ABSTRACT
For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. However, the 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other nearly-universal prokaryotic marker genes as the number of gene sequences increases in reference databases. This trend was strongly correlated with how represented a taxonomic group was in the database and indicates that, at the gene-level, the boundaries between many species might be fuzzy. Through our study, we argue that any approach that relies on a single marker to distinguish bacterial taxa is fraught even if some markers appear to be discriminative in current databases.
Subject(s)
Bacteria , Databases, Genetic , RNA, Ribosomal, 16S , RNA, Ribosomal, 16S/genetics , Bacteria/genetics , Bacteria/classification , Genetic Markers/genetics , Phylogeny , Computational Biology/methodsABSTRACT
BACKGROUND: Oxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates. Polishing tools have been developed to correct these errors, but it is uncertain if they obtain the accuracy needed for the high-resolution source tracking of foodborne illness outbreaks. RESULTS: We tested 132 combinations of assembly and short- and long-read polishing tools to assess their accuracy for reconstructing the genome sequences of 15 highly similar Salmonella enterica serovar Newport isolates from a 2020 onion outbreak. While long-read polishing alone improved accuracy, near perfect accuracy (99.9999% accuracy or ~ 5 nucleotide errors across the 4.8 Mbp genome, excluding low confidence regions) was only obtained by pipelines that combined both long- and short-read polishing tools. Notably, medaka was a more accurate and efficient long-read polisher than Racon. Among short-read polishers, NextPolish showed the highest accuracy, but Pilon, Polypolish, and POLCA performed similarly. Among the 5 best performing pipelines, polishing with medaka followed by NextPolish was the most common combination. Importantly, the order of polishing tools mattered i.e., using less accurate tools after more accurate ones introduced errors. Indels in homopolymers and repetitive regions, where the short reads could not be uniquely mapped, remained the most challenging errors to correct. CONCLUSIONS: Short reads are still needed to correct errors in nanopore sequenced assemblies to obtain the accuracy required for source tracking investigations. Our granular assessment of the performance of the polishing pipelines allowed us to suggest best practices for tool users and areas for improvement for tool developers.
Subject(s)
Benchmarking , Disease Outbreaks , Genome, Bacterial , Nanopores , Nanopore Sequencing/methods , High-Throughput Nucleotide Sequencing/methods , Salmonella enterica/genetics , Salmonella enterica/isolation & purification , Humans , PhylogenyABSTRACT
Metagenomic studies have primarily relied on de novo assembly for reconstructing genes and genomes from microbial mixtures. While reference-guided approaches have been employed in the assembly of single organisms, they have not been used in a metagenomic context. Here we describe the first effective approach for reference-guided metagenomic assembly that can complement and improve upon de novo metagenomic assembly methods for certain organisms. Such approaches will be increasingly useful as more genomes are sequenced and made publicly available.
ABSTRACT
Sound-absorbing panels are widely used in the acoustic design of aircraft parts, buildings and vehicles as well as in sound insulation and absorption in areas with heavy traffic. This paper studied the acoustic properties of sound-absorbing panels manufactured with three nozzle diameters (0.4 mm, 0.6 mm and 0.8 mm) by 3D printing from three types of polylactic acid filaments (Grey Tough PLA; Black PLA Pro; Natural PLA) and with six internal configurations with labyrinthine zigzag channels (Z1 and Z2). The absorption coefficient of the sample with the Z2 pattern, a 5.33 mm height, a 0.6 mm nozzle diameter and with Black PLA Pro showed the maximum value (α = 0.93) for the nozzle diameter of 0.6 mm. Next in position were the three samples with the Z1 pattern (4 mm height) made from all three materials used and printed with a nozzle diameter of 0.4 mm with a sound absorption coefficient value (α = 0.91) at 500 Hz. The highest value of the sound transmission loss (56 dB) was found for the sample printed with a nozzle size of 0.8 mm with the Z2 pattern (8 mm height) and with Black PLA Pro. The extruded material, the nozzle diameter and the internal configuration had a significant impact on the acoustic performance of the 3D-printed samples.
ABSTRACT
This article presents the results of an analysis regarding the microstructure, mechanical strength, and microhardness of two kinds of samples built through selective laser melting with Inconel 718, the most frequently used alloy in metal additive manufacturing due to its excellent mechanical properties. The sample geometry was made up of two types of lattice structures with spherical and hyperbolical stiffness elements. The goals of these studies are to determine how homogenization heat treatment influences the microhardness and the mechanical properties of the specimens and to identify the structure with the best mechanical properties. The analysis showed that heat treatment was beneficial because the regular dendritic structure disappears, the δ phase precipitates at the grain boundaries, and both the γ and γⳠphases dissolve. It has also been shown that the structures with hyperbolical stiffness elements have better compressive strength than the structures with the elliptical structures, with a 47.6% increase for the as-fabricated structures and an approximate 50% increase for the heat-treated structure.
ABSTRACT
Sustainable building materials with excellent thermal stability and sound insulation are crucial for eco-friendly construction. This study investigates biocomposites made from cellulose pulp reinforced with beeswax, fir resin, and natural fillers like horsetail, rice flour, and fir needles. Eight formulations were obtained, and their thermal resistance, oxidation temperature, and acoustic properties were evaluated. Biocomposites exhibited significant improvements compared to conventional materials. Oxidation temperature onset increased by 60-70 °C compared to polyurethane foam or recycled textiles, reaching 280-290 °C. Sound absorption coefficients ranged from 0.15 to 0.78, with some formulations exceeding 0.5 across mid-frequencies, indicating good sound-dampening potential. These findings demonstrate the promise of these biocomposites for sustainable construction, offering a balance of thermal and acoustic performance alongside environmental and health benefits.
ABSTRACT
The Carpathian Mountains have been constantly inhabited by grey wolves and present one of the largest distribution areas in Europe, comprising between 2300 and 2700 individuals in Romania. To date, however, relatively little is known about the Romanian wolf population. We aimed to provide a first assessment of genetic diversity, population structure and wolf-dog hybridisation based on 444 mostly non-invasively collected samples in the Eastern Romanian Carpathians. Pack reconstruction and analysis of population genetic parameters were performed with mitochondrial DNA control-region sequencing and microsatellite genotyping. We found relatively high levels of genetic diversity, which is similar to values found in previous studies on Carpathian wolves from Poland and Slovakia, as well as to the long-lasting Dinaric-Balkan wolf population. We found no significant population structure in our study region, suggesting effective dispersal and admixture. Analysis of wolf-dog hybridisation using a Single Nucleotide Polymorphism panel optimised for hybrid detection revealed low rates of admixture between wolves and domestic dogs. Our results provide evidence for the existence of a genetically viable wolf population in the Romanian Carpathians. The genetic data obtained in this study may serve as valuable baseline information for the elaboration of monitoring standards and management plans for wolves in Romania.
Subject(s)
Genetics, Population , Wolves , Animals , Wolves/genetics , Romania , Europe , Genetic VariationABSTRACT
The 3D printing process allows complex structures to be obtained with low environmental impact using biodegradable materials. This work aims to develop and acoustically characterize 3D-printed panels using three types of materials, each manufactured at five infill densities (20%, 40%, 60%, 80% and 100%) with three internal configurations based on circular, triangular, and corrugated profiles. The highest absorption coefficient values (α = 0.93) were obtained from the acoustic tests for the polylactic acid material with ground birch wood particles in the triangular configuration with an infill density of 40%. The triangular profile showed the best acoustic performance for the three types of materials analysed and, from the point of view of the mechanical tests, it was highlighted that the same triangular configuration presented the highest resistance both to compression (40 MPa) and to three-point bending (50 MPa). The 40% and 60% infill density gave the highest absorption coefficient values regardless of the material analyzed. The mechanical tests for compression and three-point bending showed higher strength values for samples manufactured from simple polylactic acid filament compared to samples manufactured from ground wood particles. The standard defects of 3D printing and the failure modes of the interior configurations of the 3D-printed samples could be observed from the microscopic analysis of the panels. Based on the acoustic results and the determined mechanical properties, one application area for these types of 3D-printed panels could be the automotive and aerospace industries.
ABSTRACT
The first part of this paper is dedicated to obtaining 3D-printed molds using poly lactic acid (PLA) incorporating specific patterns, which have the potential to serve as the foundation for sound-absorbing panels for various industries and aviation. The molding production process was utilized to create all-natural environmentally friendly composites. These composites mainly comprise paper, beeswax, and fir resin, including automotive function as the matrices and binders. In addition, fillers, such as fir needles, rice flour, and Equisetum arvense (horsetail) powder, were added in varying amounts to achieve the desired properties. The mechanical properties of the resulting green composites, including impact and compressive strength, as well as maximum bending force value, were evaluated. The morphology and internal structure of the fractured samples were analyzed using scanning electron microscopy (SEM) and an optical microscopy. The highest impact strength was measured for the composites with beeswax, fir needles, recyclable paper, and beeswax fir resin and recyclable paper, 19.42 and 19.32 kJ/m2, respectively, while the highest compressive strength was 4 MPa for the beeswax and horsetail-based green composite. Natural-material-based composites exhibited 60% higher mechanical performance compared to similar commercial products used in the automotive industry.
ABSTRACT
BACKGROUND: The Salmonella enterica serovar Newport red onion outbreak of 2020 was the largest foodborne outbreak of Salmonella in over a decade. The epidemiological investigation suggested two farms as the likely source of contamination. However, single nucleotide polymorphism (SNP) analysis of the whole genome sequencing data showed that none of the Salmonella isolates collected from the farm regions were linked to the clinical isolates-preventing the use of phylogenetics in source identification. Here, we explored an alternative method for analyzing the whole genome sequencing data driven by the hypothesis that if the outbreak strain had come from the farm regions, then the clinical isolates would disproportionately contain plasmids found in isolates from the farm regions due to horizontal transfer. RESULTS: SNP analysis confirmed that the clinical isolates formed a single, nearly-clonal clade with evidence for ancestry in California going back a decade. The clinical clade had a large core genome (4,399 genes) and a large and sparsely distributed accessory genome (2,577 genes, at least 64% on plasmids). At least 20 plasmid types occurred in the clinical clade, more than were found in the literature for Salmonella Newport. A small number of plasmids, 14 from 13 clinical isolates and 17 from 8 farm isolates, were found to be highly similar (> 95% identical)-indicating they might be related by horizontal transfer. Phylogenetic analysis was unable to determine the geographic origin, isolation source, or time of transfer of the plasmids, likely due to their promiscuous and transient nature. However, our resampling analysis suggested that observing a similar number and combination of highly similar plasmids in random samples of environmental Salmonella enterica within the NCBI Pathogen Detection database was unlikely, supporting a connection between the outbreak strain and the farms implicated by the epidemiological investigation. CONCLUSION: Horizontally transferred plasmids provided evidence for a connection between clinical isolates and the farms implicated as the source of the outbreak. Our case study suggests that such analyses might add a new dimension to source tracking investigations, but highlights the need for detailed and accurate metadata, more extensive environmental sampling, and a better understanding of plasmid molecular evolution.
Subject(s)
Salmonella enterica , Serogroup , Onions/genetics , Farms , Phylogeny , Plasmids/genetics , Disease OutbreaksABSTRACT
16S rRNA gene sequence clustering is an important tool in characterizing the diversity of microbial communities. As 16S rRNA gene data sets are growing in size, existing sequence clustering algorithms increasingly become an analytical bottleneck. Part of this bottleneck is due to the substantial computational cost expended on small clusters and singleton sequences. We propose an iterative sampling-based 16S rRNA gene sequence clustering approach that targets the largest clusters in the data set, allowing users to stop the clustering process when sufficient clusters are available for the specific analysis being targeted. We describe a probabilistic analysis of the iterative clustering process that supports the intuition that the clustering process identifies the larger clusters in the data set first. Using real data sets of 16S rRNA gene sequences, we show that the iterative algorithm, coupled with an adaptive sampling process and a mode-shifting strategy for identifying cluster representatives, substantially speeds up the clustering process while being effective at capturing the large clusters in the data set. The experiments also show that SCRAPT (Sample, Cluster, Recruit, AdaPt and iTerate) is able to produce operational taxonomic units that are less fragmented than popular tools: UCLUST, CD-HIT and DNACLUST. The algorithm is implemented in the open-source package SCRAPT. The source code used to generate the results presented in this paper is available at https://github.com/hsmurali/SCRAPT.
Subject(s)
Algorithms , Software , RNA, Ribosomal, 16S/genetics , Genes, rRNA , Cluster AnalysisABSTRACT
The paper presents the influence of the loading modelling on the simulation process results of the bending behaviour for 3D printed structures. The study is done on structures having different geometries of the cross section, and the type of structure is bar or tube. The materials used for 3D printing are poly(lactic) acid and poly(lactic acid) mixed with glass fibres. The simulation was carried out both based on a simple modelling (schematization) of the bending loading and a complex one. The complex modelling reproduces the bending of 3D printed structures more accurately but is also more time-consuming for the computer-aided design stage. Analysis of the study results shows that in terms of the Von Mises stresses determined by simulation, they are in line with those of the tests but with a slight advantage for the complex modelling compared to the simple one. In terms of deformations, the simulation introduces errors compared to the test results, but the source of the errors is the high elasticity of some 3D printed structures. The study also shows that the high elasticity is due to both the shape of the structure cross section and its arrangement during the bending test.
ABSTRACT
The human gut is home to trillions of microorganisms that are responsible for the modification of many orally administered drugs, leading to a wide range of therapeutic outcomes. Prodrugs bearing an azo bond are designed to treat inflammatory bowel disease and colorectal cancer via microbial azo reduction, allowing for topical application of therapeutic moieties to the diseased tissue in the intestines. Despite the inextricable link between microbial azo reduction and the efficacy of azo prodrugs, the prevalence, abundance, and distribution of azoreductases have not been systematically examined across the gut microbiome. Here, we curated and clustered amino acid sequences of experimentally confirmed bacterial azoreductases and conducted a hidden Markov model-driven homolog search for these enzymes across 4644 genome sequences present in the representative Unified Human Gastrointestinal Genomes collection. We identified 1958 putative azo-reducing species, corroborating previous findings that azo reduction appears to be a ubiquitous function of the gut microbiome. However, through a systematic comparison of predicted and confirmed azo-reducing strains, we hypothesize the presence of uncharacterized azoreductases in 25 prominent strains of the human gut microbiome. Finally, we confirmed the azo reduction of Acid Orange 7 by multiple strains of Fusobacterium nucleatum, Bacteroides fragilis, and Clostridium clostridioforme Together, these results suggest the presence and activity of many uncharacterized azoreductases in the human gut microbiome and motivate future studies aimed at characterizing azoreductase genes in prominent members of the human gut microbiome. SIGNIFICANCE STATEMENT: This work systematically examined the prevalence, abundance, and distribution of azoreductases across the healthy and inflammatory bowel disease human gut microbiome, revealing potentially uncharacterized azoreductase genes. It also confirmed the reduction of Acid Orange 7 by strains of Fusobacterium nucleatum, Bacteroides fragilis, and Clostridium clostridioforme.
Subject(s)
Gastrointestinal Microbiome , Inflammatory Bowel Diseases , Prodrugs , Humans , Gastrointestinal Microbiome/genetics , Prodrugs/metabolism , NADH, NADPH Oxidoreductases/genetics , NADH, NADPH Oxidoreductases/chemistry , NADH, NADPH Oxidoreductases/metabolism , Bacteria/genetics , Bacteria/metabolism , ClostridiumABSTRACT
MOTIVATION: Scientists seeking to understand the genomic basis of bacterial phenotypes, such as antibiotic resistance, today have access to an unprecedented number of complete and nearly complete genomes. Making sense of these data requires computational tools able to perform multiple-genome comparisons efficiently, yet currently available tools cannot scale beyond several tens of genomes. RESULTS: We describe PRAWNS, an efficient and scalable tool for multiple-genome analysis. PRAWNS defines a concise set of genomic features (metablocks), as well as pairwise relationships between them, which can be used as a basis for large-scale genotype-phenotype association studies. We demonstrate the effectiveness of PRAWNS by identifying genomic regions associated with antibiotic resistance in Acinetobacter baumannii. AVAILABILITY AND IMPLEMENTATION: PRAWNS is implemented in C++ and Python3, licensed under the GPLv3 license, and freely downloadable from GitHub (https://github.com/KiranJavkar/PRAWNS.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Subject(s)
Metagenomics , Software , Genomics , Genome , BacteriaABSTRACT
For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. The 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other marker genes as the number of gene sequences increases in reference databases. We demonstrate this effect through the analysis of three commonly used databases of nearly-universal prokaryotic marker genes: the SILVA 16S rRNA gene database, the Genome Taxonomy Database (GTDB), and a set of 40 taxonomically-informative single-copy genes. Our results reflect a more fundamental property of the taxonomies themselves and have broad implications for bioinformatic analyses beyond taxonomic classification. Effective solutions for fine-level taxonomic classification require a more precise, and operationally-relevant, definition of the taxonomic labels being sought, and the use of combinations of genomic markers in the classification process. Importance: The use of reference databases for assigning taxonomic labels to genomic and metagenomic sequences is a fundamental bioinformatic task in the characterization of microbial communities. The increasing accessibility of high throughput sequencing has led to a rapid increase in the size and number of sequences in databases. This has been beneficial for improving our understanding of the global microbial genetic diversity. However, there is evidence that as the microbial diversity is more densely sampled, increasingly longer genomic segments are needed to differentiate between distinct species. The scientific community needs to be aware of this issue and needs to develop methods that better account for it when assigning taxonomic labels to metagenomic sequences from microbial communities.
ABSTRACT
Introduction: A common task in the analysis of microbial communities involves assigning taxonomic labels to the sequences derived from organisms found in the communities. Frequently, such labels are assigned using machine learning algorithms that are trained to recognize individual taxonomic groups based on training data sets that comprise sequences with known taxonomic labels. Ideally, the training data should rely on labels that are experimentally verified-formal taxonomic labels require knowledge of physical and biochemical properties of organisms that cannot be directly inferred from sequence alone. However, the labels associated with sequences in biological databases are most commonly computational predictions which themselves may rely on computationally-generated data-a process commonly referred to as "transitive annotation." Methods: In this manuscript we explore the implications of training a machine learning classifier (the Ribosomal Database Project's Bayesian classifier in our case) on data that itself has been computationally generated. We generate new training examples based on 16S rRNA data from a metagenomic experiment, and evaluate the extent to which the taxonomic labels predicted by the classifier change after re-training. Results: We demonstrate that even a few computationally-generated training data points can significantly skew the output of the classifier to the point where entire regions of the taxonomic space can be disturbed. Discussion and conclusions: We conclude with a discussion of key factors that affect the resilience of classifiers to transitively-annotated training data, and propose best practices to avoid the artifacts described in our paper.
ABSTRACT
Fused Filament Fabrication (FFF) is one of the frequently used material extrusion (MEX) additive manufacturing processes due to its ability to manufacture functional components with complex geometry, but their properties depend on the process parameters. This paper focuses on studying the effects of process parameters, namely infill density (25%, 50%, 75%, and 100%), on the mechanical and thermal response of the samples made of poly(lactic acid) (PLA) reinforced with short glass fibers (GF) produced using FFF process. To perform a comprehensive analysis, tensile, flexural, compression, differential scanning calorimetry (DSC), and thermal gravimetric analysis (TGA) tests were used. The paper also aims to manufacture by FFF process of composite structures of the fuselage section type, as structural elements of an unmanned aerial vehicle (UAV), and their testing to compression loads. The results showed that the tensile, flexural and compression strength of the additive manufactured (AMed) samples increased with the increase of infill density and therefore, the samples with 100% infill density provides the highest mechanical characteristics. The AMed samples with 50% and 75% infill density exhibited a higher toughness than samples with 100% infill. DSC analyses revealed that the glass transition (Tg), and melting (Tm) temperature increases slightly as the infill density increases. Thermogravimetric analyses (TGA) show that PLA-GF filament loses its thermal stability at a temperature of about 311 °C and the increase in fill density leads to a slight increase in thermal stability and the complete degradation temperature of the AMed material. The compression tests of the fuselage sections manufactured by FFF made of PLA-GF composite showed that their stiffening with stringers oriented at an angle of ±45° ensures a higher compression strength than the stiffening with longitudinal stringers.
ABSTRACT
Reduced availability of agricultural water has spurred increased interest in using recycled irrigation water for U.S. food crop production. However, there are significant knowledge gaps concerning the microbiological quality of these water sources. To address these gaps, we used 16S rRNA gene and metagenomic sequencing to characterize taxonomic and functional variations (e.g., antimicrobial resistance) in bacterial communities across diverse recycled and surface water irrigation sources. We collected 1 L water samples (n = 410) between 2016 and 2018 from the Mid-Atlantic (12 sites) and Southwest (10 sites) U.S. Samples were filtered, and DNA was extracted. The V3-V4 regions of the 16S rRNA gene were then PCR amplified and sequenced. Metagenomic sequencing was also performed to characterize antibiotic, metal, and biocide resistance genes. Bacterial alpha and beta diversities were significantly different (p < 0.001) across water types and seasons. Pathogenic bacteria, such as Salmonella enterica, Staphylococcus aureus, and Aeromonas hydrophilia were observed across sample types. The most common antibiotic resistance genes identified coded against macrolides/lincosamides/streptogramins, aminoglycosides, rifampin and elfamycins, and their read counts fluctuated across seasons. We also observed multi-metal and multi-biocide resistance across all water types. To our knowledge, this is the most comprehensive longitudinal study to date of U.S. recycled water and surface water used for irrigation. Our findings improve understanding of the potential differences in the risk of exposure to bacterial pathogens and antibiotic resistance genes originating from diverse irrigation water sources across seasons and U.S. regions.
Subject(s)
Anti-Bacterial Agents , Disinfectants , United States , RNA, Ribosomal, 16S/genetics , Anti-Bacterial Agents/pharmacology , Longitudinal Studies , Bacteria/genetics , Drug Resistance, Microbial/genetics , Water , Agricultural Irrigation , Wastewater , Genes, BacterialABSTRACT
Additive manufacturing, through the process of thermoplastic extrusion of filament, allows the manufacture of complex composite sandwich structures in a short time with low costs. This paper presents the design and fabrication by Fused Filament Fabrication (FFF) of composite sandwich structures with short fibers, having three core types C, Z, and H, followed by mechanical performance testing of the structures for compression and bending in three points. Flatwise compression tests and three-point bending have clearly indicated the superior performance of H-core sandwich structures due to dense core structures. The main modes of failure of composite sandwich structures were analyzed microscopically, highlighting core shear buckling in compression tests and face indentation in three-point bending tests. The strength-mass ratio allowed the identification of the structures with the best performances considering the desire to reduce the mass, so: the H-core sandwich structures showed the best results in compression tests and the C-core sandwich structures in three-point bending tests. The feasibility of the FFF process and the three-point bending test of composite wing sections, which will be used on an unmanned aircraft, have also been demonstrated. The finite element analysis showed the distribution of equivalent stresses and reaction forces for the composite wing sections tested for bending, proving to validate the experimental results.
ABSTRACT
The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen .