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The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes. [ABSTRACT FROM AUTHOR]

ABSTRACT Bacteria form an intense portion of reading and learning for students enrolled in microbiology education. As a part of the foundational course outline of bacteriology, bacterial classification is a significant topic of discussion. The purpose of our study was to analyze whether bacterial classification can be taught with a phylogenetic tree approach that might be more engaging and beneficial to student learners of microbiology. This methodology is unique compared to the conventional approach applied in introductory lectures of bacteriology that relies on morphology and Gram-staining to classify bacteria. The participants of this study were students enrolled in a two-semester medical school bridge program that offers a Master’s degree in Pre-clinical Sciences. We presented bacterial origin and classification in the light of evolution and used a phylogenetic tree to signify clinically relevant groups of bacteria. Students were also taught the traditional bacterial classification using Gram stains and morphology. Both methods of classification were delivered in a didactic classroom session considering equal time spent and utilizing the same format. An online survey was distributed to the students after the session to collect their feedback. The results from the survey showed that 74% of participants would prefer learning bacterial classification using a combined approach that includes both Gram-staining and morphology as well as the phylogenetic tree. When asked if the study of bacterial classification through an evolutionary tree diagram is a clear and concise way of understanding bacteria, 79% of the students either agreed or strongly agreed with this statement. Interestingly, the alternative phylogenetic tree approach was considered more engaging and regarded as a means to expand the clinical knowledge of bacteria by 78% and 71% of the students, respectively. Overall, our study strongly supports the use of tree-based classification as an additional method to improve the learning of medically important groups of bacteria at varying levels of education.

AbstractThe rapid identification of pathogenic bacterial strains is becoming a challenging task as it causes many hospital associated infections. Many have reported on the use of fluorescence spectroscopy as an alternative to characterize bacteria. As bacteria possess intrinsic fluorophores, attempts were made to characterize eight strains using excitation emission matrix (EEM) measurements. From the results of parallel factor analysis (PARAFAC), four fluorophores, tryptophan, anthranilic acid, nicotinamide adenine dinucleotide, and flavin adenine dinucleotide, were identified with varying distributions. The data obtained from PARAFAC analysis were subjected to hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). This study demonstrates the potential of EEM technique to classify bacteria with 100% accuracy. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes.

Bacterial identification is an essential task in medical disciplines and food hygiene. The characteristics of bacteria can be examined under a microscope using culture techniques. However, traditional clinical laboratory culture methods require considerable work, primarily physical and manual effort. An automated process using deep learning technology has been widely used for increasing accuracy and decreasing working costs. In this paper, our research evaluates different types of existing deep CNN models for bacterial contamination classification when low-resource data are used. They are baseline CNN, GCNN, ResNet, and VGGNet. The performance of CNN models was also compared with the traditional machine learning method, including SIFT+SVM. The performance of the DIBaS dataset and our own collected dataset have been evaluated. The results show that VGGNet achieves the highest accuracy. In addition, data augmentation was performed to inflate the dataset. After fitting the model with augmented data, the results show that the accuracy increases significantly. This improvement is consistent in all models and both datasets.

This research paper presents an overview of contemporary machine learning methodologies and their utilization in the domain of healthcare and the prevention of infectious diseases, specifically focusing on the classification and identification of bacterial species. As deep learning techniques have gained prominence in the healthcare sector, a diverse array of architectural models has emerged. Through a comprehensive review of pertinent literature, multiple studies employing machine learning algorithms in the context of microbial diagnosis and classification are examined. Each investigation entails a tabulated presentation of data, encompassing details about the training and validation datasets, specifications of the machine learning and deep learning techniques employed, as well as the evaluation metrics utilized to gauge algorithmic performance. Notably, Convolutional Neural Networks have been the predominant selection for image classification tasks by machine learning practitioners over the last decade. This preference stems from their ability to autonomously extract pertinent and distinguishing features with minimal human intervention. A range of CNN architectures have been developed and effectively applied in the realm of image classification. However, addressing the considerable data requirements of deep learning, recent advancements encompass the application of pre-trained models using transfer learning for the identification of microbial entities. This method involves repurposing the knowledge gleaned from solving alternate image classification challenges to accurately classify microbial images. Consequently, the necessity for extensive and varied training data is significantly mitigated. This study undertakes a comparative assessment of various popular pre-trained CNN architectures for the classification of bacteria. The dataset employed is composed of approximately 660 images, representing 33 bacterial species. To enhance dataset diversity, data augmentation is implemented, followed by evaluation on multiple models including AlexNet, VGGNet, Inception networks, Residual Networks, and Densely Connected Convolutional Networks. The results indicate that the DenseNet-121 architecture yields the optimal performance, achieving a peak accuracy of 99.08%, precision of 99.06%, recall of 99.00%, and an F1-score of 98.99%. By demonstrating the proficiency of the DenseNet-121 model on a comparatively modest dataset, this study underscores the viability of transfer learning in the healthcare sector for precise and efficient microbial identification. These findings contribute to the ongoing endeavors aimed at harnessing machine learning techniques to enhance healthcare methodologies and bolster infectious disease prevention practices.

Despite the advent of third-generation sequencing technologies, modern bacterial ecology studies still use Illumina to sequence small (~400 bp) hypervariable regions of the 16S rRNA SSU for phylogenetic classification. By sequencing a larger region of the rRNA gene operons, the limitations and biases of sequencing small portions can be removed, allowing for more accurate classification with deeper taxonomic resolution. With Nanopore sequencing now providing raw simplex reads with quality scores above Q20 using the kit 12 chemistry, the ease, cost, and portability of Nanopore play a leading role in performing differential bacterial abundance analysis. Sequencing the near-entire rrn operon of bacteria and archaea enables the use of the universally conserved operon holding evolutionary polymorphisms for taxonomic resolution. Here, a reproducible and validated pipeline was developed, RRN-operon Enabled Species-level Classification Using EMU (RESCUE), to facilitate the sequencing of bacterial rrn operons and to support import into phyloseq. Benchmarking RESCUE showed that fully processed reads are now parallel or exceed the quality of Sanger, with median quality scores of approximately Q20+, using the R10.4 and Guppy SUP basecalling. The pipeline was validated through two complex mock samples, the use of multiple sample types, with actual Illumina data, and across four databases. RESCUE sequencing is shown to drastically improve classification to the species level for most taxa and resolves erroneous taxa caused by using short reads such as Illumina. [ABSTRACT FROM AUTHOR]

Despite the advent of third-generation sequencing technologies, modern bacterial ecology studies still use Illumina to sequence small (~400 bp) hypervariable regions of the 16S rRNA SSU for phylogenetic classification. By sequencing a larger region of the rRNA gene operons, the limitations and biases of sequencing small portions can be removed, allowing for more accurate classification with deeper taxonomic resolution. With Nanopore sequencing now providing raw simplex reads with quality scores above Q20 using the kit 12 chemistry, the ease, cost, and portability of Nanopore play a leading role in performing differential bacterial abundance analysis. Sequencing the near-entire rrn operon of bacteria and archaea enables the use of the universally conserved operon holding evolutionary polymorphisms for taxonomic resolution. Here, a reproducible and validated pipeline was developed, RRN-operon Enabled Species-level Classification Using EMU (RESCUE), to facilitate the sequencing of bacterial rrn operons and to support import into phyloseq. Benchmarking RESCUE showed that fully processed reads are now parallel or exceed the quality of Sanger, with median quality scores of approximately Q20+, using the R10.4 and Guppy SUP basecalling. The pipeline was validated through two complex mock samples, the use of multiple sample types, with actual Illumina data, and across four databases. RESCUE sequencing is shown to drastically improve classification to the species level for most taxa and resolves erroneous taxa caused by using short reads such as Illumina.

Microorganisms such as bacteria are the main cause of various infectious diseases such as cholera, botulism, gonorrhea, Lyme disease, sore throat, tuberculosis and so on. Therefore, identification and classification of bacteria is very important in the world of medicine to help experts diagnose diseases suffered by patients. However, manual identification and classification of bacteria takes a long time and a professional individual. With the help of artificial intelligence, we can effectively and efficiently classify bacteria and save a lot of time and human labor. In this study, a system was created to classify bacteria from microscopic image samples. This system uses deep learning with the transfer learning method. Inception V3 architecture was modified and retained using 108 image samples labeled with five types of bacteria, namely Acinetobacter baumanii, Escherichia coli, Neisseria gonorrhoeae, Propionibacterium acnes and Veionella. The data is then divided into training and validation using the k-fold cross validation method. Furthermore, the features that have been extracted by the model are trained with the configuration of minibatchsize 5, maxepoch 5, initiallearnrate 0.0001, and validation frequency 3. The model is then tested with data validation by conducting ten experiments and getting an average accuracy value of 94.42%.

Background: Nontuberculous mycobacteria (NTMs) have now emerged as important opportunistic bacterial pathogens, particularly among patients with cystic fibrosis (CF). The development of improved molecular technologies and bioinformatics and the adoption of whole-genome sequencing to more isolates have allowed for a reanalysis of the existing taxa within the genus Mycobacterium, resulting in the renaming of some existing NTM Mycobacterium species to three novel genera, viz., Mycolicibacterium gen. nov., Mycolicibacter gen. nov. and Mycobacteroides gen. nov. This has resulted in controversy, particularly within the clinical community, accompanied by a reluctance to adopt and employ these new bacterial names. Therefore, the aims of this study were (i) to identify NTM organisms associated with CF lung disease that have been reported previously in the published literature, (ii) to examine the realignment of NTM organisms previously described in CF within the revised new mycobacterial taxonomy and renaming, and (iii) to identify and explore online taxonomical tools to help educate clinical medicine about recent changes in NTM taxonomy. Methods: Three tasks were performed, namely (i) to identify NTM organisms previously associated with people with CF, (ii) to examine the extent and scope of the reclassification of CF-related NTM species affected by changes in recent taxonomy and nomenclature, and (iii) to identify and examine the educational utility of online taxonomical educational tools/software (LifeMap [http://lifemap.univ-lyon1.fr/]; National Center for Biotechnology Information [NCBI] Taxonomy browser [https://www.ncbi.nlm.nih. gov/guide/taxonomy/]; and List of Prokaryotic names with Standing in Nomenclature [LPSN] [https://lpsn.dsmz.de/]). Mycobacterium (Mycobacteroides) abscessus was selected as the species to evaluate the application of these tools. Results: Twenty-one NTM species have been reported that have been associated with CF lung disease. Of these, two have been reclassified into the Mycobacteroides genus, two into the Mycolicibacter genus, and seven into the Mycolicibacterium genus. LifeMap, NCBI Taxonomy browser, and LPSN offered interactive visual support to better understand the taxonomy and nomenclature of NTM organisms. Conclusion: We, therefore, advocate that clinical and scientific parties employ these online tools to gain a better insight into the familiarization and understanding of such evolving NTM classification, thereby aiding a better lexicon and communication among all stakeholders.

Background: Nontuberculous mycobacteria (NTMs) have now emerged as important opportunistic bacterial pathogens, particularly among patients with cystic fibrosis (CF). The development of improved molecular technologies and bioinformatics and the adoption of whole-genome sequencing to more isolates have allowed for a reanalysis of the existing taxa within the genus Mycobacterium, resulting in the renaming of some existing NTM Mycobacterium species to three novel genera, viz., Mycolicibacterium gen. nov., Mycolicibacter gen. nov. and Mycobacteroides gen. nov. This has resulted in controversy, particularly within the clinical community, accompanied by a reluctance to adopt and employ these new bacterial names. Therefore, the aims of this study were (i) to identify NTM organisms associated with CF lung disease that have been reported previously in the published literature, (ii) to examine the realignment of NTM organisms previously described in CF within the revised new mycobacterial taxonomy and renaming, and (iii) to identify and explore online taxonomical tools to help educate clinical medicine about recent changes in NTM taxonomy. Methods: Three tasks were performed, namely (i) to identify NTM organisms previously associated with people with CF, (ii) to examine the extent and scope of the reclassification of CF-related NTM species affected by changes in recent taxonomy and nomenclature, and (iii) to identify and examine the educational utility of online taxonomical educational tools/software (LifeMap [http://lifemap.univ-lyon1.fr/]; National Center for Biotechnology Information [NCBI] Taxonomy browser [https://www.ncbi.nlm.nih. gov/guide/taxonomy/]; and List of Prokaryotic names with Standing in Nomenclature [LPSN] [https://lpsn.dsmz.de/]). Mycobacterium (Mycobacteroides) abscessus was selected as the species to evaluate the application of these tools. Results: Twenty-one NTM species have been reported that have been associated with CF lung disease. Of these, two have been reclassified into the Mycobacteroides genus, two into the Mycolicibacter genus, and seven into the Mycolicibacterium genus. LifeMap, NCBI Taxonomy browser, and LPSN offered interactive visual support to better understand the taxonomy and nomenclature of NTM organisms. Conclusion: We, therefore, advocate that clinical and scientific parties employ these online tools to gain a better insight into the familiarization and understanding of such evolving NTM classification, thereby aiding a better lexicon and communication among all stakeholders. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bacteria form an intense portion of reading and learning for students enrolled in microbiology education. As a part of the foundational course outline of bacteriology, bacterial classification is a significant topic of discussion. The purpose of our study was to analyze whether bacterial classification can be taught with a phylogenetic tree approach that might be more engaging and beneficial to student learners of microbiology. This methodology is unique compared to the conventional approach applied in introductory lectures of bacteriology that relies on morphology and Gram-staining to classify bacteria. The participants of this study were students enrolled in a two-semester medical school bridge program that offers a Master's degree in Pre-clinical Sciences. We presented bacterial origin and classification in the light of evolution and used a phylogenetic tree to signify clinically relevant groups of bacteria. Students were also taught the traditional bacterial classification using Gram stains and morphology. Both methods of classification were delivered in a didactic classroom session considering equal time spent and utilizing the same format. An online survey was distributed to the students after the session to collect their feedback. The results from the survey showed that 74% of participants would prefer learning bacterial classification using a combined approach that includes both Gram-staining and morphology as well as the phylogenetic tree. When asked if the study of bacterial classification through an evolutionary tree diagram is a clear and concise way of understanding bacteria, 79% of the students either agreed or strongly agreed with this statement. Interestingly, the alternative phylogenetic tree approach was considered more engaging and regarded as a means to expand the clinical knowledge of bacteria by 78% and 71% of the students, respectively. Overall, our study strongly supports the use of tree-based classification as an additional method to improve the learning of medically important groups of bacteria at varying levels of education., Competing Interests: The authors declare no conflict of interest.

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.

The lactic acidbacteria Lactobacillus gasseri was recently reclassifiedbasedon the average nucleotide identity (ANI) value, calculatedbasedon the identity of the whole genome sequence, anda subset was reclassifiedas Lactobacillus paragasseri. L. gasseri shows high extracellular D-amino acidprod uction ability; thus, we assessed alr, racX, murI encoding alanine racemase, aspartate racemase, andglutamate racemase, andcomparedthe nucleotide sequences of these amino acidracemase genes between L. gasseri and L. paragasseri. Both alr and murI genes extractedfrom 88 genome sequences exhibitedrestriction fragment length polymorphisms (RFLP) in a speciesdependent manner. In 4 L. gasseri and18 L. paragasseri strains pre-identified using pheS and rpoA genes, the DNA fragments of alr and murI from these 22 strains also exhibitedRFLP by DraI and BamHI digestion in a speciesdependent manner. Therefore, RFLP found in the alr and murI genes is useful for rapidd ifferentiation of these two species. [ABSTRACT FROM AUTHOR]

Bacteria are everywhere and pose severe threats to human health and safety. The rapid classification and sensitive detection of bacteria are vital steps of bacterial community research and the treatment of infection. Herein, we developed optical property–superior and heavy metal–free ZnCuInSe quantum dots (QDs) for achieving rapid discrimination of Gram-positive/Gram-negative bacteria by the naked eye; driven by the structural differences of bacteria, ZnCuInSe QDs are effective in binding to Gram-positive bacteria, especially Staphylococcus aureus (S. aureus), in comparison with Gram-negative bacteria and give discernable color viewed by the naked eye. Meanwhile, based on its distinctive fluorescence response, the accurate quantification of S. aureus was investigated with a photoluminescence system in the concentration ranges of 1 × 103 to 1 × 1011 CFU/mL, with a limit of detection of 1 × 103 CFU/mL. Furthermore, we demonstrated the feasibility of ZnCuInSe QDs as a fluorescence probe for imaging S. aureus. This simple strategy based on ZnCuInSe QDs provides an unprecedented step for rapid and effective bacterial discrimination, detection, and imaging. [ABSTRACT FROM AUTHOR]

We survey here the Alphaproteobacteria, a large class encompassing physiologically diverse bacteria which are divided in several orders established since 2007. Currently, there is considerable uncertainty regarding the classification of an increasing number of marine metagenome-assembled genomes (MAGs) that remain poorly defined in their taxonomic position within Alphaproteobacteria. The traditional classification of NCBI taxonomy is increasingly complemented by the Genome Taxonomy Database (GTDB), but the two taxonomies differ considerably in the classification of several Alphaproteobacteria, especially from ocean metagenomes. We analyzed the classification of Alphaproteobacteria lineages that are most common in marine environments, using integrated approaches of phylogenomics and functional profiling of metabolic features that define their aerobic metabolism. Using protein markers such as NuoL, the largest membrane subunit of complex I, we have identified new clades of Alphaproteobacteria that are specific to marine niches with steep oxygen gradients (oxycline). These bacteria have relatives among MAGs found in anoxic strata of Lake Tanganyika and together define a lineage that is distinct from either Rhodospirillales or Sneathiellales. We characterized in particular the new ‘oxycline’ clade. Our analysis of Alphaproteobacteria also reveals new clues regarding the ancestry of mitochondria, which likely evolved in oxycline marine environments.

Bacterial 16S ribosomal gene was used to classify bacteria because it consists of both highly conservative region, as well as a hypervariable region, in its sequence. This hypervariable region serves as a discriminative factor to differentiate bacteria at taxonomic levels. In the past, many efforts have been made to correctly identify a bacterial species from environmental samples or human gut microbiome samples, yet this identification and subsequent classification task is challenging. For such bacterial taxonomic classification, several studies in the past have been performed based on k-mer frequency matching, assembly-based clustering, supervised/unsupervised machine learning models, and a very few studies with deep learning architectures. In this article, we study and propose six different deep learning architectures involving recurrent neural networks (RNNs) and convolutional neural networks to classify bacteria at a family, genus, and species taxonomic level using ∼12,900 16S ribosomal DNA sequences. The best classification accuracies achieved are 92%, 86%, and 70% at family, genus, and species taxonomic level, respectively, by variants of RNN. [ABSTRACT FROM AUTHOR]

Abstract Background Citrobacter strains are opportunistic pathogens often responsible for serious enteric as well as extra-intestinal diseases, and therefore the O-antigenic scheme, still in use in diagnostic identification, should be set for proper serotyping. The structures of more than 30 different Citrobacter O-antigens (O-polysaccharide chains of the lipopolysaccharides) of 43 Citrobacter O-serogroups have been elucidated so far. However, relationships between strains in several heterogeneous serogroups still need to be clarified by immunochemical studies. These include complex serogroups O3 and O8, represented by 20 and 7 strains, respectively, which are the subject of the present work. Earlier, the O-polysaccharide structures have been determined for Citrobacter O3 strain Be35/57 (PCM 1508) and Citrobacter O8 strain Be64/57 (PCM 1536). Results Serological studies (immunoblotting) carried out on Citrobacter lipopolysaccharides from different strains ascribed to serogroups O3 and O8 showed that each of these serogroups should be divided into non-cross-reacting subgroups. Based on the results of chemical analyses and 1H and 13C NMR spectroscopy the structure of Citrobacter O-antigens from strains PCM 1504 (O6) and PCM 1573 (O2) have been established. Chemical data combined with serological analyses showed that several Citrobacter strains should be reclassified into other serogroups. Conclusions Immunochemical studies carried out on Citrobacter LPS, described in this paper, showed the expediency of reclassification of: 1) strains PCM 1504 and PCM 1573 from serogroups O6 and O2 to serogroups O3 and O8, respectively, 2) strains PCM 1503 and PCM 1505 from serogroups O3 and O8 to new serogroups O3a and O8a, respectively.

With the advent of high-throughput DNA sequencing technologies, traditional methodologies for taxonomic classification of bacteria as DNA–DNA hybridization and 16S rRNA identity analyses are being challenged by the development of a fast-growing number of genomic metrics. The large amount of portable and digitized genome sequences available in public repositories constitutes an invaluable data for bacterial classification. Consequently, several genomic metrics and tools were developed to aid the interpretation of these massive data. Genomic metrics are based on the assumption that higher genome similarities would reflect closer phylogenetic relationships. Different metrics would vary in their methodology of analysis, resolution power, limitations and easiness of use. The aim of this review is to highlight the differences among available genome-based methods and tools to provide a guide for in silico bacterial identification and classification. [ABSTRACT FROM AUTHOR]

Traditionally, optical microscopy is used to visualize the morphological features of pathogenic bacteria, of which the features are further used for the detection and identification of the bacteria. However, due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification, the effectiveness of this optical microscopy-based method is limited. Here, we reported a pilot study on a combined use of Structured Illumination Microscopy (SIM) with machine learning for rapid bacteria identification. After applying machine learning to the SIM image datasets from three model bacteria (including Escherichia coli, Mycobacterium smegmatis, and Pseudomonas aeruginosa), we obtained a classification accuracy of up to 98%. This study points out a promising possibility for rapid bacterial identification by morphological features.

This research paper presents an overview of contemporary machine learning methodologies and their utilization in the domain of healthcare and the prevention of infectious diseases, specifically focusing on the classification and identification of bacterial species. As deep learning techniques have gained prominence in the healthcare sector, a diverse array of architectural models has emerged. Through a comprehensive review of pertinent literature, multiple studies employing machine learning algorithms in the context of microbial diagnosis and classification are examined. Each investigation entails a tabulated presentation of data, encompassing details about the training and validation datasets, specifications of the machine learning and deep learning techniques employed, as well as the evaluation metrics utilized to gauge algorithmic performance. Notably, Convolutional Neural Networks have been the predominant selection for image classification tasks by machine learning practitioners over the last decade. This preference stems from their ability to autonomously extract pertinent and distinguishing features with minimal human intervention. A range of CNN architectures have been developed and effectively applied in the realm of image classification. However, addressing the considerable data requirements of deep learning, recent advancements encompass the application of pre-trained models using transfer learning for the identification of microbial entities. This method involves repurposing the knowledge gleaned from solving alternate image classification challenges to accurately classify microbial images. Consequently, the necessity for extensive and varied training data is significantly mitigated. This study undertakes a comparative assessment of various popular pre-trained CNN architectures for the classification of bacteria. The dataset employed is composed of approximately 660 images, representing 33 bacterial species. To enhance dataset diversity, data augmentation is implemented, followed by evaluation on multiple models including AlexNet, VGGNet, Inception networks, Residual Networks, and Densely Connected Convolutional Networks. The results indicate that the DenseNet-121 architecture yields the optimal performance, achieving a peak accuracy of 99.08%, precision of 99.06%, recall of 99.00%, and an F1-score of 98.99%. By demonstrating the proficiency of the DenseNet-121 model on a comparatively modest dataset, this study underscores the viability of transfer learning in the healthcare sector for precise and efficient microbial identification. These findings contribute to the ongoing endeavors aimed at harnessing machine learning techniques to enhance healthcare methodologies and bolster infectious disease prevention practices., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wu and Gadsden.)

Near-infrared hyperspectral imaging together with versatile chemometric algorithms including invasive weed optimization (IWO) were employed for optimizing fast classification of bacterial colonies on agar plates. Hyperspectral images of colonies from six strains of bacteria were collected, and classification models were established by applying partial least squares-discriminant analysis and support vector machine (SVM) on the original as well as difference spectra. The parameters of SVM models were optimized by comparing genetic algorithm, particle swarm optimization and the proposed IWO. The results showed that difference spectra amplified the variations among the spectra of the six strains thus potential for improving classification accuracy. The best full wavelength classification model was IWO-SVM model which produced overall correct classification rates (OCCRs) of 100.0% and 97.0% for calibration and prediction, respectively. Besides, competitive adaptive reweighted sampling (CARS), GA and successive projections algorithm (SPA) were utilized to select important wavelengths to establish simplified models. Among them, the simplified IWO-SVM model based on the feature wavelengths selected by CARS gave the best classification rates of 97.2% and 96.0% for calibration and prediction, respectively. The study demonstrated that IWO was a useful tool for optimizing calibration models thus potential for usage in many other applications. [ABSTRACT FROM AUTHOR]

Traditionally, optical microscopy is used to visualize the morphological features of pathogenic bacteria, of which the features are further used for the detection and identification of the bacteria. However, due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification, the effectiveness of this optical microscopy-based method is limited. Here, we reported a pilot study on a combined use of Structured Illumination Microscopy (SIM) with machine learning for rapid bacteria identification. After applying machine learning to the SIM image datasets from three model bacteria (including Escherichia coli, Mycobacterium smegmatis, and Pseudomonas aeruginosa), we obtained a classification accuracy of up to 98%. This study points out a promising possibility for rapid bacterial identification by morphological features. [ABSTRACT FROM AUTHOR]

Microbial diversity has always presented taxonomic challenges. With the popularity of next-generation sequencing technology, more unculturable bacteria have been sequenced, facilitating the discovery of additional new species and complicated current microbial classification. The major challenge is to assign appropriate taxonomic names. Hence, assessing the consistency between taxonomy and genomic relatedness is critical. We proposed and applied a genome comparison approach to a large-scale survey to investigate the distribution of genomic differences among microorganisms. The approach applies a genome-wide criterion, homologous coverage ratio (HCR), for describing the homology between species. The survey included 7861 microbial genomes that excluded plasmids, and 1220 pairs of genera exhibited ambiguous classification. In this study, we also compared the performance of HCR and average nucleotide identity (ANI). The results indicated that HCR and ANI analyses yield comparable results, but a few examples suggested that HCR has a superior clustering effect. In addition, we used the Genome Taxonomy Database (GTDB), the gold standard for taxonomy, to validate our analysis. The GTDB offers 120 ubiquitous single-copy proteins as marker genes for species classification. We determined that the analysis of the GTDB still results in classification boundary blur between some genera and that the marker gene-based approach has limitations. Although the choice of marker genes has been quite rigorous, the bias of marker gene selection remains unavoidable. Therefore, methods based on genomic alignment should be considered for use for species classification in order to avoid the bias of marker gene selection. On the basis of our observations of microbial diversity, microbial classification should be re-examined using genome-wide comparisons.

Background: Citrobacter strains are opportunistic pathogens often responsible for serious enteric as well as extraintestinal diseases, and therefore the O-antigenic scheme, still in use in diagnostic identification, should be set for proper serotyping. The structures of more than 30 different Citrobacter O-antigens (O-polysaccharide chains of the lipopolysaccharides) of 43 Citrobacter O-serogroups have been elucidated so far. However, relationships between strains in several heterogeneous serogroups still need to be clarified by immunochemical studies. These include complex serogroups O3 and O8, represented by 20 and 7 strains, respectively, which are the subject of the present work. Earlier, the O-polysaccharide structures have been determined for Citrobacter O3 strain Be35/57 (PCM 1508) and Citrobacter O8 strain Be64/57 (PCM 1536). Results: Serological studies (immunoblotting) carried out on Citrobacter lipopolysaccharides from different strains ascribed to serogroups O3 and O8 showed that each of these serogroups should be divided into non-cross-reacting subgroups. Based on the results of chemical analyses and ¹H and 13C NMR spectroscopy the structure of Citrobacter O-antigens from strains PCM 1504 (O6) and PCM 1573 (O2) have been established. Chemical data combined with serological analyses showed that several Citrobacter strains should be reclassified into other serogroups. Conclusions: Immunochemical studies carried out on Citrobacter LPS, described in this paper, showed the expediency of reclassification of: 1) strains PCM 1504 and PCM 1573 from serogroups O6 and O2 to serogroups O3 and O8, respectively, 2) strains PCM 1503 and PCM 1505 from serogroups O3 and O8 to new serogroups O3a and O8a, respectively. [ABSTRACT FROM AUTHOR]

Confusion about strain classification and nomenclature permeates modern microbiology. Although taxonomists have traditionally acted as gatekeepers of order, the numbers of, and speed at which, new strains are identified has outpaced the opportunity for professional classification for many lineages. Furthermore, the growth of bioinformatics and database-fueled investigations have placed metadata curation in the hands of researchers with little taxonomic experience. Here I describe practical challenges facing modern microbial taxonomy, provide an overview of complexities of classification for environmentally ubiquitous taxa like Pseudomonas syringae , and emphasize that classification can be independent of nomenclature. A move toward implementation of relational classification schemes based on inherent properties of whole genomes could provide sorely needed continuity in how strains are referenced across manuscripts and data sets. [ABSTRACT FROM AUTHOR]

Four species of bacteria, E. coli, S. epidermidis, M. smegmatis, and P. aeruginosa, were harvested from agar nutrient medium growth plates and suspended in water to create liquid specimens for the testing of a new mounting protocol. Aliquots of 30 μL were deposited on standard nitrocellulose filter paper with a mean 0.45 μm pore size to create highly flat and uniform bacterial pads. The introduction of a laser-based lens-to-sample distance measuring device and a pair of matched off-axis parabolic reflectors for light collection improved both spectral reproducibility and the signal-to-noise ratio of optical emission spectra acquired from the bacterial pads by laser-induced breakdown spectroscopy. A discriminant function analysis and a partial least squares-discriminant analysis both showed improved sensitivity and specificity compared to previous mounting techniques. The behavior of the spectra as a function of suspension concentration and filter coverage was investigated, as was the effect on chemometric cell classification of sterilization via autoclaving. [ABSTRACT FROM AUTHOR]

[Display omitted] • Support vector machine (SVM) can be used as an efficient multi-classifier to differentiate bacterial strains. • FT-IR combined with multivariate analysis can classify E. coli and Shigella efficiently. • An ensemble-classifier model was constructed for E. coli and Shigella FT-IR data classification. Accurate and effective discrimination of E. coli and Shigella is an important clinical issue, and there are many limitations in traditional methods of analysis. FT-IR shows great potential in the classification of bacteria with high specificity and low cost. In this study, we evaluated the efficiency of this technique when combined with multivariate analysis for rapid classification of E. coli and Shigella, which is difficult using traditional analytical methods. Machine learning and statistical tools were employed in combination with FT-IR to classify 14 E. coli and 9 Shigella strains. The classification accuracies for select E. coli and Shigella strains from blood agar were 0.7826, 0.8696, and 0.9565 at the genus, species, and strain levels, respectively. In addition, we used the FT-IR data of select strains from three different culture media for cross-validation, yielding an accuracy of 0.3681 at the strain level. These results indicate that the bacterial culture conditions have a significant impact on the FT-IR patterns. Based on this, an improved strategy for training an ensemble classifier model considering bacterial culture factors was constructed, resulting in almost perfect separation with an accuracy of 0.9394 for strain-level classification. These results show the potential of FT-IR combined with multivariate analysis for more reliable bacterial classification. [ABSTRACT FROM AUTHOR]

The identification of pathogenic bacteria is a frequently required task. Current identification procedures are usually either time-consuming due to necessary cultivation steps or expensive and demanding in their application. Furthermore, previous treatment of a patient with antibiotics often renders routine analysis by culturing difficult. Since Raman microspectroscopy allows for the identification of single bacterial cells, it can be used to identify such difficult to culture bacteria. Yet until now, there have been no investigations whether antibiotic treatment of the bacteria influences the Raman spectroscopic identification. This study aims to rapidly identify bacteria that have been subjected to antibiotic treatment on single cell level with Raman microspectroscopy. Two strains of Escherichia coli and two species of Pseudomonas have been treated with four antibiotics, all targeting different sites of the bacteria. With Raman spectra from untreated bacteria, a linear discriminant analysis (LDA) model is built, which successfully identifies the species of independent untreated bacteria. Upon treatment of the bacteria with subinhibitory concentrations of ampicillin, ciprofloxacin, gentamicin, and sulfamethoxazole, the LDA model achieves species identification accuracies of 85.4, 95.3, 89.9, and 97.3 %, respectively. Increasing the antibiotic concentrations has no effect on the identification performance. An ampicillin-resistant strain of E. coli and a sample of P. aeruginosa are successfully identified as well. General representation of antibiotic stress in the training data improves species identification performance, while representation of a specific antibiotic improves strain distinction capability. In conclusion, the identification of antibiotically treated bacteria is possible with Raman microspectroscopy for diverse antibiotics on single cell level. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Abstract: In the genus Pseudomonas, two main lineages and 19 phylogenetic groups or subgroups have been defined based on DNA sequence studies of 16S rRNA, gyrB and rpoD genes. In the present study, 33 strains previously classified as Pseudomonas putida were phylogenetically affiliated with their closest relatives in the genus by multilocus sequence analysis. The results demonstrated that strains assigned to biovar A of the species were located in the P. putida group, though not all belonged to the species P. putida. Biovar B strains were scattered among 6 subgroups of the Pseudomonas fluorescens group and also within the P. putida group. These results were corroborated when the entire genomes of 5 strains assigned to P. putida were analyzed. The phylogenetic results show that isolates of biovars A and B are in distinct phylogenetic groups. Thus, these biotypes are not reliable taxonomic markers. [Copyright &y& Elsevier]

Abstract: The need of quick diagnostics and increasing number of bacterial species isolated necessitate development of a rapid and effective phenotypic identification method. Mass spectrometry (MS) profiling of whole cell proteins has potential to satisfy the requirements. The genus Mycobacterium contains more than 154 species that are taxonomically very close and require use of multiple genes including 16S rDNA for phylogenetic identification and classification. Six strains of five Mycobacterium species were selected as model bacteria in the present study because of their 16S rDNA similarity (98.4–99.8%) and the high similarity of the concatenated 16S rDNA, rpoB and hsp65 gene sequences (95.9–99.9%), requiring high identification resolution. The classification of the six strains by MALDI TOF MS protein barcodes was consistent with, but at much higher resolution than, that of the multi-locus sequence analysis of using 16S rDNA, rpoB and hsp65. The species were well differentiated using MALDI TOF MS and MALDI BioTyper™ software after quick preparation of whole-cell proteins. Several proteins were selected as diagnostic markers for species confirmation. An integration of MALDI TOF MS, MALDI BioTyper™ software and diagnostic protein fragments provides a robust phenotypic approach for bacterial identification and classification. [Copyright &y& Elsevier]

Abstract: The differentiation of single bacterial cells using micro-Raman spectroscopy can be hampered by large intra-strain variability of the measured microorganisms due to fluctuating culture ages, nutrition conditions, and cultivation temperatures. Gaussian mixture discriminant analysis (MDA) is an effective classification approach for this task, as it is able to model inhomogeneous and scattering class structures. On the basis of a highly diverse dataset comprising 3642 spectra of 29 different strains of bacteria, the utility of MDA for the differentiation of microorganisms by micro-Raman spectroscopy was demonstrated in comparison to various linear and nonlinear classification algorithms. The employed algorithms include partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor classifier (kNN) and support vector machines (SVMs). In a first attempt the best prediction performance was achieved by a SVM model yielding 87.3% of correctly classified spectra outperforming MDA (80.9%) and the other classification methods. The prediction accuracy of MDA can be improved markedly by establishing multiple one-class-versus-one-class models and making predictions by a major vote decision over all pairwise classifications. Using this pairwise approach the performance of MDA increased up to 86.6%, which is statistically equivalent to the performance of a support vector machine. In the case of MDA, the assessment of a posteriori probabilities allows a straightforward novelty detection procedure. Moreover, due to its cluster property, MDA can be employed to visualize the effect of varying cultivation parameters on the group-structure of the investigated dataset. The analysis demonstrates that MDA exhibits useful features for the differentiation of single bacteria by micro-Raman spectroscopy in terms of prediction accuracy, novelty detection, and interpretation of the model. [Copyright &y& Elsevier]

We survey here the Alphaproteobacteria, a large class encompassing physiologically diverse bacteria which are divided in several orders established since 2007. Currently, there is considerable uncertainty regarding the classification of an increasing number of marine metagenome-assembled genomes (MAGs) that remain poorly defined in their taxonomic position within Alphaproteobacteria. The traditional classification of NCBI taxonomy is increasingly complemented by the Genome Taxonomy Database (GTDB), but the two taxonomies differ considerably in the classification of several Alphaproteobacteria, especially from ocean metagenomes. We analyzed the classification of Alphaproteobacteria lineages that are most common in marine environments, using integrated approaches of phylogenomics and functional profiling of metabolic features that define their aerobic metabolism. Using protein markers such as NuoL, the largest membrane subunit of complex I, we have identified new clades of Alphaproteobacteria that are specific to marine niches with steep oxygen gradients (oxycline). These bacteria have relatives among MAGs found in anoxic strata of Lake Tanganyika and together define a lineage that is distinct from either Rhodospirillales or Sneathiellales. We characterized in particular the new 'oxycline' clade. Our analysis of Alphaproteobacteria also reveals new clues regarding the ancestry of mitochondria, which likely evolved in oxycline marine environments. [ABSTRACT FROM AUTHOR]

Classification of microorganisms on the basis of traditional microbiological methods (morphological, physiological and biochemical) creates a blurred image about their taxonomic status and thus needs further clarification. It should be based on a more pragmatic approach of deploying a number of methods for the complete characterization of microbes. Hence, the methods now employed for bacterial systematics include, the complete 16S rRNA gene sequencing and its comparative analysis by phylogenetic trees, DNA-DNA hybridization studies with related organisms, analyses of molecular markers and signature pattern(s), biochemical assays, physiological and morphological tests. Collectively these genotypic, chemotaxonomic and phenotypic methods for determining taxonomic position of microbes constitute what is known as the ‘polyphasic approach’ for bacterial systematics. This approach is currently the most popular choice for classifying bacteria and several microbes, which were previously placed under invalid taxa have now been resolved into new genera and species. This has been possible owing to rapid development in molecular biological techniques, automation of DNA sequencing coupled with advances in bioinformatic tools and access to sequence databases. Several DNA-based typing methods are known; these provide information for delineating bacteria into different genera and species and have the potential to resolve differences among the strains of a species. Therefore, newly isolated strains must be classified on the basis of the polyphasic approach. Also previously classified organisms, as and when required, can be reclassified on this ground in order to obtain information about their accurate position in the microbial world. Thus, current techniques enable microbiologists to decipher the natural phylogenetic relationships between microbes. [ABSTRACT FROM AUTHOR]

Honey bees are important pollinators of many major crops and add billions of dollars annually to the US economy through their services. Recent declines in the health of the honey bee have startled researchers and lay people alike as honey bees are agriculture's most important pollinator. One factor that may influence colony health is the microbial community. Although honey bee worker guts have a characteristic community of bee-specific microbes, the honey bee queen digestive tracts are colonized predominantly by a single acetic acid bacterium tentatively named ' Parasaccharibacter apium '. This bacterium is related to flower-associated microbes such as Saccharibacter floricola , and initial phylogenetic analyses placed it as sister to these environmental bacteria. We used a combination of phylogenetic and sequence identity methods to better resolve evolutionary relationships among ' P. apium ', strains in the genus Saccharibacter , and strains in the closely related genus Bombella . Interestingly, measures of genome-wide average nucleotide identity and aligned fraction, coupled with phylogenetic placement, indicate that many strains labelled as ' P. apium ' and Saccharibacter species are all the same species as Bombella apis . We propose reclassifying these strains as Bombella apis and outline the data supporting that classification below.

Antibiotics are widely used to prevent and treat diseases and promote animal growth in the livestock industry, and therefore antibiotic residues can end up in biogas digestate from processes treating animal manure (AM) and food waste (FW). These digestates represent a potential source of spread of antimicrobial resistance (AMR) when used as fertilisers. This study evaluated AMR risks associated with biogas digestates from two processes, using AM and FW as substrate, by isolation and identification of antibiotic-resistant bacteria (ARB) and testing their susceptibility to different antibiotics. ARB from the digestates were isolated by selective plating. The antibiotic susceptibility profile of isolates was determined using ampicillin, ceftazidime, meropenem, vancomycin, ciprofloxacin, rifampicin, chloramphenicol, clindamycin, erythromycin, tetracycline, gentamicin or sulfamethoxazole/trimethoprim, representing different antibiotic classes with differing mechanisms of action. In total, 30 different bacterial species belonging to seven genera were isolated and classified. Bacillus and closely related genera, including Paenibacillus , Lysinibacillus and Brevibacillus , were the dominant ARB in both digestates. Most of the ARB strains isolated were non-pathogenic and some were even known to be beneficial to plant growth. However, some were potentially pathogenic, such as an isolate identified as Bacillus cereus. Many of the isolated species showed multi resistance and the AM digestate and FW digestate both contain bacterial species resistant to all antibiotics tested here, except gentamicin. A higher level of resistance was displayed by the FW isolates, which may indicate higher antibiotic pressure in FW compared with AM digestate. Overall, the results indicate a risk of AMR spread when these digestates are used as fertiliser. However, most of the ARB identified are species commonly found in soil, where AMR in many cases is abundant already, so the contribution of digestate-based fertiliser to the spread of AMR may still be very limited. Image 1 • ARB were isolated from both animal manure and food waste digestate. • Bacillus , Paenibacillus , Lysinibacillus and Brevibacillus were the dominant ARB. • Most ARB identified were non-pathogenic, but some were potentially pathogenic. • A limited risk of AMR spread is suggested when digestate is used as fertiliser. [ABSTRACT FROM AUTHOR]

Microbial diversity has always presented taxonomic challenges. With the popularity of next-generation sequencing technology, more unculturable bacteria have been sequenced, facilitating the discovery of additional new species and complicated current microbial classification. The major challenge is to assign appropriate taxonomic names. Hence, assessing the consistency between taxonomy and genomic relatedness is critical. We proposed and applied a genome comparison approach to a large-scale survey to investigate the distribution of genomic differences among microorganisms. The approach applies a genome-wide criterion, homologous coverage ratio (HCR), for describing the homology between species. The survey included 7861 microbial genomes that excluded plasmids, and 1220 pairs of genera exhibited ambiguous classification. In this study, we also compared the performance of HCR and average nucleotide identity (ANI). The results indicated that HCR and ANI analyses yield comparable results, but a few examples suggested that HCR has a superior clustering effect. In addition, we used the Genome Taxonomy Database (GTDB), the gold standard for taxonomy, to validate our analysis. The GTDB offers 120 ubiquitous single-copy proteins as marker genes for species classification. We determined that the analysis of the GTDB still results in classification boundary blur between some genera and that the marker gene-based approach has limitations. Although the choice of marker genes has been quite rigorous, the bias of marker gene selection remains unavoidable. Therefore, methods based on genomic alignment should be considered for use for species classification in order to avoid the bias of marker gene selection. On the basis of our observations of microbial diversity, microbial classification should be re-examined using genome-wide comparisons. [ABSTRACT FROM AUTHOR]