Classification Of Microorganisms Research Articles

Clasnip: a web-based intraspecies classifier and multi-locus sequence typing for pathogenic microorganisms using fragmented sequences.

Bioinformatic approaches for the identification of microorganisms have evolved rapidly, but existing methods are time-consuming, complicated or expensive for massive screening of pathogens and their non-pathogenic relatives. Also, bioinformatic classifiers usually lack automatically generated performance statistics for specific databases. To address this problem, we developed Clasnip (www.clasnip.com), an easy-to-use web-based platform for the classification and similarity evaluation of closely related microorganisms at interspecies and intraspecies levels. Clasnip mainly consists of two modules: database building and sample classification. In database building, labeled nucleotide sequences are mapped to a reference sequence, and then single nucleotide polymorphisms (SNPs) statistics are generated. A probability model of SNPs and classification groups is built using Hidden Markov Models and solved using the maximum likelihood method. Database performance is estimated using three replicates of two-fold cross-validation. Sensitivity (recall), specificity (selectivity), precision, accuracy and other metrics are computed for all samples, training sets, and test sets. In sample classification, Clasnip accepts inputs of genes, short fragments, contigs and even whole genomes. It can report classification probability and a multi-locus sequence typing table for SNPs. The classification performance was tested using short sequences of 16S, 16-23S and 50S rRNA regions for 12 haplotypes of Candidatus Liberibacter solanacearum (CLso), a regulated plant pathogen associated with severe disease in economically important Apiaceous and Solanaceous crops. The program was able to classify CLso samples with even only 1-2 SNPs available, and achieved 97.2%, 98.8% and 100.0% accuracy based on 16S, 16-23S, and 50S rRNA sequences, respectively. In comparison with all existing 12 haplotypes, we proposed that to be classified as a new haplotype, given samples have at least 2 SNPs in the combined region of 16S rRNA (OA2/Lsc2) and 16-23S IGS (Lp Frag 4-1611F/Lp Frag 4-480R) regions, and 2 SNPs in the 50S rplJ/rplL (CL514F/CL514R) regions. Besides, we have included the databases for differentiating Dickeya spp., Pectobacterium spp. and Clavibacter spp. In addition to bacteria, we also tested Clasnip performance on potato virus Y (PVY). 251 PVY genomes were 100% correctly classified into seven groups (PVYC, PVYN, PVYO, PVYNTN, PVYN:O, Poha, and Chile3). In conclusion, Clasnip is a statistically sound and user-friendly bioinformatic application for microorganism classification at the intraspecies level. Clasnip service is freely available at www.clasnip.com.

Phylogenetic diversity of core rumen microbiota as described by cryo-ET.

Microbial taxonomy is critical for describing ecosystem composition, yet the link between taxonomy and properties of microbes, such as their cellular architecture, remains poorly defined. We hypothesized that the cellular architecture represents microbial niche adaptation. We used cryo-electron microscopy and tomography to analyze microbial morphology in order to associate cellular architecture with phylogeny and genomic contents. As a model system, we chose the core rumen microbiome and imaged a large isolate collection covering 90% of its richness at the order level. Based on quantifications of several morphological features, we found that the visual similarity of microbiota is significantly related to their phylogenetic distance. Up to the Family level, closely related microbes have similar cellular architectures, which are highly correlated with genome similarity. However, in more distantly related bacteria, the correlation both with taxonomy and genome similarity is lost. This is the first comprehensive study of microbial cellular architecture and our results highlight that structure remains an important parameter in classification of microorganisms, along with functional parameters such as metabolomics. Furthermore, the high-quality images presented in this study represent a reference database for the identification of bacteria in anaerobic ecosystems.

Bioprospecting in food production: an approximation of the current state in Colombia

Microbial bioprospecting is the study and classification of microorganisms with industrial value. Different researches word wide are focusing on the study of natural molecules that can be used for medicine, agriculture and the environment, among others. In Colombia, marine bioprospecting has become highly relevant. Also, different universities and institutes are working on the study of the biodiversity and its applications. The aim of this review was to compilate the most important laws and decrees related with the use of the resources with commercial purposes. Also, the elucidation of the current state of bioprospecting in Colombia and the principal applications of microorganisms in the food production chain. The special focus of this review is to show the potential use of bioprospection on agricultural development of the country in order to change the conventional practices to eco-friendly process in food production. Also, this review proposes the bioprospecting of lactic acid bacteria as an alternative to use their biomass and metabolites for food preservation.

GENERAL RULES FOR SAFE WORK WITH BIOLOGICAL AGENTS

Problem statement. The work was carried out on the identification and distribution by groups of various types of emergency situations that are possible in laboratories when performing procedures with various biological agents. The need to observe clearly regulated rules for safe work with various biological objects in laboratory institutions is proved. The purpose of the article. Determine the rules for safe work with various biological agents in laboratory conditions of different security levels. Method. Analytical methods, generalizing approaches to information presented in regulatory documents, literary and other information sources. Results. Existing and previously developed rules for safe work and techniques for appropriate laboratory work with biological objects are studied. The main signs of work risks according to the international classification of microorganisms are investigated. The characteristics of each hazard group are studied and examples of microbial species assigned to a particular risk group are given. The classification characteristics for four types of microbiological laboratories according to a number of features are given. It was established that the level of laboratories’ biological safety depends on the risk group of microorganisms. It is recognized that compliance with the rules of safe work with various biological agents in laboratory conditions is ensured by the use of necessary and mandatory equipment for a certain level of the laboratory. It is argued that the possibility of implementing biological safety rules in laboratories of different levels is based on the assessment of biological risks in certain conditions of a given laboratory institution. A step-by-step procedure for assessing biological risks in a certain laboratory has been developed, which is also presented in the form of a block diagram. Scientific novelty. Scientific and normative information on the rules of safe work with biological agents in laboratory conditions is analyzed and summarized. A defined sequence of actions for biological risk assessment in certain laboratory settings is proposed. Practical value. The practical possibility and necessity of managing biological risks in laboratory conditions of different biological safety levels is substantiated.

Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice.

The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a "new" technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.

Selection of an effective dose of autologous probiotics for patients in the rehabilitation period

It is known that the presence of microorganisms with probiotic properties protects the body from pathogenic bacteria. The classification of symbiotic microorganisms living in the human gastrointestinal tract is constantly changing. Probiotics do not form colonies in the intestine, therefore, to obtain a long-term positive effect, the concentrations of probiotic strains in the intestine should reach a level of at least 10⁸ CFU/ml and remain so for a long time. The biotechnology of designing probiotic drugs is based on the isolation of probiotic strains of microbes from the intestines of a healthy individual. This is the basis for a new promising direction in practical medicine — the preparation of autoprobiotics used for the treatment and prevention of various diseases. The recommended effective daily dose of probiotic should be 10⁸–10⁹ CFU, but the dose may vary depending on the probiotic strain and the pharmaceutical form. The preservation of the viability of bacteria in the required amount until the end of the drug shelf life is also crucial for its effective use. It is assumed that auto-strains of probiotics in identical doses will be effective as well.

A novel hybrid transformer-CNN architecture for environmental microorganism classification.

The success of vision transformers (ViTs) has given rise to their application in classification tasks of small environmental microorganism (EM) datasets. However, due to the lack of multi-scale feature maps and local feature extraction capabilities, the pure transformer architecture cannot achieve good results on small EM datasets. In this work, a novel hybrid model is proposed by combining the transformer with a convolution neural network (CNN). Compared to traditional ViTs and CNNs, the proposed model achieves state-of-the-art performance when trained on small EM datasets. This is accomplished in two ways. 1) Instead of the original fixed-size feature maps of the transformer-based designs, a hierarchical structure is adopted to obtain multi-scale feature maps. 2) Two new blocks are introduced to the transformer's two core sections, namely the convolutional parameter sharing multi-head attention block and the local feed-forward network block. The ways allow the model to extract more local features compared to traditional transformers. In particular, for classification on the sixth version of the EM dataset (EMDS-6), the proposed model outperforms the baseline Xception by 6.7 percentage points, while being 60 times smaller in parameter size. In addition, the proposed model also generalizes well on the WHOI dataset (accuracy of 99%) and constitutes a fresh approach to the use of transformers for visual classification tasks based on small EM datasets.

Historical Antibiotic Stress Changed the Effects of Sulfamethoxazole and Trimethoprim on Activated Sludge: ARGs and Potential Hosts

The co-exposure of antibiotics has important effects on antibiotic resistance genes (ARGs) and microbial community aggregation in wastewater treatment plants (WWTPs). However, it is unclear whether differences in historical antibiotic exposure stress can determine responses of microbes and ARGs to combined antibiotics. By selecting a high concentration (30 mg·L-1) of sulfamethoxazole (SMX) and trimethoprim (TMP) as historical exposure stress conditions, the effects of SMX and TMP-combined pollution on ARGs, bacterial communities, and their interactions were explored in short-term experiments. Based on high-throughput quantitative PCR, a total of 13 ARGs were detected, and the absolute abundance was 2.21-5.42 copies·μL-1 (logarithm, DNA, the same below). Among them, sul2, ermB, mefA, and tetM-01 were the main subtypes in the samples, and the absolute abundance was between 2.95 and 5.40 copies·μL-1. The combined contamination of SMX and TMP could cause the enrichment of ARGs and mobile genetic elements (MGEs); however, their effects on each subtype were different, and the historical legacy effect of SMX was higher than that of TMP. Under the different exposure histories, the co-occurrence and co-exclusion patterns existed between ARGs. Moreover, MGEs (especially intI-1) were significantly correlated with sulfonamides (sul1 and sul2), tetracyclines[tet(32)], and macrolide-lincosamide-streptogramin (MLSB) resistance genes (ermB). Based on the full-scale classification of microorganisms, it was found that the microbial community structure of various groups responded differently to combined pollution, and the conditionally abundant taxa (CAT) were obviously enriched. Thauera, Pseudoxanthomonas, and Paracoccus were the dominant resistant bacterial genera. Furthermore, a total of 31 potential hosts of ARGs were identified with network analysis, which were dominated with conditionally rare taxa (CRT). Particularly, Candidatus_Alysiosphaera and Fusibacter were positively correlated with most of the ARGs, being the common protentional hosts. Importantly, some rare genera (RT, Variibacter, Aeromonas, Cloacibacterium, etc.) were potential hosts of transposon IS613, which played an important role in the proliferation and spread of ARGs. In conclusion, this study revealed the legacy effects of historical antibiotic stress on ARGs and their hosts, which could provide new ideas and theoretical basis for reducing ARGs pollution in WWTPs.

16S rRNA Based Profiling of Bacterial Communities Colonizing Bakery-Production Environments.

Conventional culture-based techniques are largely inadequate in elucidating the microbiota contained in an environment, due to low recovery within a complex bacterial community. This limitation has been mitigated by the use of next-generation sequencing (NGS)-based approaches thereby facilitating the identification and classification of both culturable and uncultivable microorganisms. Amplicon targeted NGS methods, such as 16S ribosomal RNA (16S rRNA) and shotgun metagenomics, are increasingly being applied in various settings such as in food production environments to decipher the microbial consortium therein. Even though multiple food matrices/food production environments have been studied, the low-moisture environment associated with bakery food production remains to be investigated. To address this knowledge gap, in this study, we investigated the microbiome associated with two bakery production sites (designated as A and B) located in Ireland using 16S rRNA-amplicon-based sequencing. Amplicons corresponding to a hypervariable region contained within the 16S rRNA gene were amplified from DNA samples purified from environmental swabs and ingredients collected at both sites at various stages (preparation, production, postproduction, and storage) across the bakery production chain, over three seasons (winter, spring, and summer). These amplicons were sequenced, and data were analyzed using the mothur pipeline and visualized using MicrobiomeAnalyst and a series of R packages. The top seven bacterial phyla identified at both sites were composed of Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, Deinococcus-Thermus, Patescibacteria, and Verrucomicrobia. In addition, the phyla Tenericutes (Mycoplasmatota) and Acidobacteria were observed only in samples taken at site B. Different bacterial compositions were identified at each stage of production. These same bacteria were also found to be present in the final processed food suggesting the influence of the environment on the food matrix. This study is the first demonstration of the utility of 16S rRNA amplicon-based sequencing to describe the microbiota associated with bakery processing environments.

Low complexity and accurate Machine learning model for waterborne pathogen classification using only three handcrafted features from optofluidic images

Pathogenic microorganism can cause human diseases and even death, so waterborne pathogen detection is essential for water quality monitoring. Conventional microorganism detection/classification through biochemical approaches is expensive, time-consuming, labour-intensive and leaves chemical pollutions. Recently, many image processing algorithms are developed for cheaper, faster, more automatic, and more environmentally friendly microorganism classification. However, most existing classification algorithms are designed for microscopic images and focus only on the classification performance, not the computational cost. Alternatively, the optofluidic systems provide a smaller and cheaper way to capture microorganism images than conventional microscopes. Thus, in this paper, an image classification algorithm based on optofluidic images is proposed for portable, real-time water quality monitoring applications. With a comprehensive investigation on different categories of features and careful analysis on our optofluidic images, 2 morphological features were selected to classify the waterborne pathogens with a proposed feature, EHHOG (entropy of histogram of HOG) (HOG: histogram of oriented gradients). The proposed algorithm can achieve higher or similar classification accuracy as existing methods, while the computational cost is much lower with only 3 features.

Identification of Antibiotic Resistance Proteins via MiCId's Augmented Workflow. A Mass Spectrometry-Based Proteomics Approach.

Fast and accurate identifications of pathogenic bacteria along with their associated antibiotic resistance proteins are of paramount importance for patient treatments and public health. To meet this goal from the mass spectrometry aspect, we have augmented the previously published Microorganism Classification and Identification (MiCId) workflow for this capability. To evaluate the performance of this augmented workflow, we have used MS/MS datafiles from samples of 10 antibiotic resistance bacterial strains belonging to three different species: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The evaluation shows that MiCId’s workflow has a sensitivity value around 85% (with a lower bound at about 72%) and a precision greater than 95% in identifying antibiotic resistance proteins. In addition to having high sensitivity and precision, MiCId’s workflow is fast and portable, making it a valuable tool for rapid identifications of bacteria as well as detection of their antibiotic resistance proteins. It performs microorganismal identifications, protein identifications, sample biomass estimates, and antibiotic resistance protein identifications in 6–17 min per MS/MS sample using computing resources that are available in most desktop and laptop computers. We have also demonstrated other use of MiCId’s workflow. Using MS/MS data sets from samples of two bacterial clonal isolates, one being antibiotic-sensitive while the other being multidrug-resistant, we applied MiCId’s workflow to investigate possible mechanisms of antibiotic resistance in these pathogenic bacteria; the results showed that MiCId’s conclusions agree with the published study. The new version of MiCId (v.07.01.2021) is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

Identification of an Aerococcus urinaeequi isolate by whole genome sequencing and average nucleotide identity analysis

Identification and classification of microorganisms is one of the most important but difficult and challenging issues in microbiology. Whole genome sequencing (WGS), which can give a thorough understanding for the genome of bacteria strain, has been universally used for studying bacterial classification, evolution, and drug-related resistant genes. We in this study aimed to identify a Gram-positive, microaerophilic, catalase-negative cocci strain named AV208, which has shown resistance to vancomycin, by whole genome's average nucleotide identity (ANI) and high-throughput sequencing technology. The AV208 strain was identified by following commercially available identification systems, including API 20 Strep system and Vitek 2 Compact Gram-positive identification system for biochemical phenotypic test. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS) and 16S rRNA gene sequencing were used for confirmation identification. The whole genome of AV208 was sequenced by using high throughput sequencing technology and ANI between AV208, and its phylogenetic neighbours were analysed by the Orthologous Average Nucleotide Identity Tool (OAT) software. Polymerase chain reaction (PCR) and DNA sequencing were used to investigate the potential molecular mechanism for vancomycin resistance. The AV208 strain was isolated from an ascites sample from a patient with chronic kidney disease who showed extensive resistance to the drugs detected, such as vancomycin with MIC >256 μg/mL. With combination of biochemical phenotypic test, MALDI-TOF-MS and 16S rRNA gene sequencing, the AV208 strain was tentatively identified as an Aercoccus viridans. By using complete genome sequence, we found a 96.24% ANI between strain AV208 and Aerococcus urinaeequi CCUG 28094T, which was higher than that with A. viridans CCUG4311T (94.9%). The consistency of 16S rRNA sequence of strain AV208 was 100% with A. urinaeequi CCUG 28094T and 99.9% with A. viridans CCUG4311T, with only one base difference between them. PCR and sequencing for van genes revealed that AV208 was positive for the vanA gene. A Tn1546 transposon-like structure with vanA gene was found in the genome, which was predicted locating in plasmid, causing vancomycin resistance phenotypes. Average nucleotide identity analysis based on whole genome sequence is an accurate and effective method for identification of bacteria, especially for strains that are not discernible by existing methods such as Aerococcus.

Classification of Environmental Strains from Order to Genus Levels Using Lipid and Protein MALDI-ToF Fingerprintings and Chemotaxonomic Network Analysis.

During the last two decades, MALDI-ToF mass spectrometry has become an efficient and widely-used tool for identifying clinical isolates. However, its use for classification and identification of environmental microorganisms remains limited by the lack of reference spectra in current databases. In addition, the interpretation of the classical dendrogram-based data representation is more difficult when the quantity of taxa or chemotaxa is larger, which implies problems of reproducibility between users. Here, we propose a workflow including a concurrent standardized protein and lipid extraction protocol as well as an analysis methodology using the reliable spectra comparison algorithm available in MetGem software. We first validated our method by comparing protein fingerprints of highly pathogenic bacteria from the Robert Koch Institute (RKI) open database and then implemented protein fingerprints of environmental isolates from French Guiana. We then applied our workflow for the classification of a set of protein and lipid fingerprints from environmental microorganisms and compared our results to classical genetic identifications using 16S and ITS region sequencing for bacteria and fungi, respectively. We demonstrated that our protocol allowed general classification at the order and genus level for bacteria whereas only the Botryosphaeriales order can be finely classified for fungi.

Metagenomics Analysis of Breast Cancer to Study Bacterial Diversity

Introduction: Breast cancer is the swelling structures in the breast cells. After skin cancer, breast cancer is the most broadly accepted malignancy analysed in ladies in the United States. Breast cancer can happen in both males and females, yet it's undeniably more normal in ladies. Indications of mammary glands cancer may involve a ball or lumps in the breast, an alteration of breast shape, scratching of the skin, fluid coming out from areola, a recently reversed areola, or a red or textured fix of skin. In those with removed lay out of the illness, there can be bone agony, swollen lymph hubs, windedness, or yellow skin. There are certain microorganisms that are involved in breast cancer and have function thereof. The study of these microorganisms can be done through metagenomics study and comparing with normal samples.
 Methods: For current study pairwise metagenomics data for breast cancer sample was retrieved from ENA site. Four FASTQ files were retrieved with the Accession no PRJEB25419 and complete metagenomics analysis was done using GALAXY Server. Tools like FASTQC, Trim galore, KRAKEN2, Krona pie chart etc. were used for classification and identification of microorganisms in the samples.
 Results: The taxonomic and functional analysis of the metagenome of breast cancer identifies different organisms like. Actinobacteria, Bifidobacterium cuniculi, Murnine leukemia virus, Human endogenous retrovirus, Pezizomycotina. Different microbes and their percentage in the breast cancer have been predicted that are 4% of Nematocera, 2% of Brachycera, 3% of Galegeae, 2% of Nelumbo nucifera.
 Conclusion These results give insights into the metagenome of breast cancer that can be used for clinical perspectives. Detailed study is required to establish the association of these microbes with development and progression of breast cancer.
 Keywords: - Breast cancer, Metagenomics, Galaxy, ENA Database, Krona pie chart.

Fiber Probe-Based Raman Spectroscopic Identification of Pathogenic Infection Microorganisms on Agar Plates.

Rapid identification of microorganisms is clinically meaningful, and it helps to decelerate the spread of drug resistance and improve patient treatment. In this study, we present a rapid fiber probe-based Raman technique with an excitation wavelength of 785 nm, which is applied to classify and identify nine different species of microorganisms. The cost-effective fiber probe compresses the dimension of the system and provides a more reliable and stable database. All microorganisms were simply cultivated on Luria-Bertani (LB) agar, and Raman spectra were obtained directly from the microbial colonies with the fiber probe within 30 s. The classification model consists of principal component analysis (PCA) in combination with linear discriminant analysis (LDA) and was examined by applying leave-one-batch-out cross-validation (LOBOCV). This model achieved an accuracy of 98.9%. In addition, the validation and identification processes based on independent replicates achieved accuracies of 99.8% and 100%, respectively. The results demonstrated that fiber probe Raman spectroscopy in combination with chemometric analysis allowed a rapid classification and identification of microorganisms only with a normal culture. Therefore, it is promising especially for medical applications and could moreover be helpful to investigate and identify microorganisms rapidly in further studies.

Microbiological characterization and antimicrobial susceptibility pattern of infections associated with febrile neutropenia in pediatric hemato-oncological patients.

Febrile neutropenia (FN) is the most frequent hemato-oncological emergency, with high morbidi ty and mortality in pediatrics. The objective of the study was the microbiological characterization and antimicrobial susceptibility of infections associated with FN in pediatric hemato-oncological patients. Retrospective cohort study with patients aged between 1 month and 18 years, with onco-hematological pathology according to ICD-10 codes, hospitalized in a tertiary healthcare center in Bucaramanga, Colombia. Based on the medical records of the period 2013-2017, the episodes of FN were identified, and the isolated microorganisms and their susceptibility pattern were described. Biochemical identification and antimicrobial susceptibility testing were performed with the Dade Behring Microscan« automated system. The resistant microorganism classification was performed based on the Minimum Inhibitory Concentration (MIC) and the interpretation of the laboratory according to the cut-off points of the Clinical and Laboratory Standards Institute recommendations. Of 130 patients, 14.7% of the cultures obtained were positive. Bloods tream infection was observed in 17.5% of the episodes. The isolated microorganisms were mainly Gram-negative bacteria (75.8%). Enterobacteriaceae (EB) were the most frequent, led by Klebsiella pneumoniae, Escherichia coli, followed by Pseudomonas aeruginosa and coagulase-negative Staphylo cocci. Of the EBs, 40.5% showed resistance to Piperacillin/Tazobactam, 33.3% to Cefepime, and 8.2% to Meropenem. According to the antimicrobial resistance pattern, it was observed that 16.4% of the positive EB cultures had an extended-spectrum beta-lactamase pattern and 5% a pattern suggestive of carbapenemases. All Gram-positive cocci were sensitive to Vancomycin. In the studied patients, the predominant pathogenic microorganisms were Gram-negative ones with resistance in dices similar to those of developing countries.

A New Strategy for Microbial Taxonomic Identification through Micro-Biosynthetic Gold Nanoparticles and Machine Learning.

Microorganisms can serve as biological factories for the synthesis of inorganic nanomaterials that can become useful as nanocatalysts, energy-harvesting-storage components, antibacterial agents, and biomedical materials. Herein, the development of biosynthesis of inorganic nanomaterials into a simple, stable, and accurate strategy for distinguishing microorganisms from multiple classification levels (i.e., kingdom, order, genus, and species) without gene amplification, biochemical testing, or target recognition is reported. Gold nanoparticles (AuNPs) biosynthesized by different microorganisms differ in color of the solution, and their features can be characterized, including the particle size, the surface plasmon resonance (SPR) spectrum, and the surface potential. The inter-relation between the features of micro-biosynthetic AuNPs and the classification of microorganisms are exploited at different levels through machine learning to establish a taxonomic model. This model agrees well with traditional classification methods that offers a new strategy for microbial taxonomic identification. The underlying mechanism of this strategy is related to the biomolecules produced by different microorganisms including glucose, glutathione, and nicotinamide adenine dinucleotide phosphate-dependent reductase that regulate the features of micro-biosynthetic AuNPs. This work broadens the application of biosynthesis of inorganic materials through micro-biosynthetic AuNPs and machine learning, which holds great promise as a tool for biomedical research.

Study on the classification and identification of microorganisms in municipal sludge

Study on the classification and identification of microorganisms in municipal sludge

Micro-organismos: os agentes secretos da biosfera, e seus papéis determinantes na biotecnologia

Abstract We present a survey of projects that have been funded by FAPESP under the BIOTA-Microorganisms program. These projects generated a wide variety of results, including the identification of novel antibacterial-producing microorganisms, the characterization of novel microbial enzymes for industrial applications, taxonomic classification of novel microorganisms in several environments, investigation of the soil and mangrove microbial ecosystems and its influence on endangered plant species, and the sequencing of novel metagenome-assembled genomes. The results surveyed demonstrate the importance of microorganisms in environments that play important roles in human activities as well as the potential that many of these microorganisms have in contributing to biotechnological applications crucial for human survival in the 21st century.

Microorganism image classification with circle-based Multi-Region Binarization and mutual-information-based feature selection

BackgroundAn important part of water quality control is waterborne pathogen detection. Conventional microorganism detections are done by costly and complicated biochemical approaches, which leave chemical pollutions and require professional laboratories, technicians, and much time. Recently, machine-learning-based image classification algorithms are developed for faster, cheaper, simpler, and more environmentally friendly microorganism detection. Most of them are designed for conventional microscopic images. On the other hand, the optofluidic system is a smaller and cheaper alternative to conventional microscopes, so we aim to propose a microorganism classification algorithm for optofluidic images. MethodIn our previous study, the Binarized-Greyscale-Hybrid Algorithm with Multi-Region Binarization (BiGHAM) algorithm was developed for our optofluidic image dataset. In this paper, a new circle-based region partition method for the Multi-Region Binarization and a new mutual-information-based feature selection method were proposed to improve the BiGHAM method. Additionally, an implementation example of how to adapt and apply our method on an open dataset was presented. Results and conclusionWith the circle-based region partition method and the new feature combination selected based on mutual information, the improved-BiGHAM algorithm achieved higher classification accuracies, and the algorithm is simpler than the previous BiGHAM method during training. For our optofluidic image dataset, the accuracy improvement for the 1000-image-per-class case was from 94.8 to 96.3%, and for the 30-image-per-class case was from 77.8 to 85.6% with the support vector machine as the classifier. The adapted algorithm also works well on the open dataset, showing the potential to be extended to applications on different microorganism images.