multimedia presentation to minimize the overall presentation lag. We adapt techniques developed in conventional two-machine flowshop research for computing or approximating the optimal sequences. We extensively discuss a variety of settings for commonly found applications with auto-assembled multimedia presentations. For each problem setting, the formulation and solutions were explored. We implemented a prototype system with which a number of problems have been evaluated. The numerical simulations and real-life experiments with 3G wireless networks show clearly that a computed optimal or near-optimal sequence significantly reduces the presentation lag as compared to random sequences. [Copyright &y& Elsevier]

Abstract: Three-dimensional (3D) magnetic resonance imaging (MRI) has shown great potential for studying the impact of prematurity and pathology on brain development. We have investigated the potential of optimized T1-weighted 3D magnetization-prepared rapid gradient-echo imaging (MP-RAGE) for obtaining contrast between white matter (WM) and gray matter (GM) in neonates at 3 T. Using numerical simulations, we predicted that the inversion time (TI) for obtaining strongest contrast at 3 T is approximately 2 s for neonates, whereas for adults, this value is approximately 1.3 s. The optimal neonatal TI value was found to be insensitive to reasonable variations of the assumed T1 relaxation times. The maximum theoretical contrast for neonates was found to be approximately one third of that for adults. Using the optimized TI values, MP-RAGE images were obtained from seven neonates and seven adults at 3 T, and the contrast-to-noise ratio (CNR) was measured for WM versus five GM regions. Compared to adults, neonates exhibited lower CNR between cortical GM and WM and showed a different pattern of regional variation in CNR. These results emphasize the importance of sequence optimization specifically for neonates and demonstrate the challenge in obtaining strong contrast in neonatal brain with T1-weighted 3D imaging. [Copyright &y& Elsevier]

The need to validate large amounts of data with the help of the domain expert arises naturally in many dataintensive applications, including data mining, data stream, and database-related applications. This paper presents a general validation approach that generalizes different expert-driven validation methods developed for specialized validation problems. In particular, we model the validation process as a sequence of validation operators, explore various properties of such sequences, and present theoretical results that provide for better understanding of the validation process. We also address the problem of selecting the best validation sequence among the class of equivalent sequence permutations. We demonstrate that this optimization problem is NP-hard and present two heuristic algorithms for improving validation sequences. [ABSTRACT FROM AUTHOR]

In this article, a fundamental aspect of protein engineering called into question: Is it really possible to computationally redesign the sequence of a protein so as to improve its stability? Specifically, the case where almost all protein's sequence is allowed to change to any other residue type is considered. The aim is to highlight two conflicting schools of thought. First, there are compelling fundamental reasons why protein design should not be possible by optimizing a conventional energy-like function. Second, there is evidence that it is possible and has been done in practice. [ABSTRACT FROM AUTHOR]

Investigating the relative importance of protein stability, function, and folding kinetics in driving protein evolution has long been hindered by the fact that we can only compare modern natural proteins, the products of the very process we seek to understand, to each other, with no external references or baselines. Through a large-scale all-atom simulation of protein evolution, we have created a large diverse alignment of SH3 domain sequences which have been selected only for native state stability, with no other influencing factors. Although the average pairwise identity between computationally evolved and natural sequences is only 17%, the residue frequency distributions of the computationally evolved sequences are similar to natural SH3 sequences at 86% of the positions in the domain, suggesting that optimization for the native state structure has dominated the evolution of natural SH3 domains. Additionally, the positions which play a consistent role in the transition state of three well-characterized SH3 domains (by phi-value analysis) are structurally optimized for the native state, and vice versa. Indeed, we see a specific and significant correlation between sequence optimization for native state stability and conservation of transition state structure. [Copyright &y& Elsevier]

SUMMARY: Various species of microalgae have recently emerged as promising host‐organisms for use in biotechnology industries due to their unique properties. These include efficient conversion of sunlight into organic compounds, the ability to grow in extreme conditions and the occurrence of numerous post‐translational modification pathways. However, the inability to obtain high levels of nuclear heterologous gene expression in microalgae hinders the development of the entire field. To overcome this limitation, we analyzed different sequence optimization algorithms while studying the effect of transcript sequence features on heterologous expression in the model microalga Chlamydomonas reinhardtii, whose genome consists of rare features such as a high GC content. Based on the analysis of genomic data, we created eight unique sequences coding for a synthetic ferredoxin–hydrogenase enzyme, used here as a reporter gene. Following in silico design, these synthetic genes were transformed into the C. reinhardtii nucleus, after which gene expression levels were measured. The empirical data, measured in vivo show a discrepancy of up to 65‐fold between the different constructs. In this work we demonstrate how the combination of computational methods and our empirical results enable us to learn about the way gene expression is encoded in the C. reinhardtii transcripts. We describe the deleterious effect on overall expression of codons encoding for splicing signals. Subsequently, our analysis shows that utilization of a frequent subset of preferred codons results in elevated transcript levels, and that mRNA folding energy in the vicinity of translation initiation significantly affects gene expression. [ABSTRACT FROM AUTHOR]