Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming

Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence of antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs) and spread to humans. Here, we performed a longitudinal study in a large-scale commercial poultry farm in China, collecting E. coli isolates from both farm and slaughterhouse; targeting animals, carcasses, workers and their households and environment. By using whole-genome phylogenetic analysis and network analysis based on single nucleotide polymorphisms (SNPs), we found highly interrelated non-pathogenic and pathogenic E. coli strains with phylogenetic intermixing, and a high prevalence of shared multidrug resistance profiles amongst livestock, human and environment. Through an original data processing pipeline which bcombines omics, machine learning, gene sharing network and mobile genetic elements analysis, we investigated the resistance to 26 different antimicrobials and identified 361 genes associated to antimicrobial resistance (AMR) phenotypes; 58 of these were known AMR-associated genes and 35 were associated to multidrug resistance. We uncovered an extensive network of genes, correlated to AMR phenotypes, shared among livestock, humans, farm and slaughterhouse environments. We also found several human, livestock and environmental isolates sharing closely related mobile genetic elements carrying ARGs across host species and environments. In a scenario where no consensus exists on how antibiotic use in the livestock may affect antibiotic resistance in the human population, our findings provide novel insights into the broader epidemiology of antimicrobial resistance in livestock farming. Moreover, our original data analysis method has the potential to uncover AMR transmission pathways when applied to the study of other pathogens active in other anthropogenic environments characterised by complex interconnections between host species

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock

A debate is currently ongoing as to whether intensive livestock farms may constitute reservoirs of clinically relevant antimicrobial resistance (AMR), thus posing a threat to surrounding communities. Here, combining shotgun metagenome sequencing, machine learning (ML), and culture-based methods, we focused on a poultry farm and connected slaughterhouse in China, investigating the gut microbiome of livestock, workers and their households, and microbial communities in carcasses and soil. For both the microbiome and resistomes in this study, differences are observed across environments and hosts. However, at a finer scale, several similar clinically relevant antimicrobial resistance genes (ARGs) and similar associated mobile genetic elements were found in both human and broiler chicken samples. Next, we focused on Escherichia coli, an important indicator for the surveillance of AMR on the farm. Strains of E. coli were found intermixed between humans and chickens. We observed that several ARGs present in the chicken faecal resistome showed correlation to resistance/susceptibility profiles of E. coli isolates cultured from the same samples. Finally, by using environmental sensing these ARGs were found to be correlated to variations in environmental temperature and humidity. Our results show the importance of adopting a multi-domain and multi-scale approach when studying microbial communities and AMR in complex, interconnected environments

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

Affective computing is an increasingly important outgrowth of Artificial Intelligence, which is intended to deal with rich and subjective human communication. In view of the complexity of affective expression, discriminative feature extraction and corresponding high-performance classifier selection are still a big challenge. Specific features/classifiers display different performance in different datasets. There has currently been no consensus in the literature that any expression feature or classifier is always good in all cases. Although the recently updated deep learning algorithm, which uses learning deep feature instead of manual construction, appears in the expression recognition research, the limitation of training samples is still an obstacle of practical application. In this paper, we aim to find an effective solution based on a fusion and association learning strategy with typical manual features and classifiers. Taking these typical features and classifiers in facial expression area as a basis, we fully analyse their fusion performance. Meanwhile, to emphasize the major attributions of affective computing, we select facial expression relative Action Units (AUs) as basic components. In addition, we employ association rules to mine the relationships between AUs and facial expressions. Based on a comprehensive analysis from different perspectives, we propose a novel facial expression recognition approach that uses multiple features and multiple classifiers embedded into a stacking framework based on AUs. Extensive experiments on two public datasets show that our proposed multi-layer fusion system based on optimal AUs weighting has gained dramatic improvements on facial expression recognition in comparison to an individual feature/classifier and some state-of-the-art methods, including the recent deep learning based expression recognition one

A Kind of Visual Speech Feature with the Geometric and Local Inner Texture Description

In this paper, we propose a type of joint feature with geometric parameters and color moments to represent the speaking-mouth frames for image-based visual speech synthesis systems. Based on FDP around the mouth area, the geometric feature is obtained by computing Euclidean distances to describe the width of the speaking mouth, the height of the outer and inner lips and the distances between them. The color moment component in the joint feature is obtained by calculating the texture between the upper and lower inner lips to describe the visibility state of the teeth. Through analyzing the accordance between the teeth visibility and the components of RGB and HSV color space based on the samples separately, we discovered that green and blue components are good at describing the change of teeth visibility. The experiments show that the proposed joint feature can effectively provide the basis for categorizing the different speaking states especially at the sense of lip shapes and tooth visibility. The evaluation of clustering results is done by analyzing the derived parameters of the silhouette function.  The analyzing results prove that comparing with the geometric only and PCA, our proposed feature together with the shape and the local inner lip texture clues has better performance in improving the similarity between samples within the clusters. In the future, more expressive features with the shape and local texture information should be explored to increase the proportion of similar samples within the clusters to improve the descriptive ability of speaking mouths. DOI: http://dx.doi.org/10.11591/telkomnika.v11i2.204

An Integrated Model to Characterize Comprehensive Stiffness of Angular Contact Ball Bearings

The bearing dynamic behaviors will be complicated due to the changes in the geometric sizes and relative positions of the bearing components at high speed. In this paper, based on the Hertz contact theory, elastohydrodynamic lubrication (EHL) model, and Jones’ bearing theory, the comprehensive stiffness model of the angular contact ball bearing is proposed in consideration of the effects of elastic deformation, centrifugal deformation, thermal deformation, and the ball spinning motion. The influences of these factors on bearing dynamic stiffness are investigated in detail. The calculation results show that the centrifugal deformation and thermal deformation increase with the increase in rotation speed. When the centrifugal deformation and thermal deformation are considered, the bearing radial contact stiffness increases as the speed increases, whereas the axial contact stiffness and the angular contact stiffness decrease. When the deformations and the EHL are all considered, the comprehensive bearing stiffness decreases with the increasing speed. It is also found that the spinning motion of the ball causes the comprehensive bearing stiffness to increase

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

Affective computing is an increasingly important outgrowth of Artificial Intelligence, which is intended to deal with rich and subjective human communication. In view of the complexity of affective expression, discriminative feature extraction and corresponding high-performance classifier selection are still a big challenge. Specific features/classifiers display different performance in different datasets. There has currently been no consensus in the literature that any expression feature or classifier is always good in all cases. Although the recently updated deep learning algorithm, which uses learning deep feature instead of manual construction, appears in the expression recognition research, the limitation of training samples is still an obstacle of practical application. In this paper, we aim to find an effective solution based on a fusion and association learning strategy with typical manual features and classifiers. Taking these typical features and classifiers in facial expression area as a basis, we fully analyse their fusion performance. Meanwhile, to emphasize the major attributions of affective computing, we select facial expression relative Action Units (AUs) as basic components. In addition, we employ association rules to mine the relationships between AUs and facial expressions. Based on a comprehensive analysis from different perspectives, we propose a novel facial expression recognition approach that uses multiple features and multiple classifiers embedded into a stacking framework based on AUs. Extensive experiments on two public datasets show that our proposed multi-layer fusion system based on optimal AUs weighting has gained dramatic improvements on facial expression recognition in comparison to an individual feature/classifier and some state-of-the-art methods, including the recent deep learning based expression recognition one

Mg-Al Mixed Oxide Adsorbent Synthesized Using FCT Template for Fluoride Removal from Drinking Water

To make full use of natural waste, a novel Mg-Al mixed oxide adsorbent was synthesized by the dip-calcination method using the fluff of the chinar tree (FCT) and an Mg(II) and Al(III) chloride solution as raw materials. The adsorbents were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS). The effects of the Mg/Al molar ratio and calcination temperature on the performance of the novel Mg-Al mixed oxide adsorbent were investigated. The optimized Mg-Al mixed oxide adsorbent had a Langmuir adsorption capacity of 53 mg/g. This adsorption capacity was higher than that of the separate Mg oxide and Al oxide. The synergy between Mg and Al is beneficial to the adsorption performance of the material. The fluoride adsorption capacity of the optimized Mg-Al mixed oxide adsorbent is only slightly affected by ions such as Cl−, NO3−, SO42−, Na+, and K+ and is excellent for use in recycling and real water. The hydroxyl groups on the surface of the Mg-Al mixed oxide adsorbent play a key role in the adsorption of fluorine. The as-obtained novel Mg-Al mixed oxide adsorbent is an efficient and environmentally friendly agent for fluoride removal from drinking water