PeerJ
type: channel
link: https://peerj.com/articles/
description: Articles published in PeerJ
publisher: PeerJ Inc.
publication name: PeerJ
issn: 2167-8359
items: Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
items: Transmissibility of coronavirus disease 2019 in Chinese cities with different dynamics of imported cases
items: BiSCoT: improving large eukaryotic genome assemblies with optical maps
items: Diversity of the gut microbiome in three grasshopper species using 16S rRNA and determination of cellulose digestibility
items: Characterizing carbonyl compounds and their sources in Fuzhou ambient air, southeast of China
items: Nuclear microsatellites reveal population genetic structuring and fine-scale pattern of hybridization in the Japanese mantis shrimp Oratosquilla oratoria
items: Trait differences among discrete morphs of a color polymorphic lizard, Podarcis erhardii
items: Robustness analysis in an inter-cities mobility network: modeling municipal, state and federal initiatives as failures and attacks toward SARS-CoV-2 containment
items: Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs
items: An updated evolutionary study of the Notch family reveals a new ancient origin and novel invariable motifs as potential pharmacological targets

Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
type: item
type: AcademicArticle
description: Background This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. Methods This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). Results The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760–0.785]) and 0.844 (95% CI [0.839–0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726–0.729]) and 0.848 (95% CI [0.847–0.849]), respectively. Conclusions Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.
format: application/pdf
format: text/html
creator: Xiaoran Li
creator: Peilin Ge
creator: Jocelyn Zhu
creator: Haifang Li
creator: James Graham
creator: Adam Singer
creator: Paul S. Richman
creator: Tim Q. Duong
subject: Bioinformatics
subject: Epidemiology
subject: Infectious Diseases
subject: Data Mining and Machine Learning
description: doi:10.7717/peerj.10337
publisher: PeerJ Inc.
rights: © 2020 Li et al.
abstract: Background This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. Methods This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). Results The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760–0.785]) and 0.844 (95% CI [0.839–0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726–0.729]) and 0.848 (95% CI [0.847–0.849]), respectively. Conclusions Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.
bibliographic citation: Li et al. (2020) Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables. PeerJ 8:e10337 https://doi.org/10.7717/peerj.10337
date submitted: 2020-07-20
date accepted: 2020-10-20
issued: 2020-11-06
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Li
  given name: Xiaoran
author list: Agent
  family name: Ge
  given name: Peilin
author list: Agent
  family name: Zhu
  given name: Jocelyn
author list: Agent
  family name: Li
  given name: Haifang
author list: Agent
  family name: Graham
  given name: James
author list: Agent
  family name: Singer
  given name: Adam
author list: Agent
  family name: Richman
  given name: Paul S.
author list: Agent
  family name: Duong
  given name: Tim Q.
doi: 10.7717/peerj.10337
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-06
copyright: © 2020 Li et al.
attribution name: Li et al.

Transmissibility of coronavirus disease 2019 in Chinese cities with different dynamics of imported cases
type: item
type: AcademicArticle
description: Background Monitoring the reproduction number (Rt) of the disease could help determine whether there is sustained transmission in a population, but areas with similar epidemic trends could have different transmission dynamics given the risk from imported cases varied across regions. In this study, we examined the Rt of coronavirus disease 2019 (COVID-19) by taking different dynamics of imported cases into account and compared the transmissibility of COVID-19 at different intervention periods in Hangzhou and Shenzhen. Methods We obtained the daily aggregated counts of laboratory-confirmed imported and local cases of COVID-19 infections in Hangzhou and Shenzhen from January 1 to March 13, 2020. Daily Rt and piecewise Rt before and after Wuhan lockdown were estimated, accounting for imported cases. Results Since the epidemic of COVID-19 in Shenzhen was dominated by imported cases, Rt was around 0.1 to 0.7 before the Wuhan lockdown. After the lockdown of Wuhan and the initialization of measures in response to the outbreak, local transmission was well-controlled as indicated by a low estimated value of piecewise Rt, 0.15 (95% CI [0.09–0.21]). On the contrary, Rt obtained for Hangzhou ranged from 1.2 to 4.9 with a piecewise Rt of 2.55 (95% CI [2.13–2.97]) before the lockdown of Wuhan due to the surge in local cases. Because of the Wuhan lockdown and other outbreak response measures, Rt dropped below unity in mid-February. Conclusions Even though Shenzhen had more cases than Hangzhou, local transmission did not sustain probably due to limited transmission from imported cases owing to the reduction in local susceptibles as residents left the city during Chunyun. The lockdown measures and local outbreak responses helped reduce the local transmissibility.
format: application/pdf
format: text/html
creator: Ka Chun Chong
creator: Wei Cheng
creator: Shi Zhao
creator: Feng Ling
creator: Kirran N. Mohammad
creator: Maggie Wang
creator: Benny CY Zee
creator: Lai Wei
creator: Xi Xiong
creator: Hengyan Liu
creator: Jingxuan Wang
creator: Enfu Chen
subject: Epidemiology
subject: Health Policy
subject: Infectious Diseases
subject: Public Health
description: doi:10.7717/peerj.10350
publisher: PeerJ Inc.
rights: © 2020 Chong et al.
abstract: Background Monitoring the reproduction number (Rt) of the disease could help determine whether there is sustained transmission in a population, but areas with similar epidemic trends could have different transmission dynamics given the risk from imported cases varied across regions. In this study, we examined the Rt of coronavirus disease 2019 (COVID-19) by taking different dynamics of imported cases into account and compared the transmissibility of COVID-19 at different intervention periods in Hangzhou and Shenzhen. Methods We obtained the daily aggregated counts of laboratory-confirmed imported and local cases of COVID-19 infections in Hangzhou and Shenzhen from January 1 to March 13, 2020. Daily Rt and piecewise Rt before and after Wuhan lockdown were estimated, accounting for imported cases. Results Since the epidemic of COVID-19 in Shenzhen was dominated by imported cases, Rt was around 0.1 to 0.7 before the Wuhan lockdown. After the lockdown of Wuhan and the initialization of measures in response to the outbreak, local transmission was well-controlled as indicated by a low estimated value of piecewise Rt, 0.15 (95% CI [0.09–0.21]). On the contrary, Rt obtained for Hangzhou ranged from 1.2 to 4.9 with a piecewise Rt of 2.55 (95% CI [2.13–2.97]) before the lockdown of Wuhan due to the surge in local cases. Because of the Wuhan lockdown and other outbreak response measures, Rt dropped below unity in mid-February. Conclusions Even though Shenzhen had more cases than Hangzhou, local transmission did not sustain probably due to limited transmission from imported cases owing to the reduction in local susceptibles as residents left the city during Chunyun. The lockdown measures and local outbreak responses helped reduce the local transmissibility.
bibliographic citation: Chong et al. (2020) Transmissibility of coronavirus disease 2019 in Chinese cities with different dynamics of imported cases. PeerJ 8:e10350 https://doi.org/10.7717/peerj.10350
date submitted: 2020-06-29
date accepted: 2020-10-21
issued: 2020-11-06
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Chong
  given name: Ka Chun
author list: Agent
  family name: Cheng
  given name: Wei
author list: Agent
  family name: Zhao
  given name: Shi
author list: Agent
  family name: Ling
  given name: Feng
author list: Agent
  family name: Mohammad
  given name: Kirran N.
author list: Agent
  family name: Wang
  given name: Maggie
author list: Agent
  family name: Zee
  given name: Benny CY
author list: Agent
  family name: Wei
  given name: Lai
author list: Agent
  family name: Xiong
  given name: Xi
author list: Agent
  family name: Liu
  given name: Hengyan
author list: Agent
  family name: Wang
  given name: Jingxuan
author list: Agent
  family name: Chen
  given name: Enfu
doi: 10.7717/peerj.10350
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-06
copyright: © 2020 Chong et al.
attribution name: Chong et al.

BiSCoT: improving large eukaryotic genome assemblies with optical maps
type: item
type: AcademicArticle
description: Motivation Long read sequencing and Bionano Genomics optical maps are two techniques that, when used together, make it possible to reconstruct entire chromosome or chromosome arms structure. However, the existing tools are often too conservative and organization of contigs into scaffolds is not always optimal. Results We developed BiSCoT (Bionano SCaffolding COrrection Tool), a tool that post-processes files generated during a Bionano scaffolding in order to produce an assembly of greater contiguity and quality. BiSCoT was tested on a human genome and four publicly available plant genomes sequenced with Nanopore long reads and improved significantly the contiguity and quality of the assemblies. BiSCoT generates a fasta file of the assembly as well as an AGP file which describes the new organization of the input assembly. Availability BiSCoT and improved assemblies are freely available on GitHub at http://www.genoscope.cns.fr/biscot and Pypi at https://pypi.org/project/biscot/.
format: application/pdf
format: text/html
creator: Benjamin Istace
creator: Caroline Belser
creator: Jean-Marc Aury
subject: Bioinformatics
subject: Genomics
description: doi:10.7717/peerj.10150
publisher: PeerJ Inc.
rights: © 2020 Istace et al.
abstract: Motivation Long read sequencing and Bionano Genomics optical maps are two techniques that, when used together, make it possible to reconstruct entire chromosome or chromosome arms structure. However, the existing tools are often too conservative and organization of contigs into scaffolds is not always optimal. Results We developed BiSCoT (Bionano SCaffolding COrrection Tool), a tool that post-processes files generated during a Bionano scaffolding in order to produce an assembly of greater contiguity and quality. BiSCoT was tested on a human genome and four publicly available plant genomes sequenced with Nanopore long reads and improved significantly the contiguity and quality of the assemblies. BiSCoT generates a fasta file of the assembly as well as an AGP file which describes the new organization of the input assembly. Availability BiSCoT and improved assemblies are freely available on GitHub at http://www.genoscope.cns.fr/biscot and Pypi at https://pypi.org/project/biscot/.
bibliographic citation: Istace et al. (2020) BiSCoT: improving large eukaryotic genome assemblies with optical maps. PeerJ 8:e10150 https://doi.org/10.7717/peerj.10150
date submitted: 2019-12-19
date accepted: 2020-09-21
issued: 2020-11-05
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Istace
  given name: Benjamin
author list: Agent
  family name: Belser
  given name: Caroline
author list: Agent
  family name: Aury
  given name: Jean-Marc
doi: 10.7717/peerj.10150
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-05
copyright: ©2020 Istace et al.
attribution name: Istace et al.

Diversity of the gut microbiome in three grasshopper species using 16S rRNA and determination of cellulose digestibility
type: item
type: AcademicArticle
description: Background Grasshoppers are typical phytophagous pests, and they have large appetites with high utilization of plants fibers, the digestion of which may depend on the microorganisms in their intestines. Grasshoppers have the potential to be utilized in bioreactors, which could improve straw utilization efficiency in the future. In this study, we describe the gut microbiome in three species of grasshoppers, Oedaleus decorus asiaticus, Aiolopus tamulus and Shirakiacris shirakii, by constructing a 16S rDNA gene library and analyzed the digestibility of cellulose and hemicellulose in the grasshoppers by using moss black phenol colorimetry and anthrone colorimetry. Results There were 509,436 bacterial OTUs (Operational Taxonomic Units) detected in the guts of all the grasshoppers sampled. Among them, Proteobacteria and Firmicutes were the most common, Aiolopus tamulus had the highest bacterial diversity, and Shirakiacris shirakii had the highest bacterial species richness. The intestinal microflora structure varied between the different species of grasshopper, with Aiolopus tamulus and Shirakiacris shirakii being the most similar. Meanwhile, the time at which grasshopper specimens were collected also led to changes in the intestinal microflora structure in the same species of grasshoppers. Klebsiella may form the core elements of the microflora in the grasshopper intestinal tract. The digestibility of cellulose/hemicellulose among the three species grasshoppers varied (38.01/24.99%, 43.95/17.21% and 44.12/47.62%). LEfSe analysis and Spearman correlation coefficients showed that the hemicellulosic digestibility of Shirakiacris shirakii was significantly higher than that of the other two species of grasshopper, which may be related to the presence of Pseudomonas, Stenotrophomonas, Glutamicibacter, Corynebacterium, and Brachybacterium in Shirakiacris shirakii intestinal tract. Conclusion The intestinal microbial communities of the three grasshoppers species are similar on p…
format: application/pdf
format: text/html
creator: Jian-Mei Wang
creator: Jing Bai
creator: Fang-Yuan Zheng
creator: Yao Ling
creator: Xiang Li
creator: Jing Wang
creator: Yong-Chao Zhi
creator: Xin-Jiang Li
subject: Biodiversity
subject: Entomology
subject: Microbiology
description: doi:10.7717/peerj.10194
publisher: PeerJ Inc.
rights: © 2020 Wang et al.
abstract: Background Grasshoppers are typical phytophagous pests, and they have large appetites with high utilization of plants fibers, the digestion of which may depend on the microorganisms in their intestines. Grasshoppers have the potential to be utilized in bioreactors, which could improve straw utilization efficiency in the future. In this study, we describe the gut microbiome in three species of grasshoppers, Oedaleus decorus asiaticus, Aiolopus tamulus and Shirakiacris shirakii, by constructing a 16S rDNA gene library and analyzed the digestibility of cellulose and hemicellulose in the grasshoppers by using moss black phenol colorimetry and anthrone colorimetry. Results There were 509,436 bacterial OTUs (Operational Taxonomic Units) detected in the guts of all the grasshoppers sampled. Among them, Proteobacteria and Firmicutes were the most common, Aiolopus tamulus had the highest bacterial diversity, and Shirakiacris shirakii had the highest bacterial species richness. The intestinal microflora structure varied between the different species of grasshopper, with Aiolopus tamulus and Shirakiacris shirakii being the most similar. Meanwhile, the time at which grasshopper specimens were collected also led to changes in the intestinal microflora structure in the same species of grasshoppers. Klebsiella may form the core elements of the microflora in the grasshopper intestinal tract. The digestibility of cellulose/hemicellulose among the three species grasshoppers varied (38.01/24.99%, 43.95/17.21% and 44.12/47.62%). LEfSe analysis and Spearman correlation coefficients showed that the hemicellulosic digestibility of Shirakiacris shirakii was significantly higher than that of the other two species of grasshopper, which may be related to the presence of Pseudomonas, Stenotrophomonas, Glutamicibacter, Corynebacterium, and Brachybacterium in Shirakiacris shirakii intestinal tract. Conclusion The intestinal microbial communities of the three grasshoppers species are similar on p…
bibliographic citation: Wang et al. (2020) Diversity of the gut microbiome in three grasshopper species using 16S rRNA and determination of cellulose digestibility. PeerJ 8:e10194 https://doi.org/10.7717/peerj.10194
date submitted: 2020-06-03
date accepted: 2020-09-24
issued: 2020-11-05
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Wang
  given name: Jian-Mei
author list: Agent
  family name: Bai
  given name: Jing
author list: Agent
  family name: Zheng
  given name: Fang-Yuan
author list: Agent
  family name: Ling
  given name: Yao
author list: Agent
  family name: Li
  given name: Xiang
author list: Agent
  family name: Wang
  given name: Jing
author list: Agent
  family name: Zhi
  given name: Yong-Chao
author list: Agent
  family name: Li
  given name: Xin-Jiang
doi: 10.7717/peerj.10194
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-05
copyright: © 2020 Wang et al.
attribution name: Wang et al.

Characterizing carbonyl compounds and their sources in Fuzhou ambient air, southeast of China
type: item
type: AcademicArticle
description: In recent years, ozone (O3) concentrations in the southeastern coastal areas of China have shown a gradual upward trend. As precursors and intermediates in the formation of O3, carbonyl compounds play key roles in the atmospheric photochemical oxidation cycle. To explore the main pollution characteristics of carbonyl compounds in a typical coastal city in southeast China, ambient samples were collected in Fuzhou (the provincial capital of Fujian province, located on the southeast coast of China) and analyzed using high-performance liquid chromatography with ultraviolet detection. The study was continuously carried out at an urban site (Jinjishan) and a suburban site (Gushan) in Fuzhou from May 8 to 20, 2018. The total concentration of 16 carbonyl compounds at the urban site was 15.45 ± 11.18 ppbv, and the total concentration at the suburban site was 17.57 ± 12.77 ppbv. Formaldehyde (HCHO), acetaldehyde, and acetone were the main species detected in the samples, and acetone had the highest concentration among the species detected. The suburban site had a higher formaldehyde/acetaldehyde ratio and lower acetaldehyde/propionaldehyde ratio than the urban site, implying that biogenic sources potentially contributed to the carbonyl compound concentrations at the suburban site. The results of an observation-based model showed that anthropogenic hydrocarbons promoted HCHO production on May 17 at the urban site. Compared to biogenic emissions, anthropogenic activity is a more important source of carbonyl compounds.
format: application/pdf
format: text/html
creator: Zhen He
creator: Xin Zhang
creator: Yunfeng Li
creator: Xuefen Zhong
creator: Hong Li
creator: Rui Gao
creator: Jinjuan Li
subject: Atmospheric Chemistry
subject: Environmental Contamination and Remediation
subject: Environmental Impacts
description: doi:10.7717/peerj.10227
publisher: PeerJ Inc.
rights: © 2020 He et al.
abstract: In recent years, ozone (O3) concentrations in the southeastern coastal areas of China have shown a gradual upward trend. As precursors and intermediates in the formation of O3, carbonyl compounds play key roles in the atmospheric photochemical oxidation cycle. To explore the main pollution characteristics of carbonyl compounds in a typical coastal city in southeast China, ambient samples were collected in Fuzhou (the provincial capital of Fujian province, located on the southeast coast of China) and analyzed using high-performance liquid chromatography with ultraviolet detection. The study was continuously carried out at an urban site (Jinjishan) and a suburban site (Gushan) in Fuzhou from May 8 to 20, 2018. The total concentration of 16 carbonyl compounds at the urban site was 15.45 ± 11.18 ppbv, and the total concentration at the suburban site was 17.57 ± 12.77 ppbv. Formaldehyde (HCHO), acetaldehyde, and acetone were the main species detected in the samples, and acetone had the highest concentration among the species detected. The suburban site had a higher formaldehyde/acetaldehyde ratio and lower acetaldehyde/propionaldehyde ratio than the urban site, implying that biogenic sources potentially contributed to the carbonyl compound concentrations at the suburban site. The results of an observation-based model showed that anthropogenic hydrocarbons promoted HCHO production on May 17 at the urban site. Compared to biogenic emissions, anthropogenic activity is a more important source of carbonyl compounds.
bibliographic citation: He et al. (2020) Characterizing carbonyl compounds and their sources in Fuzhou ambient air, southeast of China. PeerJ 8:e10227 https://doi.org/10.7717/peerj.10227
date submitted: 2020-06-22
date accepted: 2020-10-01
issued: 2020-11-05
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: He
  given name: Zhen
author list: Agent
  family name: Zhang
  given name: Xin
author list: Agent
  family name: Li
  given name: Yunfeng
author list: Agent
  family name: Zhong
  given name: Xuefen
author list: Agent
  family name: Li
  given name: Hong
author list: Agent
  family name: Gao
  given name: Rui
author list: Agent
  family name: Li
  given name: Jinjuan
doi: 10.7717/peerj.10227
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-05
copyright: ©2020 He et al.
attribution name: He et al.

Nuclear microsatellites reveal population genetic structuring and fine-scale pattern of hybridization in the Japanese mantis shrimp Oratosquilla oratoria
type: item
type: AcademicArticle
description: The interplay between historical and contemporary processes can produce complex patterns of genetic differentiation in the marine realm. Recent mitochondrial and nuclear sequence analyses revealed cryptic speciation in the Japanese mantis shrimp Oratosquilla oratoria. Herein, we applied nuclear microsatellite markers to examine patterns and causes of genetic differentiation in this morphotaxon. Population structure analyses revealed two genetically divergent and geographically structured clades in O. oratoria, one dominating the temperate zone of the Northwestern (NW) Pacific and the other occurring in the subtropical and tropical waters where are influenced by the Kuroshio Current. Two sympatric zones, one around the Changjiang Estuary in China coast and the other in the northern Japan Sea, were demonstrated to be hybrid zones where introgressive hybridization occurred asymmetrically. The interaction between historical climate shifts and contemporary factors (e.g., freshwater discharge, temperature gradient and isolation by distance) may contribute to the present-day genetic architecture in the Japanese mantis shrimp. Range shift induced by climate changes and oceanographic factors may promote hybridization and gene flow between the O. oratoria complex. Our results provide insights into the interacting mechanisms that give rise to diversification and speciation of coastal species in the NW Pacific.
format: application/pdf
format: text/html
creator: Jiao Cheng
creator: Nan Zhang
creator: Zhongli Sha
subject: Biogeography
subject: Genetics
subject: Marine Biology
subject: Molecular Biology
subject: Population Biology
description: doi:10.7717/peerj.10270
publisher: PeerJ Inc.
rights: © 2020 Cheng et al.
abstract: The interplay between historical and contemporary processes can produce complex patterns of genetic differentiation in the marine realm. Recent mitochondrial and nuclear sequence analyses revealed cryptic speciation in the Japanese mantis shrimp Oratosquilla oratoria. Herein, we applied nuclear microsatellite markers to examine patterns and causes of genetic differentiation in this morphotaxon. Population structure analyses revealed two genetically divergent and geographically structured clades in O. oratoria, one dominating the temperate zone of the Northwestern (NW) Pacific and the other occurring in the subtropical and tropical waters where are influenced by the Kuroshio Current. Two sympatric zones, one around the Changjiang Estuary in China coast and the other in the northern Japan Sea, were demonstrated to be hybrid zones where introgressive hybridization occurred asymmetrically. The interaction between historical climate shifts and contemporary factors (e.g., freshwater discharge, temperature gradient and isolation by distance) may contribute to the present-day genetic architecture in the Japanese mantis shrimp. Range shift induced by climate changes and oceanographic factors may promote hybridization and gene flow between the O. oratoria complex. Our results provide insights into the interacting mechanisms that give rise to diversification and speciation of coastal species in the NW Pacific.
bibliographic citation: Cheng et al. (2020) Nuclear microsatellites reveal population genetic structuring and fine-scale pattern of hybridization in the Japanese mantis shrimp Oratosquilla oratoria. PeerJ 8:e10270 https://doi.org/10.7717/peerj.10270
date submitted: 2020-06-08
date accepted: 2020-10-08
issued: 2020-11-05
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Cheng
  given name: Jiao
author list: Agent
  family name: Zhang
  given name: Nan
author list: Agent
  family name: Sha
  given name: Zhongli
doi: 10.7717/peerj.10270
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-05
copyright: ©2020 Cheng et al.
attribution name: Cheng et al.

Trait differences among discrete morphs of a color polymorphic lizard, Podarcis erhardii
type: item
type: AcademicArticle
description: Color polymorphism defies evolutionary expectations as striking phenotypic variation is maintained within a single species. Color and other traits mediate social interactions, and stable polymorphism within a population is hypothesized to be related to correlational selection of other phenotypic traits among color morphs. Here, we report on a previously unknown throat color polymorphism in the Aegean Wall Lizard (Podarcis erhardii) and examine morph-correlated differences in traits important to social behavior and communication: maximum bite force capacity and chemical signal profile. We find that both sexes of P. erhardii have three color morphs: orange, yellow, and white. Moreover, orange males are significantly larger and tend to bite harder than yellow and white males. Although the established color polymorphism only partially matches the observed intraspecific variation in chemical signal signatures, the chemical profile of the secretions of orange males is significantly divergent from that of white males. Our findings suggest that morph colors are related to differences in traits that are crucial for social interactions and competitive ability, illustrating the need to look beyond color when studying polymorphism evolution.
format: application/pdf
format: text/html
creator: Kinsey M. Brock
creator: Simon Baeckens
creator: Colin M. Donihue
creator: José Martín
creator: Panayiotis Pafilis
creator: Danielle L. Edwards
subject: Animal Behavior
subject: Evolutionary Studies
subject: Zoology
description: doi:10.7717/peerj.10284
publisher: PeerJ Inc.
rights: © 2020 Brock et al.
abstract: Color polymorphism defies evolutionary expectations as striking phenotypic variation is maintained within a single species. Color and other traits mediate social interactions, and stable polymorphism within a population is hypothesized to be related to correlational selection of other phenotypic traits among color morphs. Here, we report on a previously unknown throat color polymorphism in the Aegean Wall Lizard (Podarcis erhardii) and examine morph-correlated differences in traits important to social behavior and communication: maximum bite force capacity and chemical signal profile. We find that both sexes of P. erhardii have three color morphs: orange, yellow, and white. Moreover, orange males are significantly larger and tend to bite harder than yellow and white males. Although the established color polymorphism only partially matches the observed intraspecific variation in chemical signal signatures, the chemical profile of the secretions of orange males is significantly divergent from that of white males. Our findings suggest that morph colors are related to differences in traits that are crucial for social interactions and competitive ability, illustrating the need to look beyond color when studying polymorphism evolution.
bibliographic citation: Brock et al. (2020) Trait differences among discrete morphs of a color polymorphic lizard, Podarcis erhardii. PeerJ 8:e10284 https://doi.org/10.7717/peerj.10284
date submitted: 2020-06-24
date accepted: 2020-10-09
issued: 2020-11-05
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Brock
  given name: Kinsey M.
author list: Agent
  family name: Baeckens
  given name: Simon
author list: Agent
  family name: Donihue
  given name: Colin M.
author list: Agent
  family name: Martín
  given name: José
author list: Agent
  family name: Pafilis
  given name: Panayiotis
author list: Agent
  family name: Edwards
  given name: Danielle L.
doi: 10.7717/peerj.10284
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-05
copyright: ©2020 Brock et al.
attribution name: Brock et al.

Robustness analysis in an inter-cities mobility network: modeling municipal, state and federal initiatives as failures and attacks toward SARS-CoV-2 containment
type: item
type: AcademicArticle
description: We present a robustness analysis of an inter-cities mobility complex network, motivated by the challenge of the COVID-19 pandemic and the seek for proper containment strategies. Brazilian data from 2016 are used to build a network with more than five thousand cities (nodes) and twenty-seven states with the edges representing the weekly flow of people between cities via terrestrial transports. Nodes are systematically isolated (removed from the network) either at random (failures) or guided by specific strategies (targeted attacks), and the impacts are assessed with three metrics: the number of components, the size of the giant component, and the total remaining flow of people. We propose strategies to identify which regions should be isolated first and their impact on people mobility. The results are compared with the so-called reactive strategy, which consists of isolating regions ordered by the date the first case of COVID-19 appeared. We assume that the nodes’ failures abstract individual municipal and state initiatives that are independent and possess a certain level of unpredictability. Differently, the targeted attacks are related to centralized strategies led by the federal government in agreement with municipalities and states. Removing a node means completely restricting the mobility of people between the referred city/state and the rest of the network. Results reveal that random failures do not cause a high impact on mobility restraint, but the coordinated isolation of specific cities with targeted attacks is crucial to detach entire network areas and thus prevent spreading. Moreover, the targeted attacks perform better than the reactive strategy for the three analyzed robustness metrics.
format: application/pdf
format: text/html
creator: Vander L.S. Freitas
creator: Gladston J.P. Moreira
creator: Leonardo B.L. Santos
subject: Epidemiology
subject: Computational Science
subject: Spatial and Geographic Information Science
description: doi:10.7717/peerj.10287
publisher: PeerJ Inc.
rights: © 2020 Freitas et al.
abstract: We present a robustness analysis of an inter-cities mobility complex network, motivated by the challenge of the COVID-19 pandemic and the seek for proper containment strategies. Brazilian data from 2016 are used to build a network with more than five thousand cities (nodes) and twenty-seven states with the edges representing the weekly flow of people between cities via terrestrial transports. Nodes are systematically isolated (removed from the network) either at random (failures) or guided by specific strategies (targeted attacks), and the impacts are assessed with three metrics: the number of components, the size of the giant component, and the total remaining flow of people. We propose strategies to identify which regions should be isolated first and their impact on people mobility. The results are compared with the so-called reactive strategy, which consists of isolating regions ordered by the date the first case of COVID-19 appeared. We assume that the nodes’ failures abstract individual municipal and state initiatives that are independent and possess a certain level of unpredictability. Differently, the targeted attacks are related to centralized strategies led by the federal government in agreement with municipalities and states. Removing a node means completely restricting the mobility of people between the referred city/state and the rest of the network. Results reveal that random failures do not cause a high impact on mobility restraint, but the coordinated isolation of specific cities with targeted attacks is crucial to detach entire network areas and thus prevent spreading. Moreover, the targeted attacks perform better than the reactive strategy for the three analyzed robustness metrics.
bibliographic citation: Freitas et al. (2020) Robustness analysis in an inter-cities mobility network: modeling municipal, state and federal initiatives as failures and attacks toward SARS-CoV-2 containment. PeerJ 8:e10287 https://doi.org/10.7717/peerj.10287
date submitted: 2020-06-12
date accepted: 2020-10-10
issued: 2020-11-05
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Freitas
  given name: Vander L.S.
author list: Agent
  family name: Moreira
  given name: Gladston J.P.
author list: Agent
  family name: Santos
  given name: Leonardo B.L.
doi: 10.7717/peerj.10287
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-05
copyright: © 2020 Freitas et al.
attribution name: Freitas et al.

Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs
type: item
type: AcademicArticle
description: Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.
format: application/pdf
format: text/html
creator: Shreeja Kikkisetti
creator: Jocelyn Zhu
creator: Beiyi Shen
creator: Haifang Li
creator: Tim Q. Duong
subject: Bioinformatics
subject: Infectious Diseases
subject: Radiology and Medical Imaging
subject: Respiratory Medicine
subject: Computational Science
description: doi:10.7717/peerj.10309
publisher: PeerJ Inc.
rights: © 2020 Kikkisetti et al.
abstract: Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.
bibliographic citation: Kikkisetti et al. (2020) Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs. PeerJ 8:e10309 https://doi.org/10.7717/peerj.10309
date submitted: 2020-09-02
date accepted: 2020-10-15
issued: 2020-11-05
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Kikkisetti
  given name: Shreeja
author list: Agent
  family name: Zhu
  given name: Jocelyn
author list: Agent
  family name: Shen
  given name: Beiyi
author list: Agent
  family name: Li
  given name: Haifang
author list: Agent
  family name: Duong
  given name: Tim Q.
doi: 10.7717/peerj.10309
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-05
copyright: © 2020 Kikkisetti et al.
attribution name: Kikkisetti et al.

An updated evolutionary study of the Notch family reveals a new ancient origin and novel invariable motifs as potential pharmacological targets
type: item
type: AcademicArticle
description: Notch family proteins play a key role in a variety of developmental processes by controlling cell fate decisions and operating in a great number of biological processes in several organ systems, such as hematopoiesis, somatogenesis, vasculogenesis, neurogenesis and homeostasis. The Notch signaling pathway is crucial for the majority of developmental programs and regulates multiple pathogenic processes. Notch family receptors’ activation has been largely related to its multiple effects in sustaining oncogenesis. The Notch signaling pathway constitutes an ancient and conserved mechanism for cell to cell communication. Much of what is known about Notch family proteins function comes from studies done in Caenorhabditis Elegans and Drosophila Melanogaster. Although, human Notch homologs had also been identified, the molecular mechanisms which modulate the Notch signaling pathway remained substantially unknown. In this study, an updated evolutionary analysis of the Notch family members among 603 different organisms of all kingdoms, from bacteria to humans, was performed in order to discover key regions that have been conserved throughout evolution and play a major role in the Notch signaling pathway. The major goal of this study is the presentation of a novel updated phylogenetic tree for the Notch family as a reliable phylogeny “map”, in order to correlate information of the closely related members and identify new possible pharmacological targets that can be used in pathogenic cases, including cancer.
format: application/pdf
format: text/html
creator: Dimitrios Vlachakis
creator: Louis Papageorgiou
creator: Ariadne Papadaki
creator: Maria Georga
creator: Sofia Kossida
creator: Elias Eliopoulos
subject: Bioinformatics
subject: Biotechnology
subject: Computational Biology
subject: Genetics
subject: Computational Science
description: doi:10.7717/peerj.10334
publisher: PeerJ Inc.
rights: © 2020 Vlachakis et al.
abstract: Notch family proteins play a key role in a variety of developmental processes by controlling cell fate decisions and operating in a great number of biological processes in several organ systems, such as hematopoiesis, somatogenesis, vasculogenesis, neurogenesis and homeostasis. The Notch signaling pathway is crucial for the majority of developmental programs and regulates multiple pathogenic processes. Notch family receptors’ activation has been largely related to its multiple effects in sustaining oncogenesis. The Notch signaling pathway constitutes an ancient and conserved mechanism for cell to cell communication. Much of what is known about Notch family proteins function comes from studies done in Caenorhabditis Elegans and Drosophila Melanogaster. Although, human Notch homologs had also been identified, the molecular mechanisms which modulate the Notch signaling pathway remained substantially unknown. In this study, an updated evolutionary analysis of the Notch family members among 603 different organisms of all kingdoms, from bacteria to humans, was performed in order to discover key regions that have been conserved throughout evolution and play a major role in the Notch signaling pathway. The major goal of this study is the presentation of a novel updated phylogenetic tree for the Notch family as a reliable phylogeny “map”, in order to correlate information of the closely related members and identify new possible pharmacological targets that can be used in pathogenic cases, including cancer.
bibliographic citation: Vlachakis et al. (2020) An updated evolutionary study of the Notch family reveals a new ancient origin and novel invariable motifs as potential pharmacological targets. PeerJ 8:e10334 https://doi.org/10.7717/peerj.10334
date submitted: 2020-06-05
date accepted: 2020-10-19
issued: 2020-11-05
is part of: PeerJ
license: https://creativecommons.org/licenses/by/4.0/
author list: Agent
  family name: Vlachakis
  given name: Dimitrios
author list: Agent
  family name: Papageorgiou
  given name: Louis
author list: Agent
  family name: Papadaki
  given name: Ariadne
author list: Agent
  family name: Georga
  given name: Maria
author list: Agent
  family name: Kossida
  given name: Sofia
author list: Agent
  family name: Eliopoulos
  given name: Elias
doi: 10.7717/peerj.10334
volume: 8
issn: 2167-8359
publication name: PeerJ
publication date: 2020-11-05
copyright: © 2020 Vlachakis et al.
attribution name: Vlachakis et al.

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