<?xml version="1.0" encoding="utf-8" standalone="yes"?><feed xmlns="http://www.w3.org/2005/Atom"><generator uri="https://gohugo.io" version="0.141.0">Hugo v0.141.0</generator><id>https://millironx.millironx.page/@indiewebpub/categories/paper/</id><link rel="self" type="application/atom+xml" href="https://millironx.millironx.page/@indiewebpub/categories/paper/feed.xml"/><link rel="alternate" type="text/html" href="https://millironx.millironx.page/@indiewebpub/categories/paper/"/><updated>2025-03-31T00:14:19+00:00</updated><title>All papers on Milliron X</title><entry><id>https://millironx.millironx.page/@indiewebpub/academia/taxprofiler/</id><link rel="alternate" href="https://doi.org/10.1101/2023.10.20.563221"/><title>nf-core/taxprofiler: highly parallelised and flexible pipeline for metagenomic taxonomic classification and profiling</title><published>2023-10-23T00:00:00+00:00</published><updated>2023-10-23T00:00:00+00:00</updated><author><name>Sofia Stamouli</name><uri>https://millironx.millironx.page/@indiewebpub/people/sofia-stamouli/</uri></author><author><name>Moritz E. Beber</name><uri>https://millironx.millironx.page/@indiewebpub/people/moritz-e.-beber/</uri></author><author><name>Tanja Normark</name><uri>https://millironx.millironx.page/@indiewebpub/people/tanja-normark/</uri></author><author><name>Thomas A. Christensen II</name><uri>https://millironx.millironx.page/@indiewebpub/people/thomas-a.-christensen-ii/</uri></author><author><name>Lili Andersson-Li</name><uri>https://millironx.millironx.page/@indiewebpub/people/lili-andersson-li/</uri></author><author><name>Maxime Borry</name><uri>https://millironx.millironx.page/@indiewebpub/people/maxime-borry/</uri></author><author><name>Mahwash Jamy</name><uri>https://millironx.millironx.page/@indiewebpub/people/mahwash-jamy/</uri></author><author><name>Nf-Core Community</name><uri>https://millironx.millironx.page/@indiewebpub/people/nf-core-community/</uri></author><author><name>James A. Fellows Yates</name><uri>https://millironx.millironx.page/@indiewebpub/people/james-a.-fellows-yates/</uri></author><category term="paper"/><category term="genomics"/><summary type="text">
Metagenomic classification tackles the problem of characterising the taxonomic source of all DNA sequencing reads in a sample. A common approach to address the differences and biases between the many different taxonomic classification tools is to run metagenomic data through multiple classification tools and databases. This, however, is a very time-consuming task when performed manually - particularly when combined with the appropriate preprocessing of sequencing reads before the classification. Here we present nf-core/taxprofiler, a highly parallelised read-processing and taxonomic classification pipeline. It is designed for the automated and simultaneous classification and/or profiling of both short- and long-read metagenomic sequencing libraries against a 11 taxonomic classifiers and profilers as well as databases within a single pipeline run. Implemented in Nextflow and as part of the nf-core initiative, the pipeline benefits from high levels of scalability and portability, accommodating from small to extremely large projects on a wide range of computing infrastructure. It has been developed following best-practise software development practises and community support to ensure longevity and adaptability of the pipeline, to help keep it up to date with the field of metagenomics.</summary><content type="html" xml:lang="en" xml:base="https://millironx.millironx.page/@indiewebpub/">
&lt;p>Metagenomic classification tackles the problem of characterising the taxonomic
source of all DNA sequencing reads in a sample. A common approach to address the
differences and biases between the many different taxonomic classification tools
is to run metagenomic data through multiple classification tools and databases.
This, however, is a very time-consuming task when performed manually -
particularly when combined with the appropriate preprocessing of sequencing
reads before the classification. Here we present nf-core/taxprofiler, a highly
parallelised read-processing and taxonomic classification pipeline. It is
designed for the automated and simultaneous classification and/or profiling of
both short- and long-read metagenomic sequencing libraries against a 11
taxonomic classifiers and profilers as well as databases within a single
pipeline run. Implemented in Nextflow and as part of the nf-core initiative, the
pipeline benefits from high levels of scalability and portability, accommodating
from small to extremely large projects on a wide range of computing
infrastructure. It has been developed following best-practise software
development practises and community support to ensure longevity and adaptability
of the pipeline, to help keep it up to date with the field of metagenomics.&lt;/p>
</content></entry><entry><id>https://millironx.millironx.page/@indiewebpub/academia/hydronium-pva/</id><link rel="alternate" href="https://doi.org/10.1021/acsestengg.2c00107"/><title>Investigation of Hydronium Diffusion in Poly(vinyl alcohol) Hydrogels: A Critical First Step to Describe Acid Transport for Encapsulated Bioremediation</title><published>2022-09-02T00:00:00+00:00</published><updated>2022-09-02T00:00:00+00:00</updated><author><name>Carson J. Silsby</name><uri>https://millironx.millironx.page/@indiewebpub/people/carson-j.-silsby/</uri></author><author><name>Jonathan R. Counts</name><uri>https://millironx.millironx.page/@indiewebpub/people/jonathan-r.-counts/</uri></author><author><name>Thomas A. Christensen II</name><uri>https://millironx.millironx.page/@indiewebpub/people/thomas-a.-christensen-ii/</uri></author><author><name>Mark F. Roll</name><uri>https://millironx.millironx.page/@indiewebpub/people/mark-f.-roll/</uri></author><author><name>Kristopher v. Waynant</name><uri>https://millironx.millironx.page/@indiewebpub/people/kristopher-v.-waynant/</uri></author><author><name>James G. Moberly</name><uri>https://millironx.millironx.page/@indiewebpub/people/james-g.-moberly/</uri></author><category term="paper"/><category term="diffusion"/><category term="hydrogels"/><category term="ionic strength"/><category term="polymers"/><category term="transport properties"/><summary type="text">
Bioremediation of chlorinated aliphatic hydrocarbon-contaminated aquifers can be hindered by high contaminant concentrations and acids generated during remediation. Encapsulating microbes in hydrogels may provide a protective, tunable environment from inhibiting compounds; however, current approaches to formulate successful encapsulated systems rely on trial and error rather than engineering approaches because fundamental information on mass-transfer coefficients is lacking. To address this knowledge gap, hydronium ion mass-transfer rates through two commonly used hydrogel materials, poly(vinyl alcohol) and alginic acid, under two solidification methods (chemical and cryogenic) were measured. Variations in hydrogel crosslinking conditions, polymer composition, and solvent ionic strength were investigated to understand how each influenced hydronium ion diffusivity. A three-way ANOVA indicated that the ionic strength, membrane type, and crosslinking method significantly (p &lt; 0.001) contributed to changes in hydronium ion mass transfer. Hydronium ion diffusion increased with ionic strength, counter to what is observed in aqueous-only (no polymer) solutions. Co-occurring mechanisms correlated to increased hydronium ion diffusion with ionic strength included an increased water fraction within hydrogel matrices and hydrogel contraction. Measured diffusion rates determined in this study provide first principal design information to further optimize encapsulating hydrogels for bioremediation.</summary><content type="html" xml:lang="en" xml:base="https://millironx.millironx.page/@indiewebpub/">
&lt;p>Bioremediation of chlorinated aliphatic hydrocarbon-contaminated aquifers can be
hindered by high contaminant concentrations and acids generated during
remediation. Encapsulating microbes in hydrogels may provide a protective,
tunable environment from inhibiting compounds; however, current approaches to
formulate successful encapsulated systems rely on trial and error rather than
engineering approaches because fundamental information on mass-transfer
coefficients is lacking. To address this knowledge gap, hydronium ion
mass-transfer rates through two commonly used hydrogel materials, poly(vinyl
alcohol) and alginic acid, under two solidification methods (chemical and
cryogenic) were measured. Variations in hydrogel crosslinking conditions,
polymer composition, and solvent ionic strength were investigated to understand
how each influenced hydronium ion diffusivity. A three-way ANOVA indicated that
the ionic strength, membrane type, and crosslinking method significantly (&lt;em>p&lt;/em> &amp;lt;
0.001) contributed to changes in hydronium ion mass transfer. Hydronium ion
diffusion increased with ionic strength, counter to what is observed in
aqueous-only (no polymer) solutions. Co-occurring mechanisms correlated to
increased hydronium ion diffusion with ionic strength included an increased
water fraction within hydrogel matrices and hydrogel contraction. Measured
diffusion rates determined in this study provide first principal design
information to further optimize encapsulating hydrogels for bioremediation.&lt;/p>
</content></entry><entry><id>https://millironx.millironx.page/@indiewebpub/academia/rotavirus-virome/</id><link rel="alternate" href="https://doi.org/10.1016/j.vetmic.2022.109447"/><title>Assessment of Porcine Rotavirus-associated virome variations in pigs with enteric disease</title><published>2022-04-27T00:00:00+00:00</published><updated>2022-04-27T00:00:00+00:00</updated><author><name>Tyler Doerksen</name><uri>https://millironx.millironx.page/@indiewebpub/people/tyler-doerksen/</uri></author><author><name>Thomas A. Christensen II</name><uri>https://millironx.millironx.page/@indiewebpub/people/thomas-a.-christensen-ii/</uri></author><author><name>Andrea Lu</name><uri>https://millironx.millironx.page/@indiewebpub/people/andrea-lu/</uri></author><author><name>Lance Noll</name><uri>https://millironx.millironx.page/@indiewebpub/people/lance-noll/</uri></author><author><name>Jianfa Bai</name><uri>https://millironx.millironx.page/@indiewebpub/people/jianfa-bai/</uri></author><author><name>Jamie Henningson</name><uri>https://millironx.millironx.page/@indiewebpub/people/jamie-henningson/</uri></author><author><name>Rachel Palinski</name><uri>https://millironx.millironx.page/@indiewebpub/people/rachel-palinski/</uri></author><category term="paper"/><category term="porcine rotavirus"/><category term="porcine enteric disease"/><category term="virome"/><category term="rotavirus"/><summary type="text">
Enteric disease is the predominant cause of morbidity and mortality in young mammals including pigs. Viral species involved in porcine enteric disease complex (PEDC) include rotaviruses, coronaviruses, picornaviruses, astroviruses and pestiviruses among others. The virome of three groups of swine samples submitted to the Kansas State University Veterinary Diagnostic Laboratory for routine testing were assessed, namely, a Rotavirus A positive (RVA) group, a Rotavirus co-infection (RV) group and a Rotavirus Negative (RV Neg) group. All groups were designated by qRT-PCR results testing for Porcine Rotavirus A, B, C and H such that samples positive for RVA only went in the RVA group, samples positive for >1 rotavirus went in the RV group and samples negative for all were grouped in the RVNeg group. All of the animals had clinical enteric disease resulting in scours and swollen joints/lameness, enlarged heart and/or a cough. All samples were metagenomic sequenced and analyzed for viral species composition that identified 14 viral species and eight bacterial viruses/phages. Sapovirus and Escherichia coli phages were found at a high prevalence in RVA and RV samples but were found at low or no prevalence in the RV Neg samples. Picobirnavirus was identified at a high proportion and prevalence in RV Neg and RV samples but at a low prevalence in the RVA group. A sequence analysis of the possible host of Picobirnaviruses revealed fungi as the most likely host. Non-rotaviral diversity was highest in RVA samples followed by RV then RV Neg samples. Various sequences were extracted from the sample reads and a phylogenetic update was provided showing a high prevalence of G9 and P[23] RVA genotypes. These data are important for pathogen surveillance and control measures</summary><content type="html" xml:lang="en" xml:base="https://millironx.millironx.page/@indiewebpub/">
&lt;p>Enteric disease is the predominant cause of morbidity and mortality in young
mammals including pigs. Viral species involved in porcine enteric disease
complex (PEDC) include rotaviruses, coronaviruses, picornaviruses, astroviruses
and pestiviruses among others. The virome of three groups of swine samples
submitted to the Kansas State University Veterinary Diagnostic Laboratory for
routine testing were assessed, namely, a Rotavirus A positive (RVA) group, a
Rotavirus co-infection (RV) group and a Rotavirus Negative (RV Neg) group. All
groups were designated by qRT-PCR results testing for Porcine Rotavirus A, B, C
and H such that samples positive for RVA only went in the RVA group, samples
positive for &amp;gt;1 rotavirus went in the RV group and samples negative for all were
grouped in the RVNeg group. All of the animals had clinical enteric disease
resulting in scours and swollen joints/lameness, enlarged heart and/or a cough.
All samples were metagenomic sequenced and analyzed for viral species
composition that identified 14 viral species and eight bacterial viruses/phages.
Sapovirus and Escherichia coli phages were found at a high prevalence in RVA and
RV samples but were found at low or no prevalence in the RV Neg samples.
Picobirnavirus was identified at a high proportion and prevalence in RV Neg and
RV samples but at a low prevalence in the RVA group. A sequence analysis of the
possible host of Picobirnaviruses revealed fungi as the most likely host.
Non-rotaviral diversity was highest in RVA samples followed by RV then RV Neg
samples. Various sequences were extracted from the sample reads and a
phylogenetic update was provided showing a high prevalence of G9 and P[23] RVA
genotypes. These data are important for pathogen surveillance and control
measures&lt;/p>
</content></entry></feed>