scholarly journals Overview of PCR Methods Applied for the Identification of Freshwater Toxigenic Cyanobacteria

2021 ◽  
Author(s):  
Jian Yuan ◽  
Kyoung-Jin Yoon
Keyword(s):  
Large Scale ◽  
Morphological Difference ◽  
Toxin Production ◽  
Turnaround Time ◽  
Marker Genes ◽  
Accurate Identification ◽  
Real Time Quantitative Pcr ◽  
Molecular Assays ◽  
Cyanobacterial Species ◽  
Pcr Assays

Although cyanobacteria are essential microorganisms on earth, some cyanobacteria produce toxins known as cyanotoxins, threatening humans and animals’ health. Hence, it is imperative to rapidly and accurately identify those toxic cyanobacteria. Unfortunately, traditional microscopic methods have limitations for accurate identification due to the lack of discernable morphological difference between toxic and non-toxic strains within the same cyanobacterial species or genus. In contrast, their genetic profiles are inherently conserved; therefore, nucleic acid-based assays can be more reliable for precise identification. Furthermore, molecular assays can provide high throughput and significantly reduce the turnaround time of test results. Such advantages make those assays a preferred method for rapid detection and early warning of potential toxicity. Toxigenic cyanobacterial species have synthetase genes (DNAs) for toxin production, which can be excellent marker genes. Numerous molecular assays targeting cyanotoxin synthetase genes have been developed for the identification of toxigenic cyanobacteria at various taxonomic levels. Polymerase chain reaction (PCR)-based assays are the most prevailing. Among different versions of PCR assays, the real-time quantitative PCR can be utilized to quantify the genes of interest in samples, fulfilling the purpose of both taxonomic recognition and biomass estimation. Reverse transcription (RT)-PCR assays can be used to detect transcripts (i.e., mRNAs) from toxin synthetase genes, probably enhancing the predictive value of PCR detection for toxin production from observed cyanobacterial species. Nevertheless, the utility of toxin synthetase gene- or its transcript-based PCR assays for routine cyanotoxin monitoring needs to be further evaluated on a large scale.

scholarly journals The mutL Gene as a Genome-Wide Taxonomic Marker for High Resolution Discrimination of Lactiplantibacillus plantarum and Its Closely Related Taxa

2021 ◽  
Vol 9 (8) ◽  
pp. 1570
Author(s):  
Chien-Hsun Huang ◽  
Chih-Chieh Chen ◽  
Yu-Chun Lin ◽  
Chia-Hsuan Chen ◽  
Ai-Yun Lee ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Target Genes ◽  
Marker Genes ◽  
Rrna Gene ◽  
Accurate Identification ◽  
Discrimination Power ◽  
Sequence Identity ◽  
Genome Wide ◽  
A Genome

The current taxonomy of the Lactiplantibacillus plantarum group comprises of 17 closely related species that are indistinguishable from each other by using commonly used 16S rRNA gene sequencing. In this study, a whole-genome-based analysis was carried out for exploring the highly distinguished target genes whose interspecific sequence identity is significantly less than those of 16S rRNA or conventional housekeeping genes. In silico analyses of 774 core genes by the cano-wgMLST_BacCompare analytics platform indicated that csbB, morA, murI, mutL, ntpJ, rutB, trmK, ydaF, and yhhX genes were the most promising candidates. Subsequently, the mutL gene was selected, and the discrimination power was further evaluated using Sanger sequencing. Among the type strains, mutL exhibited a clearly superior sequence identity (61.6–85.6%; average: 66.6%) to the 16S rRNA gene (96.7–100%; average: 98.4%) and the conventional phylogenetic marker genes (e.g., dnaJ, dnaK, pheS, recA, and rpoA), respectively, which could be used to separat tested strains into various species clusters. Consequently, species-specific primers were developed for fast and accurate identification of L. pentosus, L. argentoratensis, L. plantarum, and L. paraplantarum. During this study, one strain (BCRC 06B0048, L. pentosus) exhibited not only relatively low mutL sequence identities (97.0%) but also a low digital DNA–DNA hybridization value (78.1%) with the type strain DSM 20314T, signifying that it exhibits potential for reclassification as a novel subspecies. Our data demonstrate that mutL can be a genome-wide target for identifying and classifying the L. plantarum group species and for differentiating novel taxa from known species.

scholarly journals Comparison of nine different commercially available molecular assays for detection of SARS-CoV-2 RNA

2021 ◽  
Author(s):  
Ute Eberle ◽  
◽  
Clara Wimmer ◽  
Ingrid Huber ◽  
Antonie Neubauer-Juric ◽  
...  
Keyword(s):  
Real Time ◽  
Rt Pcr ◽  
Diagnostic Assays ◽  
Molecular Assays ◽  
Pcr Assays ◽  
Laboratory Experiences

AbstractTo face the COVID-19 pandemic, the need for fast and reliable diagnostic assays for the detection of SARS-CoV-2 is immense. We describe our laboratory experiences evaluating nine commercially available real-time RT-PCR assays. We found that assays differed considerably in performance and validation before routine use is mandatory.

Microcomputers in Political Science

1983 ◽  
Vol 38 ◽  
pp. 1-9
Author(s):  
Herbert F. Weisberg
Keyword(s):  
Data Analysis ◽  
Political Science ◽  
Large Scale ◽  
Turnaround Time ◽  
General Purpose ◽  
Batch Mode ◽  
New Era ◽  
Large Scale Data ◽  
The Social ◽  
Frequency Counts

We are now entering a new era of computing in political science. The first era was marked by punched-card technology. Initially, the most sophisticated analyses possible were frequency counts and tables produced on a counter-sorter, a machine that specialized in chewing up data cards. By the early 1960s, batch processing on large mainframe computers became the predominant mode of data analysis, with turnaround time of up to a week. By the late 1960s, turnaround time was cut down to a matter of a few minutes and OSIRIS and then SPSS (and more recently SAS) were developed as general-purpose data analysis packages for the social sciences. Even today, use of these packages in batch mode remains one of the most efficient means of processing large-scale data analysis.

Hunting the eagle killer: A cyanobacterial neurotoxin causes vacuolar myelinopathy

Science ◽  
2021 ◽  
Vol 371 (6536) ◽  
pp. eaax9050
Author(s):  
Steffen Breinlinger ◽  
Tabitha J. Phillips ◽  
Brigette N. Haram ◽  
Jan Mareš ◽  
José A. Martínez Yerena ◽  
...  
Keyword(s):  
United States ◽  
Aquatic Vegetation ◽  
Southeastern United States ◽  
Hydrilla Verticillata ◽  
Toxin Production ◽  
The United States ◽  
Biosynthetic Gene Cluster ◽  
Biosynthetic Gene ◽  
Bald Eagles ◽  
Cyanobacterial Species

Vacuolar myelinopathy is a fatal neurological disease that was initially discovered during a mysterious mass mortality of bald eagles in Arkansas in the United States. The cause of this wildlife disease has eluded scientists for decades while its occurrence has continued to spread throughout freshwater reservoirs in the southeastern United States. Recent studies have demonstrated that vacuolar myelinopathy is induced by consumption of the epiphytic cyanobacterial species Aetokthonos hydrillicola growing on aquatic vegetation, primarily the invasive Hydrilla verticillata. Here, we describe the identification, biosynthetic gene cluster, and biological activity of aetokthonotoxin, a pentabrominated biindole alkaloid that is produced by the cyanobacterium A. hydrillicola. We identify this cyanobacterial neurotoxin as the causal agent of vacuolar myelinopathy and discuss environmental factors—especially bromide availability—that promote toxin production.

scholarly journals CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4804
Author(s):  
Marcin Piekarczyk ◽  
Olaf Bar ◽  
Łukasz Bibrzycki ◽  
Michał Niedźwiecki ◽  
Krzysztof Rzecki ◽  
...  
Keyword(s):  
Wavelet Transforms ◽  
Exploratory Study ◽  
Large Scale ◽  
Morphological Difference ◽  
Cosmic Ray ◽  
Input Image ◽  
Image Features ◽  
The Earth ◽  
Cmos Sensor ◽  
Competition Process

Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.

scholarly journals Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr

2021 ◽  
Vol 13 (8) ◽  
pp. 1563
Author(s):  
Yuanyuan Tao ◽  
Qianxin Wang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Data Availability ◽  
Function Evaluation ◽  
Characteristic Analysis ◽  
Accurate Identification ◽  
Living Space ◽  
Evolution Characteristics

The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.

scholarly journals iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

2021 ◽  
Vol 22 (16) ◽  
pp. 8958
Author(s):  
Phasit Charoenkwan ◽  
Chanin Nantasenamat ◽  
Md. Mehedi Hasan ◽  
Mohammad Ali Moni ◽  
Pietro Lio’ ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
De Novo ◽  
Predictive Performance ◽  
Support Vector ◽  
Sufficient Information ◽  
Self Assessment ◽  
Accurate Identification ◽  
Bitter Peptides ◽  
Accurate Performance

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides

DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique

2020 ◽  
Author(s):  
Yu Wang ◽  
ZAHEER ULLAH KHAN ◽  
Shaukat Ali ◽  
Maqsood Hayat
Keyword(s):  
Feature Selection ◽  
Bacterial Infections ◽  
Large Scale ◽  
Computational Models ◽  
Genetic Material ◽  
Feature Space ◽  
Classification Model ◽  
Antibacterial Drug ◽  
Accurate Identification ◽  
Multi Level

Abstract BackgroundBacteriophage or phage is a type of virus that replicates itself inside bacteria. It consist of genetic material surrounded by a protein structure. Bacteriophage plays a vital role in the domain of phage therapy and genetic engineering. Phage and hydrolases enzyme proteins have a significant impact on the cure of pathogenic bacterial infections and disease treatment. Accurate identification of bacteriophage proteins is important in the host subcellular localization for further understanding of the interaction between phage, hydrolases, and in designing antibacterial drugs. Looking at the significance of Bacteriophage proteins, besides wet laboratory-based methods several computational models have been developed so far. However, the performance was not considerable due to inefficient feature schemes, redundancy, noise, and lack of an intelligent learning engine. Therefore we have developed an anovative bi-layered model name DeepEnzyPred. A Hybrid feature vector was obtained via a novel Multi-Level Multi-Threshold subset feature selection (MLMT-SFS) algorithm. A two-dimensional convolutional neural network was adopted as a baseline classifier.ResultsA conductive hybrid feature was obtained via a serial combination of CTD and KSAACGP features. The optimum feature was selected via a Novel Multi-Level Multi-Threshold Subset Feature selection algorithm. Over 5-fold jackknife cross-validation, an accuracy of 91.6 %, Sensitivity of 63.39%, Specificity 95.72%, MCC of 0.6049, and ROC value of 0.8772 over Layer-1 were recorded respectively. Similarly, the underline model obtained an Accuracy of 96.05%, Sensitivity of 96.22%, Specificity of 95.91%, MCC of 0.9219, and ROC value of 0.9899 over layer-2 respectivily.ConclusionThis paper presents a robust and effective classification model was developed for bacteriophage and their types. Primitive features were extracted via CTD and KSAACGP. A novel method (MLMT-SFS ) was devised for yielding optimum hybrid feature space out of primitive features. The result drew over hybrid feature space and 2D-CNN shown an excellent classification. Based on the recorded results, we believe that the developed predictor will be a valuable resource for large scale discrimination of unknown Phage and hydrolase enzymes in particular and new antibacterial drug design in pharmaceutical companies in general.

scholarly journals Advancing Knowledge on Cyanobacterial Blooms in Freshwaters

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2583
Author(s):  
Elisabeth Vardaka ◽  
Konstantinos Ar. Kormas
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Mathematical Modelling ◽  
Ecological Status ◽  
Toxin Production ◽  
Cyanobacterial Blooms ◽  
Special Issue ◽  
Research Papers ◽  
Cyanobacterial Species ◽  
Water Quality Deterioration

Cyanobacterial blooms have become a frequent phenomenon in freshwaters worldwide; they are a widely known indicator of eutrophication and water quality deterioration. Information and knowledge contributing towards the evaluation of the ecological status of freshwaters, particularly since many are used for recreation, drinking water, and aquaculture, is valuable. This Special Issue, entitled “Advancing Knowledge on Cyanobacterial Blooms in Freshwaters”, includes 11 research papers that will focus on the use of complementary approaches, from the most recently developed molecular-based methods to more classical approaches and experimental and mathematical modelling regarding the factors (abiotic and/or biotic) that control the diversity of not only the key bloom-forming cyanobacterial species, but also their interactions with other biota, either in freshwater systems or their adjacent habitats, and their role in preventing and/or promoting cyanobacterial growth and toxin production.

scholarly journals A New Genome-to-Genome Comparison Approach for Large-Scale Revisiting of Current Microbial Taxonomy

2019 ◽  
Vol 7 (6) ◽  
pp. 161 ◽  
Author(s):  
Ming-Hsin Tsai ◽  
Yen-Yi Liu ◽  
Von-Wun Soo ◽  
Chih-Chieh Chen
Keyword(s):  
Microbial Diversity ◽  
Large Scale ◽  
Gene Selection ◽  
Marker Gene ◽  
Genome Comparison ◽  
Marker Genes ◽  
Species Classification ◽  
Genome Wide ◽  
A Genome ◽  
Comparison Approach

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.

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