High-efficiency Approach Research Articles

Identification of the genetic basis of sporadic polydactyly in China by targeted sequencing

PurposePolydactyly is a highly heterogeneous group of skeletal deformities in clinical and genetic background. The variation spectrum in Chinese sporadic polydactyly has not been comprehensively analyzed. To elucidate genetic variation spectrum and genotype-phenotype correlations in Chinese patients with polydactyly, we conducted comprehensive genetic analysis of patients nationwide using targeted sequencing. MethodsA total of 181 patients diagnosed with polydactylies were recruited. We designed a targeted capture panel for sequencing 721 genes that are associated with the pathogenesis of skeletal dysplasia. We performed rigorous variant- and gene-level filtrations to identify potentially damaging variants, followed by enrichment analysis and gene prioritization. ResultsA total of 568 deleterious variants of 293 genes were identified in 173 of 181 patients with a positive rate of 95.6% by targeted sequencing. For each sample, an average of 3.17 deleterious variants were identified. Especially, 14 pathogenic or likely pathogenic variants were identified in 10 genes in 14 patients out of the 181 patients, providing a positive molecular diagnostic rate of 7.7%. ConclusionTargeted sequencing analysis provides a high efficiency approach for the genetic diagnosis of polydactyly. This is the largest next generation sequencing study performed to date in patients with polydactyly and represents the genetic basis of polydactyly typically encountered in genetics clinics.

IDDkin: network-based influence deep diffusion model for enhancing prediction of kinase inhibitors.

Protein kinases have been the focus of drug discovery research for many years because they play a causal role in many human diseases. Understanding the binding profile of kinase inhibitors is a prerequisite for drug discovery, and traditional methods of predicting kinase inhibitors are time-consuming and inefficient. Calculation-based predictive methods provide a relatively low-cost and high-efficiency approach to the rapid development and effective understanding of the binding profile of kinase inhibitors. Particularly, the continuous improvement of network pharmacology methods provides unprecedented opportunities for drug discovery, network-based computational methods could be employed to aggregate the effective information from heterogeneous sources, which have become a new way for predicting the binding profile of kinase inhibitors. In this study, we proposed a network-based influence deep diffusion model, named IDDkin, for enhancing the prediction of kinase inhibitors. IDDkin uses deep graph convolutional networks, graph attention networks and adaptive weighting methods to diffuse the effective information of heterogeneous networks. The updated kinase and compound representations are used to predict potential compound-kinase pairs. The experimental results show that the performance of IDDkin is superior to the comparison methods, including the state-of-the-art kinase inhibitor prediction method and the classic model widely used in relationship prediction. In experiments conducted to verify its generalizability and in case studies, the IDDkin model also shows excellent performance. All of these results demonstrate the powerful predictive ability of the IDDkin model in the field of kinase inhibitors. Source code and data can be downloaded from https://github.com/CS-BIO/IDDkin. Supplementary data are available at Bioinformatics online.

Multispectral Image Reconstruction From Color Images Using Enhanced Variational Autoencoder and Generative Adversarial Network

Since multispectral images (MSIs) have much more sufficient spectral information than RGB images (RGBs), reconstructing MS images from RGB images is a severely underconstrained problem. We have to generate colossally different information between the two scopes. Almost all previous approaches are based on static and dependent neural networks, which fail to explain how to supplement the massive lost information. This paper presents a low-cost and high-efficiency approach, “VAE-GAN”, based on stochastic neural networks to directly reconstruct high-quality MSIs from RGBs. Our approach combines the advantages of the Generative Adversarial Network (GAN) and the Variational Autoencoder (VAE). The VAE undertakes the generation of the lost variational MS distributions by reparameterizing the latent space vector with sampling from Gaussian distribution. The GAN is responsible for regulating the generator to produce MSI-like images. In this way, our approach can create huge missed information and make the outputs look real, which also solves the previous problem. Moreover, we use several qualitative and quantitative methods to evaluate our approach and obtain excellent results. In particular, with much less training data than the previous approaches, we obtained comparable results on the CAVE dataset and surpassed state-of-the-art results on the ICVL dataset.

Simultaneous identification of the frequencies and track irregularities of high-speed railway bridges from vehicle vibration data

Bridge frequencies and track irregularities are both the focuses of railway bridge condition assessment, which are coupled with each other in a vehicle-bridge system. Available algorithms face a great challenge when applied to simultaneously identify the natural frequencies and track irregularities of railway bridges using on-board measurement data. This paper proposes a novel algorithm for simultaneously identifying the frequencies and track irregularities of high-speed railway bridges using vehicle dynamic responses for the first time. An extended state-space model with unknown input condensation is established for time-dependent vehicle-bridge systems. We subsequently propose a new extended Kalman filter algorithm with an adaptive procedure for accelerating the convergence of estimation, which can simultaneously identify the frequencies and track irregularities of a railway bridge when a vehicle is running on it. The effectiveness of the proposed algorithm has been illustrated via numerical simulations of two real high-speed railway bridges. The proposed algorithm provides a low-cost and high-efficient approach for identifying the natural frequencies and track irregularities of high-speed railway bridges.

Metabolomics Profiling and Diagnosis Biomarkers Searching for Drug-Induced Liver Injury Implicated to Polygonum multiflorum: A Cross-Sectional Cohort Study.

Aim: The diagnosis of drug-induced liver injury (DILI) remains a challenge and the cases of Polygonum multiflorum Thunb. (PM) induced DILI (PM-DILI) have received much attention This study aimed to identify a simple and high-efficiency approach to PM-DILI diagnosis via metabolomics analysis.Methods: Plasma metabolites in 13 PM-DILI patients were profiled by liquid chromatography along with high-resolution mass spectrometry. Meanwhile, the metabolic characteristics of the PM-DILI were compared with that of autoimmune hepatitis (AIH), hepatitis B (HBV), and healthy volunteers.Results: Twenty-four metabolites were identified to present significantly different levels in PM-DILI patients compared with HBV and AIH groups. These metabolites were enriched into glucose, amino acids, and sphingolipids metabolisms. Among these essential metabolites, the ratios of P-cresol sulfate vs. phenylalanine and inosine vs. bilirubin were further selected using a stepwise decision tree to construct a classification model in order to differentiate PM-DILI from HBV and AIH. The model was highly effective with sensitivity of 92.3% and specificity of 88.9%.Conclusions: This study presents an integrated view of the metabolic features of PM-DILI induced by herbal medicine, and the four-metabolite decision tree technique imparts a potent tool in clinical diagnosis.

Evaluation of SWCNTs-loaded DNA vaccine encoding predominant antigen epitope VP4-3 against type II GCRV

Grass carp hemorrhagic disease has widely received great attention for Chinese freshwater aquaculture due to its strong infectiousness and high fatality rate. The pathogen is grass carp reovirus (GCRV). DNA vaccination is a high efficient approach for disease control. Although some encouraging progress have been made, developing a highly-efficient DNA vaccine with genetic type II GCRV predominant antigen epitope is less successful and still require further research. However, the DNA vaccine has made it challenging to avoid the problem of easily metabolized and degraded in vivo. In an effort to overcome this challenge, we constructed the DNA vaccine using single-walled carbon nanotubes (SWCNTs) as delivery vector (SWCNTs-pcDNA-vp4-3) and immunized grass carp (1.2 ± 0.2 g) by intramuscular injection. Experiments are grouped as follows: control group, pcDNA-vp4-3 immune group (10 μg per fish) and SWCNTs-pcDNA-vp4-3 immune groups (2.5, 5 and 10 μg per fish, respectively). The immune effect of DNA vaccine is evaluated comprehensively by testing immune-related parameters (the persistence and expression of DNA vaccine, serum antibody level, immune-related physiological indexes, immune-related genes expression and relative percentage survival). The results showed that serum antibody levels of grass carp were elevated significantly after immunization with DNA vaccines. And the serum antibody levels in SWCNTs-pcDNA-vp4-3 immune groups were higher than pcDNA-vp4-3 immune group. Likewise, the immune-related physiological indexes and immune-related genes expression were elevated significantly after immunization. The IgM gene expression in the SWCNTs-pcDNA-vp4-3 immune group (5 μg per fish) was 3.14 times as much as control group. The relative percentage survival in SWCNTs-pcDNA-vp4-3 immune group (5 μg per fish) reached the peak, protected 74% of grass carp from type II GCRV. Conclusively, the vp4-3-centric DNA vaccine can induce efficient immune response by injection. Single-walled carbon nanotubes extend the retention time of DNA vaccine in the body and improve its expression intensity. SWCNTs-loaded DNA vaccine induce stronger immune response and enhance the resistance to grass carp haemorrhagic disease.

Bamboo-like N/S-codoped carbon nanotube aerogels for high-power and high-energy supercapacitors

Three-dimensional carbon aerogels with high surface area, low cost and excellent structural stability, have shown extraordinary promise for supercapacitors (SCs). Pore-forming and heteroatom-doping have been reported to play an important role in improving the power and energy densities of nanostructured carbon electrodes. We propose novel bamboo-like N/S-codoped carbon nanotube aerogels (NS–CNAs) facilely fabricated through MnO2-templated polymerization, freeze-drying and calcination. The cryptomelane-type MnO2 nanowires are served as self-sacrificing templates and oxidative initiators for polymerization of pyrrole in acidic environment. XPS analysis can confirm the N content of 4.09% and the S content of 0.66%, which are mainly derived from PPy with nitrogen-containing skeletons and PSS with sulfonic acid groups respectively. Based on porous structure and N/S codoping, the assembled NS-CNA SC can provide high supercapacitive performances, such as a specific capacitance of 328 F g−1 (at 1 A g−1), the rate capability of 66.5% (from 1 A g−1 to 10 A g−1), an energy density of 45.6 Wh kg−1 (at 0.5 kW kg−1) and the capacity retention of 97.4% (after 10,000 cycles), opening up a highly-efficient approach to developing high-performance supercapacitors.

Stabilisation/solidification of municipal solid waste incineration fly ash by phosphate-enhanced calcium aluminate cement

Landfill disposal of municipal solid waste incineration fly ash (MIFA) presents significant environmental and economic burden. This study proposed a novel and high-efficiency approach for stabilisation/solidification (S/S) of MIFA by phosphate-modified calcium aluminate cement (CAC). Experimental results showed that the presence of Pb (the most leachable metal contaminant in the MIFA) retarded the early-stage reaction of CAC, resulting in an extension of setting time and a significant decline of compressive strength of CAC pastes. The incorporation of phosphate additives (10 wt% of binder), especially for trisodium phosphate, in CAC system effectively mitigated the negative impact of Pb on the CAC reaction and reduced the Pb leachability. Elemental mapping results illustrated that Pb2+ coordinated with phosphate to generate insoluble precipitates (e.g., Pb3(PO4)2). The S/S treated MIFA samples fulfilled the compressive strength and leachability requirements for on-site reuse. Overall, this study demonstrated that phosphate-modified CAC is a promising binder for S/S of hazardous MIFA.

An efficient approach for atomic-scale polishing of single-crystal silicon via plasma-based atom-selective etching

To realize the damage-free, highly efficient, and atomic-level polishing of single-crystal Si, plasma-based atom-selective etching (PASE) is proposed in this study as a generic polishing approach for Si wafers. The polishing effect of PASE is realized through the selective removal of Si atoms with more dangling bonds under high temperature. Plasma diagnostics are carried out to investigate the radical composition, the density of plasma, and the etching temperature. The key parameters of PASE are optimized, and the PASE of Si (100) with a material removal rate greater than 0.7 μm/min was realized. A ground Si (100) surface can be quickly smoothed by PASE, with the Sa roughness being reduced from 195 nm to below 1.0 nm within 5 min, and the polished surface is proven to be crystallographically perfect. The PASE of (110)- and (111)-oriented Si wafers is also proven effective, demonstrating that PASE is a generic polishing approach for Si regardless of orientation. The entire surface flattening of a 2-inch Si wafer was carried out by numerically controlled PASE, and the wafer flatness was reduced from 37.29 μm to 4.92 μm through optimized scanning conditions. Overall, this study has shown that PASE is a promising approach for high-efficiency and high-quality polishing of Si.

Designing novel organic inhibitor loaded MgAl-LDHs nanocontainer for enhanced corrosion resistance

The combination of LDHs nanocontainer and organic inhibitor demonstrates a great prospect in the field of corrosion protection. Whereas the traditional ion exchange method has a low efficiency owing to the small space and the firm force between the LDHs laminates. A high-efficiency approach of intercalating organic inhibitor into the LDHs is delivered in this work. The MgAl-LDHs loaded with 5-aminoindazole (AIA) were synthesized by separating the layered structures of hydrotalcites into single-layer nanosheets and then restructuring the exfoliated nanosheets with organic inhibitor. The as-synthesized LDHs-AIA− displayed a controlled release behavior and excellent anti-corrosion performance on the copper in 3.5 wt% NaCl solution, which were characterized by UV–vis spectra and electrochemical tests. Furthermore, a series of structure and morphology tests involving SEM, TEM, XRD, FTIR, XPS and TGA were also used for further analysis the LDHs. And the analysis of corrosion products and the anti-corrosion mechanism were carried out by SEM, XRD tests. The theoretical calculations concerning the adsorption of corrosion inhibitors on the copper surface were also investigated. It can be concluded that the inhibitor-loaded LDHs have the ability to adjustably release the corrosion inhibitor in corrosive medium, thereafter the corrosion inhibitor can adsorb onto the active sites of copper surface to prevent corrosion.

Circuit-Based Electrothermal Simulation of Multicellular SiC Power MOSFETs Using FANTASTIC

This paper discusses the benefits of an advanced highly-efficient approach to static and dynamic electrothermal simulations of multicellular silicon carbide (SiC) power MOSFETs. The strategy is based on a fully circuital representation of the device, which is discretized into an assigned number of individual cells, high enough to analyze temperature and current nonuniformities over the active area. The cells are described with subcircuits implementing a simple transistor model that accounts for the utmost influence of the traps at the SiC/SiO2 interface. The power-temperature feedback is emulated with an equivalent network corresponding to a compact thermal model automatically generated by the FANTASTIC tool from an accurate 3D mesh of the component under test. The resulting macrocircuit can be solved by any SPICE-like simulation program with low computational burden and rare occurrence of convergence issues.

Green and enhanced extraction of coumarins from Cortex Fraxini by ultrasound-assisted deep eutectic solvent extraction.

Green and enhanced extraction of bioactive ingredients from medicinal plants has become a hot research field, and deep eutectic solvents have been considered as a novel kind of sustainable solvents in the extraction process. In this study, hydrogen bond acceptor (choline chloride, etc.) and hydrogen bond donor (l-malic acid, etc.) were used to prepare different kinds of deep eutectic solvents to extract coumarins from Cortex Fraxini. The extraction conditions, including the composition and moisture content of deep eutectic solvents, extraction time, and liquid-solid ratio, were systematically optimized basing on the extraction yield of coumarins. To further investigate the extraction mechanism, Fourier transform infrared spectroscopy was performed, and the microstructures of Cortex Fraxini powders were observed before and after extraction using scanning electron microscope. Results showed that the novel ultrasound-assisted extraction with conditions of deep eutectic solvent containing betaine/glycerin (1:3), aqueous solution (20%), solid-liquid ratio (15mg/mL), and extraction time (30min) exhibited the best extraction yields for the four target coumarins and much better extraction efficiency than with conventional solvent extractions. This suggests that the new ultrasound-assisted deep eutectic solvent extraction could be used as a green and high-efficient approach for extraction of the main coumarins from Cortex Fraxini.

Recursive Method in Modal Parameter Identification of Aerospace Structures under Non-Gaussian Noise

Operational modal parameter identification is a tough problem in aerospace engineering due to the complex mechanics environment, various noises, and limited computational resources. In this paper, a novel, recursive, robust, and high-efficiency modal parameter identification approach is proposed for this issue. The kernelized time-dependent autoregressive moving average (TARMA) model is adopted to model the nonstationary responses, a recursive estimator is established based on the maximum correntropy criterion, and sliding-window technique is applied to fix the computational complexity, which ensures the approach its estimation accuracy, robustness, and high efficiency. Finally, steps of the identification procedure and model selection are presented. An experimental scheme is proposed for validation, and the proposed approach is comparatively assessed against the classical recursive pseudo-linear regression TARMA method via Monte Carole tests of a time-varying experimental system. The results of the comparative study demonstrate that the proposed method achieves similar estimation accuracy and higher computation efficiency under the Gaussian environment. Moreover, a superior estimation accuracy and enhanced robustness are rendered under additive non-Gaussian impulsive noise.

Genome-wide association analysis of root system architecture features and agronomic traits in durum wheat

Root system architecture (RSA) has positive effects on wheat growth and its yield performance. As root features are difficult to manipulate through conventional breeding strategies, marker-trait association (MTA) could be helpful for the improvement of RSA. In the present study, 112 durum wheat genotypes were investigated for several root system features as well as some agronomic traits. The population was genotyped using a 15K SNP and a total of 3321 markers were used in the association analysis. A total of 581 significant marker-trait associations were identified in all of the 14 chromosomes. The percentages of phenotypic variation (R2) of measured traits varied between 8.76 and 24.81%. Out of 581 associated markers, 125 loci were linked with multiple traits. The most significant associations on measured traits were detected for genome B compared to genome A (61% vs 39%). Also, major associated loci existed on chromosomes 1A, 1B, 2B, 3B, and 5B, which could be considered in the future breeding programs to manipulate relevant traits using marker-assisted selection procedures. Among associated SNP markers, most markers were related to the number of days to spike heading (181), anthesis (53), booting (69), physiological maturity (41), water use efficiency (109), and transpiration efficiency (24). Furthermore, the large number of QTLs (167 in total) for RSA and agronomic traits were detected. The highest numbers of QTLs were related to WUE (23), DAS (23), DB (21), and DA (20) than other traits. Among the detected QTLs, 16 QTLs for RSA overlapped with different agronomic traits, as well as 6 QTLs co-located with other RSA traits in at last one trait. These results are helpful for better understanding the genetic basis of root system features and agronomic traits. Furthermore, these results could be valuable for facilitating pyramiding of the ideal alleles using the MAS approach for favorable plant-type and high water use efficiency in the future durum wheat breeding programs.

Systematic approach to realizing optimal moving order selection for multi-axis precise motion system

This paper presents a systematic approach for selecting the optimal moving order (MO) in a multi-axis precise motion system (MPMS). According to the proposed procedure, an optimal high-efficiency MO selection approach for high-efficiency and high-accuracy requirement is introduced. First, the characteristics of the giving MPMS are analysed, and the error model is established. The orthogonal test method is used to evaluate the influence on the MO caused by different MPMS configurations. Second, the number of possible MOs can be narrowed to limited and satisfied MO types. By calculating the deviations after each step, all directional movements can be arranged. Third, considering that both the accuracy and efficiency are important indexes, a series of systematic formulations are developed to select the optimal MO to balance accuracy and efficiency. A case for which a six-axis precise platform is adopted in an optoelectronic packaging system is implemented, and the methods of high-quality MO selection are verified by performing a series of experiments, and the methods are shown to be useful and effective. To balance the proportion of efficiency and accuracy, the formula and corresponding model are proposed to select the MO. The approach is not only beneficial to the accuracy improvement and trajectory planning of MPMS, but also helpful in terms of reducing the computational processing for the following algorithm. For the engineers using in precise industry area, the proposed approach can significantly improve the operative precision of MPMS with an optimal MO. This methodology of MO can also be the basis of references to error-related analyses on MPMS.

High Efficient and Green Approach to the Synthesis of Leuprolide in Continuous-flow Microreactor†

High Efficient and Green Approach to the Synthesis of Leuprolide in Continuous-flow Microreactor†

Crater detection from commercial satellite imagery to estimate unexploded ordnance in Cambodian agricultural land.

Unexploded ordnance (UXO) pose a significant threat to post-conflict communities, and current efforts to locate bombs rely on time-intensive and dangerous in-person enumeration. Very high resolution (VHR) sub-meter satellite images may offer a low-cost and high-efficiency approach to automatically detect craters and estimate UXO density. Machine-learning methods from the meteor crater literature are ill-suited to find bomb craters, which are smaller than meteor craters and have high appearance variation, particularly in spectral reflectance and shape, due to the complex terrain environment. A two-stage learning-based framework is created to address these challenges. First, a simple and loose statistical classifier based on histogram of oriented gradient (HOG) and spectral information is used for a first pass of crater recognition. In a second stage, a patch-dependent novel spatial feature is developed through dynamic mean-shift segmentation and SIFT descriptors. We apply the model to a multispectral WorldView-2 image of a Cambodian village, which was heavily bombed during the Vietnam War. The proposed method increased true bomb crater detection by over 160 percent. Comparative analysis demonstrates that our method significantly outperforms typical object-recognition algorithms and can be used for wide-area bomb crater detection. Our model, combined with declassified records and demining reports, suggests that 44 to 50 percent of the bombs in the vicinity of this particular Cambodian village may remain unexploded.

A high-efficiency energy and storage approach for IoT applications of facial recognition

This work introduces a high-efficiency approach for face recognition applications based on features using a recent algorithm called Floor of Log (FoL). The advantage of this method is the reduction of storage and energy, maintaining accuracy. K-Nearest Neighbors and Support Vector Machine algorithm was applied to learn the better parameter of the FoL algorithm using cross-validation. Accuracy and the size after the compression process were adopted to evaluate the proposed algorithm. The FoL was tested in CelebA, Extended YaleB, AR, and LFW face datasets obtaining the same or better results when compared with the approach using the same classifiers with uncompressed features, but with a reduction of 86 to 91% compared to the original data size. The proposed method of this work presents a robust and straightforward algorithm of compression of features for face recognition applications. The FoL is a new supervised compression algorithm that can be adapted to achieve great results and integrated with edge computing systems.

Ecological carbonated steel slag pervious concrete prepared as a key material of sponge city

A pervious concrete with carbonated ground steel slag powder as binder and crushed steel slag as aggregate was prepared, which provided the key material for construction of sponge city and explored a new approach to massively utilize steel slag. This paper presented series of experimental studies on the properties of steel slag carbonized consolidation matrix and carbonated steel slag pervious concrete. Results indicated that the phosphogypsum promoted the carbonization ratio of steel slag. When the content of phosphogypsum was 2.5%, strength and carbonization ratio of carbonated steel slag from pressing formation reached 82.6 MPa and 52.1% respectively. The main product of the carbonated steel slag was calcite and C–S–H. The strength and porosity of carbonated steel slag pervious concrete reached 9.4 MPa and higher than 20% respectively. And it also had a good plantability when it is used in sponge city construction. Compared with ordinary pervious concrete, carbonated steel slag pervious concrete saved 75.8% cost of materials, was 100% made of solid waste and absorbed around 100 kg/m3 of CO2, which turns out to be a green and high-efficiency approach to utilize the solid waste steel slag, saving the natural resource and reducing the carbon footprint of the construction of sponge city.

Label-free surface-enhanced Raman spectroscopy detection of absorption manner of lysozyme based on nanodots arrays

Lysozyme plays an important role in the innate immune system of animal or human body, thus, it is vital to detect it using a high sensitivity and efficiency approach in clinic. Nanostructure-based Surface-enhanced Raman scattering (SERS) substrate is often employed to detect biomolecules. In this study, a nanodots array structure, which was utilized as a micro-nano hybrid SERS substrate, was fabricated using second-scan scratching strategy with AFM-probe. The dependence of the nanodots structure on the machining parameters such as applied normal loads and scratching angles were investigated. The effects of the nanostructure feature sizes and the Au film thickness on the measured SERS spectra that tested using Rhodamine 6G (R6G) were studied. In addition, different drying temperatures and concentrations of NaNO3 solution were used to study the absorption manner of lysozyme on an Ag substrate. The SERS spectra of a binary protein mixture, which includes lysozyme and insulin, were also investigated based on the fabricated nanostructures. Results showed that the substrates fabricated using AFM nanomachining approach may promote the SERS application in biologic field.