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Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patients WOS:000871801500001

Background Fertility intentions have been proved to be a reliable predictor of actual fertility behaviour. Also, childbirth-related fear (CBRF) has been proven to be negatively associated with childbirth readiness and fertility intentions among women, while childbirth readiness was positively related to fertility intentions. However, the associations and potential mechanisms between CBRF, childbirth readiness, and fertility intentions remain unknown. This study aimed to investigate the unique association between CBRF, childbirth readiness, and fertility intentions and whether childbirth readiness would mediate the relationship between CBRF and fertility intentions. Method A cross-sectional study of women (N = 1119, aged 16–53 years) who gave birth within 72 h was conducted. Using a convenience sampling, women were recruited from obstetric wards—10 comprehensive hospitals and 3 specialized hospitals in 7 provinces in mainland China. Pearson correlation was used to examine the relationship between CBRF, childbirth readiness, fertility intentions, and social support. Multivariate linear regression was further used to analyze the association between demographic and personal characteristics, CBRF, childbirth readiness, and fertility intentions. Mediation analysis was used to examine whether childbirth readiness mediates the relationship between CBRF and fertility intentions. Results Women with high childbirth readiness (β = 0.09, P = 0.002) had higher fertility intentions. However, women with high CBRF (β = − 0.17, P Conclusions This study established the evidence that CBRF had both direct and indirect effects on the level of fertility intentions and childbirth readiness mediated the relationship between CBRF and the level of fertility intentions. Specifically, higher CBRF was associated with lower scores of childbirth readiness, which was associated with lower levels of fertility intentions. This finding suggested that it is important for health policymakers and health providers to pay more attention to improving women’s childbirth readiness, which might reduce the negative influence of CBRF on fertility intentions, thus strengthening their fertility intentions.

Rhodosporidium toruloides is an oleaginous yeast under development with promising industrial applications. Since promoters of different strengths have been demonstrated as an efficient strategy to fine-tune gene expression in synthetic biology, a set of constitutive promoters with strengths varying over 2 orders of magnitude were identified in R. toruloides through transcriptome analysis under different growth conditions. Promoter candidates were first cloned and characterized using an enhanced green fluorescent protein (EGFP) as a reporter under eight conditions, and 31 promoters were identified with strength varied from 0.1 to 19.0 folds of the commonly used strong promoter of the glyceraldehyde-3-phosphate dehydrogenase gene (PGPD1). The resultant promoters were then used to optimize the linoleic acid biosynthetic pathway in R. toruloides in different media, including the use of lignocellulosic hydrolysate as the fermentation substrate, and improved the production of linoleic acid by up to 214.2% in minimal medium, with the highest production of 350.3 mg/L in Yeast Peptone Dextrose medium. This work has enriched the promoter library of R. toruloides, and helped develop R. toruloides as a platform organism for applications in biomanufacturing and synthetic biology.

Brain-computer interfaces (BCIs) based on the electroencephalogram (EEG) generated during motor imagery (MI) have the potential to be used in brain-controlled prosthetics, neurorehabilitation and gaming. Many MI EEG classification systems segment EEG into windows for classification. However, a comprehensive analysis of decision fusion based on the segmented EEG data, within the context of different classifiers, has not been carried out. This study presents a multi-segment majority voting (MSMV) decision fusion approach in which an EEG trial is segmented using overlapping windows. Segments are labelled and a final classification label for the trial is derived through majority voting, using the common spatial pattern (CSP) features. The impact of the MSMV approach on the classification accuracy of six classifiers was investigated. The effects of window size and overlap were analysed. Results were generated using five different subsets of EEG channels, and channel subsets for static EEG analysis are also proposed. The BCI Competition III dataset IVa was used. The MSMV decision fusion approach was found to significantly improve the classification accuracy for linear discriminant analysis (LDA), support vector machine (SVM), naive-Bayes (NB) and random forest (RF) classifiers. The classification accuracy was improved by 5.02%, 4.41%, 1.25% and 3.62% for the SVM, LDA, NB and RF classifiers, respectively. The channel analysis indicated the importance of central-parietal and central-frontal electrode regions for MI EEG classification. MSMV decision fusion improved MI EEG classification performance and could be considered for future studies, particularly in online systems that deal with buffered data.

Grain crops are easily contaminated by fungi due to the existence of various microorganisms in the storage process, especially in humid and warm storage conditions. Compared with conventional methods, surface-enhanced Raman scattering (SERS) has paved the way for the detection of fungi in grain crops as it is a rapid, nondestructive, and sensitive analytical method. In this work, Aspergillus niger, Saccharomyces cerevisiae, Fusarium moniliforme and Trichoderma viride in grain crops were detected using colloidal Au nanoparticles and SERS. The results indicated that different fungi showed different Raman phenotypes, which could be easily characterized by SERS. Combined with multivariate statistical analysis, identification of a variety of fungi could be accomplished rapidly and accurately. This research can be applied for the rapid detection of fungi in the food and biomedical industries.

SiamPRN algorithm performs well in visual tracking, but it is easy to drift under occlusion and fast motion scenes because it uses $$\ell _1$$ ℓ 1 -smooth loss function to measure the regression location of bounding box. In this paper, we propose a multivariate intersection over union (MIOU) loss in SiamRPN tracking framework. Firstly, MIOU loss includes three geometric factors in regression: the overlap area ratio, the center distance ratio, and the aspect ratio, which can better reflect the coincidence degree of target box and prediction box. Secondly, we improve the definition of aspect ratio loss to avoid gradient explosion, improve the optimization performance of prediction box. Finally, based on SiamPRN tracker, we compared the tracking performance of $$\ell _1$$ ℓ 1 -smooth loss, IOU loss, GIOU loss, DIOU loss, and MIOU loss. Experimental results show that the MIOU loss has better target location regression than other loss functions on the OTB2015 and VOT2016 benchmark, especially for the challenges of occlusion, illumination change and fast motion.

Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute (PfAgo)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate 101 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments. PlasmidMaker should greatly expand the potential of synthetic biology.

The severe situation caused by THE COVID-19 epidemic has not only hindered the steady development of social economy, but also had a great impact on the development of e-commerce logistics. For e-commerce enterprises, logistics cost is an important factor that affects the operation effect and consumer experience. Based on this, this study proposes cost control methods for e-commerce logistics in the prevention and control of COVID-19 environment. In this study, based on the actual environment of COVID-19 prevention and control, the logistics cost algorithm during the epidemic period is designed on the basis of the analysis of the influencing factors of e-commerce logistics cost, and the cross-border logistics strategy that conforms to the background of COVID-19 prevention and control and the demand of e-commerce logistics cost control is developed to better reduce the operating cost of logistics enterprises. The e-commerce logistics cost control method proposed in this article is effective in the prevention and control of new crown pneumonia, and the overall actual cost is within the budgeted cost range. The experimental results prove that the e-commerce logistics cost control method designed in this paper can help e-commerce companies achieve good economic benefits and proves that it has higher application advantages.

The selection of the mutation strategy for differential evolution (DE) algorithm plays an important role in the optimization performance, such as exploration ability, convergence accuracy and convergence speed. To improve these performances, an improved differential evolution algorithm with neighborhood mutation operators and opposition-based learning, namely NBOLDE, is developed in this paper. In the proposed NBOLDE, the new evaluation parameters and weight factors are introduced into the neighborhood model to propose a new neighborhood strategy. On this basis, a new neighborhood mutation strategy based on DE/current-to-best/1, namely DE/neighbor-to-neighbor/1, is designed in order to replace large-scale global mutation by local neighborhood mutation with high search efficiency. Then, a generalized opposition-based learning is employed to optimize the initial population and select the better solution between the current solution and reverse solution in order to approximate global optimal solution, which can amend the convergence direction, accelerate convergence, improve efficiency, enhance the stability and avoid premature convergence. Finally, the proposed NBOLDE is compared with four state-of-the-art DE variants by 12 benchmark functions with low-dimension and high-dimension. The experiment results indicate that the proposed NBOLDE has a faster convergence speed, higher convergence accuracy, and better optimization capabilities in solving high-dimensional complex functions.

In this paper, the problem of the robust tracking for two-arm condenser cleaning crawler-type mobile manipulators (CCCMM) with delayed angle-velocity uncertainties is original investigated. The two-arm condenser cleaning crawler-type mobile manipulators are composed of a crawler-type mobile platform and two-arm industrial manipulators.The uncertainty is nonlinear time-varying and does not require a matching condition. A wavelet transform and probabilistic neural network (WTPNN) system is used to approximate an unknown controlled system from the strategic manipulation of the model following tracking errors. Based on the Lyapunov method and the linear matrix inequality (LMI) approach, several sufficient conditions, which guarantee the state variables of the closed loop system to converge, globally, uniformly and exponentially, to a ball in the state space with any pre-specified convergence rate, are derived. Experiment results are given to illustrate the superior control performance of the proposed intelligent control method.

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) are emerging as promising building blocks of high-performance photocatalysts for visible-light-driven water splitting because of their unique physical, chemical, electronic, and optical properties. This review focuses on the fundamentals of 2D TMDC-based mixed-dimensional heterostructures and their unique properties as visible-light-driven photocatalysts from the perspective of dimensionality and interface engineering. First, we discuss the approaches and advantages of surface modification and functionalization of 2D TMDCs for photocatalytic water splitting under visible-light illumination. We then classify the strategies for improving the photocatalytic activity of 2D TMDCs via combination with various low-dimensional nanomaterials to form mixed-dimensional heterostructures. Further, we highlight recent advances in the use of these mixed-dimensional heterostructures as high-efficiency visible-light-driven photocatalysts, particularly focusing on synthesis routes, modification approaches, and physiochemical mechanisms for improving their photoactivity. Finally, we provide our perspectives on future opportunities and challenges in promoting real-world photocatalytic applications of 2D TMDC-based heterostructures.

Enzymes are increasingly explored for use in asymmetric synthesis1-3, but their applications are generally limited by the reactions available to naturally occurring enzymes. Recently, interest in photocatalysis4 has spurred the discovery of novel reactivity from known enzymes5. However, so far photoinduced enzymatic catalysis6 has not been used for the cross-coupling of two molecules. For example, the intermolecular coupling of alkenes with α-halo carbonyl compounds through a visible-light-induced radical hydroalkylation, which could provide access to important γ-chiral carbonyl compounds, has not yet been achieved by enzymes. The major challenges are the inherent poor photoreactivity of enzymes and the difficulty in achieving stereochemical control of the remote prochiral radical intermediate7. Here we report a visible-light-induced intermolecular radical hydroalkylation of terminal alkenes that does not occur naturally, catalysed by an 'ene' reductase using readily available α-halo carbonyl compounds as reactants. This method provides an efficient approach to the synthesis of various carbonyl compounds bearing a γ-stereocentre with excellent yields and enantioselectivities (up to 99 per cent yield with 99 per cent enantiomeric excess), which otherwise are difficult to access using chemocatalysis. Mechanistic studies suggest that the formation of the complex of the substrates (α-halo carbonyl compounds) and the 'ene' reductase triggers the enantioselective photoinduced radical reaction. Our work further expands the reactivity repertoire of biocatalytic, synthetically useful asymmetric transformations by the merger of photocatalysis and enzyme catalysis.

Here, we report a rapid CRISPR–Cas9-mediated gene knock-in strategy that uses Cas9 ribonucleoprotein and 5′-modified double-stranded DNA donors with 50-base-pair homology arms and achieved unprecedented 65/40% knock-in rates for 0.7/2.5 kilobase inserts, respectively, in human embryonic kidney 293T cells. The identified 5′-end modification led to up to a fivefold increase in gene knock-in rates at various genomic loci in human cancer and stem cells. A simple and effective strategy is introduced to increase CRISPR–Cas9-mediated gene knock-in rates by using 5′-modified double-stranded DNA donors with short homology arms.

Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited. Here, we report ECNet (evolutionary context-integrated neural network), a deep-learning algorithm that exploits evolutionary contexts to predict functional fitness for protein engineering. This algorithm integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe. As such, it enables accurate mapping from sequence to function and provides generalization from low-order mutants to higher-order mutants. We show that ECNet predicts the sequence-function relationship more accurately as compared to existing machine learning algorithms by using ~50 deep mutational scanning and random mutagenesis datasets. Moreover, we used ECNet to guide the engineering of TEM-1 β-lactamase and identified variants with improved ampicillin resistance with high success rates., Protein engineering is an active area of research in which machine learning has proven quite powerful. Here, the authors present a deep learning method that integrates both general and protein-specific sequence representations to improve the engineering of one’s protein of interest.

Background Production of biofuels and green chemicals by microbes is currently of great interest due to the increasingly limited reserves of fossil fuels. Biodiesel, especially fatty acid ethyl esters (FAEEs), is considered as an attractive alternative because of its similarity with petrodiesel and compatibility with existing infrastructures. Cost-efficient bio-production of FAEEs requires a highly lipogenic production host that is suitable for large-scale fermentation. As a non-model oleaginous yeast that can be cultured to an extremely high cell density and accumulate over 70% cell mass as lipids, Rhodotorula toruloides represents an attractive host for FAEEs production. Results We first constructed the FAEE biosynthetic pathways in R. toruloides by introducing various wax ester synthase genes from different sources, and the bifunctional wax ester synthase/acyl-CoA-diacyglycerol acyltransferase (WS/DGAT) gene from Acinetobacter baylyi was successfully expressed, leading to a production of 826 mg/L FAEEs through shake-flask cultivation. We then mutated this bifunctional enzyme to abolish the DGAT activity, and further improved the titer to 1.02 g/L. Finally, to elevate the performance of Δku70-AbWS* in a bioreactor, both batch and fed-batch cultivation strategies were performed. The FAEEs titer, productivity and yield were 4.03 g/L, 69.5 mg/L/h and 57.9 mg/g (mg FAEEs/g glucose) under batch cultivation, and 9.97 g/L, 90.6 mg/L/h, and 86.1 mg/g under fed-batch cultivation. It is worth mentioning that most of the produced FAEEs were secreted out of the cell, which should greatly reduce the cost of downstream processing. Conclusion We achieved the highest FAEEs production in yeast with a final titer of 9.97 g/L and demonstrated that the engineered R. toruloides has the potential to serve as a platform strain for efficient production of fatty acid-derived molecules.

The working conditions of rolling bearings during the running change in real time. Aiming at the problem of fault diagnosis of rolling bearing under complex working conditions, a new fault diagnosis (VHDBN) method based on variation mode decomposition (VMD), Hilbert transform (HT) and deep belief network (DBN) is proposed in this paper. Firstly, the proposed fault diagnosis method performs the VMD decomposition for the time domain signal in order to obtain a series of intrinsic mode functions (IMFs), and Hilbert envelope spectrum is obtained by Hilbert transform. The Hilbert envelope spectrum is used to construct the feature matrix, which is used as an input of the DBN network in order to obtain a fault diagnosis model. In order to test and verify the effectiveness of the proposed fault diagnosis method, the experimental data of rolling bearings under variable load is used in here. The experimental results show that the VMD-Hilbert envelope spectrum can better reflect the fault characteristics than the time domain spectrum, and the proposed fault diagnosis method under variable load has higher recognition accuracy than other comparison methods.

Based on the characteristics of inductance, resistance and capacitance of millimeter-sized metal single-turn (or multi-turn) coil, it is predicted that array formed by millimeter-sized metal coils with high-density distribution may hold near-zero permeability in a broadband of microwave frequency range. Simulations and experiments show that real part of relative permeability of ring-shaped array formed by copper coils is smaller than one and will decrease with the increase in either cross-sectional area or number of the coils; the imaginary part of the permeability remains small always, which is consistent with theoretical analysis. Our work may provide a convenient way to fabricate the broadband low-microwave-permeability metamaterials with low energy losses by using millimeter-sized single-turn (or multi-turn) coils.

Over the past few decades, the enzyme-mimicking activity of metal–organic frameworks (MOFs) accompanied with structural characteristics has aroused much attention. However, pure MOFs have low affinity with DNA. Here, iron-based MOFs with acidized carbon nanotubes (CNTs) via a simple hydrothermal process have been synthesized, named as MIL-88(NH2)-Fe@CNTs. CNTs can enhance the affinity between MOF and DNA, achieving flexible regulation of their catalytic activity benefiting from the strong π − π stacking between CNTs and DNA. Meanwhile, in comparison with conventional iron-based MOFs, the addition of CNTs, which contributes to the acceleration of electron transfer, endowing as-prepared nanocomposites remarkably enhanced peroxidase-like activity to achieve an ultrasensitive detection of H2O2 with the LOD of 17.64 μg/L. Notably, the as-prepared nanocomposites with adsorbed DNA displayed excellent affinity towards both TMB (3, 3′, 5, 5′-tetramethylbenzidine) substrates and H2O2 as well as high catalytic velocity. On the basis of their switchable peroxidase-like activity regulated by different length or sequence of ssDNA, it is believed that our-prepared MOF-based nanomaterials would be promising for fabricating versatile and sensitive label-free colorimetric assays for diverse targets.

Visual object tracking is to locate an object of interest in a sequence of consecutive video frames, which is widely applied in many high-level computer vision tasks such as intelligent video surveillance and robotics. It is of great challenges for visual tracking methods to handle large target appearance variations caused by pose deformation, fast motion, occlusion, and surrounding environments in real-time videos. In this paper, inspired by human attention cognitive saliency model, we propose a visual tracking method based on salient superpixels which integrates the target appearance similarity and cognitive saliency, and helps to location inference and appearance model updating. The saliency of superpixel is detected by graph model and manifold ranking. We cluster the superpixels of the first four target boxes into a set corresponding to object foreground and model the target appearance with color descriptors. While tracking, the relevance is computed between the candidate superpixels and the target appearance set. We also propose an iterative threshold segmentation method to distinguish the foreground and background of superpixels based on saliency and relevance. To increase the accuracy of location inference, we explore particle filter in both confidence estimation and sampling procedures. We compared our method with the existing techniques in OTB100 dataset in terms of precision based on center location error and success rate based on overlap, and the experimental results show that our proposed method achieved substantially better performance. Promising results have shown that the proposed salient superpixel-based approach is effective to deformation, occlusion, and other challenges in object tracking.

Here we report a transcription factor decoy strategy for targeted activation of eight large silent polyketide synthase and non-ribosomal peptide synthetase gene clusters, ranging from 50 to 134 kilobases in multiple streptomycetes, and characterization of a novel oxazole family compound produced by a 98-kb biosynthetic gene cluster. Due to its simplicity and ease, this strategy can be readily scaled up for discovery of natural products in streptomycetes., Graphical abstract

Summary The outbreak of coronavirus disease 2019 (COVID-19) posed an unprecedented threat to health care providers (HCPs) in Wuhan, China, especially for nurses who were frequently exposed to infected or suspected patients. Limited information was available about the working experience of nurses in fighting against the pandemic. To learn the physical and psychological responses of nurses during the pandemic and explore the potential determinants, we conducted a large-scale survey in Wuhan. This multicenter cross-sectional study enrolled 5521 nurses who worked in designated hospitals, mobile cabins, or shelters during the pandemic. A structured online questionnaire was distributed to assess the physical discomforts, emotional distress and cognitive reactions of nurses at work, and the log-binomial regression analysis was performed to explore potential determinants. A considerable proportion of nurses had symptoms of physical discomforts [3677 (66.6%)] and emotional distress [4721 (85.5%)]. Nurses who were directly involved in the care of patients (i.e., care for severe patients: RR, 2.35; 95% CI, 1.95–2.84), with irregular work schedules (RR, 2.36; 95% CI, 1.95–2.87), and working overtime (RR, 1.34; 95% CI, 1.08–1.65) were at a higher risk for physical discomforts. Nurses who were directly involved in the care of patients (i.e., care for severe patients: RR, 1.78; 95% CI, 1.40–2.29), with irregular work schedules (RR, 3.39; 95% CI, 2.43–4.73), and working overtime (RR, 1.51; 95% CI, 1.12–2.04) were at a higher risk for emotional distress. Therefore, formulating reasonable work schedules and improving workforce systems are necessary to alleviate the physical and emotional distress of nurses during the pandemic.

Background The increasing of older adults has led to enormous demand for medical care. However, as a group with unique needs and characteristics, older adults are often discriminated against in the medical field. In this paper, we aimed to translate the Fraboni Scale of Ageism (FSA) into Chinese and examine its construct validity, content validity, and reliability in Chinese mainland medical students. In order to evaluate the prevalence of ageism in Chinese medical students and prompt medical college to adopt necessary teaching methods to mitigate ageism in medical students. Methods By Brislin’s translation guidelines, FSA was translated to Chinese. The convenient sampling method was used to select samples for this survey, including 1,974 students from two medical schools in central and north China. Construct validity was verified by the exploratory and confirmatory factor analysis. The content validity index (CVI) was used to assess content validity. The Cronbach’s alpha coefficients and intraclass correlation coefficient (ICC) were used to estimate reliability. Results The alpha coefficients for FSA (Chinese version) was 0.81 and ICC was 0.87. The CVI was 0.93. Three factors were identified by exploratory factor analysis explaining 34.84% of the total variance and a three-factor model was confirmed to fit by confirmatory factor analysis. Conclusions FSA (Chinese version) is a reliable and valid scale for measuring discrimination degree against older adults in Chinese medical students.

We developed a CRISPR-Cas9- and homology-directed-repair-assisted genome-scale engineering method named CHAnGE that can rapidly output tens of thousands of specific genetic variants in yeast. More than 98% of target sequences were efficiently edited with an average frequency of 82%. We validate the single-nucleotide resolution genome-editing capability of this technology by creating a genome-wide gene disruption collection and apply our method to improve tolerance to growth inhibitors.

In this paper, we present an efficient framework to cognitively detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, the thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, cognitive fusion of these two modalities provides an effective solution to tackle this problem. First, a background model is extracted followed by a two-stage background subtraction for foreground detection in visible and thermal images. To deal with cases of occlusion or overlap, knowledge-based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. To evaluate the proposed method, a publicly available color-thermal benchmark dataset Object Tracking and Classification in and Beyond the Visible Spectrum is employed here. For our foreground detection evaluation, objective and subjective analysis against several state-of-the-art methods have been done on our manually segmented ground truth. For our object tracking evaluation, comprehensive qualitative experiments have also been done on all video sequences. Promising results have shown that the proposed fusion-based approach can successfully detect and track multiple human objects in most scenes regardless of any light change or occlusion problem.

Aiming at the problem that the most existing fault diagnosis methods could not effectively recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy information entropy, improved particle swarm optimization algorithm and least squares support vector machines are introduced into the fault diagnosis to propose a novel intelligent diagnosis method, which is applied to diagnose the faults of the motor bearing in this paper. In the proposed method, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by using empirical mode decomposition method. The fuzzy information entropy values of IMFs are calculated to reveal the intrinsic characteristics of the vibration signal and considered as feature vectors. Then the diversity mutation strategy, neighborhood mutation strategy, learning factor strategy and inertia weight strategy for basic particle swarm optimization (PSO) algorithm are used to propose an improved PSO algorithm. The improved PSO algorithm is used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier, which is used to classify the fault. Finally, the proposed fault diagnosis method is fully evaluated by experiments and comparative studies for motor bearing. The experiment results indicate that the fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods in this paper and published in the literature. It provides a new method for fault diagnosis of rotating machinery.

Identification of the upstream regulators of a gene is important to characterize the transcriptional pathway and the function of the gene. Previously, we found that a zinc finger protein (ThZFP1) is involved in abiotic stress tolerance of Tamarix hispida. In the present study, we further investigated the transcriptional pathway of ThZFP1. Dof motifs are abundant in the ThZFP1 promoter; therefore, we used them to screen for transcriptional regulators of ThZFP1. A Dof protein, ThDof1.4, binds to the Dof motif specifically, and was hypothesized as the upstream regulator of ThZFP1. Further study showed that overexpression of ThDof1.4 in T. hispida activated the expression of GUS controlled by the ThZFP1 promoter. In T. hispida, transient overexpression of ThDof1.4 increased the transcripts of ThZFP1; conversely, transient RNAi-silencing of ThDof1.4 reduced the expression of ThZFP1. Chromatin immunoprecipitation indicated that ThDof1.4 binds to the ThZFP1 promoter. Additionally, ThDof1.4 and ThZFP1 share similar expression patterns in response to salt or drought stress. Furthermore, like ThZFP1, ThDof1.4 could increase the proline level and enhance ROS scavenging capability to improve salt and osmotic stress tolerance. Together, these results suggested that ThDof1.4 and ThZFP1 form a transcriptional regulatory cascade involved in abiotic stress resistance in T. hispida.

In this paper, we shall show first that both reflection and transmission can be significantly amplified when a beam of electromagnetic wave is normally incident into an active (even lossy) metamaterial slab with near-zero-real-part-of-impedance, which is attributed to effect of interference between forward and backward waves in the slab. Then we demonstrate that extended steady electromagnetic waves can be achieved using a double-slab structure, which may be applied to generate coherent electromagnetic wave with very narrow frequency band and very low divergence angle. This work provides feasible scheme to construct a new type of source of coherent electromagnetic radiation based on new physical mechanism.

Although content-based image retrieval (CBIR) has been an active research theme in the computer vision community for over two decades, there are still challenging problems in properly understanding the process in feature extraction and image matching. Consequently, significant research is still required to develop solutions for practical applications, especially in exploring and making the best using of the cognitive aspects of the human vision system. Motivated by three cognitive properties of human vision, namely hierarchical structuring, color perception and embedded compressed sensing, we proposed a novel framework for CBIR. First, we use a hierarchical approach to perform discrete cubic partitioning of the image in the HSV space. Then, we propose a new hierarchical mapping of the image data through the use of hierarchical operators: SGLCM. These features are then integrated in a 2D CS model, which extracts refined features and suppresses noise. Finally, the resultant features are used for similarity-based ranking to perform CBIR. Experiments were performed using two Corel image datasets, i.e., the Corel-1000 dataset which contains 1000 images in 10 image categories and the Corel-10000 dataset which contains 10000 images in 100 image categories where each category contains 100 images. In comparison with three other state-of-the-art approaches, the proposed method has demonstrated much improved retrieval accuracy, especially for images with rich color contents and detail, yet the computational complexity has been significantly reduced to meet the needs for real-time online applications. The implication of the study is that the exploitation of cognitive properties of our human vision systems in effective CBIR. Future research work can be further explored to address some limitations for optimized parameter setting, adaptive feature fusion and improved machine learning.

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.

In this paper, a novel scheme is proposed for multi-sourced signal fusion and secure processing. Within a distributed compressed sensing (DCS) framework, traditional sampling, compression and encryption for signal acquisition are unified under the secure multiparty computation protocol. In the proposed scheme, generation of the pseudo-random sensing matrix offers a natural method for data encryption in DCS, allowing for joint recovery of multiparty data at legal users' side. Experimental analysis and results indicate that the secure signal processing and recovery in DCS domain is feasible, and requires fewer measurements than the achievable approach of separate CS and Nyquist processing. The proposed scheme can be also extended to other cloud-based collaborative secure signal processing and data-mining applications.

Here we report an efficient CRISPR-Cas9 knock-in strategy to activate silent biosynthetic gene clusters (BGCs) in streptomycetes. We applied this one-step strategy to activate multiple BGCs of different classes in five Streptomyces species and triggered the production of unique metabolites, including a novel pentangular type II polyketide in Streptomyces viridochromogenes. This potentially scalable strategy complements existing activation approaches and facilitates discovery efforts to uncover new compounds with interesting bioactivities., Graphical abstract

A novel photoelectrochemical (PEC) aptasensor with graphitic-phase carbon nitride quantum dots (g-C3N4; QDs) and reduced graphene oxide (rGO) was fabricated. The g-C3N4 QDs possess enhanced emission quantum yield (with an emission peak at 450 nm), improved charge separation ability and effective optical absorption, while rGO has excellent electron transfer capability. Altogether, this results in improved PEC performance. The method is making use of an aptamer against sulfadimethoxine (SDM) that was immobilized on electrode through π stacking interaction. Changes of the photocurrent occur because SDM as a photogenerated hole acceptor can further accelerate the separation of photoexcited carriers. Under optimized conditions and at an applied potential of +0.2 V, the aptasensor has a linear response in the 0.5 nM to 80 nM SDM concentration range, with a 0.1 nM detection limit (at S/N = 3). The method was successfully applied to the analysis of SDM in tap, lake and waste water samples.

RNAs are well-suited to act as cellular sensors that detect and respond to metabolite changes in the environment, due to their ability to fold into complex structures. Here, we introduce a genome-wide strategy called PARCEL that experimentally identifies RNA aptamers in vitro, in a high-throughput manner. By applying PARCEL to a collection of prokaryotic and eukaryotic organisms, we have revealed 58 new RNA aptamers to three key metabolites, greatly expanding the list of natural RNA aptamers. The newly identified RNA aptamers exhibit significant sequence conservation, are highly structured and show an unexpected prevalence in coding regions. We identified a prokaryotic precursor tmRNA that binds vitamin B2 (FMN) to facilitate its maturation, as well as eukaryotic mRNAs that bind and respond to FMN, suggesting FMN as the second RNA-binding ligand to affect eukaryotic expression. PARCEL results show that RNA-based sensing and gene regulation is more widespread than previously appreciated in different organisms., Riboswitches recognize and respond to specific metabolites by altering gene expression. Here, the authors developed a high-throughput method (PARCEL) to experimentally identify RNA aptamers across transcriptomes.

Chaotic ant colony optimization (CACO) algorithm is an effective optimization algorithm that simulates the self-organization and chaotic behavior of ants. However, in the research and application of the CACO algorithm for solving complex optimization problems, the CACO algorithm presents some disadvantages. In order to resolve these disadvantages, an improved CACO algorithm based on adaptive multi-variant strategies (CACOAMS) is proposed in this paper. The CACOAMS algorithm takes full advantage of multi-population strategy, the neighborhood comprehensive learning strategy, the fine search strategy, the chaotic optimization strategy, the super excellent ant strategy, the punishment strategy and min---max ant strategy in order to avoid the local optimization solution and stagnation, guarantee learning rate of the different dimensions for each ant and the diversity of the search, eliminate the self-locking trap between environmental boundary and obstacles, improve the search efficiency, search accuracy and robustness of the algorithm. In order to testify to the performance of the CACOAMS algorithm, the CACOAMS algorithm is applied to test the benchmark functions and dynamically adjust the values of PID parameters. The simulation results show that the CACOAMS algorithm takes on the strong flexibility, adaptability and robustness. It can effectively improve system control precision and guarantee feasibility and effectiveness.

It is important to develop efficient technologies on removal of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) from water due to their wide distribution and potential threat to human health. In this work, a durable and convenient electrosorption device was designed in continuous flow mode to investigate the adsorption of PFOX (X = A and S) on multiwalled carbon nanotubes (MWNTs) from water under electrochemical assistance. The electrosorption experiments were conducted under different influent and electrolyte concentrations, hydraulic retention time (HRT) and electrode distance to optimize the operation. The results showed that the highest removal efficiencies toward both PFOA and PFOS could come up to 90 % at 1 V. Compared with open circuit (OC) adsorption under the same conditions, the removal efficiencies were improved by 4.9 times (PFOA) and 4.2 times (PFOS) respectively. In addition, the MWNTs electrode was found to be reusable. These findings provide an efficient method to remove PFOX from water by electrosorption in continuous flow mode.

We report on a fluorescent assay for oxytetracycline (OTC) using a fluorescein-labeled long-chain aptamer assembled onto reduced graphene oxide (rGO). The π-π stacking interaction between aptamer and rGO causes the fluorescence of the label to be almost completely quenched via energy transfer so that the system has very low background fluorescence. The addition of OTC leads to the formation of G-quadruplex OTC complexes and prevents the adsorption of labeled aptamer on the surface of rGO. As a result, fluorescence is restored, and this effect allows for a quantitative assay of OTC over the 0.1–2 μM concentration range and with a detection limit of 10 nM. This method is simple, rapid, selective and sensitive. It may be applied to other small molecule analytes by applying appropriate aptamers.

Phloroglucinol synthase PhlD is a type III polyketide synthase capable of directly converting three molecules of malonyl-CoA to an industrially important chemical—phloroglucinol (1, 3, 5-trihydroxylbenzene). Although this enzymatic process provides an attractive biosynthetic route to phloroglucinol, the low productivity of PhlD limits its further practical application. Here we used protein engineering coupled with in situ product removal to improve the productivity of phoroglucinol biosynthesis in recombinant Escherichia coli. Specifically, directed evolution was used to obtain a series of thermostable PhlD mutants with the best one showing over 24-fold longer half-life of thermal inactivation than the wild-type enzyme at 37 °C. When introduced into a malonyl-CoA overproducing E. coli strain, one of the mutants showed 30 % improvement in phloroglucinol productivity compared to the wild-type enzyme in a shake-flask study and the final phloroglucinol concentration reached 2.35 g/L with 25 % of theoretical yield. A continuous product extraction strategy was designed to remove the toxic phloroglucinol product from the cell media, which further increased the titer of phloroglucinol to 3.65 g/L, which is the highest phloroglucinol titer ever reported to date.

n-Butanol has several favourable properties as an advanced fuel or a platform chemical. Bio-based production of n-butanol is becoming increasingly important for sustainable chemical industry. Synthesis of n-butanol can be achieved via more than one metabolic pathway. Here we report the metabolic engineering of Saccharomyces cerevisiae to produce n-butanol through a synergistic pathway: the endogenous threonine pathway and the introduced citramalate pathway. Firstly, we characterized and optimized the endogenous threonine pathway; then, a citramalate synthase (CimA) mediated pathway was introduced to construct the synergistic pathway; next, the synergistic pathway was optimized by additional overexpression of relevant genes identified previously; meanwhile, the n-butanol production was also improved by overexpression of keto-acid decarboxylases (KDC) and alcohol dehydrogenase (ADH). After combining these strategies with co-expression of LEU1 (two copies), LEU4, LEU2 (two copies), LEU5, CimA, NFS1, ADH7 and ARO10*, we achieved an n-butanol production of 835 mg/L in the final engineered strain, which is almost 7-fold increase compared to the initial strain. Furthermore, the production showed a 3-fold of the highest titer ever reported in yeast. Therefore, the engineered yeast strain represents a promising alternative platform for n-butanol production.

We report on a photoelectrochemical immunoassay for the determination of microcystin-LR (MC-LR). It is based on the unique photoelectrochemical properties of a CdS-graphene composite that was deposited on fluorine-doped tin oxide glass prior to immobilization of the antibody (Ab) against MC-LR. The electrode was used for the label-free determination of MC-LR by monitoring the decrease in the photocurrent that result from the immunoreaction. The electrode displays a linear response to MC-LR in the range from 0.1 to 25 μg L–1, a detection limit of 0.021 μg L–1 (at S/N = 3), and can be applied to determine MC-LR in environmental water samples.

To enhance the photocatalytic activity of TiO2 nanotubes, tetracycline hydrochloride (TC) molecularly imprinted titania modified TiO2 nanotubes (MIP-TiO2) was prepared by liquid phase deposition, which improved the molecular recognition ability of the photocatalyst toward template molecules. This MIP-TiO2 photocatalyst was characterized by ESEM and XRD, which showed that the imprinted titania was deposited on the nanotube uniformly and was of well-crystalized anatase-type. In the adsorption experiments, MIP-TiO2 exhibited a high adsorption capacity (about 1.6 times higher than that of TiO2 nanotubes) for TC mainly because of its imprinted sites and high surface area. Under UV irradiation MIP-TiO2 showed enhanced photocatalytic activity with an apparent first-order rate constant 1.9-fold that of TiO2 nanotubes.

The residues of tetracycline antibiotics in water have attracted many concerns due to their harmful impact to human health. This paper reports an electrochemical sensor for the determination of tetracycline (TC) by the microelectrode, which was fabricated by electrodeposited gold colloids on tungsten tip. Cyclic voltammerty was used to study the electrochemical behavior of TC on the microelectrode. Well anodic wave was obtained at about 1.5 V in acidic solutions. Electrochemical determination of tetracycline was investigated using microelectrode by cyclic voltammetry. Under optimized conditions, the calibration curves for TC were obtained. The oxidation peak currents were linearly related to TC concentrations in the range of 1–10 mg·L−1 and 10–100 mg·L−1, respectively. The detection limit was 0.09 mg·L−1 (S/N = 3).

There are currently many active movements towards computerizing patient healthcare information. As Electronic Medical Record (EMR) systems are being increasingly adopted in healthcare facilities, however, there is a big challenge in effectively utilizing this massive information source. It is very time-consuming for healthcare providers to dig into the voluminous medical records of a patient to find the few that are indeed relevant to the patient's current problem. Due to the complex semantic relationships among medical concepts and use of many synonyms, antonyms, and hypernym/hyponym, simple word-based information retrieval does not produce satisfactory results. In this paper, we propose an EMR retrieval system that leverages semantic query expansion to retrieve medical records that are relevant to the patient's current symptom/problem. The proposed framework integrates various technologies, including information retrieval, domain ontologies, automatic semantic relationship learning, as well as a body of domain knowledge elicited from healthcare experts. Knowledge of semantic relationships among medical concepts, such as symptoms, exams and tests, diagnoses, and treatments, as well as knowledge of synonyms and hypernym/hyponyms, is used to expand and enhance initial queries posed by a user. We have implemented a preliminary prototype and conducted a pilot testing using sample nursing notes drawn from the EMR system of a community health center.

Software engineering efforts can potentially benefit much from a good understanding of the structures of existing software systems and the processes governing their development. Towards that end, we study software systems by means of the complex network analysis framework. We model a software package as a network, with nodes representing the functions in the package and edges representing the dependencies among the functions. Our empirical analysis of five widely-adopted open-source software packages reveals a set of interesting features of such networks, which cannot be adequately reproduced by existing complex network models. We then set out to develop a new network growth model, explicitly imitating generally-advocated software development principals, such as divide-and-conquer, modularization, high intra-module cohesion, and low inter-module coupling. Results of our analytical derivations and numeric studies show that our model can more closely reproduce the particular features exhibited by real-world software packages, thus hopefully better explaining the phenomena of concern.

Palladium loaded carbon nanotubes cathode with well-aligned nanotubes array was successfully fabricated on a titanium foil (Pd/CNTs/Ti) using a chemical vapor deposition technique and subsequent electrochemical deposition method. Pd particles were well dispersed on CNTs wall surfaces with average sizes no more than 20 nm, in most cases around 10 nm. Experiments for dechlorination of 2,4,5-trichlorobiphenyl (PCB 29) in methanol/water solution were carried out for the first time using Pd/CNTs/Ti cathode for investigation of its performance in electrocatalytic dechlorination of PCBs. Results show that Pd/CNTs/Ti presented better dechlorination efficiency (up to 90% in 6 h) than Pd/Ti and Pd/graphite cathodes owing to unique properties owned by CNTs, which benefited enhanced dechlorination of PCB 29. Complete dechlorination for PCB 29 was observed and biphenyl was identified to be final dechlorination product. The amount of Pd loading, cathode potential, and electrolytes were found to be key factors influencing dechlorination performance.

The objective of this study was to enhance the activity of aniline dioxygenase (AtdA), a multi-component Rieske non-heme iron dioxygenase enzyme isolated from Acinetobacter sp. strain YAA, so as to create an enhanced biocatalyst for the bioremediation of aromatic amines. Previously, the mutation V205A was found to widen the substrate specificity of AtdA to accept 2-isopropylaniline (2IPA) for which the wild-type enzyme has no activity (Ang EL, Obbard JP, Zhao HM, FEBS J, 274:928-939, 2007). Using mutant V205A as the parent and applying one round of saturation mutagenesis followed by a round of random mutagenesis, the activity of the final mutant, 3-R21, was increased by 8.9-, 98.0-, and 2.0-fold for aniline, 2,4-dimethylaniline (24DMA), and 2-isopropylaniline (2IPA), respectively, over the mutant V205A. In particular, the activity of the mutant 3-R21 for 24DMA, which is a carcinogenic aromatic amine pollutant, was increased by 3.5-fold over the wild-type AtdA, while the AN activity was restored to the wild-type level, thus yielding a mutant aniline dioxygenase with enhanced activity and capable of hydroxylating a wider range of aromatic amines than the wild type.