Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic Screening of Antimicrobial Resistance Markers in UK and US Campylobacter Isolates Highlights Stability of Resistance over an 18-Year Period

Campylobacter jejuni and Campylobacter coli are important bacterial causes of human foodborne illness. Despite several years of reduced antibiotics usage in livestock production in the United Kingdom (UK) and United States (US), a high prevalence of antimicrobial resistance (AMR) persists in Campylobacter. ABSTRACT Campylobacter jejuni and Campylobacter coli are important bacterial causes of human foodborne illness. Despite several years of reduced antibiotics usage in livestock production in the United Kingdom (UK) and United States (US), a high prevalence of antimicrobial resistance (AMR) persists in Campylobacter. Both countries have instigated genome sequencing-based surveillance programs for Campylobacter, and in this study, we have identified AMR genes in 32,256 C. jejuni and 8,776 C. coli publicly available genome sequences to compare the prevalence and trends of AMR in Campylobacter isolated in the UK and US between 2001 and 2018. AMR markers were detected in 68% of C. coli and 53% of C. jejuni isolates, with 15% of C. coli isolates being multidrug resistant (MDR), compared to only 2% of C. jejuni isolates. The prevalence of aminoglycoside, macrolide, quinolone, and tetracycline resistance remained fairly stable from 2001 to 2018 in both C. jejuni and C. coli, but statistically significant differences were observed between the UK and US. There was a statistically significant higher prevalence of aminoglycoside and tetracycline resistance for US C. coli and C. jejuni isolates and macrolide resistance for US C. coli isolates. In contrast, UK C. coli and C. jejuni isolates showed a significantly higher prevalence of quinolone resistance. Specific multilocus sequence type (MLST) clonal complexes (e.g., ST-353/464) showed >95% quinolone resistance. This large-scale comparison of AMR prevalence has shown that the prevalence of AMR remains stable for Campylobacter in the UK and the US. This suggests that antimicrobial stewardship and restricted antibiotic usage may help contain further expansion of AMR prevalence in Campylobacter but are unlikely to reduce it in the short term.
Characterization of the Flagellar Collar Reveals Structural Plasticity Essential for Spirochete Motility

Many spirochetes cause serious human diseases. They are well recognized by their distinct morphology and motility. ABSTRACT Spirochetes are a remarkable group of bacteria with distinct morphology and periplasmic flagella that enable motility in viscous environments, such as host connective tissues. The collar, a spirochete-specific complex of the periplasmic flagellum, is required for this unique spirochete motility, yet it has not been clear how the collar assembles and enables spirochetes to transit between complex host environments. Here, we characterize the collar complex in the Lyme disease spirochete Borrelia burgdorferi. We discover as well as delineate the distinct functions of two novel collar proteins, FlcB and FlcC, by combining subtractive bioinformatic, genetic, and cryo-electron tomography approaches. Our high-resolution in situ structures reveal that the multiprotein collar has a remarkable structural plasticity essential not only for assembly of flagellar motors in the highly curved membrane of spirochetes but also for generation of the high torque necessary for spirochete motility. IMPORTANCE Many spirochetes cause serious human diseases. They are well recognized by their distinct morphology and motility. Spirochete motility is driven by a periplasmic flagellum, which possesses a unique collar essential for flagellar assembly and spirochete motility. Here, we discover two novel collar proteins in the Lyme disease spirochete Borrelia burgdorferi. We demonstrate, for the first time, that the collar is a multiprotein complex with a remarkable plasticity that enables the motor to accommodate the highly curved membrane of spirochetes and generate the high torque necessary for spirochete motility.
Plasma Contains Ultra-short Single-stranded DNA in Addition to Nucleosomal cfDNA

Plasma cell-free DNA is a widely used biomarker for diagnostic screening. We introduce uscfDNA-seq, a single-stranded cell-free DNA NGS pipeline, which bypasses previous limitations to reveal a novel population of ultrashort single-stranded cell-free DNA in plasma with a modal size of 50nt. This species of cfDNA aligns predominantly to the nuclear genome and could potentially be used for novel biomarker discovery.
A modular platform for on-demand vaccine self-assembly enabled by decoration of bacterial outer membrane vesicles with biotinylated antigens

Engineered outer membrane vesicles (OMVs) derived from laboratory strains of bacteria are a promising technology for the creation of non-infectious, nanoparticle vaccines against diverse pathogens. As mimics of the bacterial cell surface, OMVs offer a molecularly-defined architecture for programming repetitive, high-density display of heterologous antigens in conformations that elicit strong B and T cell immune responses. However, antigen display on the surface of OMVs can be difficult to control and highly variable due to bottlenecks in protein expression and localization to the outer membrane of the host cell, especially for bulky and/or complex antigens. To address this shortcoming, we created a universal approach called AddVax (avidin-based dock- and-display for vaccine antigen cross (x)-linking) whereby virtually any antigen that is amenable to biotinylation can be linked to the exterior of OMVs whose surfaces are remodeled with multiple copies of a synthetic antigen receptor (SNARE) comprised of an outer membrane scaffold protein fused to a member of the avidin family. We show that SNARE-OMVs can be readily decorated with a molecularly diverse array of biotinylated subunit antigens, including globular and membrane proteins, glycans and glycoconjugates, haptens, lipids, and short peptides. When the resulting OMV formulations were injected in wild-type BALB/c mice, strong antigen-specific antibody responses were observed that depended on the physical coupling between the antigen and SNARE-OMV delivery vehicle. Overall, these results demonstrate AddVax as a modular platform for rapid self-assembly of antigen-studded OMVs with the potential to accelerate vaccine generation, respond rapidly to pathogen threats in humans and animals, and simplify vaccine stockpiling.
Visualization of seasonal phosphorus re-translocation, and expression profile of phosphate transporters in a shortened annual cycle system of the deciduous poplar tree

Phosphorus (P) is an essential macronutrient for plant growth. In deciduous trees, P is remobilized from senescing leaves and stored in perennial tissues during winter for further growth. Annual internal recycling and accumulation of P is considered an important strategy to support vigorous growth of trees. However, the pathways of seasonal re-translocation of P and the molecular mechanisms of this transport have not been clarified. Here we show the seasonal P re-translocation route visualized using the real-time radioisotope imaging and the macro- and micro-autoradiography. We analyzed the seasonal re-translocation P in poplar (Populus alba. L) cultivated under “a shortened annual cycle system”, which mimicked seasonal phenology in a laboratory. From growing to senescing season, sink tissues of 32P and/or 33P shifted from young leaves and the apex to the lower stem and roots. The radioisotope P re-translocated from a leaf was stored in phloem and xylem parenchyma cells and redistributed to new shoots after dormancy. Seasonal expression profile of phosphate transporters (PHT1, PHT5 and PHO1 family) was obtained in the same system. Our results reveal the seasonal P re-translocation routes at the organ and tissue levels and provide a foothold for elucidating its molecular mechanisms.
Rod–sphere cluster irradiation with femtosecond laser pulses: cut and paste at the nanoscale

Abstract We report on the irradiation of gold rod–sphere assemblies with ultrashort laser pulses, producing structures that are very difficult to obtain by other methods. The optical response of these assemblies displays several peaks arising from the interaction of the plasmon modes of the individual particles, offering thus great flexibility to control the energy deposited on the individual particles. Judicious selection of the wavelength and fluence of the laser pulses allow fine control over the changes produced: the particles can be melted, welded and/or the organic links cleaved. In this way, it is possible to generate structures “à la carte” with a degree of control unmatched by other synthetic protocols. The method is exemplified with gold nanoparticles, but it can be easily implemented on particles composed of different metals, widening considerably the range of possibilities. The final structures are excellent candidates for surface-enhanced spectroscopies or plasmonic photothermal therapy as they have a very intense electric field located outside the structure, not in the gaps.
Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam

Abstract This study examines rainfall forecasting for the Perfume (Huong) River basin using the machine learning method. To be precise, statistical measurement indicators are deployed to evaluate the reliability of the actual accumulated data. At the same time, this study applied and compared two popular models of multi-layer perceptron and the k-nearest neighbors (k-NN) with different configurations. The calculated rainfall data are obtained from the Hue, Aluoi, and Namdong hydrological stations, where the rainfall demonstrated a giant impact on the downstream from 1980 to 2018. This study result shows that both models, once fine-tuned properly, enjoyed the performance with standard metrics of R_squared, mean absolute error, Nash–Sutcliffe efficiency, and root-mean-square error. In particular, once Adam stochastic is deployed, the implementation of the MLP model is significantly improving. The promising forecast results encourage us to consider applying these models with future data to help natural disaster non-stop mitigation in the Perfume River basin.
Social Media Debate Position 3: Social Media as a Credible Source of Information

Abstract This article examines the ways in which social media has grown into a commonly used source of information and news. It considers the ways social media can be both an asset and a hindrance to the spread of true, verifiable information. The article delves into the many ways that social media is utilized by society, from situations in which it is used to spread useful information during public emergencies to the unfortunate reality of the spread of misinformation. Social media can be a place for people to come together and share true and accurate information, as well as a place where misinformation is spread. Society must be made aware of the risks that an over reliance on social media poses, especially in terms of how it can spread misinformation and cause division in the world.
A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

The vehicle detection and classification (VDC) problem has received much attention recently due to the increased security threats and the need to develop intelligent transportation systems. A large number of approaches have been proposed for the VDC problem using neural networks. To determine how neural networks‐based approaches have developed for the VDC in recent years, this paper surveys the VDC approaches through a literature review with the range Jan. 2012 through Apr. 2021. To do this, we introduce a new comparison framework to classify and compare the VDC approaches. Our proposed framework is composed of nine comparison dimensions: input data type, vehicle type, scale, scope, dynamicity, vehicle detection method, vehicle classification method, application, and evaluation method. Next, using the proposed framework, we discuss the evolution of the VDC approaches and identify several open issues that have emerged in the field. This paper provides a guide for researchers to use or design robust VDC systems with proper characteristics based on their needs.
Toward vehicular cloud/fog communication: A survey on data dissemination in vehicular ad hoc networks using vehicular cloud/fog computing

The digitization of objects has given birth to the concept of the Internet of Things (IoT) which is revolutionizing traditional objects by replacing them with intelligent objects such as smart vehicles, smartphones, smart home…etc. In this context, and with the emergence of vehicle networks, is born the need to increase the vehicular resources in order to benefit from several applications facilitating driving and ensuring the drivers safety, even going to think of the automated driving. The use of cloud computing has become the key solution to the lack of resources required to run compute‐intensive applications and the lack of storage space to back up all data related to roads and applications. In addition, we are witnessing the birth of the fog computing paradigm, which brings the functionalities of cloud computing at the edge of the network, thereby solving the latency problem for some time‐sensitive applications and also saving the bandwidth of the network because vehicle requests will not need to cross the entire network to be processed at the cloud level. In this paper, we discuss the different principles of cloud/fog computing and compare the two paradigms. We present a classification of data dissemination schemes in vehicular ad hoc networks (VANETs), vehicular cloud, and vehicular fog computing with their different architectures proposed. We finally present several cloud/fog computing applications.
Performance evaluation on the node mobility with respect to human driver behavior prediction in vehicular ad hoc network using adaptive deer hunting Optimized Link State Routing Protocol

In vehicular ad hoc network (VANET) broadcasting is considered as the critical area of research. The vehicles are connecting in an ad hoc manner to create a network of a wider range. In an intelligent transport system (ITS), with vehicle to vehicle (V2V) communication in the VANET is used to support the network backbone and which the accident prevention technique has been deployed for ensuring the road safety. The major reason for the road accident is the abnormal driving behavior of human drivers. The movement pattern of the vehicle is the main factor of the network topology so that the impact of human driving pattern influences the performance and behavior of the network. Driving behavior can be classified as reckless, normal, drunken, and fatigue driving. During driving, the behavior of human drivers was predicted in this paper, thereby providing the performance analysis on the mobility of the network. For the improvement of a driver behavior, the hybridized mega‐trend diffusion (MTD) with optimized deep belief network–sunflower optimization (DBN‐SFO) is used. Here, the network performance of the adaptive deer hunting Optimized Link State Routing Protocol (ADHOLSR) is analyzed in NS2 simulation platform.
A detailed tutorial survey on VANETs: Emerging architectures, applications, security issues, and solutions

Vehicular ad‐hoc network (VANET) is an emerging field of mobile ad‐hoc network (MANET) which facilitates intelligent transport system (ITS). It thus helps in enhancing the real‐time driving experience and road safety. Due to association of VANET with life‐critical applications, there is a dire need to understand each and every aspect of it in terms of architecture, security, applications, routing, and so forth, before bringing it into implementation. Although there exist numerous studies on VANETs, they tend to cover only individual aspects of it. This first of its kind survey aims to provide a combined and thorough study of each aspect of VANET along with their advantages and limitations in a single solid document. Different architectures proposed till date have been explored along with their shortcomings and the possible applications which are supported in VANETs. Comparison of various wireless technologies used in VANET based on the range requirement of different VANET applications and various routing protocols that are best suited for VANET on the basis of the application requirement is done. It also discusses the various security attacks possible in VANET, the risks associated with them, and the possible security solutions available for these attacks. Various optimization and bio‐inspired techniques as well as intrusion detection systems for enhancing security in VANET are also analyzed. Finally, various simulators have been investigated, and comprehensive analysis has been done with respect to the feasibility of implementation of VANETs in these simulators. This research is believed to assist the researchers in understanding the current state of research in VANETs.
An improved brain storm optimisation algorithm for energy-efficient train operation problem

This paper presents a new method to determine the optimal driving strategies of the train using an improved brain storm optimisation (IBSO) algorithm. In the proposed method, the idea of successful-parent-selecting frame is applied to improve the original brain storm optimisation (BSO) algorithm avoiding premature convergence in evolutionary process while dealing with complex problems. The objective of the algorithm is to minimise energy consumption of the train by finding the switching points. Furthermore, the speed limits, gradients, maximum acceleration and deceleration as well as the maximum traction and braking force varying with speed are taken into consideration to meet practical constraints. Finally the comparison simulations among four algorithms show that the energy-efficient train operation strategy obtained by IBSO algorithm are more superior under the same conditions.
Sensitive pH probe developed with water-soluble fluorescent carbon dots from chocolate by one-step hydrothermal method

ABSTRACT In present work, fluorescent carbon dots (CDs) with an average diameter of 2.5 nm were firstly synthesised by a simple, convenient and low-cost hydrothermal method from chocolate. The obtained CDs possessed fine monodispersity and bright blue fluorescence that was strongest at an excitation wavelength of 360 nm and had a comparable quantum yield of 12% (in case of dots prepared in presence of nitric acid). The emission peak depended on the excitation wavelength in the range from 320 to 440 nm. Ionic strength had a weak effect up to 1.0 M and then no significant change was found in the range of 1.0–4.5 M. The fluorescence intensity of CDs displayed good pH adaptability and a linear dependence on the pH change in the range of pH 1.0–3.0 and pH 9.0–12.5, which have promising potentials for pH sensor.
Hydrogen Adsorption on Mg-DFT Zeolite Cluster: A Density Functional Theory Study

Hydrogen molecule adsorption has been investigated on additional framework of Mg atom in DFT type zeolite by way of Density Functional Theory. The electronegativity, HOMO and LUMO energies chemical hardness, chemical potential, adsorption enthalpy and adsorption energy values have been calculated on a 16T zeolite cluster model and compared with those of Mg exchanged ERI and LTL zeolite structures. Hydrogen adsorption enthalpy value has been computed as -26.2 kJ/ mol. This enthalpy value is meaningfully higher than the hydrogen molecule’s liquefaction enthalpy value. This accordingly specifies that Mg-DFT zeolite structure appears to be an encouraging candidate cryoadsorbent for hydrogen storage.
A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN

In this paper, aiming at the pressure fluctuation problem of high-pressure common rail system, based on the convolution neural network of WaveNet structure, a pressure fluctuation prediction algorithm is proposed. Compared with other pressure fluctuation algorithms, deep learning is applied in this algorithm. The effectiveness of the proposed algorithm is approved in the experiment in different target rated injection pressure, different injection interval and other working conditions. The results of the experiment show that the average accuracy of the prediction algorithm is about 99.1%, and meets the demand. The accuracy of the prediction the pressure fluctuation from the injection interval to the starting point of the main injection is higher, and the accuracy of the prediction of the pressure fluctuation and its attenuation process slightly decreases after the main injection duration.
An accurate automated technique for quasi-optics measurement of the microwave diagnostics for fusion plasma

A new integrated technique for fast and accurate measurement of the quasi-optics, especially for the microwave/millimeter wave diagnostic systems of fusion plasma, has been developed. Using the LabVIEW-based comprehensive scanning system, we can realize not only automatic but also fast and accurate measurement, which will help to eliminate the effects of temperature drift and standing wave/multi-reflection. With the Matlab-based asymmetric two-dimensional Gaussian fitting method, all the desired parameters of the microwave beam can be obtained. This technique can be used in the design and testing of microwave diagnostic systems such as reflectometers and the electron cyclotron emission imaging diagnostic systems of the Experimental Advanced Superconducting Tokamak.
Performance Comparison of Deep CNN Models for Disease Diagnosis on Apple Leaves

The apple is one of the most cultivated fruits in the world. They are round in shape and their color varies from green to red. Apple Orchards face constant threats from a large number of insects and pathogens and the early detection of these diseases can help in mitigating these harmful effects. An apple tree takes around six to ten years to mature and produce fruit and therefore, the production costs are high and there is no room for such diseases to get a healthy fruit and a profitable yield. Delayed or incorrect diagnosis of these diseases can lead to using either inadequate or more than required chemicals or using a wrong chemical altogether to treat the plant. Historically, this problem was solved using conventional machine learning algorithms like SVMs, Decision Trees and Random Forests. However, in recent times, the approach to solve this problem has shifted to deep learning, specifically Convolutional Neural Networks. CNN’s are powerful tools that can be used for image classification. We can get state-ofthe-art results even by using small amounts of data and little to no data preprocessing. In this work, we are going to compare some of the state of the art CNN architectures on the task of accurately classifying a given image into different categories of diseases or as a healthy leaf. Finally, experimental results are conveyed and performance analysis of these various architectures has been done.