Miracle-dz/UniFAR-FTUs huggingface dataset:
Cointegration as a mechanism for the evolution of a KPC-producing multidrug resistance plasmid in Proteus mirabilis

ABSTRACT The incidence and transmission of Klebsiella pneumoniae carbapenemase (KPC) producing plasmids have been well documented. However, the evolutionary dynamics of KPC plasmids and their fitness costs are not well characterized. Here, two carbapenemase-producing plasmids from Proteus mirabilis, pT18 and pT211 (both carrying bla KPC-2), were characterized through whole genome sequencing. pT211 is a 24.2 kbp N-type plasmid that contains bla KPC-2 and a single copy of the IS6-family insertion sequence IS26. pT18 is a 59 kbp cointegrate plasmid comprised of sequences derived from three different plasmids: a close relative of pT211 (containing bla KPC-2), an FII-33 plasmid (bla TEM-1B, bla CTX-M-65, rmtB and fosA3) and a rolling-circle plasmid. The segments of pT18 derived from each of the different plasmids are separated by copies of IS26, and sequence analysis indicated that pT18 was likely generated by both conservative and replicative IS26-mediated cointegrate formation. pT18 and pT211 were transferred into Escherichia coli DH5α separately to assess the impact of plasmids on host fitness. Only DH5α harbouring pT18 grew slower than the wild type in antibiotic-free media. However, in sub-inhibitory concentrations of fosfomycin and amikacin, cells containing pT18 grew faster than the wild type, and the minimum concentrations of fosfomycin and amikacin required to observe an advantage for plasmid-carrying cells were 1/3 and 1/20 the DH5α MIC, respectively. This study highlights the importance of the role of cointegrate plasmids in the dissemination of antibiotic resistance genes between pathogenic bacterial species, and highlights the importance of sub-inhibitory concentrations of antibiotics to the persistence of such plasmids.
Hierarchical structure of genetic distances: Effects of matrix size, spatial distribution and correlation structure among gene frequencies

Geographic structure of genetic distances among local populations within species, based on allozyme data, has usually been evaluated by estimating genetic distances clustered with hierarchical algorithms, such as the unweighted pair-group method by arithmetic averages (UPGMA). The distortion produced in the clustering process is estimated by the cophenetic correlation coefficient. This hierarchical approach, however, can fail to produce an accurate representation of genetic distances among populations in a low dimensional space, especially when continuous (clinal) or reticulate patterns of variation exist. In the present study, we analyzed 50 genetic distance matrices from the literature, for animal taxa ranging from Platyhelminthes to Mammalia, in order to determine in which situations the UPGMA is useful to understand patterns of genetic variation among populations. The cophenetic correlation coefficients, derived from UPGMA based on three types of genetic distance coefficients, were correlated with other parameters of each matrix, including number of populations, loci, alleles, maximum geographic distance among populations, relative magnitude of the first eigenvalue of covariance matrix among alleles and logarithm of body size. Most cophenetic correlations were higher than 0.80, and the highest values appeared for Nei's and Rogers' genetic distances. The relationship between cophenetic correlation coefficients and the other parameters analyzed was defined by an "envelope space", forming triangles in which higher values of cophenetic correlations are found for higher values in the parameters, though low values do not necessarily correspond to high cophenetic correlations. We concluded that UPGMA is useful to describe genetic distances based on large distance matrices (both in terms of elevated number of populations or alleles), when dimensionality of the system is low (matrices with large first eigenvalues) or when local populations are separated by large geographical distances.
An Improved Computational Method for Vibration Response and Radiation Noise Analysis of Two-Stage Gearbox

In this work, an improved computational method for the prediction of the vibration and noise of a gearbox that considers the flexibility of the shaft is developed. Based on the finite element method (FEM), a coupled dynamic model of a spur gear-shaft-bearing system is established, and the time-varying mesh stiffness (TVMS), the time-varying bearing stiffness (TVBS), and the flexibility of the shaft are considered. The Newmark integration method (NIM) is utilized to obtain the dynamic load of the bearing. Furthermore, the proposed model is validated by experiments. The bearing load is then considered to be the excitation of the housing, and the radiated noise is calculated via the finite element method/boundary element method (FEM/BEM). The effects of the shaft flexibility on the bearing response and radiated noise are discussed based on the proposed method. The results demonstrate that, when the shaft flexibility is considered, the system undergoes the bending vibration of the shaft, and the vibration amplitude and excitation frequency components of the bearing load decrease significantly. Additionally, the main resonance mode of the gearbox is changed, and the radiated noise is enhanced. The effects of the input speed and shaft stiffness on the bearing response and radiated noise are also investigated. The results provide a theoretical basis for the further development of the vibration and noise reduction of gearboxes.
3-D Printed Strain Sensor for Structural Health Monitoring

Additive manufacturing, or 3D printing, is evolving from a technology that can only aid rapid prototyping, to one that can be used to directly manufacture large-scale, real-world equipment. Gravity Separation Spirals (GSS) are vital to the mining industry for separating mineral-rich slurry into its different density components. In order to overcome inherent drawbacks of the traditional mould base manufacturing methods, including significant tooling costs, limited customisation and worker exposure to hazardous materials, a 3D printer is under development to directly print spirals. By embedding small Internet of Things (IoT) sensors inside the GSS, it is possible to remotely determine the operation conditions, predict faults, and use collected data to optimise production output. This work presents a 3D printed strain sensor, which can be directly printed into the GSS. This approach uses a carbon-based conductive filament to print a strain gauge on top of a Polylactic Acid (PLA) base material. Printed sensors have been tested using an Instron E10000 testing machine with an optical extensometer to improve accuracy. Testing was conducted by both loading and unloading conditions to understand the effect of hysteresis. Test results show a near-linear relationship between strain and measured resistance, and show a 6.05% increase in resistance after the test, which indicates minor hysteresis. Moreover, the impact of viscoelastic behaviour is identified, where the resistance response lags the strain. Results from both conductive and non-conductive material show the impact of the conductive carbon upon the tensile strength, which will help to inform future decisions about sensor placement.
Fast Grid SLAM Based on Particle Filter with Scan Matching and Multithreading

This paper presents a SLAM solution based on Rao-Blackwellized particle filters supported by a fast scan matching algorithm for pose refinement and a flexible space management data structure. By taking advantage of independence between particles its computational efficiency is further improved through multithreading. We have evaluated the efficiency of the solution by using several publicly available datasets and compared the results with the popular solution GMapping. The obtained results show the proposed approach provides a fast and accurate particle filter SLAM suitable for real-time operations.
Randomized Least Frequently Used Cache Replacement Strategy for Named Data Networking

To accommodate the rapidly changing Internet requirements, Information-Centric Networking (ICN) was recently introduced as a promising architecture for the future Internet. One of the ICN primary features is ‘in-network caching’; due to its ability to minimize network traffic and respond faster to users’ requests. Therefore, various caching algorithms have been presented that aim to enhance the network performance using different measures, such as cache hit ratio and cache hit distance. Choosing a caching strategy is critical, and an adequate replacement strategy is also required to decide which content should be dropped. Thus, in this paper, we propose a content replacement scheme for ICN, called Randomized LFU that is implemented with respect to content popularity taking the time complexity into account. We use Abilene and Tree network topologies in our simulation models. The proposed replacement achieves encouraging results in terms of the cache hit ratio, inner hit, and hit distance and it outperforms FIFO, LRU, and Random replacement strategies.
Effects of Clove Oil, Tricaine, and CO2 on Gamete Quality in Steelhead and White Sturgeon

Abstract This study examined the direct impact of three commonly used anesthetics—clove oil (86% eugenol), tricaine (tricaine methanesulfonate [MS-222]), and CO2—on egg quality for steelhead Oncorhynchus mykiss and that of clove oil and tricaine on the motility of sperm from both steelhead and white sturgeon Acipenser transmontanus. Exposure of unfertilized eggs for 6 or 24 h to 150 mg clove oil/L, 225 mg tricaine/L, or less than 100 mm Hg CO2 resulted in no reduction in egg fertility or embryo survival to hatch. However, higher doses (1,500 mg clove oil/L; 2,250 and 22,500 mg tricaine/L; or more than 125 mm Hg CO2) negatively impacted egg quality. Similarly, a 3-h exposure to clove oil (150 mg/L) or tricaine (225 mg/L) produced no significant effect on sperm motility in steelhead. White sturgeon sperm showed a modest decline in motility after incubation in clove oil. Thus, the data suggest that clove oil and tricaine can be used on fish prior to gamete harvesting without significant adverse impacts on ga.
An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Motor imagery (MI) is a typical BCI paradigm and has been widely applied into many aspects (e.g. brain-driven wheelchair and motor function rehabilitation training). Although significant achievements have been achieved, multiple motor imagery decoding is still unsatisfactory. To deal with this challenging issue, firstly, a segment of electroencephalogram was extracted and preprocessed. Secondly, we applied a filter bank common spatial pattern (FBCSP) with one-vs-rest (OVR) strategy to extract the spatio-temporal-frequency features of multiple MI. Thirdly, the F-score was employed to optimise and select these features. Finally, the optimized features were fed to the spiking neural networks (SNN) for classification. Evaluation was conducted on two public multiple MI datasets (Dataset IIIa of the BCI competition III and Dataset IIa of the BCI competition IV). Experimental results showed that the average accuracy of the proposed framework reached up to 90.09% (kappa: 0.868) and 81.33% (kappa: 0.751) on the two public datasets, respectively. The achieved performance (accuracy and kappa) was comparable to the best one of the compared methods. This study demonstrated that the proposed method can be used as an alternative approach for multiple MI decoding and it provided a potential solution for online multiple MI detection.
Multi-Time Scale Regional Autonomous Operation Strategy for Multi-Microgrids With Three-Phase/Single-Phase Hybrid Structure

In the case of islanding from the distribution network, problems such as source-load power loss under multi-zone island isolation will challenge the autonomous operation of multi-microgrids with three-phase/single-phase hybrid structure. In order to make full use of the resources in the multi-microgrids to realize the reliable and efficient operation of the system as a whole, this paper proposes a multi-time scale regional autonomous control strategy for multi-microgrids with three-phase/single-phase hybrid structure. The dynamic update islanding combination is controlled by the hourly dynamic island combination and the simultaneous search switching to achieve the overall resource allocation optimization. The source-storage-load power and state redistribution are dynamically realized through minute-level centralized coordination and optimization, so that the constraints of the three-phase unbalance are met and the source-load shedding are minimized. The power fluctuations of the sources and loads within each sub-microgrid are suppressed by using the second-level off-grid real-time power control. Moreover, the tie-line power can be maintained as the original given value and the three-phase unbalance degree index can be achieved. The multi-microgrids model with three-phase/single-phase hybrid structure based on DIgSILENT platform is used to verify the correctness and effectiveness of the proposed strategy.
Biotechnology and Crop Improvement

Plant biotechnology, a major component of agricultural biotechnology, deals with various aspects of plant tissue culture, genetic transformation, and molecular biology techniques. Tissue culture methods offer a rich scope for creation, conservation, and utilization of genetic variability for the improvement of field, fruit, vegetable, and forest crops, and medicinal/aromatic plants. Micropropagation technology ensures true to type, rapid and mass multiplication of plants that possesses special significance in vegetatively propagated plant species. This technology has witnessed a huge expansion globally, with an estimated global market of 15 billion US$/annum for tissue-culture products. Some basic techniques of tissue culture, such as anther/microspore culture, somaclonal variation, embryo culture, and somatic hybridization, are being exploited to generate useful genetic variability for obtaining incremental improvement in commercial cultivars. Production of secondary metabolites, such as food flavors, food colors, dyes, perfumes, drugs, and scented oils used in aromatherapy, through cell cultures and hairy root cultures, are leading examples of molecular farming. Cryopreservation of germplasm at the cell/tissue/organ levels, in liquid nitrogen at −196°C, is highly rewarding for establishing germplasm banks, especially for vegetatively propagated crops and rare, endangered plant species. During the past 15 years, remarkable achievements have been made in the production, characterization, field evaluation, and release of transgenic varieties/hybrids in several crops. Transgenic varieties/hybrids of maize, cotton, soybean, potato, tomato, and papaya are now being commercially grown on about 134 million hectares spread across 25 countries. Research in genomics allows high-resolution genetic analysis for physical mapping and positional gene cloning of useful genes for crop improvement. Molecular (DNA) markers help in precise characterization of germplasm, construction of saturated linkage maps, and DNA fingerprinting of crop varieties. Molecular markers are now increasingly being used for marker-assisted gene pyramiding and alien gene introgression. Current research, involving large-scale DNA sequencing, microarrays, and robotics, is heading towards gene revolution and nanobiotechnology.
CGAN-based Synthetic Medical Image Augmentation between Retinal Fundus Images and Vessel Segmented Images

Computer-aided medical diagnosis is widely used to assist in the interpretation of medical images. Early screening of diseases has a great value for the clinical diagnosis. However, medical images are scarce and fraught with legal concerns about patient privacy. Inspired by the prevailing learning capability of Generative Adversarial Network (GAN), We propose simulated learning: it first learns a mapping from retinal fundus images to vessel segmented images and then trains a learning model to generate synthetic fundus images. In the results, we evaluated our proposed method from both quantitative and qualitative aspects. For generated segmented images, our method achieves a dice coefficient of 0.838 and an AUC of 0.985 on the DRIVE datasets. You can also see more information about our medical image algorithm results at https://github.com/qiqi7788/Bidirectional-Mapping-formedical-image. These results also verify the effectiveness of our algorithm.
Fractional-order Sliding Mode Based Variable Stiffness and Damping Impedance Control with Disturbance Observer

This paper proposes a fractional-order sliding mode based variable stiffness and damping impedance controller. A new closed-loop impedance architecture containing the negative power type stiffness and damping parameters is built, and an integral fractional-order sliding surface synthesizes constant and variable impedance parameters to construct a reduced order system, which indicates an impedance behavior. The external disturbance easily propagates to the preset impedance architecture to degenerate the stable performance of reduced order system, and it is well estimated by a fractional-order disturbance observer to reduce the adverse effect by partially eliminating the disturbance. The proposed impedance architecture owns better transient performance compared with the trivial spring-damper impedance architecture, which is well illustrated by the verified numerical results.
The Impact of Constructors on the Validity of Class Cohesion Metrics

Class cohesion is a measure of the degree to which a class’s inner elements, like methods and attributes, are bound or related to one another. There have been over thirty different formulas proposed in order to calculate the metric. None of them are explicitly designed to deal with constructors in any different way than with regular methods—they simply treat them as identical entities. However, as many object-oriented theorists say, constructors play a very specific role in object life-cycle. In the scope of this empirical research, five different formulas were implemented in two ways: including constructors and excluding them. Then both set of formulas were applied to the same set of 1000 mid-size open source Java projects. The results obtained demonstrated how much of a distraction constructors were bringing into metric calculations.
Multi-Level Attentional Network for Aspect-based Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) has attracted much attention due to its capacity to determine the sentiment polarity of the certain aspect in a sentence. In previous works, great significance of the interaction between aspect and sentence has been exhibited in ABSA. In consequence, a Multi-Level Attentional Networks (MLAN) is proposed. MLAN consists of four parts: Embedding Layer, Encoding Layer, Multi-Level Attentional (MLA) Layers and Final Prediction Layer. Among these parts, MLA Layers including Aspect Level Attentional (ALA) Layer and Interactive Attentional (ILA) Layer is the innovation of MLAN, and its function is to focus on the important information and obtain multiple levels’ attentional weighted representation of aspect and sentence, which makes MLAN superior than the most of deep learning models for ABSA. In the experiments, MLAN is compared with classical TD-LSTM, MemNet, RAM, ATAE-LSTM, IAN, AOA, LCR-Rot and AEN-Globe on SemEval 2014 Dataset. The experimental results show that MLAN outperforms those state-of-the-art models greatly. And in case study, the works of ALA Layer and ILA Layer have been proven to be effective and interpretable.
Survey on Scenario-Based Safety Assessment of Automated Vehicles

When will automated vehicles come onto the market? This question has puzzled the automotive industry and society for years. The technology and its implementation have made rapid progress over the last decade, but the challenge of how to prove the safety of these systems has not yet been solved. Since a market launch without proof of safety would neither be accepted by society nor by legislators, much time and many resources have been invested into safety assessment in recent years in order to develop new approaches for an efficient assessment. This paper therefore provides an overview of various approaches, and gives a comprehensive survey of the so-called scenario-based approach. The scenario-based approach is a promising method, in which individual traffic situations are typically tested by means of virtual simulation. Since an infinite number of different scenarios can theoretically occur in real-world traffic, even the scenario-based approach leaves the question unanswered as to how to break these down into a finite set of scenarios, and find those which are representative in order to render testing more manageable. This paper provides a comprehensive literature review of related safety-assessment publications that deal precisely with this question. Therefore, this paper develops a novel taxonomy for the scenario-based approach, and classifies all literature sources. Based on this, the existing methods will be compared with each other and, as one conclusion, the alternative concept of formal verification will be combined with the scenario-based approach. Finally, future research priorities are derived.
How ‘micro’ are your services?

Microservice is an architectural style that proposes that a complex system should be developed from small and independent services that work together. There is not a welldefined boundary about when a software architecture can be considered based on microservices or not. Because of that, defining microservices context and infrastructure is challenging, especially to characterize aspects related to microservice size, data consistency, and microservices coupling. Thus, it is crucial to understand the microservices-based software characteristics, to comprehend the impact of some evolutions on architecture, and evaluate how much a particular architecture fits the microservices architectural style. Therefore, based on bibliographic research and case studies conducted in academical and industrial environments, we aim to propose a model to characterize the architecture structure based on the main guidelines of the microservice architectural style. This model introduces dimensions that measure characteristics based on modules size, coupling to data sources, and service collaboration. This study should facilitate the mapping, measurement, and monitoring of different impacts generated in the software architecture from increments and refactoring performed. This work is on the initial development stage and as a result, we expected that the model supports architectural decisions that consider different quality attributes to achieve the right balance between service independence and collaboration for a given system.
Using the Rgb Image of Machine Code to Classify the Malware

Aiming at the problem of high complexity caused by the excessive dependence of feature set on expert experience and high feature dimension in current malware classification methods, this paper proposes a classification method that is based on rgb image of assembly instructions and Convolutional Neural Network (CNN). This paper firstly gets the assembly code file of the executable malware sample, then generates rgb pixels by analyzing machine codes for each kind of assembly instructions (such as mov and add), then generates rgb images through these rgb pixels. Finally, the classification model is trained on the training sample set by using CNN. We have carried out a series of experiments, and through these experiments, it is proved that our method can be got a good result in malware classification.