Carinacuma umesi, a new genus and species of bodotriid cumacean (Crustacea: Malacostraca: Peracarida) from shallow waters of the Maryland Coastal Bays, Mid-Atlantic region, USA
Carinacuma, a new bodotriid cumacean genus, is diagnosed to receive Spilocuma watlingi Omholt & Heard as its type species and C. umesi sp. nov., described from shallow waters (0.8 to 2.8 m) on the Mid-Atlantic coast of North America. Carinacuma gen. nov. has its closest affinities to the North American genera Spilocuma and Mancocuma, but can be distinguished from them and the other genera within the subfamily Vaunthompsoniinae by a combination of characters, including the presence of a dorsal carina or keel on pereonite 3 of the female, morphology of the antenna, absence of pleopods in the male, and the setation and segmentation of the uropods. Carinacuma umesi sp. nov., can be differentiated from its northern Gulf of Mexico cognate, C. watlingi comb. nov. by several characters, including: (1) maxilliped 3 carpus of female with inner margin bearing four to five simple setae, (2) uropod peduncle of female with inner margin bearing one sub-distal micro-serrate seta with single sub-terminal medial setule, (3) male antennule peduncle articles 1–2 sub-equal length, and (4) male antennule accessory flagellum slightly longer than basal article of main flagellum. A key to the five known males lacking pleopods within Vaunthompsoniinae is provided.
Morales-Núñez et al. (2021) Carinacuma umesi, a new genus and species of bodotriid cumacean (Crustacea: Malacostraca: Peracarida) from shallow waters of the Maryland Coastal Bays, Mid-Atlantic region, USA. PeerJ 9:e11740 https://doi.org/10.7717/peerj.11740
In silico predictions of protein interactions between Zika virus and human host
Background The ZIKA virus (ZIKV) belongs to the Flaviviridae family, was first isolated in the 1940s, and remained underreported until its global threat in 2016, where drastic consequences were reported as Guillan-Barre syndrome and microcephaly in newborns. Understanding molecular interactions of ZIKV proteins during the host infection is important to develop treatments and prophylactic measures; however, large-scale experimental approaches normally used to detect protein-protein interaction (PPI) are onerous and labor-intensive. On the other hand, computational methods may overcome these challenges and guide traditional approaches on one or few protein molecules. The prediction of PPIs can be used to study host-parasite interactions at the protein level and reveal key pathways that allow viral infection. Results Applying Random Forest and Support Vector Machine (SVM) algorithms, we performed predictions of PPI between two ZIKV strains and human proteomes. The consensus number of predictions of both algorithms was 17,223 pairs of proteins. Functional enrichment analyses were executed with the predicted networks to access the biological meanings of the protein interactions. Some pathways related to viral infection and neurological development were found for both ZIKV strains in the enrichment analysis, but the JAK-STAT pathway was observed only for strain PE243 when compared with the FSS13025 strain. Conclusions The consensus network of PPI predictions made by Random Forest and SVM algorithms allowed an enrichment analysis that corroborates many aspects of ZIKV infection. The enrichment results are mainly related to viral infection, neuronal development, and immune response, and presented differences among the two compared ZIKV strains. Strain PE243 presented more predicted interactions between proteins from the JAK-STAT signaling pathway, which could lead to a more inflammatory immune response when compared with the FSS13025 strain. These results show that the methodology employed in this study can potentially reveal new interactions between the ZIKV and human cells.
Crhisllane Rafaele dos Santos Vasconcelos
Pitta et al. (2021) In silico predictions of protein interactions between Zika virus and human host. PeerJ 9:e11770 https://doi.org/10.7717/peerj.11770
Genome-wide analysis of BpDof genes and the tolerance to drought stress in birch (Betula platyphylla)
Background DNA binding with one finger (Dof) proteins are plant-specific transcription factors playing vital roles in developmental processes and stress responses in plants. Nevertheless, the characterizations, expression patterns, and functions of the Dof family under drought stress (a key determinant of plant physiology and metabolic homeostasis) in woody plants remain unclear. Methods The birch (Betula platyphylla var. mandshuric) genome and plant TFDB database were used to identify Dof gene family members in birch plants. ClustalW2 of BioEdit v7.2.1, MEGA v7.0, ExPASy ProtParam tool, Subloc, TMHMM v2.0, GSDS v2.0, MEME, TBtools, KaKs Calculator v2.0, and PlantCARE were respectively used to align the BpDof sequences, build a phylogenetic tree, identify the physicochemical properties, analyze the chromosomal distribution and synteny, and identify the cis-elements in the promoter regions of the 26 BpDof genes. Additionally, the birch seedlings were exposed to PEG6000-simulated drought stress, and the expression patterns of the BpDof genes in different tissues were analyzed by qRT-PCR. The histochemical staining and the evaluation of physiological indexes were performed to assess the plant tolerance to drought with transient overexpression of BpDof4, BpDof11, and BpDof17 genes. SPSS software and ANOVA were used to conduct all statistical analyses and determine statistically significant differences between results. Results A total of 26 BpDof genes were identified in birch via whole-genome analysis. The conserved Dof domain with a C(x)2C(x)21C(x)2C zinc finger motif was present in all BpDof proteins. These birch BpDofs were classified into four groups (A to D) according to the phylogenetic analysis of Arabidopsis thaliana Dof genes. BpDof proteins within the same group mostly possessed similar motifs, as detected by conserved motif analysis. The exon–intron analysis revealed that the structures of BpDof genes differed, indicating probable gene gain and lose during the BpDof evolution. The chromosomal distribution and synteny analysis showed that the 26 BpDofs were unevenly distributed on 14 chromosomes, and seven duplication events among six chromosomes were found. Cis-acting elements were abundant in the promoter regions of the 26 BpDof genes. qRT-PCR revealed that the expression of the 26 BpDof genes was differentially regulated by drought stress among roots, stems, and leaves. Most BpDof genes responded to drought stress, and BpDof4, BpDof11, and BpDof17­ were significantly up-regulated. Therefore, plants overexpressing these three genes were generated to investigate drought stress tolerance. The BpDof4-, BpDof11-, and BpDof17­-overexpressing plants showed promoted reactive oxygen species (ROS) scavenging capabilities and less severe cell damage, suggesting that they conferred enhanced drought tolerance in birch. This study provided an in-depth insight into the structure, evolution, expression, and function of the Dof gene family in plants.
Sun et al. (2021) Genome-wide analysis of BpDof genes and the tolerance to drought stress in birch (Betula platyphylla). PeerJ 9:e11938 https://doi.org/10.7717/peerj.11938
Quorum sensing in Aliivibrio wodanis 06/09/139 and its role in controlling various phenotypic traits
Background Quorum Sensing (QS) is a cell-to-cell communication system that bacteria utilize to adapt to the external environment by synthesizing and responding to signalling molecules called autoinducers. The psychrotrophic bacterium Aliivibrio wodanis 06/09/139, originally isolated from a winter ulcer of a reared Atlantic salmon, produces the autoinducer N-3-hydroxy-decanoyl-homoserine-lactone (3OHC10-HSL) and encodes the QS systems AinS/R and LuxS/PQ, and the master regulator LitR. However, the role of QS in this bacterium has not been investigated yet. Results In the present work we show that 3OHC10-HSL production is cell density and temperature-dependent in A. wodanis 06/09/139 with the highest production occurring at a low temperature (6 °C). Gene inactivation demonstrates that AinS is responsible for 3OHC10-HSL production and positively regulated by LitR. Inactivation of ainS and litR further show that QS is involved in the regulation of growth, motility, hemolysis, protease activity and siderophore production. Of these QS regulated activities, only the protease activity was found to be independent of LitR. Lastly, supernatants harvested from the wild type and the ΔainS and ΔlitR mutants at high cell densities show that inactivation of QS leads to a decreased cytopathogenic effect (CPE) in a cell culture assay, and strongest attenuation of the CPE was observed with supernatants harvested from the ΔlitR mutant. Conclusion A. wodanis 06/09/139 use QS to regulate a number of activities that may prove important for host colonization or interactions. The temperature of 6 °C that is in the temperature range at which winter ulcer occurs, plays a role in AHL production and development of CPE on a Chinook Salmon Embryo (CHSE) cell line.
Maharajan et al. (2021) Quorum sensing in Aliivibrio wodanis 06/09/139 and its role in controlling various phenotypic traits. PeerJ 9:e11980 https://doi.org/10.7717/peerj.11980
Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach
Background A feasible and accurate risk prediction systems for emergency department (ED) patients is urgently required. The Modified Early Warning Score (MEWS) is a wide-used tool to predict clinical outcomes in ED. Literatures showed that machine learning (ML) had better predictability in specific patient population than traditional scoring system. By analyzing a large multicenter dataset, we aim to develop a ML model to predict in-hospital morality of the adult non traumatic ED patients for different time stages, and comparing performance with other ML models and MEWS. Methods A retrospective observational cohort study was conducted in five Taiwan EDs including two tertiary medical centers and three regional hospitals. All consecutively adult (>17 years old) non-traumatic patients admit to ED during a 9-year period (January first, 2008 to December 31th, 2016) were included. Exclusion criteria including patients with (1) out-of-hospital cardiac arrest and (2) discharge against medical advice and transferred to other hospital (3) missing collect variables. The primary outcome was in-hospital mortality and were categorized into 6, 24, 72, 168 hours mortality. MEWS was calculated by systolic blood pressure, pulse rate, respiratory rate, body temperature, and level of consciousness. An ensemble supervised stacking ML model was developed and compared to sensitive and unsensitive Xgboost, Random Forest, and Adaboost. We conducted a performance test and examine both the area under the receiver operating characteristic (AUROC) and the area under the precision and recall curve (AUPRC) as the comparative measures. Result After excluding 182,001 visits (7.46%), study group was consisted of 24,37,326 ED visits. The dataset was split into 67% training data and 33% test data for ML model development. There was no statistically difference found in the characteristics between two groups. For the prediction of 6, 24, 72, 168 hours in-hospital mortality, the AUROC of MEW and ML mode was 0.897, 0.865, 0.841, 0.816 and 0.939, 0.928, 0.913, 0.902 respectively. The stacking ML model outperform other ML model as well. For the prediction of in-hospital mortality over 48-hours, AUPRC performance of MEWS drop below 0.1, while the AUPRC of ML mode was 0.317 in 6 hours and 0.2150 in 168 hours. For each time frame, ML model achieved statistically significant higher AUROC and AUPRC than MEWS (all P < 0.001). Both models showed decreasing prediction ability as time elapse, but there was a trend that the gap of AUROC values between two model increases gradually (P < 0.001). Three MEWS thresholds (score >3, >4, and >5) were determined as baselines for comparison, ML mode consistently showed improved or equally performance in sensitivity, PPV, NPV, but not in specific. Conclusion Stacking ML methods improve predicted in-hospital mortality than MEWS in adult non-traumatic ED patients, especially in the prediction of delayed mortality.
Wu et al. (2021) Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach. PeerJ 9:e11988 https://doi.org/10.7717/peerj.11988
Molecular and morphological evidence for a new species of Leptopus (Phyllanthaceae) from Southeast Yunnan, China
Leptopus malipoensis, a new species of Phyllanthaceae from Southeast Yunnan Province, China, is described. The phylogenetic position of the new species within the genus Leptopus was analyzed based on nuclear ribosomal Internal Transcribed Spacer (nrITS) and plastid matK sequence data. The results show that L. malipoensis is highly supported to be the sister of L. fangdingianus (P. T. Li) Voronts. & Petra Hoff., a species endemic to western Guangxi Province, China. Morphologically, the new species can be distinguished from all known congeneric taxa by its long and slim branches usually pendulous or procumbent, some of its leaf laminas up to 15 cm long and 7 cm wide. It further differs from its sister species by its hirsute stems, leaves and pedicel of female flowers, longer pedicel of male flowers, 3-locular ovary and three styles. A key to all 10 accepted Leptopus species is provided.
Zhang et al. (2021) Molecular and morphological evidence for a new species of Leptopus (Phyllanthaceae) from Southeast Yunnan, China. PeerJ 9:e11989 https://doi.org/10.7717/peerj.11989
The modulatory properties of Astragalus membranaceus treatment on endometrial cancer: an integrated pharmacological method
Astragalus membranaceus is a traditional Chinese medicine and has been used for adjuvant clinical therapy for a variety of cancers. However, the mechanism of its action on endometrial carcinoma is unclear. Based on the Gene Expression Omnibus (GEO) database, the Cancer Genome Atlas (TCGA) database, and the Traditional Chinese Medicine System Pharmacology Database (TCMSP™), the drug and target compounds were initially screened to construct a common network module. Twenty active compounds in Astragalus membranaceus were successfully identified, which hit by 463 potential targets related to endometrial cancer. Eight of the more highly predictive compounds (such as Jaranol, Bifendate, Isorhamnetin, Calycosin, 7-O-methylisomucronulatol, Formononetin, Kaempferol, Quercetin) were involved in DNA integrity checkpoint, cyclin-dependent protein kinase holoenzyme complex, and histone kinase activity. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway confirmed that Astragalus membranaceus might play a role in the treatment of endometrial cancer through p53 signalling pathway, transcriptional misregulation in cancer, and endometrial cancer signalling pathway. Drug-target-pathway networks were constructed using Cytoscape to provide a visual perspective. In addition, we verified that formononetin inhibited the proliferation of endometrial cancer cells through cell viability tests and clone formation tests. And qPCR and western blot found that formononetin exerts anti-cancer effects by promoting the expression of estrogen receptor beta (ERβ) and p53. Based on a systematic network pharmacology approach, our works successfully predict the active ingredients and potential targets of Astragalus membranaceus for application to endometrial cancer and helps to illustrate mechanism of action on a comprehensive level.
Zhang et al. (2021) The modulatory properties of Astragalus membranaceus treatment on endometrial cancer: an integrated pharmacological method. PeerJ 9:e11995 https://doi.org/10.7717/peerj.11995
Protein function prediction with gene ontology: from traditional to deep learning models
Protein function prediction is a crucial part of genome annotation. Prediction methods have recently witnessed rapid development, owing to the emergence of high-throughput sequencing technologies. Among the available databases for identifying protein function terms, Gene Ontology (GO) is an important resource that describes the functional properties of proteins. Researchers are employing various approaches to efficiently predict the GO terms. Meanwhile, deep learning, a fast-evolving discipline in data-driven approach, exhibits impressive potential with respect to assigning GO terms to amino acid sequences. Herein, we reviewed the currently available computational GO annotation methods for proteins, ranging from conventional to deep learning approach. Further, we selected some suitable predictors from among the reviewed tools and conducted a mini comparison of their performance using a worldwide challenge dataset. Finally, we discussed the remaining major challenges in the field, and emphasized the future directions for protein function prediction with GO.
Vu et al. (2021) Protein function prediction with gene ontology: from traditional to deep learning models. PeerJ 9:e12019 https://doi.org/10.7717/peerj.12019
Genetic diversity and population structure of Euscaphis japonica, a monotypic species
Background Understanding plant genetic diversity is important for effective conservation and utilization of genetic resources. Euscaphis japonica (Thunb.) Dippel, is a monotypic species with high phenotypic diversity, narrow distribution, and small population size. In this study, we estimated the genetic diversity and population structure of E. japonica using nine natural populations and inter-simple sequence repeat (ISSR) markers. Our results could provide a theoretical reference for future conservation and utilization of E. japonica. Results We obtained a total of 122 DNA bands, of which 121 (99.18%) were polymorphic. The average number of effective alleles (Ne = 1.4975), Nei’s gene diversity index (H = 0.3016), and Shannon’s information index (I = 0.4630) revealed that E. japonica possessed a high level of genetic diversity. We observed that E. japonica consisted of both deciduous and evergreen populations. UPGMA tree showed that the evergreen and deciduous E. japonica form a sister group. There is little genetic differentiation among geographic populations based on STRUCTURE analysis. The Dice’s similarity coefficient between the deciduous and evergreen populations was low, and the Fst value was high, indicating that these two types of groups have high degree of differentiation. Conclusion Rich genetic diversity has been found in E. japonica, deciduous E. japonica and evergreen E. japonica populations, and genetic variation mainly exists within the population. The low-frequency gene exchange between deciduous and evergreen populations may be the result of the differentiation of deciduous and evergreen populations. We suggest that in-situ protection, seed collection, and vegetative propagation could be the methods for maintenance and conservation of E. japonica populations.
Sun et al. (2021) Genetic diversity and population structure of Euscaphis japonica, a monotypic species. PeerJ 9:e12024 https://doi.org/10.7717/peerj.12024
Discriminating three motor imagery states of the same joint for brain-computer interface
The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.
Guan et al. (2021) Discriminating three motor imagery states of the same joint for brain-computer interface. PeerJ 9:e12027 https://doi.org/10.7717/peerj.12027