PeerJ
type: channel
link: https://peerj.com/articles/
description: Articles published in PeerJ
publisher: PeerJ Inc.
publication name: PeerJ
issn: 2167-8359
items: Evolutionary history of dimethylsulfoniopropionate (DMSP) demethylation enzyme DmdA in marine bacteria
items: https://peerj.com/articles/9862
items: A survey of RNA secondary structural propensity encoded within human herpesvirus genomes: global comparisons and local motifs
items: Evaluation of different culture media to support in vitro growth and biofilm formation of bacterial vaginosis-associated anaerobes
items: A multi-class classification model for supporting the diagnosis of type II diabetes mellitus
items: KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models. I. review and model concept
items: Omics approaches in Allium research: Progress and way ahead
items: Assessment of postoperative health functioning after knee arthroplasty in relation to pain catastrophizing: a 6-month follow-up cohort study
items: Effect of nest age and habitat variables on nest survival in Marsh Harrier (Circus aeruginosus) in a fishpond habitat
items: A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Evolutionary history of dimethylsulfoniopropionate (DMSP) demethylation enzyme DmdA in marine bacteria
type: item
type: AcademicArticle
description: Dimethylsulfoniopropionate (DMSP), an osmolyte produced by oceanic phytoplankton and bacteria, is primarily degraded by bacteria belonging to the Roseobacter lineage and other marine Alphaproteobacteria via DMSP-dependent demethylase A protein (DmdA). To date, the evolutionary history of DmdA gene family is unclear. Some studies indicate a common ancestry between DmdA and GcvT gene families and a co-evolution between Roseobacter and the DMSP-producing-phytoplankton around 250 million years ago (Mya). In this work, we analyzed the evolution of DmdA under three possible evolutionary scenarios: (1) a recent common ancestor of DmdA and GcvT, (2) a coevolution between Roseobacter and the DMSP-producing-phytoplankton, and (3) an enzymatic adaptation for utilizing DMSP in marine bacteria prior to Roseobacter origin. Our analyses indicate that DmdA is a new gene family originated from GcvT genes by duplication and functional divergence driven by positive selection before a coevolution between Roseobacter and phytoplankton. Our data suggest that Roseobacter acquired dmdA by horizontal gene transfer prior to an environment with higher DMSP. Here, we propose that the ancestor that carried the DMSP demethylation pathway genes evolved in the Archean, and was exposed to a higher concentration of DMSP in a sulfur-rich atmosphere and anoxic ocean, compared to recent Roseobacter eco-orthologs (orthologs performing the same function under different conditions), which should be adapted to lower concentrations of DMSP.
format: application/pdf
format: text/html
creator: Laura Hernández
creator: Alberto Vicens
creator: Luis E. Eguiarte
creator: Valeria Souza
creator: Valerie De Anda
creator: José M. González
subject: Ecology
subject: Evolutionary Studies
subject: Genomics
subject: Microbiology
description: doi:10.7717/peerj.9861
publisher: PeerJ Inc.
rights: © 2020 Hernández et al.
abstract: Dimethylsulfoniopropionate (DMSP), an osmolyte produced by oceanic phytoplankton and bacteria, is primarily degraded by bacteria belonging to the Roseobacter lineage and other marine Alphaproteobacteria via DMSP-dependent demethylase A protein (DmdA). To date, the evolutionary history of DmdA gene family is unclear. Some studies indicate a common ancestry between DmdA and GcvT gene families and a co-evolution between Roseobacter and the DMSP-producing-phytoplankton around 250 million years ago (Mya). In this work, we analyzed the evolution of DmdA under three possible evolutionary scenarios: (1) a recent common ancestor of DmdA and GcvT, (2) a coevolution between Roseobacter and the DMSP-producing-phytoplankton, and (3) an enzymatic adaptation for utilizing DMSP in marine bacteria prior to Roseobacter origin. Our analyses indicate that DmdA is a new gene family originated from GcvT genes by duplication and functional divergence driven by positive selection before a coevolution between Roseobacter and phytoplankton. Our data suggest that Roseobacter acquired dmdA by horizontal gene transfer prior to an environment with higher DMSP. Here, we propose that the ancestor that carried the DMSP demethylation pathway genes evolved in the Archean, and was exposed to a higher concentration of DMSP in a sulfur-rich atmosphere and anoxic ocean, compared to recent Roseobacter eco-orthologs (orthologs performing the same function under different conditions), which should be adapted to lower concentrations of DMSP.
bibliographic citation: Hernández et al. (2020) Evolutionary history of dimethylsulfoniopropionate (DMSP) demethylation enzyme DmdA in marine bacteria. PeerJ 8:e9861 https://doi.org/10.7717/peerj.9861
date submitted: 2020-04-06
date accepted: 2020-08-12
issued: 2020-09-10
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Hernández
  given name: Laura
author list: Agent
  family name: Vicens
  given name: Alberto
author list: Agent
  family name: Eguiarte
  given name: Luis E.
author list: Agent
  family name: Souza
  given name: Valeria
author list: Agent
  family name: De Anda
  given name: Valerie
author list: Agent
  family name: González
  given name: José M.
doi: 10.7717/peerj.9861
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-10
copyright: © 2020 Hernández et al.
attribution name: Hernández et al.

https://peerj.com/articles/9862
type: item
type: AcademicArticle
title: Consistent administration of cetuximab is associated with favorable outcomes in recurrent/metastatic head and neck squamous cell carcinoma in an endemic carcinogen exposure area: a retrospective observational study
description: Background This study aimed to analyze the clinical outcomes associated with patients with recurrent/metastatic head and neck squamous cell carcinoma (RM HNSCC) who received cetuximab-based chemotherapy in a real-world clinical setting. Methods Clinical data were extracted from RM HNSCC patients diagnosed between 2016 and 2019. Kaplan–Meier survival estimates and Cox proportional hazards model were used for survival analyses. Results Of 106 RM HNSCC patients (mean age = 55.1 years), 38.7% exhibited recurrent disease and 61.3% had metastatic disease. The majority of patients showed a habit of addictive substance use, including alcohol (67.0%), betel nuts (71.7%), or tobacco (74.5%). The primary tumor sites included the oral cavity (64.1%), hypopharynx (19.8%), and oropharynx (16.0%). The median number of cetuximab cycles for the 106 patients was 11 (2–24). The disease control rate (DCR) was 48.1%, and the overall response rate (ORR) was 28.3%. The median progression-free survival (PFS) and overall survival (OS) were 5.0 and 9.23 months, respectively. Patients treated with more than 11 cycles of cetuximab exhibited a longer median PFS and median OS than did patients treated with less than 11 cycles (median PFS: 7.0 vs. 3.0 months, p < 0.001; OS: 12.43 vs. 4.46 months, p = 0.001). Patients without previous concurrent chemoradiotherapy (CRT) had a better median PFS than did those with previous CRT (6.0 vs. 4.0 months, p = 0.046). Multivariable analysis revealed that perineural invasion and fewer cycles of cetuximab (<11 cycles) were independent risk factors associated with disease progression. In addition, the reduction in treatment cycles of cetuximab and advanced lymph node metastasis were independent prognostic factors predicting poorer overall survival. Conclusion Our study provides important real-world data regarding cetuximab-containing treatment in RM HNSCC. Consistent administration of cetuximab could be associated with more favorable outcomes in RM HNSCC in end…
format: application/pdf
format: text/html
creator: Hui-Ching Wang
creator: Pei-Lin Liu
creator: Pei-Chuan Lo
creator: Yi-Tzu Chang
creator: Leong-Perng Chan
creator: Tsung-Jang Yeh
creator: Hui-Hua Hsiao
creator: Shih-Feng Cho
subject: Drugs and Devices
subject: Oncology
subject: Otorhinolaryngology
description: doi:10.7717/peerj.9862
publisher: PeerJ Inc.
rights: © 2020 Wang et al.
abstract: Background This study aimed to analyze the clinical outcomes associated with patients with recurrent/metastatic head and neck squamous cell carcinoma (RM HNSCC) who received cetuximab-based chemotherapy in a real-world clinical setting. Methods Clinical data were extracted from RM HNSCC patients diagnosed between 2016 and 2019. Kaplan–Meier survival estimates and Cox proportional hazards model were used for survival analyses. Results Of 106 RM HNSCC patients (mean age = 55.1 years), 38.7% exhibited recurrent disease and 61.3% had metastatic disease. The majority of patients showed a habit of addictive substance use, including alcohol (67.0%), betel nuts (71.7%), or tobacco (74.5%). The primary tumor sites included the oral cavity (64.1%), hypopharynx (19.8%), and oropharynx (16.0%). The median number of cetuximab cycles for the 106 patients was 11 (2–24). The disease control rate (DCR) was 48.1%, and the overall response rate (ORR) was 28.3%. The median progression-free survival (PFS) and overall survival (OS) were 5.0 and 9.23 months, respectively. Patients treated with more than 11 cycles of cetuximab exhibited a longer median PFS and median OS than did patients treated with less than 11 cycles (median PFS: 7.0 vs. 3.0 months, p < 0.001; OS: 12.43 vs. 4.46 months, p = 0.001). Patients without previous concurrent chemoradiotherapy (CRT) had a better median PFS than did those with previous CRT (6.0 vs. 4.0 months, p = 0.046). Multivariable analysis revealed that perineural invasion and fewer cycles of cetuximab (<11 cycles) were independent risk factors associated with disease progression. In addition, the reduction in treatment cycles of cetuximab and advanced lymph node metastasis were independent prognostic factors predicting poorer overall survival. Conclusion Our study provides important real-world data regarding cetuximab-containing treatment in RM HNSCC. Consistent administration of cetuximab could be associated with more favorable outcomes in RM HNSCC in end…
bibliographic citation: Wang et al. (2020) Consistent administration of cetuximab is associated with favorable outcomes in recurrent/metastatic head and neck squamous cell carcinoma in an endemic carcinogen exposure area: a retrospective observational study. PeerJ 8:e9862 https://doi.org/10.7717/peerj.9862
date submitted: 2020-02-13
date accepted: 2020-08-12
issued: 2020-09-10
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Wang
  given name: Hui-Ching
author list: Agent
  family name: Liu
  given name: Pei-Lin
author list: Agent
  family name: Lo
  given name: Pei-Chuan
author list: Agent
  family name: Chang
  given name: Yi-Tzu
author list: Agent
  family name: Chan
  given name: Leong-Perng
author list: Agent
  family name: Yeh
  given name: Tsung-Jang
author list: Agent
  family name: Hsiao
  given name: Hui-Hua
author list: Agent
  family name: Cho
  given name: Shih-Feng
doi: 10.7717/peerj.9862
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-10
copyright: ©2020 Wang et al.
attribution name: Wang et al.

A survey of RNA secondary structural propensity encoded within human herpesvirus genomes: global comparisons and local motifs
type: item
type: AcademicArticle
description: There are nine herpesviruses known to infect humans, of which Epstein–Barr virus (EBV) is the most widely distributed (>90% of adults infected). This ubiquitous virus is implicated in a variety of cancers and autoimmune diseases. Previous analyses of the EBV genome revealed numerous regions with evidence of generating unusually stable and conserved RNA secondary structures and led to the discovery of a novel class of EBV non-coding (nc)RNAs: the stable intronic sequence (sis)RNAs. To gain a better understanding of the roles of RNA structure in EBV biology and pathogenicity, we revisit EBV using recently developed tools for genome-wide motif discovery and RNA structural characterization. This corroborated previous results and revealed novel motifs with potential functionality; one of which has been experimentally validated. Additionally, since many herpesviruses increasingly rival the seroprevalence of EBV (VZV, HHV-6 and HHV-7 being the most notable), analyses were expanded to include all sequenced human Herpesvirus RefSeq genomes, allowing for genomic comparisons. In total 10 genomes were analyzed, for EBV (types 1 and 2), HCMV, HHV-6A, HHV-6B, HHV-7, HSV-1, HSV-2, KSHV, and VZV. All resulting data were archived in the RNAStructuromeDB (https://structurome.bb.iastate.edu/herpesvirus) to make them available to a wide array of researchers.
format: application/pdf
format: text/html
creator: Ryan J. Andrews
creator: Collin A. O’Leary
creator: Walter N. Moss
subject: Bioinformatics
subject: Computational Biology
subject: Genomics
subject: Microbiology
subject: Virology
description: doi:10.7717/peerj.9882
publisher: PeerJ Inc.
rights: © 2020 Andrews et al.
abstract: There are nine herpesviruses known to infect humans, of which Epstein–Barr virus (EBV) is the most widely distributed (>90% of adults infected). This ubiquitous virus is implicated in a variety of cancers and autoimmune diseases. Previous analyses of the EBV genome revealed numerous regions with evidence of generating unusually stable and conserved RNA secondary structures and led to the discovery of a novel class of EBV non-coding (nc)RNAs: the stable intronic sequence (sis)RNAs. To gain a better understanding of the roles of RNA structure in EBV biology and pathogenicity, we revisit EBV using recently developed tools for genome-wide motif discovery and RNA structural characterization. This corroborated previous results and revealed novel motifs with potential functionality; one of which has been experimentally validated. Additionally, since many herpesviruses increasingly rival the seroprevalence of EBV (VZV, HHV-6 and HHV-7 being the most notable), analyses were expanded to include all sequenced human Herpesvirus RefSeq genomes, allowing for genomic comparisons. In total 10 genomes were analyzed, for EBV (types 1 and 2), HCMV, HHV-6A, HHV-6B, HHV-7, HSV-1, HSV-2, KSHV, and VZV. All resulting data were archived in the RNAStructuromeDB (https://structurome.bb.iastate.edu/herpesvirus) to make them available to a wide array of researchers.
bibliographic citation: Andrews et al. (2020) A survey of RNA secondary structural propensity encoded within human herpesvirus genomes: global comparisons and local motifs. PeerJ 8:e9882 https://doi.org/10.7717/peerj.9882
date submitted: 2020-04-08
date accepted: 2020-08-16
issued: 2020-09-10
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Andrews
  given name: Ryan J.
author list: Agent
  family name: O’Leary
  given name: Collin A.
author list: Agent
  family name: Moss
  given name: Walter N.
doi: 10.7717/peerj.9882
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-10
copyright: ©2020 Andrews et al.
attribution name: Andrews et al.

Evaluation of different culture media to support in vitro growth and biofilm formation of bacterial vaginosis-associated anaerobes
type: item
type: AcademicArticle
description: Background Bacterial vaginosis (BV) is one of the most common vaginal infections worldwide. It is associated with the presence of a dense polymicrobial biofilm on the vaginal epithelium, formed mainly by Gardnerella species. The biofilm also contains other anaerobic species, but little is known about their role in BV development. Aim To evaluate the influence of different culture media on the planktonic and biofilm growth of six cultivable anaerobes frequently associated with BV, namely Gardnerella sp., Atopobium vaginae, Lactobacillus iners, Mobiluncus curtisii, Peptostreptococcus anaerobius and Prevotella bivia. Methods A total of nine different culture media compositions, including commercially available and chemically defined media simulating genital tract secretions, were tested in this study. Planktonic cultures and biofilms were grown under anaerobic conditions (10% carbon dioxide, 10% helium and 80% nitrogen). Planktonic growth was assessed by optical density measurements, and biofilm formation was quantified by crystal violet staining. Results Significant planktonic growth was observed for Gardnerella sp., A. vaginae and L. iners in New York City III broth, with or without ascorbic acid supplementation. Biofilm quantification showed high in vitro biofilm growth for Gardnerella sp., P. anaerobius and P. bivia in almost all culture media excluding Brucella broth. Contrary, only New York City III broth was able to promote biofilm formation for A. vaginae, L. iners and M. curtisii. Conclusions Our data demonstrate that New York City III broth relative to the other tested media is the most conducive for future studies addressing polymicrobial biofilms development as this culture medium allowed the formation of significant levels of single-species biofilms.
format: application/pdf
format: text/html
creator: Aliona S. Rosca
creator: Joana Castro
creator: Nuno Cerca
subject: Microbiology
subject: Gynecology and Obstetrics
subject: Women’s Health
description: doi:10.7717/peerj.9917
publisher: PeerJ Inc.
rights: © 2020 Rosca et al.
abstract: Background Bacterial vaginosis (BV) is one of the most common vaginal infections worldwide. It is associated with the presence of a dense polymicrobial biofilm on the vaginal epithelium, formed mainly by Gardnerella species. The biofilm also contains other anaerobic species, but little is known about their role in BV development. Aim To evaluate the influence of different culture media on the planktonic and biofilm growth of six cultivable anaerobes frequently associated with BV, namely Gardnerella sp., Atopobium vaginae, Lactobacillus iners, Mobiluncus curtisii, Peptostreptococcus anaerobius and Prevotella bivia. Methods A total of nine different culture media compositions, including commercially available and chemically defined media simulating genital tract secretions, were tested in this study. Planktonic cultures and biofilms were grown under anaerobic conditions (10% carbon dioxide, 10% helium and 80% nitrogen). Planktonic growth was assessed by optical density measurements, and biofilm formation was quantified by crystal violet staining. Results Significant planktonic growth was observed for Gardnerella sp., A. vaginae and L. iners in New York City III broth, with or without ascorbic acid supplementation. Biofilm quantification showed high in vitro biofilm growth for Gardnerella sp., P. anaerobius and P. bivia in almost all culture media excluding Brucella broth. Contrary, only New York City III broth was able to promote biofilm formation for A. vaginae, L. iners and M. curtisii. Conclusions Our data demonstrate that New York City III broth relative to the other tested media is the most conducive for future studies addressing polymicrobial biofilms development as this culture medium allowed the formation of significant levels of single-species biofilms.
bibliographic citation: Rosca et al. (2020) Evaluation of different culture media to support in vitro growth and biofilm formation of bacterial vaginosis-associated anaerobes. PeerJ 8:e9917 https://doi.org/10.7717/peerj.9917
date submitted: 2020-04-22
date accepted: 2020-08-20
issued: 2020-09-10
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Rosca
  given name: Aliona S.
author list: Agent
  family name: Castro
  given name: Joana
author list: Agent
  family name: Cerca
  given name: Nuno
doi: 10.7717/peerj.9917
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-10
copyright: © 2020 Rosca et al.
attribution name: Rosca et al.

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus
type: item
type: AcademicArticle
description: Background Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided. Methods This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance. Results The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-…
format: application/pdf
format: text/html
creator: Kuang-Ming Kuo
creator: Paul Talley
creator: YuHsi Kao
creator: Chi Hsien Huang
subject: Diabetes and Endocrinology
subject: Public Health
subject: Computational Science
description: doi:10.7717/peerj.9920
publisher: PeerJ Inc.
rights: © 2020 Kuo et al.
abstract: Background Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided. Methods This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance. Results The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-…
bibliographic citation: Kuo et al. (2020) A multi-class classification model for supporting the diagnosis of type II diabetes mellitus. PeerJ 8:e9920 https://doi.org/10.7717/peerj.9920
date submitted: 2020-05-21
date accepted: 2020-08-20
issued: 2020-09-10
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Kuo
  given name: Kuang-Ming
author list: Agent
  family name: Talley
  given name: Paul
author list: Agent
  family name: Kao
  given name: YuHsi
author list: Agent
  family name: Huang
  given name: Chi Hsien
doi: 10.7717/peerj.9920
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-10
copyright: © 2020 Kuo et al.
attribution name: Kuo et al.

KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models. I. review and model concept
type: item
type: AcademicArticle
description: The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.
format: application/pdf
format: text/html
creator: Gaby Deckmyn
creator: Omar Flores
creator: Mathias Mayer
creator: Xavier Domene
creator: Andrea Schnepf
creator: Katrin Kuka
creator: Kris Van Looy
creator: Daniel P. Rasse
creator: Maria J.I. Briones
creator: Sébastien Barot
creator: Matty Berg
creator: Elena Vanguelova
creator: Ivika Ostonen
creator: Harry Vereecken
creator: Laura M. Suz
creator: Beat Frey
creator: Aline Frossard
creator: Alexei Tiunov
creator: Jan Frouz
creator: Tine Grebenc
creator: Maarja Öpik
creator: Mathieu Javaux
creator: Alexei Uvarov
creator: Olga Vindušková
creator: Paul Henning Krogh
creator: Oskar Franklin
creator: Juan Jiménez
creator: Jorge Curiel Yuste
subject: Ecology
subject: Ecosystem Science
subject: Soil Science
description: doi:10.7717/peerj.9750
publisher: PeerJ Inc.
rights: © 2020 Deckmyn et al.
abstract: The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.
bibliographic citation: Deckmyn et al. (2020) KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models. I. review and model concept. PeerJ 8:e9750 https://doi.org/10.7717/peerj.9750
date submitted: 2020-02-11
date accepted: 2020-07-27
issued: 2020-09-09
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Deckmyn
  given name: Gaby
author list: Agent
  family name: Flores
  given name: Omar
author list: Agent
  family name: Mayer
  given name: Mathias
author list: Agent
  family name: Domene
  given name: Xavier
author list: Agent
  family name: Schnepf
  given name: Andrea
author list: Agent
  family name: Kuka
  given name: Katrin
author list: Agent
  family name: Van Looy
  given name: Kris
author list: Agent
  family name: Rasse
  given name: Daniel P.
author list: Agent
  family name: Briones
  given name: Maria J.I.
author list: Agent
  family name: Barot
  given name: Sébastien
author list: Agent
  family name: Berg
  given name: Matty
author list: Agent
  family name: Vanguelova
  given name: Elena
author list: Agent
  family name: Ostonen
  given name: Ivika
author list: Agent
  family name: Vereecken
  given name: Harry
author list: Agent
  family name: Suz
  given name: Laura M.
author list: Agent
  family name: Frey
  given name: Beat
author list: Agent
  family name: Frossard
  given name: Aline
author list: Agent
  family name: Tiunov
  given name: Alexei
author list: Agent
  family name: Frouz
  given name: Jan
author list: Agent
  family name: Grebenc
  given name: Tine
author list: Agent
  family name: Öpik
  given name: Maarja
author list: Agent
  family name: Javaux
  given name: Mathieu
author list: Agent
  family name: Uvarov
  given name: Alexei
author list: Agent
  family name: Vindušková
  given name: Olga
author list: Agent
  family name: Henning Krogh
  given name: Paul
author list: Agent
  family name: Franklin
  given name: Oskar
author list: Agent
  family name: Jiménez
  given name: Juan
doi: 10.7717/peerj.9750
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-09
copyright: © 2020 Deckmyn et al.
attribution name: Deckmyn et al.

Omics approaches in Allium research: Progress and way ahead
type: item
type: AcademicArticle
description: Background The genus Allium (Family: Amaryllidaceae) is an economically important group of crops cultivated worldwide for their use as a vegetable and spices. Alliums are also well known for their nutraceutical properties. Among alliums, onion, garlic, leek, and chives cultivated worldwide. Despite their substantial economic and medicinal importance, the genome sequence of any of the Allium is not available, probably due to their large genome sizes. Recently evolved omics technologies are highly efficient and robust in elucidating molecular mechanisms of several complex life processes in plants. Omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, metagenomics, etc. have the potential to open new avenues in research and improvement of allium crops where genome sequence information is limited. A significant amount of data has been generated using these technologies for various Allium species; it will help in understanding the key traits in Allium crops such as flowering, bulb development, flavonoid biosynthesis, male sterility and stress tolerance at molecular and metabolite level. This information will ultimately assist us in speeding up the breeding in Allium crops. Method In the present review, major omics approaches, and their progress, as well as potential applications in Allium crops, could be discussed in detail. Results Here, we have discussed the recent progress made in Allium research using omics technologies such as genomics, transcriptomics, micro RNAs, proteomics, metabolomics, and metagenomics. These omics interventions have been used in alliums for marker discovery, the study of the biotic and abiotic stress response, male sterility, organ development, flavonoid and bulb color, micro RNA discovery, and microbiome associated with Allium crops. Further, we also emphasized the integrated use of these omics platforms for a better understanding of the complex molecular mechanisms to speed up the breeding programs for better culti…
format: application/pdf
format: text/html
creator: Kiran Khandagale
creator: Ram Krishna
creator: Praveen Roylawar
creator: Avinash B. Ade
creator: Ashwini Benke
creator: Bharat Shinde
creator: Major Singh
creator: Suresh J. Gawande
creator: Ashutosh Rai
subject: Agricultural Science
subject: Biotechnology
subject: Genomics
subject: Plant Science
description: doi:10.7717/peerj.9824
publisher: PeerJ Inc.
rights: © 2020 Khandagale et al.
abstract: Background The genus Allium (Family: Amaryllidaceae) is an economically important group of crops cultivated worldwide for their use as a vegetable and spices. Alliums are also well known for their nutraceutical properties. Among alliums, onion, garlic, leek, and chives cultivated worldwide. Despite their substantial economic and medicinal importance, the genome sequence of any of the Allium is not available, probably due to their large genome sizes. Recently evolved omics technologies are highly efficient and robust in elucidating molecular mechanisms of several complex life processes in plants. Omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, metagenomics, etc. have the potential to open new avenues in research and improvement of allium crops where genome sequence information is limited. A significant amount of data has been generated using these technologies for various Allium species; it will help in understanding the key traits in Allium crops such as flowering, bulb development, flavonoid biosynthesis, male sterility and stress tolerance at molecular and metabolite level. This information will ultimately assist us in speeding up the breeding in Allium crops. Method In the present review, major omics approaches, and their progress, as well as potential applications in Allium crops, could be discussed in detail. Results Here, we have discussed the recent progress made in Allium research using omics technologies such as genomics, transcriptomics, micro RNAs, proteomics, metabolomics, and metagenomics. These omics interventions have been used in alliums for marker discovery, the study of the biotic and abiotic stress response, male sterility, organ development, flavonoid and bulb color, micro RNA discovery, and microbiome associated with Allium crops. Further, we also emphasized the integrated use of these omics platforms for a better understanding of the complex molecular mechanisms to speed up the breeding programs for better culti…
bibliographic citation: Khandagale et al. (2020) Omics approaches in Allium research: Progress and way ahead. PeerJ 8:e9824 https://doi.org/10.7717/peerj.9824
date submitted: 2019-10-21
date accepted: 2020-08-05
issued: 2020-09-09
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Khandagale
  given name: Kiran
author list: Agent
  family name: Krishna
  given name: Ram
author list: Agent
  family name: Roylawar
  given name: Praveen
author list: Agent
  family name: Ade
  given name: Avinash B.
author list: Agent
  family name: Benke
  given name: Ashwini
author list: Agent
  family name: Shinde
  given name: Bharat
author list: Agent
  family name: Singh
  given name: Major
author list: Agent
  family name: Gawande
  given name: Suresh J.
author list: Agent
  family name: Rai
  given name: Ashutosh
doi: 10.7717/peerj.9824
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-09
copyright: ©2020 Khandagale et al.
attribution name: Khandagale et al.

Assessment of postoperative health functioning after knee arthroplasty in relation to pain catastrophizing: a 6-month follow-up cohort study
type: item
type: AcademicArticle
description: Background Knee arthroplasty (KA) is a typically successful surgical procedure commonly performed to alleviate painin participants with end-stage knee osteoarthritis. Despite its beneficial effects, a significant proportion of individuals with KA continue experiencing persistent pain and functional limitations. The purpose of this study was to assess the postoperative outcomes after KA in relation to postoperative pain catastrophizing. Methods Participants were recruited at a domiciliary physiotherapy service, using a prospective, observational, hypothesis-generating cohort design. Participants were divided into two groups based on their Pain Catastrophizing Scale (PCS) total score (50th percentile), which resulted in high and low PCS groups. The primary outcome measure was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). In addition, quality of life, walking speed, physical performance, range of motion, and pain were measured. Outcome measures were collected at baseline (1 week postoperatively) and at follow-up (1, 3, and 6 months postoperatively). Results A total of 60 participants (21 total KA and 39 unicompartmental KA) were recruited. Individuals with a higher degree of pain catastrophizing showed significantly higher WOMAC total scores at every follow-up, indicating poorer health functioning (p < 0.01). Similarly, the high PCS group showed higher WOMAC pain, stiffness and disability subscale scores (p < 0.05), poorer quality of life (p < 0.01), and poorer physical performance (p < 0.05) at every follow-up. In addition, the high PCS group achieved a slower walking speed at baseline and at 3 months follow-up (p < 0.05), and a higher degree of pain at rest, on walking and on knee flexion at every follow-up (p < 0.01, p < 0.05 and p < 0.05, respectively) except for walking pain at 3 months follow-up. No significant differences were observed between groups in range of motion, except for active knee extension at the 6-month follow-up (p < …
format: application/pdf
format: text/html
creator: Marc Terradas-Monllor
creator: Mirari Ochandorena-Acha
creator: Julio Salinas-Chesa
creator: Sergi Ramírez
creator: Hector Beltran-Alacreu
subject: Anesthesiology and Pain Management
subject: Kinesiology
subject: Orthopedics
subject: Rheumatology
subject: Translational Medicine
description: doi:10.7717/peerj.9903
publisher: PeerJ Inc.
rights: © 2020 Terradas-Monllor et al.
abstract: Background Knee arthroplasty (KA) is a typically successful surgical procedure commonly performed to alleviate painin participants with end-stage knee osteoarthritis. Despite its beneficial effects, a significant proportion of individuals with KA continue experiencing persistent pain and functional limitations. The purpose of this study was to assess the postoperative outcomes after KA in relation to postoperative pain catastrophizing. Methods Participants were recruited at a domiciliary physiotherapy service, using a prospective, observational, hypothesis-generating cohort design. Participants were divided into two groups based on their Pain Catastrophizing Scale (PCS) total score (50th percentile), which resulted in high and low PCS groups. The primary outcome measure was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). In addition, quality of life, walking speed, physical performance, range of motion, and pain were measured. Outcome measures were collected at baseline (1 week postoperatively) and at follow-up (1, 3, and 6 months postoperatively). Results A total of 60 participants (21 total KA and 39 unicompartmental KA) were recruited. Individuals with a higher degree of pain catastrophizing showed significantly higher WOMAC total scores at every follow-up, indicating poorer health functioning (p < 0.01). Similarly, the high PCS group showed higher WOMAC pain, stiffness and disability subscale scores (p < 0.05), poorer quality of life (p < 0.01), and poorer physical performance (p < 0.05) at every follow-up. In addition, the high PCS group achieved a slower walking speed at baseline and at 3 months follow-up (p < 0.05), and a higher degree of pain at rest, on walking and on knee flexion at every follow-up (p < 0.01, p < 0.05 and p < 0.05, respectively) except for walking pain at 3 months follow-up. No significant differences were observed between groups in range of motion, except for active knee extension at the 6-month follow-up (p < …
bibliographic citation: Terradas-Monllor et al. (2020) Assessment of postoperative health functioning after knee arthroplasty in relation to pain catastrophizing: a 6-month follow-up cohort study. PeerJ 8:e9903 https://doi.org/10.7717/peerj.9903
date submitted: 2020-03-24
date accepted: 2020-08-18
issued: 2020-09-09
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Terradas-Monllor
  given name: Marc
author list: Agent
  family name: Ochandorena-Acha
  given name: Mirari
author list: Agent
  family name: Salinas-Chesa
  given name: Julio
author list: Agent
  family name: Ramírez
  given name: Sergi
author list: Agent
  family name: Beltran-Alacreu
  given name: Hector
doi: 10.7717/peerj.9903
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-09
copyright: ©2020 Terradas-Monllor et al.
attribution name: Terradas-Monllor et al.

Effect of nest age and habitat variables on nest survival in Marsh Harrier (Circus aeruginosus) in a fishpond habitat
type: item
type: AcademicArticle
description: Background One important anti‐predator strategy adopted by birds involves nest site selection and timing of breeding. Nest-site selection by marsh-nesting birds often involves nest concealment and water depth as key features influencing nest survival. Marsh Harrier (Circus aeruginosus) is an obligate ground nester, which sets it apart from other raptors. The aim of the present study was to identify for the first time possible temporal and habitat factors affecting nest survival in Marsh Harrier. Understanding features which affect nest survival are essential for assessing relevant conservation strategies. Methods To understand the relative contributions of different temporal and habitat variables to brood losses, it is useful to determine the daily survival rate (DSR). We examined 82 Marsh Harrier nests located on fishponds in eastern Poland, where predation is the main cause of nest loss. Six habitat variables were measured for each active nest. DSR was calculated using known-fate models with the RMark package. Results The best-supported model predicted that DSR decreased with nest age and was positively affected by the water depth and the diameter of reed stems, but not by the height or density of vegetation at the nest site. The distances of nests to the fishpond dyke and to open water received no support in the models. The chances of nest survival were lower if a neighbouring nest had been depredated. This result suggests that the Marsh Harrier is more susceptible to mammalian than avian predation and confirms the high level of predator pressure in fishpond habitats.
format: application/pdf
format: text/html
creator: Urszula Zaremba
creator: Zbigniew Kasprzykowski
creator: Artur Golawski
subject: Conservation Biology
subject: Ecology
subject: Zoology
description: doi:10.7717/peerj.9929
publisher: PeerJ Inc.
rights: © 2020 Zaremba et al.
abstract: Background One important anti‐predator strategy adopted by birds involves nest site selection and timing of breeding. Nest-site selection by marsh-nesting birds often involves nest concealment and water depth as key features influencing nest survival. Marsh Harrier (Circus aeruginosus) is an obligate ground nester, which sets it apart from other raptors. The aim of the present study was to identify for the first time possible temporal and habitat factors affecting nest survival in Marsh Harrier. Understanding features which affect nest survival are essential for assessing relevant conservation strategies. Methods To understand the relative contributions of different temporal and habitat variables to brood losses, it is useful to determine the daily survival rate (DSR). We examined 82 Marsh Harrier nests located on fishponds in eastern Poland, where predation is the main cause of nest loss. Six habitat variables were measured for each active nest. DSR was calculated using known-fate models with the RMark package. Results The best-supported model predicted that DSR decreased with nest age and was positively affected by the water depth and the diameter of reed stems, but not by the height or density of vegetation at the nest site. The distances of nests to the fishpond dyke and to open water received no support in the models. The chances of nest survival were lower if a neighbouring nest had been depredated. This result suggests that the Marsh Harrier is more susceptible to mammalian than avian predation and confirms the high level of predator pressure in fishpond habitats.
bibliographic citation: Zaremba et al. (2020) Effect of nest age and habitat variables on nest survival in Marsh Harrier (Circus aeruginosus) in a fishpond habitat. PeerJ 8:e9929 https://doi.org/10.7717/peerj.9929
date submitted: 2020-03-30
date accepted: 2020-08-21
issued: 2020-09-09
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Zaremba
  given name: Urszula
author list: Agent
  family name: Kasprzykowski
  given name: Zbigniew
author list: Agent
  family name: Golawski
  given name: Artur
doi: 10.7717/peerj.9929
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-09
copyright: © 2020 Zaremba et al.
attribution name: Zaremba et al.

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome
type: item
type: AcademicArticle
description: Background The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients. Methods The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients’ clinical prognoses. Results Seven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42–16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13–11.25]). Conclusion We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk f…
format: application/pdf
format: text/html
creator: Jie Wang
creator: Heping Yu
creator: Qingquan Hua
creator: Shuili Jing
creator: Zhifen Liu
creator: Xiang Peng
creator: Cheng’an Cao
creator: Yongwen Luo
subject: Emergency and Critical Care
subject: Epidemiology
subject: Global Health
subject: Public Health
subject: Statistics
description: doi:10.7717/peerj.9945
publisher: PeerJ Inc.
rights: © 2020 Wang et al.
abstract: Background The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients. Methods The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients’ clinical prognoses. Results Seven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42–16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13–11.25]). Conclusion We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk f…
bibliographic citation: Wang et al. (2020) A descriptive study of random forest algorithm for predicting COVID-19 patients outcome. PeerJ 8:e9945 https://doi.org/10.7717/peerj.9945
date submitted: 2020-05-04
date accepted: 2020-08-25
issued: 2020-09-09
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Wang
  given name: Jie
author list: Agent
  family name: Yu
  given name: Heping
author list: Agent
  family name: Hua
  given name: Qingquan
author list: Agent
  family name: Jing
  given name: Shuili
author list: Agent
  family name: Liu
  given name: Zhifen
author list: Agent
  family name: Peng
  given name: Xiang
author list: Agent
  family name: Cao
  given name: Cheng’an
author list: Agent
  family name: Luo
  given name: Yongwen
doi: 10.7717/peerj.9945
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-09-09
copyright: © 2020 Wang et al.
attribution name: Wang et al.

PeerJ
type: Periodical
uri: PeerJ
issn: 2167-8359

Agent
family name: Curiel Yuste
given name: Jorge