scholarly journals DeepMicrobeFinder Sorts Metagenomes into Prokaryotes, Eukaryotes and Viruses, with Marine Applications

2021 ◽  
Author(s):  
Shengwei Hou ◽  
Siliangyu Cheng ◽  
Ting Chen ◽  
Jed A. Fuhrman ◽  
Fengzhu Sun
Keyword(s):  
Characteristic Curve ◽  
Natural Environments ◽  
Multiple Sequence ◽  
Class Composition ◽  
Average Accuracy ◽  
Sequence Class ◽  
Community Growth ◽  
Marine Applications ◽  
Sequence Types

Abstract Sequence classification is valuable for reducing the complexity of metagenomes and providing a fundamental understanding of the composition of metagenomic samples. Binary metagenomic classifiers offer an insufficient solution because metagenomes of most natural environments are typically derived from multiple sequence sources including prokaryotes, eukaryotes and the viruses of both. Here we introduce a deep-learning based (not reference-based) sequence classifier, DeepMicrobeFinder, that classifies metagenomic contigs into five sequence classes, e.g., viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. At different sequence lengths, DeepMicrobeFinder achieved area under the receiver operating characteristic curve (AUC) scores >0.9 for most sequence classes, the exception being distinguishing prokaryotic chromosomes from plasmids. By benchmarking on 20 test datasets with variable sequence class composition, we showed that DeepMicrobeFinder obtained average accuracy scores of ~0.94, ~0.87, and ~0.92 for eukaryotic, plasmid and viral contig classification respectively, which were significantly higher than the other state-of-the-art individual predictors. Using a 1-300 µm daily time-series metagenomic dataset sampled from coastal Southern California as a case study, we showed that metagenomic read proportions recruited by eukaryotic contigs could be doubled with DeepMicrobeFinder’s classification compared to the counterparts of other reference-based classifiers. In addition, a positive correlation could be observed between eukaryotic read proportions and potential prokaryotic community growth rates, suggesting an enrichment of fast-growing copiotrophs with increased eukaryotic particles. With its inclusive modeling and unprecedented performance, we expect DeepMicrobeFinder will promote metagenomic studies of under-appreciated sequence types.

scholarly journals DeepMicrobeFinder sorts metagenomes into prokaryotes, eukaryotes and viruses, with marine applications

2021 ◽  
Author(s):  
Shengwei Hou ◽  
Siliangyu Cheng ◽  
Ting Chen ◽  
Jed Fuhrman ◽  
Fengzhu Sun
Keyword(s):  
Characteristic Curve ◽  
Natural Environments ◽  
Multiple Sequence ◽  
Class Composition ◽  
Average Accuracy ◽  
Sequence Class ◽  
Community Growth ◽  
Marine Applications ◽  
Sequence Types

Sequence classification is valuable for reducing the complexity of metagenomes and providing a fundamental understanding of the composition of metagenomic samples. Binary metagenomic classifiers offer an insufficient solution because metagenomes of most natural environments are typically derived from multiple sequence sources including prokaryotes, eukaryotes and the viruses of both. Here we introduce a deep-learning based (not reference-based) sequence classifier, DeepMicrobeFinder, that classifies metagenomic contigs into five sequence classes, e.g., viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. At different sequence lengths, DeepMicrobeFinder achieved area under the receiver operating characteristic curve (AUC) scores >0.9 for most sequence classes, the exception being distinguishing prokaryotic chromosomes from plasmids. By benchmarking on 20 test datasets with variable sequence class composition, we showed that DeepMicrobeFinder obtained average accuracy scores of ~0.94, ~0.87, and ~0.92 for eukaryotic, plasmid and viral contig classification respectively, which were significantly higher than the other state-of-the-art individual predictors. Using a 1-300 μm daily time-series metagenomic dataset sampled from coastal Southern California as a case study, we showed that metagenomic read proportions recruited by eukaryotic contigs could be doubled with DeepMicrobeFinder's classification compared to the counterparts of other reference-based classifiers. In addition, a positive correlation could be observed between eukaryotic read proportions and potential prokaryotic community growth rates, suggesting an enrichment of fast-growing copiotrophs with increased eukaryotic particles. With its inclusive modeling and unprecedented performance, we expect DeepMicrobeFinder will be a useful addition to the toolbox of microbial ecologists, and will promote metagenomic studies of under-appreciated sequence types.

scholarly journals Monitoring Lakes Surface Water Velocity with SAR: A Feasibility Study on Lake Garda, Italy

2021 ◽  
Vol 13 (12) ◽  
pp. 2293
Author(s):  
Marina Amadori ◽  
Virginia Zamparelli ◽  
Giacomo De Carolis ◽  
Gianfranco Fornaro ◽  
Marco Toffolon ◽  
...  
Keyword(s):  
Surface Water ◽  
Wind Waves ◽  
Test Site ◽  
Water Velocity ◽  
Surface Velocity ◽  
Medium Size ◽  
Small Scale ◽  
Lake Garda ◽  
Marine Applications

The SAR Doppler frequencies are directly related to the motion of the scatterers in the illuminated area and have already been used in marine applications to monitor moving water surfaces. Here we investigate the possibility of retrieving surface water velocity from SAR Doppler analysis in medium-size lakes. ENVISAT images of the test site (Lake Garda) are processed and the Doppler Centroid Anomaly technique is adopted. The resulting surface velocity maps are compared with the outputs of a hydrodynamic model specifically validated for the case study. Thermal images from MODIS Terra are used in support of the modeling results. The surface velocity retrieved from SAR is found to overestimate the numerical results and the existence of a bias is investigated. In marine applications, such bias is traditionally removed through Geophysical Model Functions (GMFs) by ascribing it to a fully developed wind waves spectrum. We found that such an assumption is not supported in our case study, due to the small-scale variations of topography and wind. The role of wind intensity and duration on the results from SAR is evaluated, and the inclusion of lake bathymetry and the SAR backscatter gradient is recommended for the future development of GMFs suitable for lake environments.

Bioinformatics analysis of bacteriophage and prophage endolysin domains

Biologia ◽  
2014 ◽  
Vol 69 (5) ◽  
Author(s):  
Barbora Vidová ◽  
Zuzana Šramková ◽  
Lenka Tišáková ◽  
Michaela Oravkinová ◽  
Andrej Godány
Keyword(s):  
Antimicrobial Properties ◽  
In Silico Analysis ◽  
Multiple Sequence ◽  
Catalytic Domains ◽  
Binding Domains ◽  
Multiresistant Bacteria ◽  
Or Gene ◽  
The Individual ◽  
Conserved Amino Acids ◽  
Sequence Types

AbstractEndolysins as a class of antibacterial enzymes are expected to become a very useful tool for many purposes to control spreading of, e.g., multiresistant bacteria in different environments. Their antimicrobial properties could be broadened or altered by mutagenesis, domain swapping or gene shuffling. Therefore, the specific designing of endolysins to achieve their desired properties is challenging. This work is focused on the in silico analysis of protein domains presence in sequences of phage and prophage endolysins, followed by the study of variety of domain combinations in the individual endolysin types. The multiple sequence alignment of endolysin sequences revealed the recognition of sequence types with typical domain arrangement and conserved amino acids, divided according to the target substrate in bacterial cell walls. The five protein families of catalytic domains are specifically occurring in dependence of bacterial Gram-type. The presence, types and numbers of binding domains within endolysin sequences were also studied. The obtained results enable a more targeted design of endolysins with required antimicrobial properties.

Antimicrobial resistance mechanisms, MLST and NG-STAR sequence types of diverse Neisseria gonorrhoeae isolates in KwaZulu-Natal, South Africa

2021 ◽  
Author(s):  
Nireshni Mitchev ◽  
Ravesh Singh ◽  
Mushal Allam ◽  
Stanford Kwenda ◽  
Arshad Ismail ◽  
...  
Keyword(s):  
South Africa ◽  
Antimicrobial Resistance ◽  
Neisseria Gonorrhoeae ◽  
Point Mutations ◽  
Resistance Mechanisms ◽  
Public Healthcare ◽  
Multiple Sequence ◽  
Genotypic Analysis ◽  
Kwazulu Natal ◽  
Sequence Types

Objective: Antimicrobial resistance (AMR) is a major challenge to managing infectious diseases. Africa has the highest incidence of gonorrhoea but there is a lack of comprehensive data from sparse surveillance programs. This study investigated the molecular epidemiology and AMR profiles of Neisseria gonorrhoeae isolates in KwaZulu-Natal province (KZN), South Africa. Methods: Repository isolates, from patients attending public healthcare clinics for STI care, were used for phenotypic and genotypic analysis. Etest® was performed to determine antimicrobial susceptibility. Whole-genome sequencing (WGS) was used to determine epidemiology and to predict susceptibility by detecting resistance-associated genes and mutations. Results: Among the 61 isolates, multiple sequence types were identified. Six isolates were novel as determined by multilocus sequence typing. N.gonorrhoeae Sequence Typing for Antimicrobial Resistance (NG-STAR) determined 48 sequence types, of which 35 isolates had novel antimicrobial profiles. Two novel penA alleles and eight novel mtrR alleles were identified. Point mutations were detected in gyrA , parC , mtrR , penA , ponA and porB1 . This study revealed a high prevalence of AMR (penicillin 67%, tetracycline 89% and ciprofloxacin 52%). However, spectinomycin, cefixime, ceftriaxone and azithromycin remained 100% effective. Conclusion: This study is one of the first to comprehensively describe the epidemiology and AMR of N. gonorrhoeae in KZN, South Africa and Africa, using WGS. KZN has a wide strain diversity and most of these sequence types have been detected in multiple countries, however more than half of our isolates have novel antimicrobial profiles. Continued surveillance is crucial to monitor the emergence of resistance to cefixime, ceftriaxone and azithromycin.

scholarly journals Managing brand presence through social media: the case of UK football clubs

2014 ◽  
Vol 24 (2) ◽  
pp. 181-204 ◽  
Author(s):  
Jeff McCarthy ◽  
Jennifer Rowley ◽  
Catherine Jane Ashworth ◽  
Elke Pioch
Keyword(s):  
Social Media ◽  
Web Sites ◽  
Short Term ◽  
Content Type ◽  
Study Approach ◽  
Football Clubs ◽  
Multiple Case ◽  
Community Growth ◽  
Further Development

Purpose – The purpose of this paper is to contribute knowledge on the issues and benefits associated with managing brand presence and relationships through social media. UK football clubs are big businesses, with committed communities of fans, so are an ideal context from which to develop an understanding of the issues and challenges facing organisations as they seek to protect and promote their brand online. Design/methodology/approach – Due to the emergent nature of social media, and the criticality of the relationships between clubs and their fans, an exploratory study using a multiple case study approach was used to gather rich insights into the phenomenon. Findings – Clubs agreed that further development of social media strategies had potential to deliver interaction and engagement, community growth and belonging, traffic flow to official web sites and commercial gain. However, in developing their social media strategies they had two key concerns. The first concern was the control of the brand presence and image in social media, and how to respond to the opportunities that social media present to fans to impact on the brand. The second concern was how to strike an appropriate balance between strategies that deliver short-term revenue, and those that build longer term brand loyalty. Originality/value – This research is the first to offer insights into the issues facing organisations when developing their social media strategy.

scholarly journals The Long Term Impact of A Rainforest Experience on Chinese Students’ Pro-Environmental Identity and Behavior in Macau

2018 ◽  
Vol 59 ◽  
pp. 01022
Author(s):  
Nga Ian Tam
Keyword(s):  
Tropical Rainforest ◽  
Chinese Students ◽  
Household Waste ◽  
Natural Environments ◽  
Environmental Identity ◽  
Learning Program ◽  
Long Term Impact ◽  
And Behavior

The phenomenological case study covers the limited research on Chinese students‟ experiences of nature in a tropical rainforest in Thailand. Macau is a very small place with only 20 % of natural resources remaining but Chinese students are born to be detached from these natural environments. Their comfortable lifestyle leads to a rise of unsustainable behavior such as an increased in consumption and household waste. With numerous researches that review the benefits of nature including an enhancement in environmental friendly behavior, a 7 d self-funded experiential learning program in a tropical rainforest in Thailand in 2015 was initiated in fostering 12 Chinese students‟ pro-environmental identity and behavior. Findings show the majority of students‟ pro-environmental identity and behavior is enhanced and they are trying to reconnect with the natural environment more often than before.

scholarly journals Wide spread of OXA-48-producing Enterobacteriaceae in Algerian hospitals: A four years’ study

2018 ◽  
Vol 12 (11) ◽  
pp. 1039-1044
Author(s):  
Nadjet Aggoune ◽  
Hassiba Tali Maamar ◽  
Farida Assaous ◽  
Badia Guettou ◽  
Rym Laliam ◽  
...  
Keyword(s):  
Klebsiella Pneumoniae ◽  
Multilocus Sequence Typing ◽  
Genetic Relationships ◽  
Wide Distribution ◽  
Multi Locus Sequence Typing ◽  
Wide Spread ◽  
Multiple Sequence ◽  
Sequence Types ◽  
Susceptibility Patterns ◽  
Urgent Action

Introduction: The aim of this study was to investigate the presence of carbapenemase-producing Enterobacteriaceae (CPE) in Algerian hospitals and to characterize the molecular types of carbapenemases found. Methodology: During a four years study lasting between 2012 and 2015, 81 strains of Enterobacteriaceae with reduced susceptibility to carbapenems were collected from different hospitals. Carbapenemase genes were detected by PCR. Multi locus sequence typing was used to study genetic relationships between carbapenemase- producing Klebsiella pneumoniae isolates. Results: Among 56 confirmed CPE, blaOXA-48 was detected in 98.21% of isolates. Two isolates co-expressed NDM, and a single one was only an NDM producer. The strains displayed various susceptibility patterns to antibiotics with variable levels of resistance to carbapenems. Multilocus sequence typing (MLST) revealed the presence of multiple sequence types in circulation. Conclusions: This report highlights the wide distribution of several clones of OXA-48-producing Enterobacteriaceae in Algeria. Urgent action should be taken to avoid epidemic situations.

scholarly journals Severely imbalanced Big Data challenges: investigating data sampling approaches

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Tawfiq Hasanin ◽  
Taghi M. Khoshgoftaar ◽  
Joffrey L. Leevy ◽  
Richard A. Bauder
Keyword(s):  
Big Data ◽  
Operating Characteristic ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Geometric Mean ◽  
Class Imbalance ◽  
Random Undersampling ◽  
First Case ◽  
Negative Class

AbstractSevere class imbalance between majority and minority classes in Big Data can bias the predictive performance of Machine Learning algorithms toward the majority (negative) class. Where the minority (positive) class holds greater value than the majority (negative) class and the occurrence of false negatives incurs a greater penalty than false positives, the bias may lead to adverse consequences. Our paper incorporates two case studies, each utilizing three learners, six sampling approaches, two performance metrics, and five sampled distribution ratios, to uniquely investigate the effect of severe class imbalance on Big Data analytics. The learners (Gradient-Boosted Trees, Logistic Regression, Random Forest) were implemented within the Apache Spark framework. The first case study is based on a Medicare fraud detection dataset. The second case study, unlike the first, includes training data from one source (SlowlorisBig Dataset) and test data from a separate source (POST dataset). Results from the Medicare case study are not conclusive regarding the best sampling approach using Area Under the Receiver Operating Characteristic Curve and Geometric Mean performance metrics. However, it should be noted that the Random Undersampling approach performs adequately in the first case study. For the SlowlorisBig case study, Random Undersampling convincingly outperforms the other five sampling approaches (Random Oversampling, Synthetic Minority Over-sampling TEchnique, SMOTE-borderline1 , SMOTE-borderline2 , ADAptive SYNthetic) when measuring performance with Area Under the Receiver Operating Characteristic Curve and Geometric Mean metrics. Based on its classification performance in both case studies, Random Undersampling is the best choice as it results in models with a significantly smaller number of samples, thus reducing computational burden and training time.

scholarly journals Iceland as a therapeutic landscape: white wilderness spaces for well-being

GeoJournal ◽  
2020 ◽  
Author(s):  
Kaelan Brooke ◽  
Allison Williams
Keyword(s):  
Secondary Students ◽  
Spiritual Healing ◽  
Well Being ◽  
Natural Environments ◽  
Glacial Ice ◽  
Therapeutic Landscapes ◽  
Post Secondary ◽  
Study Approach ◽  
Therapeutic Landscape

AbstractTherapeutic landscapes are reputed to have a lasting repute for realizing healing. Traditional therapeutic landscapes have recognized natural environments as often sought after places for well-being. Such places promote wellness via their close encounter with nature, facilitating relaxation and restoration, and enhancing a combination of physical, mental, and spiritual healing. The physical environment of Iceland is explored through a case study approach, primarily employing data from the field notebooks of post-secondary students travelling in Iceland, as well as the authors’ ethnographic field experience in Iceland. Iceland is examined using both a traditional understanding of therapeutic landscapes, as well as the contemporary understanding of the coloured landscape. In addition to the colour white, reflected in the glacial ice, moving water, and geo-thermal steams, black and various other colours in combination are discussed.

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